diff --git a/ck/README.md b/ck/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7b55ae0a3fe1e5e41b9f87fc72d6c8f29f524cbd --- /dev/null +++ b/ck/README.md @@ -0,0 +1,4 @@ +### 大作业 地址 +https://github.com/Chi-Kai/mywork + +### 该大作业可能被宋老师识别出来 diff --git a/lazy1/face_dataset.py b/lazy1/face_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d31e752e5871cd78aefebc91bce73be3817d5c90 --- /dev/null +++ b/lazy1/face_dataset.py @@ -0,0 +1,68 @@ +import os +import numpy as np +import cv2 + +# 定义图片尺寸 +IMAGE_SIZE = 64 + + +# 按照定义图像大小进行尺度调整 +def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): + top, bottom, left, right = 0, 0, 0, 0 + # 获取图像尺寸 + h, w, _ = image.shape + # 找到图片最长的一边 + longest_edge = max(h, w) + # 计算短边需要填充多少使其与长边等长 + if h < longest_edge: + d = longest_edge - h + top = d // 2 + bottom = d // 2 + elif w < longest_edge: + d = longest_edge - w + left = d // 2 + right = d // 2 + else: + pass + # 设置填充颜色 + BLACK = [0, 0, 0] + # 对原始图片进行填充操作 + constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) + # 调整图像大小并返回 + return cv2.resize(constant, (height, width)) + + +images, labels = list(), list() +# 读取训练数据 +def read_path(path_name): + for dir_item in os.listdir(path_name): + # 合并成可识别的操作路径 + full_path = os.path.abspath(os.path.join(path_name, dir_item)) + # 如果是文件夹，则继续递归调用 + if os.path.isdir(full_path): + read_path(full_path) + else: + if dir_item.endswith('.jpg'): + # print(dir_item) + image = cv2.imread(full_path) + image = resize_image(image, IMAGE_SIZE, IMAGE_SIZE) + images.append(image) + labels.append(path_name) + print(labels) + return images, labels + + +# 从指定路径读取训练数据 +def load_dataset(path_name): + images, labels = read_path(path_name) + # 由于图片是基于矩阵计算的， 将其转为矩阵 + images = np.array(images) + print(images.shape) + labels = np.array([0 if label.endswith('me') else 1 for label in labels]) + #print(labels) + return images, labels + + +if __name__ == '__main__': + images, labels = load_dataset("D:\data") + print('裁剪完毕') \ No newline at end of file diff --git a/lazy1/find_path.py b/lazy1/find_path.py new file mode 100644 index 0000000000000000000000000000000000000000..00eca00e613da5d98fe7b97bfb5bd9db3650ebe4 --- /dev/null +++ b/lazy1/find_path.py @@ -0,0 +1,2 @@ +import cv2 +print(cv2.__file__) \ No newline at end of file diff --git a/lazy1/get_data.py b/lazy1/get_data.py new file mode 100644 index 0000000000000000000000000000000000000000..013bf947f20109d6b1008b2d71046a0ad9ef4c47 --- /dev/null +++ b/lazy1/get_data.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import cv2 +def CatchPICFromVideo(catch_num, path_name): + face_cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # haarcascade_frontalface_alt2 + camera = cv2.VideoCapture(0+cv2.CAP_DSHOW) + num = 0 + while True: + ret, frame = camera.read() + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + for (x, y, w, h) in faces: + img_name = f'{path_name}/{str(num)}.jpg' + image = frame[y:y + h, x:x + w] + print(img_name) + cv2.imwrite(img_name, image) + num += 1 + if num > catch_num: + break + # 画出矩形框圈出人脸 + cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + # 显示捕捉了多少张人脸 + font = cv2.FONT_HERSHEY_SIMPLEX + cv2.putText(frame, f'num:{str(num)}', (x + 30, y + 30), font, 1, (255, 0, 255), 4) + if num > catch_num: + break + # 显示图像 + cv2.imshow('camera', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + camera.release() + cv2.destroyAllWindows() + + +if __name__ == '__main__': + CatchPICFromVideo(1000 ,'D:\data\others') \ No newline at end of file diff --git a/lazy1/main.py b/lazy1/main.py new file mode 100644 index 0000000000000000000000000000000000000000..556a6c813bc6abe7a5efc0f456139afde28ca23d --- /dev/null +++ b/lazy1/main.py @@ -0,0 +1,183 @@ +#导入相关包 +import PySimpleGUI as sg +import numpy as np +import cv2 +from predict import * +# 打开内置摄像头 +cap = cv2.VideoCapture(0) +# 背景色 +sg.theme('Reddit') +# 定义窗口布局，创建不同功能的按钮 +layout = [ + [sg.Image(filename='', key='image')], + [sg.Radio('默认状态', 'Radio', True, size=(10, 1))], + [sg.Radio('阈值二值化', 'Radio', size=(10, 1), key='thresh'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(40, 15), key='thresh_slider')], + [sg.Radio('边缘检测', 'Radio', size=(10, 1), key='canny'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_a'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_b'), + sg.Checkbox('加入降噪',key='gass'), + ], + [sg.Radio('轮廓检测', 'Radio', size=(10, 1), key='contour'), + sg.Slider((0, 255), 127, 1, orientation='h', size=(40, 15), key='base_slider')], + [sg.Radio('高斯滤波', 'Radio', size=(10, 1), key='blur'), + sg.Slider((1, 11), 1, 1, orientation='h', size=(40, 15), key='blur_slider'), + sg.Checkbox('线性滤波', key='line'), + ], + [sg.Radio('色彩转换', 'Radio', size=(10, 1), key='hue'), + sg.Slider((0, 225), 0, 1, orientation='h', size=(40, 15), key='hue_slider')], + [sg.Radio('调节对比度', 'Radio', size=(10, 1), key='enhance'), + sg.Slider((1, 255), 128, 1, orientation='h', size=(40, 15), key='enhance_slider')], + [sg.Radio('人脸识别','Radio',size=(10,1),key='face'), sg.Radio('笑脸识别','Radio',size=(10,1),key='smile')], + [sg.Radio('强化识别','Radio',size=(10,1),key='Big')], + [sg.Button('Exit', size=(10, 1))] +] +# 窗口设计 +window = sg.Window('实时图像处理系统', + layout, + location=(500, 500), + finalize=True, + font='宋体', + ) +# 导入识别模型 +face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') +eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') +smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_smile.xml') +##主体部分 +while True: + event, values = window.read(timeout=0, timeout_key='timeout') + # 实时读取图像,逐帧捕获 + ret, frame = cap.read() + ###########阈值二分类############# + if values['thresh']: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + #该函数有两个输出，只取第二个，为阈值之后的图像 + frame = cv2.threshold(frame, values['thresh_slider'], 255, cv2.THRESH_BINARY)[1] + ###########边缘检测############# + if values['canny']: + if values['gass']: + # 加入降噪 + frame = cv2.GaussianBlur(frame, (5, 5), 0) + #不加入降噪 + frame = cv2.Canny(frame, values['canny_slider_a'], values['canny_slider_b']) + ###########轮廓检测############# + if values['contour']: + #需要先转化为二值图 + gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + gray=cv2.threshold(gray,values['base_slider'],255,cv2.THRESH_BINARY)[1] + #21维的高斯滤波降噪 + hue = cv2.GaussianBlur(gray, (21, 21), 1) + #三个参数，输入图像、层次类型、轮廓逼近方法，第一个返回值为修改后的图像，也就是轮廓 + cnts = cv2.findContours(hue, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + #绘制轮廓,cnt为存储了多个轮廓的数组，color代表线条的颜色， + cv2.drawContours(frame, cnts, -1, (0, 0, 255), 3) + ###########轮廓检测############# + if values['blur']: + #高斯滤波 + frame = cv2.GaussianBlur(frame, (21, 21), values['blur_slider']) + if values['line']: + kernel=np.ones((5,5),np.float32)/25 + frame=cv2.filter2D(frame,-1,kernel) + ###########颜色转换############# + if values['hue']: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2YCrCb) + frame[:, :, 0] += int(values['hue_slider']) + #再映射回RGB空间 + frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR) + ###########调节对比度############# + if values['enhance']: + ##得到一个提高度 + enh_val = values['enhance_slider'] / 40 + #直方图均衡化的函数进行实例化，选择（8,8）的网格 + clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8)) + lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB) + #将这个函数应用到当前图像上，得到最终图像 + lab[:, :, 0] = clahe.apply(lab[:, :, 0]) + frame = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) + ###########人脸识别################## + if values['face']: + #导入分类函数，找到人脸的大致位置 + faces = face_cascade.detectMultiScale(frame, 1.3, 2) + for (x, y, w, h) in faces: + # 画出人脸框，蓝色，画笔宽度微 + face = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + # 框选出人脸区域，在人脸区域而不是全图中进行人眼检测，节省计算资源 + face_area = face[y:y + h, x:x + w] + ## 人眼检测 + # 用人眼级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表 + eyes = eye_cascade.detectMultiScale(face_area, 1.3, 10) + for (ex, ey, ew, eh) in eyes: + # 画出人眼框，绿色，画笔宽度为1 + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 1) + ###########笑脸识别############ + if values['smile']: + faces = face_cascade.detectMultiScale(frame, 1.3, 2) + for (x, y, w, h) in faces: + face = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + face_area = face[y:y + h, x:x + w] + eyes = eye_cascade.detectMultiScale(face_area, 1.3, 10) + for (ex, ey, ew, eh) in eyes: + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 1) + ## 微笑检测 + # 用微笑级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表 + smiles = smile_cascade.detectMultiScale(face_area, scaleFactor=1.16, minNeighbors=65, minSize=(25, 25), + flags=cv2.CASCADE_SCALE_IMAGE) + for (ex, ey, ew, eh) in smiles: + # 画出微笑框，红色（BGR色彩体系），画笔宽度为1 + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 0, 255), 1) + cv2.putText(frame, 'Smile', (x, y - 7), 3, 1.2, (0, 0, 255), 2, cv2.LINE_AA) + ############强化版人脸识别################# + if values['Big']: + #初始化模型 + my_model = keras.models.load_model('D:\model\model.h5') + #转化为灰度图像 + gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) + # 利用分类器识别出哪个区域为人脸 + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + if len(faces) > 0: + for (x, y, w, h) in faces: + # 截取脸部图像提交给模型识别这是谁 + image = frame[y: y + h, x: x + w] + faceID = face_predict(image, my_model) + + # 如果是“我” + if faceID == 0: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) + # 文字提示是谁 + cv2.putText(frame, 'me', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) # 字的线宽 + else: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) + # 文字提示是谁 + cv2.putText(frame, 'others', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) + """ + 暂时停止使用的功能，因为太卡了 + if values['finger']: + skinCrCbHist = np.zeros((256, 256), dtype=np.uint8) + cv2.ellipse(skinCrCbHist, (113, 155), (23, 25), 43, 0, 360, (255, 255, 255), -1) # 绘制椭圆弧线 + YCrCb = cv2.cvtColor(frame, cv2.COLOR_BGR2YCR_CB) # 转换至YCrCb空间 + (y, Cr, Cb) = cv2.split(YCrCb) # 拆分出Y,Cr,Cb值 + skin = np.zeros(Cr.shape, dtype=np.uint8) # 掩膜 + (x, y) = Cr.shape + for i in range(0, x): + for j in range(0, y): + if skinCrCbHist[Cr[i][j], Cb[i][j]] > 0: # 若不在椭圆区间中 + skin[i][j] = 255 + frame= cv2.bitwise_and(frame, frame, mask=skin)""" + if event == 'Exit' or event is None: + break + # GUI实时更新，捕捉当前图像，然后转化为byte形式 + imgbytes = cv2.imencode('.png', frame)[1].tobytes() + # 更新图像 + window['image'].update(data=imgbytes) +#关闭GUI界面 +window.close() diff --git a/lazy1/make_cnn.py b/lazy1/make_cnn.py new file mode 100644 index 0000000000000000000000000000000000000000..305cabea2481af6c865cdc2526857e31d51bb852 --- /dev/null +++ b/lazy1/make_cnn.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +__author__ = 'Seven' +import random +import numpy as np +from sklearn.model_selection import train_test_split +from keras.preprocessing.image import ImageDataGenerator +from keras.models import Sequential +from keras.layers import Dense, Activation, Flatten, Dropout +from keras.layers import Conv2D, MaxPool2D +from keras.optimizers import SGD +from keras.utils import np_utils +from keras.models import load_model +from keras import backend as K +from face_dataset import load_dataset, resize_image, IMAGE_SIZE +import warnings +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +warnings.filterwarnings('ignore') + + +class Dataset: + def __init__(self, path_name): + # 训练集 + self.train_images = None + self.train_labels = None + # 验证集 + # self.valid_images = None + # self.valid_labels = None + # 测试集 + self.test_images = None + self.test_labels = None + # 数据加载路径 + self.path_name = path_name + # 当前库采用的维度顺序 + self.input_shape = None + + def load(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2): + # 加载数据集至内存 + images, labels = load_dataset(self.path_name) + train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.3, + random_state=random.randint(0, 10)) + if K.image_data_format() == 'channels_first': + train_images = train_images.reshape(train_images.shape[0], img_channels, img_rows, img_cols) + test_images = test_images.reshape(test_images.shape[0], img_channels, img_rows, img_cols) + self.input_shape = (img_channels, img_rows, img_cols) + else: + train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, img_channels) + test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, img_channels) + self.input_shape = (img_rows, img_cols, img_channels) + + # 输出训练集、测试集的数量 + print(train_images.shape[0], 'train samples') + print(test_images.shape[0], 'test samples') + # 我们的模型使用categorical_crossentropy作为损失函数，因此需要根据类别数量nb_classes将 + # 类别标签进行one-hot编码使其向量化，在这里我们的类别只有两种，经过转化后标签数据变为二维 + train_labels = np_utils.to_categorical(train_labels, nb_classes) + test_labels = np_utils.to_categorical(test_labels, nb_classes) + # 像素数据浮点化以便归一化 + train_images = train_images.astype('float32') + test_images = test_images.astype('float32') + # 将其归一化,图像的各像素值归一化到0~1区间 + train_images /= 255.0 + test_images /= 255.0 + self.train_images = train_images + self.test_images = test_images + self.train_labels = train_labels + self.test_labels = test_labels + + +# CNN网络模型类 +class Model: + def __init__(self): + self.model = None + + # 建立模型 + def build_model(self, dataset, nb_classes=2): + # 构建一个空的网络模型，它是一个线性堆叠模型，各神经网络层会被顺序添加，专业名称为序贯模型或线性堆叠模型 + self.model = Sequential() + + # 以下代码将顺序添加CNN网络需要的各层，一个add就是一个网络层 + self.model.add(Conv2D(32, 3, 3, border_mode='same', + input_shape=dataset.input_shape)) # 1 2维卷积层 + self.model.add(Activation('relu')) # 2 激活函数层 + + self.model.add(Conv2D(32, 3, 3)) # 3 2维卷积层 + self.model.add(Activation('relu')) # 4 激活函数层 + + self.model.add(MaxPool2D(pool_size=(2, 2))) # 5 池化层 + self.model.add(Dropout(0.25)) # 6 Dropout层 + + self.model.add(Conv2D(64, 3, 3, border_mode='same')) # 7 2维卷积层 + self.model.add(Activation('relu')) # 8 激活函数层 + + self.model.add(Conv2D(64, 3, 3)) # 9 2维卷积层 + self.model.add(Activation('relu')) # 10 激活函数层 + + self.model.add(MaxPool2D(pool_size=(2, 2))) # 11 池化层 + self.model.add(Dropout(0.25)) # 12 Dropout层 + + self.model.add(Flatten()) # 13 Flatten层 + self.model.add(Dense(512)) # 14 Dense层,又被称作全连接层 + self.model.add(Activation('relu')) # 15 激活函数层 + self.model.add(Dropout(0.5)) # 16 Dropout层 + self.model.add(Dense(nb_classes)) # 17 Dense层 + self.model.add(Activation('softmax')) # 18 分类层，输出最终结果 + + # 输出模型概况 + self.model.summary() + + # 训练模型 + def train(self, dataset, batch_size=20, nb_epoch=100, data_augmentation=True): + sgd = SGD(lr=0.01, decay=1e-6, + momentum=0.9, nesterov=True) # 采用SGD+momentum的优化器进行训练，首先生成一个优化器对象 + self.model.compile(loss='categorical_crossentropy', + optimizer=sgd, + metrics=['accuracy']) # 完成实际的模型配置工作 + + # 不使用数据提升，所谓的提升就是从我们提供的训练数据中利用旋转、翻转、加噪声等方法创造新的 + # 训练数据，有意识的提升训练数据规模，增加模型训练量 + if not data_augmentation: + self.model.fit(dataset.train_images, + dataset.train_labels, + batch_size=batch_size, + nb_epoch=nb_epoch, + validation_data=(dataset.test_images, dataset.test_labels), + shuffle=True) + # 使用实时数据提升 + else: + # 定义数据生成器用于数据提升，其返回一个生成器对象datagen，datagen每被调用一 + # 次其生成一组数据（顺序生成），节省内存，其实就是python的数据生成器 + datagen = ImageDataGenerator( + featurewise_center=False, # 是否使输入数据去中心化（均值为0）， + samplewise_center=False, # 是否使输入数据的每个样本均值为0 + featurewise_std_normalization=False, # 是否数据标准化（输入数据除以数据集的标准差） + samplewise_std_normalization=False, # 是否将每个样本数据除以自身的标准差 + zca_whitening=False, # 是否对输入数据施以ZCA白化 + rotation_range=20, # 数据提升时图片随机转动的角度(范围为0～180) + width_shift_range=0.2, # 数据提升时图片水平偏移的幅度（单位为图片宽度的占比，0~1之间的浮点数） + height_shift_range=0.2, # 同上，只不过这里是垂直 + horizontal_flip=True, # 是否进行随机水平翻转 + vertical_flip=False) # 是否进行随机垂直翻转 + + # 计算整个训练样本集的数量以用于特征值归一化、ZCA白化等处理 + datagen.fit(dataset.train_images) + + # 利用生成器开始训练模型 + self.model.fit_generator(datagen.flow(dataset.train_images, dataset.train_labels, + batch_size=batch_size), + samples_per_epoch=dataset.train_images.shape[0], + nb_epoch=nb_epoch, + validation_data=(dataset.test_images, dataset.test_labels)) + + MODEL_PATH = 'D:/model/model.h5' + + def save_model(self, file_path=MODEL_PATH): + self.model.save(file_path) + + def load_model(self, file_path=MODEL_PATH): + self.model = load_model(file_path) + + def evaluate(self, dataset): + score = self.model.evaluate(dataset.test_images, dataset.test_labels, verbose=1) + # print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100)) + print(f'{self.model.metrics_names[1]}:{score[1] * 100}%') + + # 识别人脸 + def face_predict(self, image): + # 依然是根据后端系统确定维度顺序 + if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE): + image = resize_image(image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE + image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测 + elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3): + image = resize_image(image) + image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) + + # 浮点并归一化 + image = image.astype('float32') + image /= 255.0 + + # 给出输入属于各个类别的概率，我们是二值类别，则该函数会给出输入图像属于0和1的概率各为多少 + result = self.model.predict_proba(image) + print('result:', result) + + # 给出类别预测：0或者1 + result = self.model.predict_classes(image) + + # 返回类别预测结果 + return result[0] + + +if __name__ == '__main__': + dataset = Dataset('D:/data') + dataset.load() + + #训练模型 + model = Model() + model.build_model(dataset) + #测试训练函数 + model.train(dataset) + model.save_model(file_path='D:/model/model.h5') + #评估模型 + #model = Model() + #model.load_model(file_path='D:/model/model.h5') + #model.evaluate(dataset) diff --git a/lazy1/predict.py b/lazy1/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd9a167112034b7d32e9dbebe50be9f8fd963be --- /dev/null +++ b/lazy1/predict.py @@ -0,0 +1,112 @@ +import tensorflow.keras as keras +import cv2 +IMAGE_SIZE=64 +#裁剪照片得到64*64的尺寸 +def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): + top, bottom, left, right = (0, 0, 0, 0) + + # 获取图像尺寸 + h, w, _ = image.shape + + # 对于长宽不相等的图片，找到最长的一边 + longest_edge = max(h, w) + + # 计算短边需要增加多上像素宽度使其与长边等长 + if h < longest_edge: + dh = longest_edge - h + top = dh // 2 + bottom = dh - top + elif w < longest_edge: + dw = longest_edge - w + left = dw // 2 + right = dw - left + else: + pass + + # RGB颜色 + BLACK = [0, 0, 0] + # 给图像增加边界，是图片长、宽等长，cv2.BORDER_CONSTANT指定边界颜色由value指定 + constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) + # 调整图像大小并返回 + return cv2.resize(constant, (height, width)) +##########预测函数######################## +def face_predict(image,model): + image = resize_image(image) + image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) + image=image.astype('float32') + image/=255 + # 给出输入属于各个类别的概率与误差 + result = model.predict_proba(image) + print('属于该类的概率与误差是:', result) + + # 给出类别预测：0或者1 + result = model.predict_classes(image) + + # 返回类别预测结果 + return result[0] + +####如果带不动就展示这个程序咯 +if __name__ == '__main__': + # 加载模型 + my_model = keras.models.load_model('D:\model\model.h5') + + # 框住人脸的矩形边框颜色 + color = (0, 255, 0) + + # 捕获指定摄像头的实时视频流 + camera = cv2.VideoCapture(0) + + # 人脸识别分类器本地存储路径 + cascade_path = "/anaconda3/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml" + + # 循环检测识别人脸 + while True: + ret, frame = camera.read() # 读取一帧视频 + + # 图像灰化，降低计算复杂度 + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + # 使用人脸识别分类器，读入分类器 + face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') + + # 利用分类器识别出哪个区域为人脸 + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + if len(faces) > 0: + for (x, y, w, h) in faces: + # 截取脸部图像提交给模型识别这是谁 + image = frame[y: y + h, x: x + w] + faceID = face_predict(image,my_model) + + # 如果是“我” + if faceID == 0: + cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness=2) + + # 文字提示是谁 + cv2.putText(frame, 'me', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) # 字的线宽 + else: + cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness=2) + + # 文字提示是谁 + cv2.putText(frame, 'others', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) + + cv2.imshow("camera", frame) + + # 等待1毫秒看是否有按键输入 + k = cv2.waitKey(1) + # 如果输入q则退出循环 + if k & 0xFF == ord('q'): + break + + # 释放摄像头并销毁所有窗口 + camera.release() + cv2.destroyAllWindows() \ No newline at end of file diff --git "a/lazy1/\345\244\215\344\271\240.pptx" "b/lazy1/\345\244\215\344\271\240.pptx" new file mode 100644 index 0000000000000000000000000000000000000000..437150ac45fb1162da2d88f3559e2409514cff6c --- /dev/null +++ "b/lazy1/\345\244\215\344\271\240.pptx" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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100644 index 0000000000000000000000000000000000000000..b963ef653a73d6df9f114bd7f495777525ce8f1d --- /dev/null +++ "b/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253.py" @@ -0,0 +1,51 @@ +""" 从视频读取帧保存为图片""" +import cv2 +import numpy as np +#cap = cv2.VideoCapture("C:/Users/lenovo/Videos/1.mp4")#读取文件 +cap = cv2.VideoCapture(0)#读取摄像头 + +#皮肤检测 +def A(img): + + YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB) #转换至YCrCb空间 + (y,cr,cb) = cv2.split(YCrCb) #拆分出Y,Cr,Cb值 + cr1 = cv2.GaussianBlur(cr, (5,5), 0) + _, skin = cv2.threshold(cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) #Ostu处理 + res = cv2.bitwise_and(img,img, mask = skin) + return res + +def B(img): + + #binaryimg = cv2.Canny(Laplacian, 50, 200) #二值化，canny检测 + h = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) #寻找轮廓 + contour = h[0] + contour = sorted(contour, key = cv2.contourArea, reverse=True)#已轮廓区域面积进行排序 + #contourmax = contour[0][:, 0, :]#保留区域面积最大的轮廓点坐标 + bg = np.ones(dst.shape, np.uint8) *255#创建白色幕布 + ret = cv2.drawContours(bg,contour[0],-1,(0,0,0),3) #绘制黑色轮廓 + return ret + + +while(True): + + ret, frame = cap.read() + #下面三行可以根据自己的电脑进行调节 + src = cv2.resize(frame,(400,350), interpolation=cv2.INTER_CUBIC)#窗口大小 + cv2.rectangle(src, (90, 60), (300, 300 ), (0, 255, 0))#框出截取位置 + roi = src[60:300 , 90:300] # 获取手势框图 + + res = A(roi) # 进行肤色检测 + cv2.imshow("0",roi) + + gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY) + dst = cv2.Laplacian(gray, cv2.CV_16S, ksize = 3) + Laplacian = cv2.convertScaleAbs(dst) + + contour = B(Laplacian)#轮廓处理 + cv2.imshow("2",contour) + + key = cv2.waitKey(50) & 0xFF + if key == ord('q'): + break +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git "a/\345\244\215\344\271\240/~$\345\244\215\344\271\240.pptx" "b/\345\244\215\344\271\240/~$\345\244\215\344\271\240.pptx" new file mode 100644 index 0000000000000000000000000000000000000000..41e5629cec2873a749bd7a89d77087f4763155ec --- /dev/null +++ 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https://git-lfs.github.com/spec/v1 +oid sha256:68a67a4f04c3cee30edef60f47affcb2dd070b8dfaf9d9c6179b54880b02bbf0 +size 750054 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/GUI.fig" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/GUI.fig" new file mode 100644 index 0000000000000000000000000000000000000000..38a4062c50e9dcce41f463bef8c253c35db02d5b --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/GUI.fig" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0dad3d17f65a283b1e09e7fc8a4a06e618727bbf454eea158ce05607d4965e8c +size 73770 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/GUI.m" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/GUI.m" new file mode 100644 index 0000000000000000000000000000000000000000..42f47a8432bc70151a3a1368f9ae100eba77b259 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/GUI.m" @@ -0,0 +1,990 @@ +function varargout = GUI(varargin) +clc; +gui_Singleton = 1; +gui_State = struct('gui_Name', mfilename, ... + 'gui_Singleton', gui_Singleton, ... + 'gui_OpeningFcn', @GUI_OpeningFcn, ... + 'gui_OutputFcn', @GUI_OutputFcn, ... + 'gui_LayoutFcn', [] , ... + 'gui_Callback', []); +if nargin && ischar(varargin{1}) + gui_State.gui_Callback = str2func(varargin{1}); +end + +if nargout + [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); +else + gui_mainfcn(gui_State, varargin{:}); +end +function GUI_OpeningFcn(hObject, eventdata, handles, varargin) +handles.output = hObject; +guidata(hObject, handles); +function varargout = GUI_OutputFcn(hObject, eventdata, handles) +function axes1_ButtonDownFcn(hObject, eventdata, handles) +function pushbutton7_Callback(hObject, eventdata, handles) +function axes4_CreateFcn(hObject, eventdata, handles) +function axes3_ButtonDownFcn(hObject, eventdata, handles) +function edit1_Callback(hObject, eventdata, handles) +set(handles.edit1,'userdata',get(handles.edit1,'String')); +key=get(handles.edit1,'userdata') +function edit1_CreateFcn(hObject, eventdata, handles) +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end +function edit2_Callback(hObject, eventdata, handles) +function edit2_CreateFcn(hObject, eventdata, handles) +if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) + set(hObject,'BackgroundColor','white'); +end +function pushbutton8_Callback(hObject, eventdata, handles) +im1=get(findobj('tag','pushbutton1'),'userdata'); +im2=get(findobj('tag','qianru2'),'userdata'); +dif=abs(double(im1)-double(im2)); +axes(handles.axes7); +imshow(dif); + + +function pushbutton1_Callback(hObject, eventdata, handles) +axis off; +[filename_img pathname_img]=uigetfile({'*.bmp';'*.jpg';'*.*'},'ѡͼƬ'); +str=[pathname_img filename_img];%ƴpathnamefilenameγ· +img=imread(str); +set(handles.pushbutton1,'userdata',img); +axes(handles.axes1);%axes1ľо +imshow(img); +function pushbutton2_Callback(hObject, eventdata, handles) +axis off; +[filename_hide,pathname_hide] = uigetfile({'*.jpg';'*.bmp';'*.*'},'ѡˮӡͼƬ'); +str1=[pathname_hide filename_hide]; +imhide=imread(str1); +set(handles.pushbutton2,'userdata',imhide); +axes(handles.axes3); +imshow(imhide); + + +function qianru1_Callback(hObject, eventdata, handles) +axis off; +img=get(findobj('tag','pushbutton1'),'userdata'); +imhide=get(findobj('tag','pushbutton2'),'userdata'); +[m,n,h]=size(img); +[mhide,nhide,hhide]=size(imhide); +imgR=img(:,:,1); +imgG=img(:,:,2); +imgB=img(:,:,3); +imhideR=imhide(:,:,1); +imhideG=imhide(:,:,2); +imhideB=imhide(:,:,3); +wcyuan=zeros(1,8); +wcyin=zeros(1,8); + +imgRnew=imgR; +for i=1:mhide + for j=1:nhide + [wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)]=dec2bin_z(imgR(i,j)); + [wcyin(1),wcyin(2),wcyin(3),wcyin(4),wcyin(5),wcyin(6),wcyin(7),wcyin(8)]=dec2bin_z(imhideR(i,j)); + wcyuan(5)=wcyin(1);wcyuan(6)=wcyin(2);wcyuan(7)=wcyin(3);wcyuan(8)=wcyin(4); + imgRnew(i,j)=bin2dec_z(wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)); + end +end +imgGnew=imgG; +for i=1:mhide + for j=1:nhide + [wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)]=dec2bin_z(imgG(i,j)); + [wcyin(1),wcyin(2),wcyin(3),wcyin(4),wcyin(5),wcyin(6),wcyin(7),wcyin(8)]=dec2bin_z(imhideG(i,j)); + wcyuan(5)=wcyin(1);wcyuan(6)=wcyin(2);wcyuan(7)=wcyin(3);wcyuan(8)=wcyin(4); + imgGnew(i,j)=bin2dec_z(wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)); + end +end +imgBnew=imgB; +for i=1:mhide + for j=1:nhide + [wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)]=dec2bin_z(imgB(i,j)); + [wcyin(1),wcyin(2),wcyin(3),wcyin(4),wcyin(5),wcyin(6),wcyin(7),wcyin(8)]=dec2bin_z(imhideB(i,j)); + wcyuan(5)=wcyin(1);wcyuan(6)=wcyin(2);wcyuan(7)=wcyin(3);wcyuan(8)=wcyin(4); + imgBnew(i,j)=bin2dec_z(wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)); + end +end + +%Ƕɣʼ +imgNew=zeros(m,n,h); +imgNew(:,:,1)=imgRnew; +imgNew(:,:,2)=imgGnew; +imgNew(:,:,3)=imgBnew; +axes(handles.axes4); +imshow(uint8(imgNew)); +set(handles.qianru1,'userdata',imgNew); +h3=warndlg('','ʾ') +function baocun1_Callback(hObject, eventdata, handles) +imgNew=get(findobj('tag','qianru1'),'userdata'); +[filename,filepath]=uiputfile({'*.bmp'},'ļ'); +str=[filepath,filename]; +imwrite(uint8(imgNew),str); +function jiema1_Callback(hObject, eventdata, handles) +[filename filepath]=uigetfile({'*.bmp';'*.*'},'ͼƬ'); +str=[filepath filename]; +Img=imread(str); +[m,n,h]=size(Img); +%ֻ֪ͼƬԭ״ȡˮӡ, +%ԿˮӡͼĸߺͿ346350Կ346350 +str1=get(findobj('tag','edit1'),'userdata'); +key1=str1 +mhide=str2double([str1(1),str1(2),str1(3)]);% +nhide=str2double([str1(4),str1(5),str1(6)]);% + +imgR=Img(:,:,1); +imgG=Img(:,:,2); +imgB=Img(:,:,3); +% Ϣȡ +%ֻ֪ͼƬԭ״ȡˮӡ + +%׼ +wcyuan=zeros(1,8); +wcyin=zeros(1,8); +imgtiquR=zeros(mhide,nhide); +imgtiquG=zeros(mhide,nhide); +imgtiquB=zeros(mhide,nhide); +%R +for i=1:mhide + for j=1:nhide + [wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)]=dec2bin_z(imgR(i,j)); + %λת + wcyin(1)=wcyuan(5);wcyin(2)=wcyuan(6);wcyin(3)=wcyuan(7);wcyin(4)=wcyuan(8); + %0 + wcyin(5)=1;wcyin(6)=1;wcyin(7)=1;wcyin(8)=1; + imgtiquR(i,j)=bin2dec_z(wcyin(1),wcyin(2),wcyin(3),wcyin(4),wcyin(5),wcyin(6),wcyin(7),wcyin(8)); + end +end + +%G +for i=1:mhide + for j=1:nhide + [wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)]=dec2bin_z(imgG(i,j)); + %λת + wcyin(1)=wcyuan(5);wcyin(2)=wcyuan(6);wcyin(3)=wcyuan(7);wcyin(4)=wcyuan(8); + %0 + wcyin(5)=1;wcyin(6)=1;wcyin(7)=1;wcyin(8)=1; + imgtiquG(i,j)=bin2dec_z(wcyin(1),wcyin(2),wcyin(3),wcyin(4),wcyin(5),wcyin(6),wcyin(7),wcyin(8)); + end +end + +%B +for i=1:mhide + for j=1:nhide + [wcyuan(1),wcyuan(2),wcyuan(3),wcyuan(4),wcyuan(5),wcyuan(6),wcyuan(7),wcyuan(8)]=dec2bin_z(imgB(i,j)); + %λת + wcyin(1)=wcyuan(5);wcyin(2)=wcyuan(6);wcyin(3)=wcyuan(7);wcyin(4)=wcyuan(8); + %0 + wcyin(5)=1;wcyin(6)=1;wcyin(7)=1;wcyin(8)=1; + imgtiquB(i,j)=bin2dec_z(wcyin(1),wcyin(2),wcyin(3),wcyin(4),wcyin(5),wcyin(6),wcyin(7),wcyin(8)); + end +end + +%ȡϣʼ +tiqu=zeros(mhide,nhide,3); +tiqu(:,:,1)=imgtiquR; +tiqu(:,:,2)=imgtiquG; +tiqu(:,:,3)=imgtiquB; +axis off; +axes(handles.axes5); +imshow(uint8(tiqu)); +set(handles.jiema1,'userdata',tiqu); +h3=warndlg('','ʾ') + +function qianru2_Callback(hObject, eventdata, handles) +axis off; +img=get(findobj('tag','pushbutton1'),'userdata'); +imhide=get(findobj('tag','pushbutton2'),'userdata'); +[m,n,h]=size(img); +[mhide,nhide]=size(imhide); +imgR=img(:,:,1); +imgG=img(:,:,2); +imgB=img(:,:,3); +imgRline=imgR(:); +imgGline=imgG(:); +imgBline=imgB(:); +imhideline=imhide(:); +yr=4; +yg=5; +yb=3; +y=zeros(1,8); + +imhidelinebin=zeros(mhide*nhide*8,1); +for ii=1:mhide*nhide + [imhidelinebin(ii*8),imhidelinebin(ii*8-1),imhidelinebin(ii*8-2),imhidelinebin(ii*8-3),... + imhidelinebin(ii*8-4),imhidelinebin(ii*8-5),imhidelinebin(ii*8-6),imhidelinebin(ii*8-7)]... + =dec2bin_z(imhideline(ii)); +end + +%ʼǶ +flag=0; +embedSum=0; +%B +imgBlineNew=imgBline; +for ii=1:m*n + if(flag==1) + break; + end +[y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgBline(ii)); +fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1));%һ0λ +embedNum=fn-yb; + if embedNum>0 + for jj=1:embedNum + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(jj)=imhidelinebin(embedSum); + end + imgBlineNew(ii)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end + +%R +%flag=0; +imgRlineNew=imgRline; +for ii=1:m*n + if(flag==1) + break; + end + +[y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgRline(ii)); +fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); +embedNum=fn-yr; + if embedNum>0 + for jj=1:embedNum + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(jj)=imhidelinebin(embedSum); + end + imgRlineNew(ii)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end + +%G +%flag=0; +imgGlineNew=imgGline; +for ii=1:m*n + if(flag==1) + break; + end + +[y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgGline(ii)); +fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); +embedNum=fn-yg; + if embedNum>0 + for jj=1:embedNum + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(jj)=imhidelinebin(embedSum); + end + imgGlineNew(ii)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end + + +%Ƕɣʼ +imgNew=zeros(m,n,h); +imgR2=reshape(imgRlineNew,[m,n]); +imgG2=reshape(imgGlineNew,[m,n]); +imgB2=reshape(imgBlineNew,[m,n]); +imgNew(:,:,1)=imgR2; +imgNew(:,:,2)=imgG2; +imgNew(:,:,3)=imgB2; +axes(handles.axes4); +imshow(uint8(imgNew)); +set(handles.qianru2,'userdata',imgNew); +h3=warndlg('','ʾ') +function baocun2_Callback(hObject, eventdata, handles) +imgNew=get(findobj('tag','qianru2'),'userdata'); +[filename,filepath]=uiputfile({'*.bmp'},'ļ'); +str=[filepath,filename]; +imwrite(uint8(imgNew),str); +function jiema2_Callback(hObject, eventdata, handles) +[filename filepath]=uigetfile({'*.bmp';'*.*'},'ͼƬ'); +str=[filepath filename]; +Img=imread(str); +[m,n,h]=size(Img); +%ֻ֪ͼƬԭ״ȡˮӡ, +%ԿˮӡͼĸߺͿ346350Կ346350 +str1=get(findobj('tag','edit1'),'userdata'); +key=str1 + +mhide=str2double([str1(1),str1(2),str1(3)]);% +nhide=str2double([str1(4),str1(5),str1(6)]);% +%*************************************************** +%жܲװɫͼԵĻҪռ +judge=0; +if(m*n>mhide*nhide*3) + judge=1; +end +if(judge==1) + nhide=nhide*3; +end +%*************************************************** +%׼ +yr=4; +yg=5; +yb=3; +ImgR=Img(:,:,1); +ImgG=Img(:,:,2); +ImgB=Img(:,:,3); +ImgRline=ImgR(:); +ImgGline=ImgG(:); +ImgBline=ImgB(:); + +% Ϣȡ +Imhidelinebin=zeros(8*mhide*nhide,1); +%׼ +y=zeros(8,1); + +%B +flag=0; +extractSum=0; +for ii=1:m*n + if flag==1 + break; + end + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgBline(ii)); + fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + embedNum=fn-yb; + if embedNum>0 + for jj=1:embedNum + extractSum=extractSum+1; + if extractSum>8*mhide*nhide + flag=1; + break; + end + Imhidelinebin(extractSum)=y(jj); + end + end +end + + +%R +%flag=0; +%extractSum=0; +for ii=1:m*n + if flag==1 + break; + end + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgRline(ii)); + fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + embedNum=fn-yr; + if embedNum>0 + for jj=1:embedNum + extractSum=extractSum+1; + if extractSum>8*mhide*nhide + flag=1; + break; + end + Imhidelinebin(extractSum)=y(jj); + end + end +end + + +%G +%flag=0; +%extractSum=0; +for ii=1:m*n + if flag==1 + break; + end + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgGline(ii)); + fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + embedNum=fn-yg; + if embedNum>0 + for jj=1:embedNum + extractSum=extractSum+1; + if extractSum>8*mhide*nhide + flag=1; + break; + end + Imhidelinebin(extractSum)=y(jj); + end + end +end + +%awc=embedNum +%awb=extractSum +%ȡϣʼ +Imhideline=zeros(mhide*nhide,1); +for ii=1:mhide*nhide + Imhideline(ii)=bin2dec_z(Imhidelinebin(ii*8),Imhidelinebin(ii*8-1),Imhidelinebin(ii*8-2),Imhidelinebin(ii*8-3),... + Imhidelinebin(ii*8-4),Imhidelinebin(ii*8-5),Imhidelinebin(ii*8-6),Imhidelinebin(ii*8-7)); +end +%*********************************************** +%жǷװ²ɫͼ +if(judge==1) + Imhide=reshape(Imhideline,[mhide,nhide/3,3]); +end +if(judge==0) + Imhide=reshape(Imhideline,[mhide,nhide]); +end +%*********************************************** +axis off; +axes(handles.axes5); +imshow(uint8(Imhide)); +set(handles.jiema2,'userdata',Imhide); +h3=warndlg('','ʾ') + +function qianru3_Callback(hObject, eventdata, handles) +axis off; +img=get(findobj('tag','pushbutton1'),'userdata'); +imhide=get(findobj('tag','pushbutton2'),'userdata'); +%жά +imghide_size=size(imhide); +dimension=numel(imghide_size); + +[m,n,h]=size(img); +[mhide,nhide]=size(imhide); +%ȡԿ +cipher_m=mhide; +if(dimension==3) + cipher_n=nhide/3; +end +if(dimension==2) + cipher_n=nhide; +end +%Կ +change=zeros(8,1); +cipher=cipher_m+cipher_n; +cipher=mod(cipher,255); +[change(1),change(2),change(3),change(4),change(5),change(6),change(7),change(8)]=dec2bin_z(cipher); + +%׼ +imgR=img(:,:,1); +imgG=img(:,:,2); +imgB=img(:,:,3); +imgRline=imgR(:); +imgGline=imgG(:); +imgBline=imgB(:); +imhideline=imhide(:); +yr=4; +yg=5; +yb=3; +y=zeros(1,8); + +imhidelinebin=zeros(mhide*nhide*8,1); +for ii=1:mhide*nhide + [imhidelinebin(ii*8),imhidelinebin(ii*8-1),imhidelinebin(ii*8-2),imhidelinebin(ii*8-3),... + imhidelinebin(ii*8-4),imhidelinebin(ii*8-5),imhidelinebin(ii*8-6),imhidelinebin(ii*8-7)]... + =dec2bin_z(imhideline(ii)); + % + imhidelinebin(ii*8)=xor(imhidelinebin(ii*8),change(1)); + imhidelinebin(ii*8-1)=xor(imhidelinebin(ii*8-1),change(2)); + imhidelinebin(ii*8-2)=xor(imhidelinebin(ii*8-2),change(3)); + imhidelinebin(ii*8-3)=xor(imhidelinebin(ii*8-3),change(4)); + imhidelinebin(ii*8-4)=xor(imhidelinebin(ii*8-4),change(5)); + imhidelinebin(ii*8-5)=xor(imhidelinebin(ii*8-5),change(6)); + imhidelinebin(ii*8-6)=xor(imhidelinebin(ii*8-6),change(7)); + imhidelinebin(ii*8-7)=xor(imhidelinebin(ii*8-7),change(8)); +end + +%ʼǶ +flag=0; +embedSum=0; +%B +imgBlineNew=imgBline; +for ii=1:m*n + if(flag==1) + break; + end + +[y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgBline(ii)); +fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1));%һ0λ +embedNum=fn-yb; + if embedNum>0 + for jj=1:embedNum + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(jj)=imhidelinebin(embedSum); + end + imgBlineNew(ii)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end + +%R +%flag=0; +imgRlineNew=imgRline; +for ii=1:m*n + if(flag==1) + break; + end + +[y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgRline(ii)); +fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); +embedNum=fn-yr; + if embedNum>0 + for jj=1:embedNum + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(jj)=imhidelinebin(embedSum); + end + imgRlineNew(ii)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end + +%G +%flag=0; +imgGlineNew=imgGline; +for ii=1:m*n + if(flag==1) + break; + end + +[y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgGline(ii)); +fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); +embedNum=fn-yg; + if embedNum>0 + for jj=1:embedNum + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(jj)=imhidelinebin(embedSum); + end + imgGlineNew(ii)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end + + +%Ƕɣʼ +imgNew=zeros(m,n,h); +imgR2=reshape(imgRlineNew,[m,n]); +imgG2=reshape(imgGlineNew,[m,n]); +imgB2=reshape(imgBlineNew,[m,n]); +imgNew(:,:,1)=imgR2; +imgNew(:,:,2)=imgG2; +imgNew(:,:,3)=imgB2; +axes(handles.axes4); +imshow(uint8(imgNew)); +set(handles.qianru3,'userdata',imgNew); +h3=warndlg('','ʾ') +function baocun3_Callback(hObject, eventdata, handles) +imgNew=get(findobj('tag','qianru3'),'userdata'); +[filename,filepath]=uiputfile({'*.bmp'},'ļ'); +str=[filepath,filename]; +imwrite(uint8(imgNew),str); +function jiema3_Callback(hObject, eventdata, handles) +[filename filepath]=uigetfile({'*.bmp';'*.*'},'ͼƬ'); +str=[filepath filename]; +Img=imread(str); +[m,n,h]=size(Img); +%ֻ֪ͼƬԭ״ȡˮӡ, +%ԿˮӡͼĸߺͿ346350Կ346350 +str1=get(findobj('tag','edit1'),'userdata'); +key=str1 + +mhide=str2double([str1(1),str1(2),str1(3)]);% +nhide=str2double([str1(4),str1(5),str1(6)]);% +%Կ +change=zeros(8,1); +cipher=mhide+nhide; +cipher=mod(cipher,255); +[change(1),change(2),change(3),change(4),change(5),change(6),change(7),change(8)]=dec2bin_z(cipher); +%*************************************************** +%жܲװɫͼԵĻҪռ +judge=0; +if(m*n>mhide*nhide*3) + judge=1; +end +if(judge==1) + nhide=nhide*3; +end +%*************************************************** +%׼ +yr=4; +yg=5; +yb=3; +ImgR=Img(:,:,1); +ImgG=Img(:,:,2); +ImgB=Img(:,:,3); +ImgRline=ImgR(:); +ImgGline=ImgG(:); +ImgBline=ImgB(:); + +% Ϣȡ +Imhidelinebin=zeros(8*mhide*nhide,1); +%׼ +y=zeros(8,1); + +%B +flag=0; +extractSum=0; +for ii=1:m*n + if flag==1 + break; + end + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgBline(ii)); + fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + embedNum=fn-yb; + if embedNum>0 + for jj=1:embedNum + extractSum=extractSum+1; + if extractSum>8*mhide*nhide + flag=1; + break; + end + Imhidelinebin(extractSum)=y(jj); + end + end +end + + +%R +%flag=0; +%extractSum=0; +for ii=1:m*n + if flag==1 + break; + end + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgRline(ii)); + fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + embedNum=fn-yr; + if embedNum>0 + for jj=1:embedNum + extractSum=extractSum+1; + if extractSum>8*mhide*nhide + flag=1; + break; + end + Imhidelinebin(extractSum)=y(jj); + end + end +end + + +%G +%flag=0; +%extractSum=0; +for ii=1:m*n + if flag==1 + break; + end + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgGline(ii)); + fn=firstNzreo(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + embedNum=fn-yg; + if embedNum>0 + for jj=1:embedNum + extractSum=extractSum+1; + if extractSum>8*mhide*nhide + flag=1; + break; + end + Imhidelinebin(extractSum)=y(jj); + end + end +end + +%awc=embedNum +%awb=extractSum +%ȡϣʼ +Imhideline=zeros(mhide*nhide,1); +for ii=1:mhide*nhide + % + Imhidelinebin(ii*8)=xor(Imhidelinebin(ii*8),change(1)); + Imhidelinebin(ii*8-1)=xor(Imhidelinebin(ii*8-1),change(2)); + Imhidelinebin(ii*8-2)=xor(Imhidelinebin(ii*8-2),change(3)); + Imhidelinebin(ii*8-3)=xor(Imhidelinebin(ii*8-3),change(4)); + Imhidelinebin(ii*8-4)=xor(Imhidelinebin(ii*8-4),change(5)); + Imhidelinebin(ii*8-5)=xor(Imhidelinebin(ii*8-5),change(6)); + Imhidelinebin(ii*8-6)=xor(Imhidelinebin(ii*8-6),change(7)); + Imhidelinebin(ii*8-7)=xor(Imhidelinebin(ii*8-7),change(8)); + %ת + Imhideline(ii)=bin2dec_z(Imhidelinebin(ii*8),Imhidelinebin(ii*8-1),Imhidelinebin(ii*8-2),Imhidelinebin(ii*8-3),... + Imhidelinebin(ii*8-4),Imhidelinebin(ii*8-5),Imhidelinebin(ii*8-6),Imhidelinebin(ii*8-7)); + +end +%*********************************************** +%жǷװ²ɫͼ +if(judge==1) + Imhide=reshape(Imhideline,[mhide,nhide/3,3]); +end +if(judge==0) + Imhide=reshape(Imhideline,[mhide,nhide]); +end +%*********************************************** +axis off; +axes(handles.axes5); +imshow(uint8(Imhide)); +set(handles.jiema3,'userdata',Imhide); +h3=warndlg('','ʾ') + +function qianru4_Callback(hObject, eventdata, handles) +axis off; +img=get(findobj('tag','pushbutton1'),'userdata'); +imhide=get(findobj('tag','pushbutton2'),'userdata'); + +[m,n,h]=size(img); +[mhide,nhide]=size(imhide); +zm=img; +%׼ +imgR=img(:,:,1); +imgG=img(:,:,2); +imgB=img(:,:,3); +imhideline=imhide(:); +%ͼ +zm_book=zeros(16,1);%zm_book¼ˣi¼˶ɫ +%ɫȡ +for i=1:m + for j=1:n + for k=1:16 + if(zm(i,j)<16*k)%ɫķ + zm_book(k)=zm_book(k)+1;%k+1 + %zm_hide(i,j)ṹ¼˵iɫĵjɫλ + zm_hide(k,zm_book(k)).x=i; + zm_hide(k,zm_book(k)).y=j; + break; + end + end + end +end +%ͼ +imhidelinebin=zeros(mhide*nhide*8,1); +for ii=1:mhide*nhide + [imhidelinebin(ii*8),imhidelinebin(ii*8-1),imhidelinebin(ii*8-2),imhidelinebin(ii*8-3),... + imhidelinebin(ii*8-4),imhidelinebin(ii*8-5),imhidelinebin(ii*8-6),imhidelinebin(ii*8-7)]... + =dec2bin_z(imhideline(ii)); +end + +%ʼǶ +flag=0; +embedSum=0; +y=zeros(8,1); +%R +for i=1:16 + if(flag==1) + break; + end + wc=zm_book(i); + for j=1:wc + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgR(zm_hide(i,j).x,zm_hide(i,j).y)); + for k=1:4 + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(k)=imhidelinebin(embedSum); + end + imgR(zm_hide(i,j).x,zm_hide(i,j).y)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end +%G +for i=1:16 + if(flag==1) + break; + end + wc=zm_book(i); + for j=1:wc + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgG(zm_hide(i,j).x,zm_hide(i,j).y)); + for k=1:4 + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(k)=imhidelinebin(embedSum); + end + imgG(zm_hide(i,j).x,zm_hide(i,j).y)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end +%B +for i=1:16 + if(flag==1) + break; + end + wc=zm_book(i); + for j=1:wc + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(imgB(zm_hide(i,j).x,zm_hide(i,j).y)); + for k=1:4 + embedSum=embedSum+1; + if embedSum>mhide*nhide*8 + flag=1; + break; + end + y(k)=imhidelinebin(embedSum); + end + imgB(zm_hide(i,j).x,zm_hide(i,j).y)=bin2dec_z(y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)); + end +end + +%Ƕɣʼ +imgNew=zeros(m,n,h); +imgNew(:,:,1)=imgR; +imgNew(:,:,2)=imgG; +imgNew(:,:,3)=imgB; +axes(handles.axes4); +imshow(uint8(imgNew)); +set(handles.qianru4,'userdata',imgNew); +h3=warndlg('','ʾ') +function baocun4_Callback(hObject, eventdata, handles) +imgNew=get(findobj('tag','qianru4'),'userdata'); +[filename,filepath]=uiputfile({'*.bmp'},'ļ'); +str=[filepath,filename]; +imwrite(uint8(imgNew),str); +function jiema4_Callback(hObject, eventdata, handles) +[filename filepath]=uigetfile({'*.bmp';'*.*'},'ͼƬ'); +str=[filepath filename]; +Img=imread(str); +[m,n,h]=size(Img); +%ֻ֪ͼƬԭ״ȡˮӡ, +%ԿˮӡͼĸߺͿ346350Կ346350 +str1=get(findobj('tag','edit1'),'userdata'); +key=str1 + +mhide=str2double([str1(1),str1(2),str1(3)]);% +nhide=str2double([str1(4),str1(5),str1(6)]);% +%*************************************************** +%жܲװɫͼԵĻҪռ +judge=0; +if(m*n>mhide*nhide*3) + judge=1; +end +if(judge==1) + nhide=nhide*3; +end +%*************************************************** +%ȡɫϢ + +%ͼ +zm=Img; +zm_book=zeros(16,1);%zm_book¼ˣi¼˶ɫ +%ɫȡͼϢ +for i=1:m + for j=1:n + for k=1:16 + if(zm(i,j)<16*k)%ɫķ + zm_book(k)=zm_book(k)+1;%k+1 + %zm_hide(i,j)ṹ¼˵iɫĵjɫλ + zm_hide(k,zm_book(k)).x=i; + zm_hide(k,zm_book(k)).y=j; + break; + end + end + end +end + + +%׼ +ImgR=Img(:,:,1); +ImgG=Img(:,:,2); +ImgB=Img(:,:,3); +y=zeros(8,1); +Imhidelinebin=zeros(8*mhide*nhide,1); +%R +flag=0; +extractSum=0; +for i=1:16 + if(flag==1) + break; + end + wc=zm_book(i); + for j=1:wc + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgR(zm_hide(i,j).x,zm_hide(i,j).y)); + for k=1:4 + extractSum=extractSum+1; + if extractSum>mhide*nhide*8 + flag=1; + break; + end + Imhidelinebin(extractSum)=y(k); + end + end +end +%G +%flag=0; +%extractSum=0; +for i=1:16 + if(flag==1) + break; + end + wc=zm_book(i); + for j=1:wc + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgG(zm_hide(i,j).x,zm_hide(i,j).y)); + for k=1:4 + extractSum=extractSum+1; + if extractSum>mhide*nhide*8 + flag=1; + break; + end + Imhidelinebin(extractSum)=y(k); + end + end +end +%B +%flag=0; +%extractSum=0; +for i=1:16 + if(flag==1) + break; + end + wc=zm_book(i); + for j=1:wc + [y(8),y(7),y(6),y(5),y(4),y(3),y(2),y(1)]=dec2bin_z(ImgB(zm_hide(i,j).x,zm_hide(i,j).y)); + for k=1:4 + extractSum=extractSum+1; + if extractSum>mhide*nhide*8 + flag=1; + break; + end + Imhidelinebin(extractSum)=y(k); + end + end +end + + +%awc=embedNum +%awb=extractSum +%ȡϣʼ +Imhideline=zeros(mhide*nhide,1); +for ii=1:mhide*nhide + %ת + Imhideline(ii)=bin2dec_z(Imhidelinebin(ii*8),Imhidelinebin(ii*8-1),Imhidelinebin(ii*8-2),Imhidelinebin(ii*8-3),... + Imhidelinebin(ii*8-4),Imhidelinebin(ii*8-5),Imhidelinebin(ii*8-6),Imhidelinebin(ii*8-7)); + +end +%*********************************************** +%жǷװ²ɫͼ +if(judge==1) + Imhide=reshape(Imhideline,[mhide,nhide/3,3]); +end +if(judge==0) + Imhide=reshape(Imhideline,[mhide,nhide]); +end +%*********************************************** +axis off; +axes(handles.axes5); +imshow(uint8(Imhide)); +set(handles.jiema4,'userdata',Imhide); +h3=warndlg('','ʾ') + +function zongbaocun_Callback(hObject, eventdata, handles) +imgNew=get(findobj('tag','jiema1'),'userdata'); +if isempty(imgNew) + imgNew=get(findobj('tag','jiema2'),'userdata'); + if isempty(imgNew) + imgNew=get(findobj('tag','jiema3'),'userdata'); + if isempty(imgNew) + imgNew=get(findobj('tag','jiema4'),'userdata'); + end + end +end +[filename,filepath]=uiputfile({'*.bmp'},'ļ'); +str=[filepath,filename]; +imwrite(uint8(imgNew),str); diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190526154147.jpg" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190526154147.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..cf71a411088b84115d8c7f59b0d1f12856bc046e --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190526154147.jpg" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:81bbb7d2577f82ffac8a49f03b95bdcaa0afec63a616ab25919270de42a322fc +size 14676 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190526154304.jpg" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190526154304.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..7b818f946211945d7d03e8cd199f8b774d77a1cd --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190526154304.jpg" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b49952620cfb3c8687f0555e7c995c625ca6144298c1fe993a82942dc9a42b9 +size 3776 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190608183654.jpg" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190608183654.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..3397b28687788f559a2531c9d23204dee29cd05d --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/QQ\346\210\252\345\233\27620190608183654.jpg" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f0967e743d462c8f6fd187388050be67679591624eb0994f66019b84e20c143 +size 28578 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/bin2dec_z.m" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/bin2dec_z.m" new file mode 100644 index 0000000000000000000000000000000000000000..4abd6a8fb8a65572d1b71b62601a49a8bfe2fbc1 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/bin2dec_z.m" @@ -0,0 +1,5 @@ +%function bin2dec_z +%תʮ +function dec=bin2dec_z(y8,y7,y6,y5,y4,y3,y2,y1) + dec=y1+2*y2+4*y3+8*y4+16*y5+32*y6+64*y7+128*y8; +end \ No newline at end of file diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/dec2bin_z.m" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/dec2bin_z.m" new file mode 100644 index 0000000000000000000000000000000000000000..1a66e9ae57e73fdd6c3708dcad8b0675c1487d8d --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/dec2bin_z.m" @@ -0,0 +1,12 @@ +%function dec2bin_z +%ʮת +function [y8,y7,y6,y5,y4,y3,y2,y1]=dec2bin_z(dec) +y1=mod(dec,2); dec=(dec-y1)/2; +y2=mod(dec,2); dec=(dec-y2)/2; +y3=mod(dec,2); dec=(dec-y3)/2; +y4=mod(dec,2); dec=(dec-y4)/2; +y5=mod(dec,2); dec=(dec-y5)/2; +y6=mod(dec,2); dec=(dec-y6)/2; +y7=mod(dec,2); dec=(dec-y7)/2; +y8=mod(dec,2); +end \ No newline at end of file diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/firstNzreo.m" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/firstNzreo.m" new file mode 100644 index 0000000000000000000000000000000000000000..f894c934fe96d9e52d1b889fe0bc22eb754c43d9 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/firstNzreo.m" @@ -0,0 +1,13 @@ +%function firstNzreo +%һλ +function po=firstNzreo(y8,y7,y6,y5,y4,y3,y2,y1) +if y8~=0 po=8; +elseif y7~=0 po=7; +elseif y6~=0 po=6; +elseif y5~=0 po=5; +elseif y4~=0 po=4; +elseif y3~=0 po=3; +elseif y2~=0 po=2; +else po=1; + +end diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/wc.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/wc.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..584eca0503429d4cea967e110d82f9ee3512f04b --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/wc.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb5ef712c7ef541756e6a0a288239a95a291ee9ec6e39fae4246e2642d865b97 +size 750054 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2451.0.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2451.0.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..4b7becfcdce1f2942f7d921ac8acbff75cebf1d4 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2451.0.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f855d4b5ca8643f110228a25287dcc94e41698f41fc614e4288e495b8e679bd9 +size 750054 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2452.0.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2452.0.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..b9f3424b983998497655b8e165a516d61c2c63d7 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2452.0.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68a67a4f04c3cee30edef60f47affcb2dd070b8dfaf9d9c6179b54880b02bbf0 +size 750054 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2452.1.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2452.1.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..7db47f61f0d9046ffa440739363e7275576dba39 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2452.1.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b71b31a7567179de2bb1f9b7e735d925ece81ed2361c7410f264bdb9eedd3ec +size 750054 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2453.0.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2453.0.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..0d2ba1fa941e3097c9e88c180bda54a50afc9d29 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2453.0.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0df7b85d5395afd737e12e6e5fdd76623cf4f9b24dad89b33e533e0755805021 +size 750054 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2454.0.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2454.0.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..18a14f50dfd11a82ca8349f11f6b21dab23bc76b --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\345\265\214\345\205\2454.0.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f716f8ecba9a4dea1e08f5cf549983bfbeb6e47e5d13b74248369617717b3448 +size 750054 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2061.0.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2061.0.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..c85d3068c9b9edc6a4b18f0bcbe750832e8bee11 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2061.0.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8094ae30b20bbf5b339b029c4a0ec94926c98e77659c235fe0ff2c7e6792df39 +size 537894 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2062.0.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2062.0.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..2ae66a953f45f16c41e95801841dbaa56211f55b --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2062.0.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2833e6860096ca1b243ea0b48ed918729fbc4db97bd3da1ffdfd509084973d54 +size 180358 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2062.1.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2062.1.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..c5309c242483d76f7e93ed6a57239d27ce604efb --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2062.1.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f524bf8c39b430bf754ee36a80b2ca05da151ad049a7a96aa3f25e2eea56e21 +size 127062 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2063.0.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2063.0.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..c5309c242483d76f7e93ed6a57239d27ce604efb --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2063.0.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f524bf8c39b430bf754ee36a80b2ca05da151ad049a7a96aa3f25e2eea56e21 +size 127062 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2063.1.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2063.1.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..9f8de334ae5738fe03255d63d035ded8f48f11f0 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2063.1.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e0307f012a57efd9f32c3a076e51ed9ba79ba11ebe851060ea83a16706bb2b9 +size 127062 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2064.0.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2064.0.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..c5309c242483d76f7e93ed6a57239d27ce604efb --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2064.0.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f524bf8c39b430bf754ee36a80b2ca05da151ad049a7a96aa3f25e2eea56e21 +size 127062 diff --git "a/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2064.1.bmp" "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2064.1.bmp" new file mode 100644 index 0000000000000000000000000000000000000000..36908406bf39c320436a67543237dfa644a5fc30 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\345\244\247\344\275\234\344\270\232/\347\216\213\346\231\250_2220171938/\345\256\236\351\252\214\346\272\220\347\240\201/\350\247\243\345\257\2064.1.bmp" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6084b0af74dac13c2f0591781c767452168425b336f4773edf9fafa452c99f6b +size 127062 diff --git "a/\345\244\232\345\252\222\344\275\223\347\250\213\345\272\217\350\256\276\350\256\241\347\275\221\350\257\276\345\261\225\347\244\272.pptx" "b/\345\244\232\345\252\222\344\275\223\347\250\213\345\272\217\350\256\276\350\256\241\347\275\221\350\257\276\345\261\225\347\244\272.pptx" new file mode 100644 index 0000000000000000000000000000000000000000..11352d95b710014549c157dd7f7eed6da47f6404 --- /dev/null +++ "b/\345\244\232\345\252\222\344\275\223\347\250\213\345\272\217\350\256\276\350\256\241\347\275\221\350\257\276\345\261\225\347\244\272.pptx" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f266ca42b6104d047ab334bb21a5bc5cca9610a96e8c205ef4583d3bdebbc2b +size 2129193 diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README (2).md" "b/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README (2).md" new file mode 100644 index 0000000000000000000000000000000000000000..7b55ae0a3fe1e5e41b9f87fc72d6c8f29f524cbd --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README (2).md" @@ -0,0 +1,4 @@ +### 大作业 地址 +https://github.com/Chi-Kai/mywork + +### 该大作业可能被宋老师识别出来 diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README (3).md" "b/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README (3).md" new file mode 100644 index 0000000000000000000000000000000000000000..7b55ae0a3fe1e5e41b9f87fc72d6c8f29f524cbd --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README (3).md" @@ -0,0 +1,4 @@ +### 大作业 地址 +https://github.com/Chi-Kai/mywork + +### 该大作业可能被宋老师识别出来 diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README.md" "b/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README.md" new file mode 100644 index 0000000000000000000000000000000000000000..7b55ae0a3fe1e5e41b9f87fc72d6c8f29f524cbd --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/ck/README.md" @@ -0,0 +1,4 @@ +### 大作业 地址 +https://github.com/Chi-Kai/mywork + +### 该大作业可能被宋老师识别出来 diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/.gitignore" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/.gitignore" new file mode 100644 index 0000000000000000000000000000000000000000..50d9d22a71b16113919f9a33b76591075029e58b --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/.gitignore" @@ -0,0 +1,3 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/inspectionProfiles/Project_Default.xml" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/inspectionProfiles/Project_Default.xml" new file mode 100644 index 0000000000000000000000000000000000000000..078ff066dd377e54535172d509e446f2ffe8f93e --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/inspectionProfiles/Project_Default.xml" @@ -0,0 +1,12 @@ + + + + \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/inspectionProfiles/profiles_settings.xml" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/inspectionProfiles/profiles_settings.xml" new file mode 100644 index 0000000000000000000000000000000000000000..105ce2da2d6447d11dfe32bfb846c3d5b199fc99 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/inspectionProfiles/profiles_settings.xml" @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/lazy1.iml" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/lazy1.iml" new file mode 100644 index 0000000000000000000000000000000000000000..d9e6024fc5bad71514b25a89333834e81d5cd8f4 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/lazy1.iml" @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/misc.xml" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/misc.xml" new file mode 100644 index 0000000000000000000000000000000000000000..f5a93a6f9a35a3100c538c4d086f1a49b5cf2158 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/misc.xml" @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/modules.xml" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/modules.xml" new file mode 100644 index 0000000000000000000000000000000000000000..3afadfe8ebfcf8ff04e6a532bb3fa99809c20e29 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/modules.xml" @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/workspace.xml" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/workspace.xml" new file mode 100644 index 0000000000000000000000000000000000000000..69970c90b8cbc89950f0a0b58632373621683ad9 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/.idea/workspace.xml" @@ -0,0 +1,54 @@ + + + + + + + + + + + + + + + + + + + + + + + 1734576410303 + + + + \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/__pycache__/predict.cpython-310.pyc" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/__pycache__/predict.cpython-310.pyc" new file mode 100644 index 0000000000000000000000000000000000000000..25dc5a25eca169295143f1c5eafe473323c59d6d --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/__pycache__/predict.cpython-310.pyc" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2654b83c9563b29c940dcc3b9dd697b14b9ebe3864809b683c9e46653f50e732 +size 2146 diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset (2).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset (2).py" new file mode 100644 index 0000000000000000000000000000000000000000..d31e752e5871cd78aefebc91bce73be3817d5c90 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset (2).py" @@ -0,0 +1,68 @@ +import os +import numpy as np +import cv2 + +# 定义图片尺寸 +IMAGE_SIZE = 64 + + +# 按照定义图像大小进行尺度调整 +def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): + top, bottom, left, right = 0, 0, 0, 0 + # 获取图像尺寸 + h, w, _ = image.shape + # 找到图片最长的一边 + longest_edge = max(h, w) + # 计算短边需要填充多少使其与长边等长 + if h < longest_edge: + d = longest_edge - h + top = d // 2 + bottom = d // 2 + elif w < longest_edge: + d = longest_edge - w + left = d // 2 + right = d // 2 + else: + pass + # 设置填充颜色 + BLACK = [0, 0, 0] + # 对原始图片进行填充操作 + constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) + # 调整图像大小并返回 + return cv2.resize(constant, (height, width)) + + +images, labels = list(), list() +# 读取训练数据 +def read_path(path_name): + for dir_item in os.listdir(path_name): + # 合并成可识别的操作路径 + full_path = os.path.abspath(os.path.join(path_name, dir_item)) + # 如果是文件夹，则继续递归调用 + if os.path.isdir(full_path): + read_path(full_path) + else: + if dir_item.endswith('.jpg'): + # print(dir_item) + image = cv2.imread(full_path) + image = resize_image(image, IMAGE_SIZE, IMAGE_SIZE) + images.append(image) + labels.append(path_name) + print(labels) + return images, labels + + +# 从指定路径读取训练数据 +def load_dataset(path_name): + images, labels = read_path(path_name) + # 由于图片是基于矩阵计算的， 将其转为矩阵 + images = np.array(images) + print(images.shape) + labels = np.array([0 if label.endswith('me') else 1 for label in labels]) + #print(labels) + return images, labels + + +if __name__ == '__main__': + images, labels = load_dataset("D:\data") + print('裁剪完毕') \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset (3).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset (3).py" new file mode 100644 index 0000000000000000000000000000000000000000..d31e752e5871cd78aefebc91bce73be3817d5c90 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset (3).py" @@ -0,0 +1,68 @@ +import os +import numpy as np +import cv2 + +# 定义图片尺寸 +IMAGE_SIZE = 64 + + +# 按照定义图像大小进行尺度调整 +def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): + top, bottom, left, right = 0, 0, 0, 0 + # 获取图像尺寸 + h, w, _ = image.shape + # 找到图片最长的一边 + longest_edge = max(h, w) + # 计算短边需要填充多少使其与长边等长 + if h < longest_edge: + d = longest_edge - h + top = d // 2 + bottom = d // 2 + elif w < longest_edge: + d = longest_edge - w + left = d // 2 + right = d // 2 + else: + pass + # 设置填充颜色 + BLACK = [0, 0, 0] + # 对原始图片进行填充操作 + constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) + # 调整图像大小并返回 + return cv2.resize(constant, (height, width)) + + +images, labels = list(), list() +# 读取训练数据 +def read_path(path_name): + for dir_item in os.listdir(path_name): + # 合并成可识别的操作路径 + full_path = os.path.abspath(os.path.join(path_name, dir_item)) + # 如果是文件夹，则继续递归调用 + if os.path.isdir(full_path): + read_path(full_path) + else: + if dir_item.endswith('.jpg'): + # print(dir_item) + image = cv2.imread(full_path) + image = resize_image(image, IMAGE_SIZE, IMAGE_SIZE) + images.append(image) + labels.append(path_name) + print(labels) + return images, labels + + +# 从指定路径读取训练数据 +def load_dataset(path_name): + images, labels = read_path(path_name) + # 由于图片是基于矩阵计算的， 将其转为矩阵 + images = np.array(images) + print(images.shape) + labels = np.array([0 if label.endswith('me') else 1 for label in labels]) + #print(labels) + return images, labels + + +if __name__ == '__main__': + images, labels = load_dataset("D:\data") + print('裁剪完毕') \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset.py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset.py" new file mode 100644 index 0000000000000000000000000000000000000000..d31e752e5871cd78aefebc91bce73be3817d5c90 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/face_dataset.py" @@ -0,0 +1,68 @@ +import os +import numpy as np +import cv2 + +# 定义图片尺寸 +IMAGE_SIZE = 64 + + +# 按照定义图像大小进行尺度调整 +def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): + top, bottom, left, right = 0, 0, 0, 0 + # 获取图像尺寸 + h, w, _ = image.shape + # 找到图片最长的一边 + longest_edge = max(h, w) + # 计算短边需要填充多少使其与长边等长 + if h < longest_edge: + d = longest_edge - h + top = d // 2 + bottom = d // 2 + elif w < longest_edge: + d = longest_edge - w + left = d // 2 + right = d // 2 + else: + pass + # 设置填充颜色 + BLACK = [0, 0, 0] + # 对原始图片进行填充操作 + constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) + # 调整图像大小并返回 + return cv2.resize(constant, (height, width)) + + +images, labels = list(), list() +# 读取训练数据 +def read_path(path_name): + for dir_item in os.listdir(path_name): + # 合并成可识别的操作路径 + full_path = os.path.abspath(os.path.join(path_name, dir_item)) + # 如果是文件夹，则继续递归调用 + if os.path.isdir(full_path): + read_path(full_path) + else: + if dir_item.endswith('.jpg'): + # print(dir_item) + image = cv2.imread(full_path) + image = resize_image(image, IMAGE_SIZE, IMAGE_SIZE) + images.append(image) + labels.append(path_name) + print(labels) + return images, labels + + +# 从指定路径读取训练数据 +def load_dataset(path_name): + images, labels = read_path(path_name) + # 由于图片是基于矩阵计算的， 将其转为矩阵 + images = np.array(images) + print(images.shape) + labels = np.array([0 if label.endswith('me') else 1 for label in labels]) + #print(labels) + return images, labels + + +if __name__ == '__main__': + images, labels = load_dataset("D:\data") + print('裁剪完毕') \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path (2).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path (2).py" new file mode 100644 index 0000000000000000000000000000000000000000..00eca00e613da5d98fe7b97bfb5bd9db3650ebe4 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path (2).py" @@ -0,0 +1,2 @@ +import cv2 +print(cv2.__file__) \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path (3).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path (3).py" new file mode 100644 index 0000000000000000000000000000000000000000..00eca00e613da5d98fe7b97bfb5bd9db3650ebe4 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path (3).py" @@ -0,0 +1,2 @@ +import cv2 +print(cv2.__file__) \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path.py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path.py" new file mode 100644 index 0000000000000000000000000000000000000000..00eca00e613da5d98fe7b97bfb5bd9db3650ebe4 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/find_path.py" @@ -0,0 +1,2 @@ +import cv2 +print(cv2.__file__) \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data (2).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data (2).py" new file mode 100644 index 0000000000000000000000000000000000000000..013bf947f20109d6b1008b2d71046a0ad9ef4c47 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data (2).py" @@ -0,0 +1,37 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import cv2 +def CatchPICFromVideo(catch_num, path_name): + face_cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # haarcascade_frontalface_alt2 + camera = cv2.VideoCapture(0+cv2.CAP_DSHOW) + num = 0 + while True: + ret, frame = camera.read() + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + for (x, y, w, h) in faces: + img_name = f'{path_name}/{str(num)}.jpg' + image = frame[y:y + h, x:x + w] + print(img_name) + cv2.imwrite(img_name, image) + num += 1 + if num > catch_num: + break + # 画出矩形框圈出人脸 + cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + # 显示捕捉了多少张人脸 + font = cv2.FONT_HERSHEY_SIMPLEX + cv2.putText(frame, f'num:{str(num)}', (x + 30, y + 30), font, 1, (255, 0, 255), 4) + if num > catch_num: + break + # 显示图像 + cv2.imshow('camera', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + camera.release() + cv2.destroyAllWindows() + + +if __name__ == '__main__': + CatchPICFromVideo(1000 ,'D:\data\others') \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data (3).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data (3).py" new file mode 100644 index 0000000000000000000000000000000000000000..013bf947f20109d6b1008b2d71046a0ad9ef4c47 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data (3).py" @@ -0,0 +1,37 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import cv2 +def CatchPICFromVideo(catch_num, path_name): + face_cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # haarcascade_frontalface_alt2 + camera = cv2.VideoCapture(0+cv2.CAP_DSHOW) + num = 0 + while True: + ret, frame = camera.read() + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + for (x, y, w, h) in faces: + img_name = f'{path_name}/{str(num)}.jpg' + image = frame[y:y + h, x:x + w] + print(img_name) + cv2.imwrite(img_name, image) + num += 1 + if num > catch_num: + break + # 画出矩形框圈出人脸 + cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + # 显示捕捉了多少张人脸 + font = cv2.FONT_HERSHEY_SIMPLEX + cv2.putText(frame, f'num:{str(num)}', (x + 30, y + 30), font, 1, (255, 0, 255), 4) + if num > catch_num: + break + # 显示图像 + cv2.imshow('camera', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + camera.release() + cv2.destroyAllWindows() + + +if __name__ == '__main__': + CatchPICFromVideo(1000 ,'D:\data\others') \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data.py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data.py" new file mode 100644 index 0000000000000000000000000000000000000000..013bf947f20109d6b1008b2d71046a0ad9ef4c47 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/get_data.py" @@ -0,0 +1,37 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import cv2 +def CatchPICFromVideo(catch_num, path_name): + face_cascade = cv2.CascadeClassifier( + cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') # haarcascade_frontalface_alt2 + camera = cv2.VideoCapture(0+cv2.CAP_DSHOW) + num = 0 + while True: + ret, frame = camera.read() + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + for (x, y, w, h) in faces: + img_name = f'{path_name}/{str(num)}.jpg' + image = frame[y:y + h, x:x + w] + print(img_name) + cv2.imwrite(img_name, image) + num += 1 + if num > catch_num: + break + # 画出矩形框圈出人脸 + cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + # 显示捕捉了多少张人脸 + font = cv2.FONT_HERSHEY_SIMPLEX + cv2.putText(frame, f'num:{str(num)}', (x + 30, y + 30), font, 1, (255, 0, 255), 4) + if num > catch_num: + break + # 显示图像 + cv2.imshow('camera', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): + break + camera.release() + cv2.destroyAllWindows() + + +if __name__ == '__main__': + CatchPICFromVideo(1000 ,'D:\data\others') \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main (2).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main (2).py" new file mode 100644 index 0000000000000000000000000000000000000000..556a6c813bc6abe7a5efc0f456139afde28ca23d --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main (2).py" @@ -0,0 +1,183 @@ +#导入相关包 +import PySimpleGUI as sg +import numpy as np +import cv2 +from predict import * +# 打开内置摄像头 +cap = cv2.VideoCapture(0) +# 背景色 +sg.theme('Reddit') +# 定义窗口布局，创建不同功能的按钮 +layout = [ + [sg.Image(filename='', key='image')], + [sg.Radio('默认状态', 'Radio', True, size=(10, 1))], + [sg.Radio('阈值二值化', 'Radio', size=(10, 1), key='thresh'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(40, 15), key='thresh_slider')], + [sg.Radio('边缘检测', 'Radio', size=(10, 1), key='canny'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_a'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_b'), + sg.Checkbox('加入降噪',key='gass'), + ], + [sg.Radio('轮廓检测', 'Radio', size=(10, 1), key='contour'), + sg.Slider((0, 255), 127, 1, orientation='h', size=(40, 15), key='base_slider')], + [sg.Radio('高斯滤波', 'Radio', size=(10, 1), key='blur'), + sg.Slider((1, 11), 1, 1, orientation='h', size=(40, 15), key='blur_slider'), + sg.Checkbox('线性滤波', key='line'), + ], + [sg.Radio('色彩转换', 'Radio', size=(10, 1), key='hue'), + sg.Slider((0, 225), 0, 1, orientation='h', size=(40, 15), key='hue_slider')], + [sg.Radio('调节对比度', 'Radio', size=(10, 1), key='enhance'), + sg.Slider((1, 255), 128, 1, orientation='h', size=(40, 15), key='enhance_slider')], + [sg.Radio('人脸识别','Radio',size=(10,1),key='face'), sg.Radio('笑脸识别','Radio',size=(10,1),key='smile')], + [sg.Radio('强化识别','Radio',size=(10,1),key='Big')], + [sg.Button('Exit', size=(10, 1))] +] +# 窗口设计 +window = sg.Window('实时图像处理系统', + layout, + location=(500, 500), + finalize=True, + font='宋体', + ) +# 导入识别模型 +face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') +eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') +smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_smile.xml') +##主体部分 +while True: + event, values = window.read(timeout=0, timeout_key='timeout') + # 实时读取图像,逐帧捕获 + ret, frame = cap.read() + ###########阈值二分类############# + if values['thresh']: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + #该函数有两个输出，只取第二个，为阈值之后的图像 + frame = cv2.threshold(frame, values['thresh_slider'], 255, cv2.THRESH_BINARY)[1] + ###########边缘检测############# + if values['canny']: + if values['gass']: + # 加入降噪 + frame = cv2.GaussianBlur(frame, (5, 5), 0) + #不加入降噪 + frame = cv2.Canny(frame, values['canny_slider_a'], values['canny_slider_b']) + ###########轮廓检测############# + if values['contour']: + #需要先转化为二值图 + gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + gray=cv2.threshold(gray,values['base_slider'],255,cv2.THRESH_BINARY)[1] + #21维的高斯滤波降噪 + hue = cv2.GaussianBlur(gray, (21, 21), 1) + #三个参数，输入图像、层次类型、轮廓逼近方法，第一个返回值为修改后的图像，也就是轮廓 + cnts = cv2.findContours(hue, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + #绘制轮廓,cnt为存储了多个轮廓的数组，color代表线条的颜色， + cv2.drawContours(frame, cnts, -1, (0, 0, 255), 3) + ###########轮廓检测############# + if values['blur']: + #高斯滤波 + frame = cv2.GaussianBlur(frame, (21, 21), values['blur_slider']) + if values['line']: + kernel=np.ones((5,5),np.float32)/25 + frame=cv2.filter2D(frame,-1,kernel) + ###########颜色转换############# + if values['hue']: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2YCrCb) + frame[:, :, 0] += int(values['hue_slider']) + #再映射回RGB空间 + frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR) + ###########调节对比度############# + if values['enhance']: + ##得到一个提高度 + enh_val = values['enhance_slider'] / 40 + #直方图均衡化的函数进行实例化，选择（8,8）的网格 + clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8)) + lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB) + #将这个函数应用到当前图像上，得到最终图像 + lab[:, :, 0] = clahe.apply(lab[:, :, 0]) + frame = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) + ###########人脸识别################## + if values['face']: + #导入分类函数，找到人脸的大致位置 + faces = face_cascade.detectMultiScale(frame, 1.3, 2) + for (x, y, w, h) in faces: + # 画出人脸框，蓝色，画笔宽度微 + face = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + # 框选出人脸区域，在人脸区域而不是全图中进行人眼检测，节省计算资源 + face_area = face[y:y + h, x:x + w] + ## 人眼检测 + # 用人眼级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表 + eyes = eye_cascade.detectMultiScale(face_area, 1.3, 10) + for (ex, ey, ew, eh) in eyes: + # 画出人眼框，绿色，画笔宽度为1 + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 1) + ###########笑脸识别############ + if values['smile']: + faces = face_cascade.detectMultiScale(frame, 1.3, 2) + for (x, y, w, h) in faces: + face = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + face_area = face[y:y + h, x:x + w] + eyes = eye_cascade.detectMultiScale(face_area, 1.3, 10) + for (ex, ey, ew, eh) in eyes: + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 1) + ## 微笑检测 + # 用微笑级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表 + smiles = smile_cascade.detectMultiScale(face_area, scaleFactor=1.16, minNeighbors=65, minSize=(25, 25), + flags=cv2.CASCADE_SCALE_IMAGE) + for (ex, ey, ew, eh) in smiles: + # 画出微笑框，红色（BGR色彩体系），画笔宽度为1 + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 0, 255), 1) + cv2.putText(frame, 'Smile', (x, y - 7), 3, 1.2, (0, 0, 255), 2, cv2.LINE_AA) + ############强化版人脸识别################# + if values['Big']: + #初始化模型 + my_model = keras.models.load_model('D:\model\model.h5') + #转化为灰度图像 + gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) + # 利用分类器识别出哪个区域为人脸 + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + if len(faces) > 0: + for (x, y, w, h) in faces: + # 截取脸部图像提交给模型识别这是谁 + image = frame[y: y + h, x: x + w] + faceID = face_predict(image, my_model) + + # 如果是“我” + if faceID == 0: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) + # 文字提示是谁 + cv2.putText(frame, 'me', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) # 字的线宽 + else: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) + # 文字提示是谁 + cv2.putText(frame, 'others', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) + """ + 暂时停止使用的功能，因为太卡了 + if values['finger']: + skinCrCbHist = np.zeros((256, 256), dtype=np.uint8) + cv2.ellipse(skinCrCbHist, (113, 155), (23, 25), 43, 0, 360, (255, 255, 255), -1) # 绘制椭圆弧线 + YCrCb = cv2.cvtColor(frame, cv2.COLOR_BGR2YCR_CB) # 转换至YCrCb空间 + (y, Cr, Cb) = cv2.split(YCrCb) # 拆分出Y,Cr,Cb值 + skin = np.zeros(Cr.shape, dtype=np.uint8) # 掩膜 + (x, y) = Cr.shape + for i in range(0, x): + for j in range(0, y): + if skinCrCbHist[Cr[i][j], Cb[i][j]] > 0: # 若不在椭圆区间中 + skin[i][j] = 255 + frame= cv2.bitwise_and(frame, frame, mask=skin)""" + if event == 'Exit' or event is None: + break + # GUI实时更新，捕捉当前图像，然后转化为byte形式 + imgbytes = cv2.imencode('.png', frame)[1].tobytes() + # 更新图像 + window['image'].update(data=imgbytes) +#关闭GUI界面 +window.close() diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main (3).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main (3).py" new file mode 100644 index 0000000000000000000000000000000000000000..556a6c813bc6abe7a5efc0f456139afde28ca23d --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main (3).py" @@ -0,0 +1,183 @@ +#导入相关包 +import PySimpleGUI as sg +import numpy as np +import cv2 +from predict import * +# 打开内置摄像头 +cap = cv2.VideoCapture(0) +# 背景色 +sg.theme('Reddit') +# 定义窗口布局，创建不同功能的按钮 +layout = [ + [sg.Image(filename='', key='image')], + [sg.Radio('默认状态', 'Radio', True, size=(10, 1))], + [sg.Radio('阈值二值化', 'Radio', size=(10, 1), key='thresh'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(40, 15), key='thresh_slider')], + [sg.Radio('边缘检测', 'Radio', size=(10, 1), key='canny'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_a'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_b'), + sg.Checkbox('加入降噪',key='gass'), + ], + [sg.Radio('轮廓检测', 'Radio', size=(10, 1), key='contour'), + sg.Slider((0, 255), 127, 1, orientation='h', size=(40, 15), key='base_slider')], + [sg.Radio('高斯滤波', 'Radio', size=(10, 1), key='blur'), + sg.Slider((1, 11), 1, 1, orientation='h', size=(40, 15), key='blur_slider'), + sg.Checkbox('线性滤波', key='line'), + ], + [sg.Radio('色彩转换', 'Radio', size=(10, 1), key='hue'), + sg.Slider((0, 225), 0, 1, orientation='h', size=(40, 15), key='hue_slider')], + [sg.Radio('调节对比度', 'Radio', size=(10, 1), key='enhance'), + sg.Slider((1, 255), 128, 1, orientation='h', size=(40, 15), key='enhance_slider')], + [sg.Radio('人脸识别','Radio',size=(10,1),key='face'), sg.Radio('笑脸识别','Radio',size=(10,1),key='smile')], + [sg.Radio('强化识别','Radio',size=(10,1),key='Big')], + [sg.Button('Exit', size=(10, 1))] +] +# 窗口设计 +window = sg.Window('实时图像处理系统', + layout, + location=(500, 500), + finalize=True, + font='宋体', + ) +# 导入识别模型 +face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') +eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') +smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_smile.xml') +##主体部分 +while True: + event, values = window.read(timeout=0, timeout_key='timeout') + # 实时读取图像,逐帧捕获 + ret, frame = cap.read() + ###########阈值二分类############# + if values['thresh']: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + #该函数有两个输出，只取第二个，为阈值之后的图像 + frame = cv2.threshold(frame, values['thresh_slider'], 255, cv2.THRESH_BINARY)[1] + ###########边缘检测############# + if values['canny']: + if values['gass']: + # 加入降噪 + frame = cv2.GaussianBlur(frame, (5, 5), 0) + #不加入降噪 + frame = cv2.Canny(frame, values['canny_slider_a'], values['canny_slider_b']) + ###########轮廓检测############# + if values['contour']: + #需要先转化为二值图 + gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + gray=cv2.threshold(gray,values['base_slider'],255,cv2.THRESH_BINARY)[1] + #21维的高斯滤波降噪 + hue = cv2.GaussianBlur(gray, (21, 21), 1) + #三个参数，输入图像、层次类型、轮廓逼近方法，第一个返回值为修改后的图像，也就是轮廓 + cnts = cv2.findContours(hue, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + #绘制轮廓,cnt为存储了多个轮廓的数组，color代表线条的颜色， + cv2.drawContours(frame, cnts, -1, (0, 0, 255), 3) + ###########轮廓检测############# + if values['blur']: + #高斯滤波 + frame = cv2.GaussianBlur(frame, (21, 21), values['blur_slider']) + if values['line']: + kernel=np.ones((5,5),np.float32)/25 + frame=cv2.filter2D(frame,-1,kernel) + ###########颜色转换############# + if values['hue']: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2YCrCb) + frame[:, :, 0] += int(values['hue_slider']) + #再映射回RGB空间 + frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR) + ###########调节对比度############# + if values['enhance']: + ##得到一个提高度 + enh_val = values['enhance_slider'] / 40 + #直方图均衡化的函数进行实例化，选择（8,8）的网格 + clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8)) + lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB) + #将这个函数应用到当前图像上，得到最终图像 + lab[:, :, 0] = clahe.apply(lab[:, :, 0]) + frame = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) + ###########人脸识别################## + if values['face']: + #导入分类函数，找到人脸的大致位置 + faces = face_cascade.detectMultiScale(frame, 1.3, 2) + for (x, y, w, h) in faces: + # 画出人脸框，蓝色，画笔宽度微 + face = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + # 框选出人脸区域，在人脸区域而不是全图中进行人眼检测，节省计算资源 + face_area = face[y:y + h, x:x + w] + ## 人眼检测 + # 用人眼级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表 + eyes = eye_cascade.detectMultiScale(face_area, 1.3, 10) + for (ex, ey, ew, eh) in eyes: + # 画出人眼框，绿色，画笔宽度为1 + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 1) + ###########笑脸识别############ + if values['smile']: + faces = face_cascade.detectMultiScale(frame, 1.3, 2) + for (x, y, w, h) in faces: + face = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + face_area = face[y:y + h, x:x + w] + eyes = eye_cascade.detectMultiScale(face_area, 1.3, 10) + for (ex, ey, ew, eh) in eyes: + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 1) + ## 微笑检测 + # 用微笑级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表 + smiles = smile_cascade.detectMultiScale(face_area, scaleFactor=1.16, minNeighbors=65, minSize=(25, 25), + flags=cv2.CASCADE_SCALE_IMAGE) + for (ex, ey, ew, eh) in smiles: + # 画出微笑框，红色（BGR色彩体系），画笔宽度为1 + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 0, 255), 1) + cv2.putText(frame, 'Smile', (x, y - 7), 3, 1.2, (0, 0, 255), 2, cv2.LINE_AA) + ############强化版人脸识别################# + if values['Big']: + #初始化模型 + my_model = keras.models.load_model('D:\model\model.h5') + #转化为灰度图像 + gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) + # 利用分类器识别出哪个区域为人脸 + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + if len(faces) > 0: + for (x, y, w, h) in faces: + # 截取脸部图像提交给模型识别这是谁 + image = frame[y: y + h, x: x + w] + faceID = face_predict(image, my_model) + + # 如果是“我” + if faceID == 0: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) + # 文字提示是谁 + cv2.putText(frame, 'me', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) # 字的线宽 + else: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) + # 文字提示是谁 + cv2.putText(frame, 'others', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) + """ + 暂时停止使用的功能，因为太卡了 + if values['finger']: + skinCrCbHist = np.zeros((256, 256), dtype=np.uint8) + cv2.ellipse(skinCrCbHist, (113, 155), (23, 25), 43, 0, 360, (255, 255, 255), -1) # 绘制椭圆弧线 + YCrCb = cv2.cvtColor(frame, cv2.COLOR_BGR2YCR_CB) # 转换至YCrCb空间 + (y, Cr, Cb) = cv2.split(YCrCb) # 拆分出Y,Cr,Cb值 + skin = np.zeros(Cr.shape, dtype=np.uint8) # 掩膜 + (x, y) = Cr.shape + for i in range(0, x): + for j in range(0, y): + if skinCrCbHist[Cr[i][j], Cb[i][j]] > 0: # 若不在椭圆区间中 + skin[i][j] = 255 + frame= cv2.bitwise_and(frame, frame, mask=skin)""" + if event == 'Exit' or event is None: + break + # GUI实时更新，捕捉当前图像，然后转化为byte形式 + imgbytes = cv2.imencode('.png', frame)[1].tobytes() + # 更新图像 + window['image'].update(data=imgbytes) +#关闭GUI界面 +window.close() diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main.py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main.py" new file mode 100644 index 0000000000000000000000000000000000000000..556a6c813bc6abe7a5efc0f456139afde28ca23d --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/main.py" @@ -0,0 +1,183 @@ +#导入相关包 +import PySimpleGUI as sg +import numpy as np +import cv2 +from predict import * +# 打开内置摄像头 +cap = cv2.VideoCapture(0) +# 背景色 +sg.theme('Reddit') +# 定义窗口布局，创建不同功能的按钮 +layout = [ + [sg.Image(filename='', key='image')], + [sg.Radio('默认状态', 'Radio', True, size=(10, 1))], + [sg.Radio('阈值二值化', 'Radio', size=(10, 1), key='thresh'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(40, 15), key='thresh_slider')], + [sg.Radio('边缘检测', 'Radio', size=(10, 1), key='canny'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_a'), + sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_b'), + sg.Checkbox('加入降噪',key='gass'), + ], + [sg.Radio('轮廓检测', 'Radio', size=(10, 1), key='contour'), + sg.Slider((0, 255), 127, 1, orientation='h', size=(40, 15), key='base_slider')], + [sg.Radio('高斯滤波', 'Radio', size=(10, 1), key='blur'), + sg.Slider((1, 11), 1, 1, orientation='h', size=(40, 15), key='blur_slider'), + sg.Checkbox('线性滤波', key='line'), + ], + [sg.Radio('色彩转换', 'Radio', size=(10, 1), key='hue'), + sg.Slider((0, 225), 0, 1, orientation='h', size=(40, 15), key='hue_slider')], + [sg.Radio('调节对比度', 'Radio', size=(10, 1), key='enhance'), + sg.Slider((1, 255), 128, 1, orientation='h', size=(40, 15), key='enhance_slider')], + [sg.Radio('人脸识别','Radio',size=(10,1),key='face'), sg.Radio('笑脸识别','Radio',size=(10,1),key='smile')], + [sg.Radio('强化识别','Radio',size=(10,1),key='Big')], + [sg.Button('Exit', size=(10, 1))] +] +# 窗口设计 +window = sg.Window('实时图像处理系统', + layout, + location=(500, 500), + finalize=True, + font='宋体', + ) +# 导入识别模型 +face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') +eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml') +smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_smile.xml') +##主体部分 +while True: + event, values = window.read(timeout=0, timeout_key='timeout') + # 实时读取图像,逐帧捕获 + ret, frame = cap.read() + ###########阈值二分类############# + if values['thresh']: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + #该函数有两个输出，只取第二个，为阈值之后的图像 + frame = cv2.threshold(frame, values['thresh_slider'], 255, cv2.THRESH_BINARY)[1] + ###########边缘检测############# + if values['canny']: + if values['gass']: + # 加入降噪 + frame = cv2.GaussianBlur(frame, (5, 5), 0) + #不加入降噪 + frame = cv2.Canny(frame, values['canny_slider_a'], values['canny_slider_b']) + ###########轮廓检测############# + if values['contour']: + #需要先转化为二值图 + gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + gray=cv2.threshold(gray,values['base_slider'],255,cv2.THRESH_BINARY)[1] + #21维的高斯滤波降噪 + hue = cv2.GaussianBlur(gray, (21, 21), 1) + #三个参数，输入图像、层次类型、轮廓逼近方法，第一个返回值为修改后的图像，也就是轮廓 + cnts = cv2.findContours(hue, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + #绘制轮廓,cnt为存储了多个轮廓的数组，color代表线条的颜色， + cv2.drawContours(frame, cnts, -1, (0, 0, 255), 3) + ###########轮廓检测############# + if values['blur']: + #高斯滤波 + frame = cv2.GaussianBlur(frame, (21, 21), values['blur_slider']) + if values['line']: + kernel=np.ones((5,5),np.float32)/25 + frame=cv2.filter2D(frame,-1,kernel) + ###########颜色转换############# + if values['hue']: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2YCrCb) + frame[:, :, 0] += int(values['hue_slider']) + #再映射回RGB空间 + frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR) + ###########调节对比度############# + if values['enhance']: + ##得到一个提高度 + enh_val = values['enhance_slider'] / 40 + #直方图均衡化的函数进行实例化，选择（8,8）的网格 + clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8)) + lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB) + #将这个函数应用到当前图像上，得到最终图像 + lab[:, :, 0] = clahe.apply(lab[:, :, 0]) + frame = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) + ###########人脸识别################## + if values['face']: + #导入分类函数，找到人脸的大致位置 + faces = face_cascade.detectMultiScale(frame, 1.3, 2) + for (x, y, w, h) in faces: + # 画出人脸框，蓝色，画笔宽度微 + face = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + # 框选出人脸区域，在人脸区域而不是全图中进行人眼检测，节省计算资源 + face_area = face[y:y + h, x:x + w] + ## 人眼检测 + # 用人眼级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表 + eyes = eye_cascade.detectMultiScale(face_area, 1.3, 10) + for (ex, ey, ew, eh) in eyes: + # 画出人眼框，绿色，画笔宽度为1 + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 1) + ###########笑脸识别############ + if values['smile']: + faces = face_cascade.detectMultiScale(frame, 1.3, 2) + for (x, y, w, h) in faces: + face = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) + face_area = face[y:y + h, x:x + w] + eyes = eye_cascade.detectMultiScale(face_area, 1.3, 10) + for (ex, ey, ew, eh) in eyes: + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 1) + ## 微笑检测 + # 用微笑级联分类器引擎在人脸区域进行人眼识别，返回的eyes为眼睛坐标列表 + smiles = smile_cascade.detectMultiScale(face_area, scaleFactor=1.16, minNeighbors=65, minSize=(25, 25), + flags=cv2.CASCADE_SCALE_IMAGE) + for (ex, ey, ew, eh) in smiles: + # 画出微笑框，红色（BGR色彩体系），画笔宽度为1 + cv2.rectangle(face_area, (ex, ey), (ex + ew, ey + eh), (0, 0, 255), 1) + cv2.putText(frame, 'Smile', (x, y - 7), 3, 1.2, (0, 0, 255), 2, cv2.LINE_AA) + ############强化版人脸识别################# + if values['Big']: + #初始化模型 + my_model = keras.models.load_model('D:\model\model.h5') + #转化为灰度图像 + gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) + # 利用分类器识别出哪个区域为人脸 + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + if len(faces) > 0: + for (x, y, w, h) in faces: + # 截取脸部图像提交给模型识别这是谁 + image = frame[y: y + h, x: x + w] + faceID = face_predict(image, my_model) + + # 如果是“我” + if faceID == 0: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) + # 文字提示是谁 + cv2.putText(frame, 'me', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) # 字的线宽 + else: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness=2) + # 文字提示是谁 + cv2.putText(frame, 'others', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) + """ + 暂时停止使用的功能，因为太卡了 + if values['finger']: + skinCrCbHist = np.zeros((256, 256), dtype=np.uint8) + cv2.ellipse(skinCrCbHist, (113, 155), (23, 25), 43, 0, 360, (255, 255, 255), -1) # 绘制椭圆弧线 + YCrCb = cv2.cvtColor(frame, cv2.COLOR_BGR2YCR_CB) # 转换至YCrCb空间 + (y, Cr, Cb) = cv2.split(YCrCb) # 拆分出Y,Cr,Cb值 + skin = np.zeros(Cr.shape, dtype=np.uint8) # 掩膜 + (x, y) = Cr.shape + for i in range(0, x): + for j in range(0, y): + if skinCrCbHist[Cr[i][j], Cb[i][j]] > 0: # 若不在椭圆区间中 + skin[i][j] = 255 + frame= cv2.bitwise_and(frame, frame, mask=skin)""" + if event == 'Exit' or event is None: + break + # GUI实时更新，捕捉当前图像，然后转化为byte形式 + imgbytes = cv2.imencode('.png', frame)[1].tobytes() + # 更新图像 + window['image'].update(data=imgbytes) +#关闭GUI界面 +window.close() diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn (2).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn (2).py" new file mode 100644 index 0000000000000000000000000000000000000000..305cabea2481af6c865cdc2526857e31d51bb852 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn (2).py" @@ -0,0 +1,206 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +__author__ = 'Seven' +import random +import numpy as np +from sklearn.model_selection import train_test_split +from keras.preprocessing.image import ImageDataGenerator +from keras.models import Sequential +from keras.layers import Dense, Activation, Flatten, Dropout +from keras.layers import Conv2D, MaxPool2D +from keras.optimizers import SGD +from keras.utils import np_utils +from keras.models import load_model +from keras import backend as K +from face_dataset import load_dataset, resize_image, IMAGE_SIZE +import warnings +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +warnings.filterwarnings('ignore') + + +class Dataset: + def __init__(self, path_name): + # 训练集 + self.train_images = None + self.train_labels = None + # 验证集 + # self.valid_images = None + # self.valid_labels = None + # 测试集 + self.test_images = None + self.test_labels = None + # 数据加载路径 + self.path_name = path_name + # 当前库采用的维度顺序 + self.input_shape = None + + def load(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2): + # 加载数据集至内存 + images, labels = load_dataset(self.path_name) + train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.3, + random_state=random.randint(0, 10)) + if K.image_data_format() == 'channels_first': + train_images = train_images.reshape(train_images.shape[0], img_channels, img_rows, img_cols) + test_images = test_images.reshape(test_images.shape[0], img_channels, img_rows, img_cols) + self.input_shape = (img_channels, img_rows, img_cols) + else: + train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, img_channels) + test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, img_channels) + self.input_shape = (img_rows, img_cols, img_channels) + + # 输出训练集、测试集的数量 + print(train_images.shape[0], 'train samples') + print(test_images.shape[0], 'test samples') + # 我们的模型使用categorical_crossentropy作为损失函数，因此需要根据类别数量nb_classes将 + # 类别标签进行one-hot编码使其向量化，在这里我们的类别只有两种，经过转化后标签数据变为二维 + train_labels = np_utils.to_categorical(train_labels, nb_classes) + test_labels = np_utils.to_categorical(test_labels, nb_classes) + # 像素数据浮点化以便归一化 + train_images = train_images.astype('float32') + test_images = test_images.astype('float32') + # 将其归一化,图像的各像素值归一化到0~1区间 + train_images /= 255.0 + test_images /= 255.0 + self.train_images = train_images + self.test_images = test_images + self.train_labels = train_labels + self.test_labels = test_labels + + +# CNN网络模型类 +class Model: + def __init__(self): + self.model = None + + # 建立模型 + def build_model(self, dataset, nb_classes=2): + # 构建一个空的网络模型，它是一个线性堆叠模型，各神经网络层会被顺序添加，专业名称为序贯模型或线性堆叠模型 + self.model = Sequential() + + # 以下代码将顺序添加CNN网络需要的各层，一个add就是一个网络层 + self.model.add(Conv2D(32, 3, 3, border_mode='same', + input_shape=dataset.input_shape)) # 1 2维卷积层 + self.model.add(Activation('relu')) # 2 激活函数层 + + self.model.add(Conv2D(32, 3, 3)) # 3 2维卷积层 + self.model.add(Activation('relu')) # 4 激活函数层 + + self.model.add(MaxPool2D(pool_size=(2, 2))) # 5 池化层 + self.model.add(Dropout(0.25)) # 6 Dropout层 + + self.model.add(Conv2D(64, 3, 3, border_mode='same')) # 7 2维卷积层 + self.model.add(Activation('relu')) # 8 激活函数层 + + self.model.add(Conv2D(64, 3, 3)) # 9 2维卷积层 + self.model.add(Activation('relu')) # 10 激活函数层 + + self.model.add(MaxPool2D(pool_size=(2, 2))) # 11 池化层 + self.model.add(Dropout(0.25)) # 12 Dropout层 + + self.model.add(Flatten()) # 13 Flatten层 + self.model.add(Dense(512)) # 14 Dense层,又被称作全连接层 + self.model.add(Activation('relu')) # 15 激活函数层 + self.model.add(Dropout(0.5)) # 16 Dropout层 + self.model.add(Dense(nb_classes)) # 17 Dense层 + self.model.add(Activation('softmax')) # 18 分类层，输出最终结果 + + # 输出模型概况 + self.model.summary() + + # 训练模型 + def train(self, dataset, batch_size=20, nb_epoch=100, data_augmentation=True): + sgd = SGD(lr=0.01, decay=1e-6, + momentum=0.9, nesterov=True) # 采用SGD+momentum的优化器进行训练，首先生成一个优化器对象 + self.model.compile(loss='categorical_crossentropy', + optimizer=sgd, + metrics=['accuracy']) # 完成实际的模型配置工作 + + # 不使用数据提升，所谓的提升就是从我们提供的训练数据中利用旋转、翻转、加噪声等方法创造新的 + # 训练数据，有意识的提升训练数据规模，增加模型训练量 + if not data_augmentation: + self.model.fit(dataset.train_images, + dataset.train_labels, + batch_size=batch_size, + nb_epoch=nb_epoch, + validation_data=(dataset.test_images, dataset.test_labels), + shuffle=True) + # 使用实时数据提升 + else: + # 定义数据生成器用于数据提升，其返回一个生成器对象datagen，datagen每被调用一 + # 次其生成一组数据（顺序生成），节省内存，其实就是python的数据生成器 + datagen = ImageDataGenerator( + featurewise_center=False, # 是否使输入数据去中心化（均值为0）， + samplewise_center=False, # 是否使输入数据的每个样本均值为0 + featurewise_std_normalization=False, # 是否数据标准化（输入数据除以数据集的标准差） + samplewise_std_normalization=False, # 是否将每个样本数据除以自身的标准差 + zca_whitening=False, # 是否对输入数据施以ZCA白化 + rotation_range=20, # 数据提升时图片随机转动的角度(范围为0～180) + width_shift_range=0.2, # 数据提升时图片水平偏移的幅度（单位为图片宽度的占比，0~1之间的浮点数） + height_shift_range=0.2, # 同上，只不过这里是垂直 + horizontal_flip=True, # 是否进行随机水平翻转 + vertical_flip=False) # 是否进行随机垂直翻转 + + # 计算整个训练样本集的数量以用于特征值归一化、ZCA白化等处理 + datagen.fit(dataset.train_images) + + # 利用生成器开始训练模型 + self.model.fit_generator(datagen.flow(dataset.train_images, dataset.train_labels, + batch_size=batch_size), + samples_per_epoch=dataset.train_images.shape[0], + nb_epoch=nb_epoch, + validation_data=(dataset.test_images, dataset.test_labels)) + + MODEL_PATH = 'D:/model/model.h5' + + def save_model(self, file_path=MODEL_PATH): + self.model.save(file_path) + + def load_model(self, file_path=MODEL_PATH): + self.model = load_model(file_path) + + def evaluate(self, dataset): + score = self.model.evaluate(dataset.test_images, dataset.test_labels, verbose=1) + # print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100)) + print(f'{self.model.metrics_names[1]}:{score[1] * 100}%') + + # 识别人脸 + def face_predict(self, image): + # 依然是根据后端系统确定维度顺序 + if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE): + image = resize_image(image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE + image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测 + elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3): + image = resize_image(image) + image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) + + # 浮点并归一化 + image = image.astype('float32') + image /= 255.0 + + # 给出输入属于各个类别的概率，我们是二值类别，则该函数会给出输入图像属于0和1的概率各为多少 + result = self.model.predict_proba(image) + print('result:', result) + + # 给出类别预测：0或者1 + result = self.model.predict_classes(image) + + # 返回类别预测结果 + return result[0] + + +if __name__ == '__main__': + dataset = Dataset('D:/data') + dataset.load() + + #训练模型 + model = Model() + model.build_model(dataset) + #测试训练函数 + model.train(dataset) + model.save_model(file_path='D:/model/model.h5') + #评估模型 + #model = Model() + #model.load_model(file_path='D:/model/model.h5') + #model.evaluate(dataset) diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn (3).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn (3).py" new file mode 100644 index 0000000000000000000000000000000000000000..305cabea2481af6c865cdc2526857e31d51bb852 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn (3).py" @@ -0,0 +1,206 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +__author__ = 'Seven' +import random +import numpy as np +from sklearn.model_selection import train_test_split +from keras.preprocessing.image import ImageDataGenerator +from keras.models import Sequential +from keras.layers import Dense, Activation, Flatten, Dropout +from keras.layers import Conv2D, MaxPool2D +from keras.optimizers import SGD +from keras.utils import np_utils +from keras.models import load_model +from keras import backend as K +from face_dataset import load_dataset, resize_image, IMAGE_SIZE +import warnings +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +warnings.filterwarnings('ignore') + + +class Dataset: + def __init__(self, path_name): + # 训练集 + self.train_images = None + self.train_labels = None + # 验证集 + # self.valid_images = None + # self.valid_labels = None + # 测试集 + self.test_images = None + self.test_labels = None + # 数据加载路径 + self.path_name = path_name + # 当前库采用的维度顺序 + self.input_shape = None + + def load(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2): + # 加载数据集至内存 + images, labels = load_dataset(self.path_name) + train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.3, + random_state=random.randint(0, 10)) + if K.image_data_format() == 'channels_first': + train_images = train_images.reshape(train_images.shape[0], img_channels, img_rows, img_cols) + test_images = test_images.reshape(test_images.shape[0], img_channels, img_rows, img_cols) + self.input_shape = (img_channels, img_rows, img_cols) + else: + train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, img_channels) + test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, img_channels) + self.input_shape = (img_rows, img_cols, img_channels) + + # 输出训练集、测试集的数量 + print(train_images.shape[0], 'train samples') + print(test_images.shape[0], 'test samples') + # 我们的模型使用categorical_crossentropy作为损失函数，因此需要根据类别数量nb_classes将 + # 类别标签进行one-hot编码使其向量化，在这里我们的类别只有两种，经过转化后标签数据变为二维 + train_labels = np_utils.to_categorical(train_labels, nb_classes) + test_labels = np_utils.to_categorical(test_labels, nb_classes) + # 像素数据浮点化以便归一化 + train_images = train_images.astype('float32') + test_images = test_images.astype('float32') + # 将其归一化,图像的各像素值归一化到0~1区间 + train_images /= 255.0 + test_images /= 255.0 + self.train_images = train_images + self.test_images = test_images + self.train_labels = train_labels + self.test_labels = test_labels + + +# CNN网络模型类 +class Model: + def __init__(self): + self.model = None + + # 建立模型 + def build_model(self, dataset, nb_classes=2): + # 构建一个空的网络模型，它是一个线性堆叠模型，各神经网络层会被顺序添加，专业名称为序贯模型或线性堆叠模型 + self.model = Sequential() + + # 以下代码将顺序添加CNN网络需要的各层，一个add就是一个网络层 + self.model.add(Conv2D(32, 3, 3, border_mode='same', + input_shape=dataset.input_shape)) # 1 2维卷积层 + self.model.add(Activation('relu')) # 2 激活函数层 + + self.model.add(Conv2D(32, 3, 3)) # 3 2维卷积层 + self.model.add(Activation('relu')) # 4 激活函数层 + + self.model.add(MaxPool2D(pool_size=(2, 2))) # 5 池化层 + self.model.add(Dropout(0.25)) # 6 Dropout层 + + self.model.add(Conv2D(64, 3, 3, border_mode='same')) # 7 2维卷积层 + self.model.add(Activation('relu')) # 8 激活函数层 + + self.model.add(Conv2D(64, 3, 3)) # 9 2维卷积层 + self.model.add(Activation('relu')) # 10 激活函数层 + + self.model.add(MaxPool2D(pool_size=(2, 2))) # 11 池化层 + self.model.add(Dropout(0.25)) # 12 Dropout层 + + self.model.add(Flatten()) # 13 Flatten层 + self.model.add(Dense(512)) # 14 Dense层,又被称作全连接层 + self.model.add(Activation('relu')) # 15 激活函数层 + self.model.add(Dropout(0.5)) # 16 Dropout层 + self.model.add(Dense(nb_classes)) # 17 Dense层 + self.model.add(Activation('softmax')) # 18 分类层，输出最终结果 + + # 输出模型概况 + self.model.summary() + + # 训练模型 + def train(self, dataset, batch_size=20, nb_epoch=100, data_augmentation=True): + sgd = SGD(lr=0.01, decay=1e-6, + momentum=0.9, nesterov=True) # 采用SGD+momentum的优化器进行训练，首先生成一个优化器对象 + self.model.compile(loss='categorical_crossentropy', + optimizer=sgd, + metrics=['accuracy']) # 完成实际的模型配置工作 + + # 不使用数据提升，所谓的提升就是从我们提供的训练数据中利用旋转、翻转、加噪声等方法创造新的 + # 训练数据，有意识的提升训练数据规模，增加模型训练量 + if not data_augmentation: + self.model.fit(dataset.train_images, + dataset.train_labels, + batch_size=batch_size, + nb_epoch=nb_epoch, + validation_data=(dataset.test_images, dataset.test_labels), + shuffle=True) + # 使用实时数据提升 + else: + # 定义数据生成器用于数据提升，其返回一个生成器对象datagen，datagen每被调用一 + # 次其生成一组数据（顺序生成），节省内存，其实就是python的数据生成器 + datagen = ImageDataGenerator( + featurewise_center=False, # 是否使输入数据去中心化（均值为0）， + samplewise_center=False, # 是否使输入数据的每个样本均值为0 + featurewise_std_normalization=False, # 是否数据标准化（输入数据除以数据集的标准差） + samplewise_std_normalization=False, # 是否将每个样本数据除以自身的标准差 + zca_whitening=False, # 是否对输入数据施以ZCA白化 + rotation_range=20, # 数据提升时图片随机转动的角度(范围为0～180) + width_shift_range=0.2, # 数据提升时图片水平偏移的幅度（单位为图片宽度的占比，0~1之间的浮点数） + height_shift_range=0.2, # 同上，只不过这里是垂直 + horizontal_flip=True, # 是否进行随机水平翻转 + vertical_flip=False) # 是否进行随机垂直翻转 + + # 计算整个训练样本集的数量以用于特征值归一化、ZCA白化等处理 + datagen.fit(dataset.train_images) + + # 利用生成器开始训练模型 + self.model.fit_generator(datagen.flow(dataset.train_images, dataset.train_labels, + batch_size=batch_size), + samples_per_epoch=dataset.train_images.shape[0], + nb_epoch=nb_epoch, + validation_data=(dataset.test_images, dataset.test_labels)) + + MODEL_PATH = 'D:/model/model.h5' + + def save_model(self, file_path=MODEL_PATH): + self.model.save(file_path) + + def load_model(self, file_path=MODEL_PATH): + self.model = load_model(file_path) + + def evaluate(self, dataset): + score = self.model.evaluate(dataset.test_images, dataset.test_labels, verbose=1) + # print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100)) + print(f'{self.model.metrics_names[1]}:{score[1] * 100}%') + + # 识别人脸 + def face_predict(self, image): + # 依然是根据后端系统确定维度顺序 + if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE): + image = resize_image(image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE + image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测 + elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3): + image = resize_image(image) + image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) + + # 浮点并归一化 + image = image.astype('float32') + image /= 255.0 + + # 给出输入属于各个类别的概率，我们是二值类别，则该函数会给出输入图像属于0和1的概率各为多少 + result = self.model.predict_proba(image) + print('result:', result) + + # 给出类别预测：0或者1 + result = self.model.predict_classes(image) + + # 返回类别预测结果 + return result[0] + + +if __name__ == '__main__': + dataset = Dataset('D:/data') + dataset.load() + + #训练模型 + model = Model() + model.build_model(dataset) + #测试训练函数 + model.train(dataset) + model.save_model(file_path='D:/model/model.h5') + #评估模型 + #model = Model() + #model.load_model(file_path='D:/model/model.h5') + #model.evaluate(dataset) diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn.py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn.py" new file mode 100644 index 0000000000000000000000000000000000000000..305cabea2481af6c865cdc2526857e31d51bb852 --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/make_cnn.py" @@ -0,0 +1,206 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +__author__ = 'Seven' +import random +import numpy as np +from sklearn.model_selection import train_test_split +from keras.preprocessing.image import ImageDataGenerator +from keras.models import Sequential +from keras.layers import Dense, Activation, Flatten, Dropout +from keras.layers import Conv2D, MaxPool2D +from keras.optimizers import SGD +from keras.utils import np_utils +from keras.models import load_model +from keras import backend as K +from face_dataset import load_dataset, resize_image, IMAGE_SIZE +import warnings +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +warnings.filterwarnings('ignore') + + +class Dataset: + def __init__(self, path_name): + # 训练集 + self.train_images = None + self.train_labels = None + # 验证集 + # self.valid_images = None + # self.valid_labels = None + # 测试集 + self.test_images = None + self.test_labels = None + # 数据加载路径 + self.path_name = path_name + # 当前库采用的维度顺序 + self.input_shape = None + + def load(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2): + # 加载数据集至内存 + images, labels = load_dataset(self.path_name) + train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.3, + random_state=random.randint(0, 10)) + if K.image_data_format() == 'channels_first': + train_images = train_images.reshape(train_images.shape[0], img_channels, img_rows, img_cols) + test_images = test_images.reshape(test_images.shape[0], img_channels, img_rows, img_cols) + self.input_shape = (img_channels, img_rows, img_cols) + else: + train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, img_channels) + test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, img_channels) + self.input_shape = (img_rows, img_cols, img_channels) + + # 输出训练集、测试集的数量 + print(train_images.shape[0], 'train samples') + print(test_images.shape[0], 'test samples') + # 我们的模型使用categorical_crossentropy作为损失函数，因此需要根据类别数量nb_classes将 + # 类别标签进行one-hot编码使其向量化，在这里我们的类别只有两种，经过转化后标签数据变为二维 + train_labels = np_utils.to_categorical(train_labels, nb_classes) + test_labels = np_utils.to_categorical(test_labels, nb_classes) + # 像素数据浮点化以便归一化 + train_images = train_images.astype('float32') + test_images = test_images.astype('float32') + # 将其归一化,图像的各像素值归一化到0~1区间 + train_images /= 255.0 + test_images /= 255.0 + self.train_images = train_images + self.test_images = test_images + self.train_labels = train_labels + self.test_labels = test_labels + + +# CNN网络模型类 +class Model: + def __init__(self): + self.model = None + + # 建立模型 + def build_model(self, dataset, nb_classes=2): + # 构建一个空的网络模型，它是一个线性堆叠模型，各神经网络层会被顺序添加，专业名称为序贯模型或线性堆叠模型 + self.model = Sequential() + + # 以下代码将顺序添加CNN网络需要的各层，一个add就是一个网络层 + self.model.add(Conv2D(32, 3, 3, border_mode='same', + input_shape=dataset.input_shape)) # 1 2维卷积层 + self.model.add(Activation('relu')) # 2 激活函数层 + + self.model.add(Conv2D(32, 3, 3)) # 3 2维卷积层 + self.model.add(Activation('relu')) # 4 激活函数层 + + self.model.add(MaxPool2D(pool_size=(2, 2))) # 5 池化层 + self.model.add(Dropout(0.25)) # 6 Dropout层 + + self.model.add(Conv2D(64, 3, 3, border_mode='same')) # 7 2维卷积层 + self.model.add(Activation('relu')) # 8 激活函数层 + + self.model.add(Conv2D(64, 3, 3)) # 9 2维卷积层 + self.model.add(Activation('relu')) # 10 激活函数层 + + self.model.add(MaxPool2D(pool_size=(2, 2))) # 11 池化层 + self.model.add(Dropout(0.25)) # 12 Dropout层 + + self.model.add(Flatten()) # 13 Flatten层 + self.model.add(Dense(512)) # 14 Dense层,又被称作全连接层 + self.model.add(Activation('relu')) # 15 激活函数层 + self.model.add(Dropout(0.5)) # 16 Dropout层 + self.model.add(Dense(nb_classes)) # 17 Dense层 + self.model.add(Activation('softmax')) # 18 分类层，输出最终结果 + + # 输出模型概况 + self.model.summary() + + # 训练模型 + def train(self, dataset, batch_size=20, nb_epoch=100, data_augmentation=True): + sgd = SGD(lr=0.01, decay=1e-6, + momentum=0.9, nesterov=True) # 采用SGD+momentum的优化器进行训练，首先生成一个优化器对象 + self.model.compile(loss='categorical_crossentropy', + optimizer=sgd, + metrics=['accuracy']) # 完成实际的模型配置工作 + + # 不使用数据提升，所谓的提升就是从我们提供的训练数据中利用旋转、翻转、加噪声等方法创造新的 + # 训练数据，有意识的提升训练数据规模，增加模型训练量 + if not data_augmentation: + self.model.fit(dataset.train_images, + dataset.train_labels, + batch_size=batch_size, + nb_epoch=nb_epoch, + validation_data=(dataset.test_images, dataset.test_labels), + shuffle=True) + # 使用实时数据提升 + else: + # 定义数据生成器用于数据提升，其返回一个生成器对象datagen，datagen每被调用一 + # 次其生成一组数据（顺序生成），节省内存，其实就是python的数据生成器 + datagen = ImageDataGenerator( + featurewise_center=False, # 是否使输入数据去中心化（均值为0）， + samplewise_center=False, # 是否使输入数据的每个样本均值为0 + featurewise_std_normalization=False, # 是否数据标准化（输入数据除以数据集的标准差） + samplewise_std_normalization=False, # 是否将每个样本数据除以自身的标准差 + zca_whitening=False, # 是否对输入数据施以ZCA白化 + rotation_range=20, # 数据提升时图片随机转动的角度(范围为0～180) + width_shift_range=0.2, # 数据提升时图片水平偏移的幅度（单位为图片宽度的占比，0~1之间的浮点数） + height_shift_range=0.2, # 同上，只不过这里是垂直 + horizontal_flip=True, # 是否进行随机水平翻转 + vertical_flip=False) # 是否进行随机垂直翻转 + + # 计算整个训练样本集的数量以用于特征值归一化、ZCA白化等处理 + datagen.fit(dataset.train_images) + + # 利用生成器开始训练模型 + self.model.fit_generator(datagen.flow(dataset.train_images, dataset.train_labels, + batch_size=batch_size), + samples_per_epoch=dataset.train_images.shape[0], + nb_epoch=nb_epoch, + validation_data=(dataset.test_images, dataset.test_labels)) + + MODEL_PATH = 'D:/model/model.h5' + + def save_model(self, file_path=MODEL_PATH): + self.model.save(file_path) + + def load_model(self, file_path=MODEL_PATH): + self.model = load_model(file_path) + + def evaluate(self, dataset): + score = self.model.evaluate(dataset.test_images, dataset.test_labels, verbose=1) + # print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100)) + print(f'{self.model.metrics_names[1]}:{score[1] * 100}%') + + # 识别人脸 + def face_predict(self, image): + # 依然是根据后端系统确定维度顺序 + if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE): + image = resize_image(image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE + image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测 + elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3): + image = resize_image(image) + image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) + + # 浮点并归一化 + image = image.astype('float32') + image /= 255.0 + + # 给出输入属于各个类别的概率，我们是二值类别，则该函数会给出输入图像属于0和1的概率各为多少 + result = self.model.predict_proba(image) + print('result:', result) + + # 给出类别预测：0或者1 + result = self.model.predict_classes(image) + + # 返回类别预测结果 + return result[0] + + +if __name__ == '__main__': + dataset = Dataset('D:/data') + dataset.load() + + #训练模型 + model = Model() + model.build_model(dataset) + #测试训练函数 + model.train(dataset) + model.save_model(file_path='D:/model/model.h5') + #评估模型 + #model = Model() + #model.load_model(file_path='D:/model/model.h5') + #model.evaluate(dataset) diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict (2).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict (2).py" new file mode 100644 index 0000000000000000000000000000000000000000..7bd9a167112034b7d32e9dbebe50be9f8fd963be --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict (2).py" @@ -0,0 +1,112 @@ +import tensorflow.keras as keras +import cv2 +IMAGE_SIZE=64 +#裁剪照片得到64*64的尺寸 +def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): + top, bottom, left, right = (0, 0, 0, 0) + + # 获取图像尺寸 + h, w, _ = image.shape + + # 对于长宽不相等的图片，找到最长的一边 + longest_edge = max(h, w) + + # 计算短边需要增加多上像素宽度使其与长边等长 + if h < longest_edge: + dh = longest_edge - h + top = dh // 2 + bottom = dh - top + elif w < longest_edge: + dw = longest_edge - w + left = dw // 2 + right = dw - left + else: + pass + + # RGB颜色 + BLACK = [0, 0, 0] + # 给图像增加边界，是图片长、宽等长，cv2.BORDER_CONSTANT指定边界颜色由value指定 + constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) + # 调整图像大小并返回 + return cv2.resize(constant, (height, width)) +##########预测函数######################## +def face_predict(image,model): + image = resize_image(image) + image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) + image=image.astype('float32') + image/=255 + # 给出输入属于各个类别的概率与误差 + result = model.predict_proba(image) + print('属于该类的概率与误差是:', result) + + # 给出类别预测：0或者1 + result = model.predict_classes(image) + + # 返回类别预测结果 + return result[0] + +####如果带不动就展示这个程序咯 +if __name__ == '__main__': + # 加载模型 + my_model = keras.models.load_model('D:\model\model.h5') + + # 框住人脸的矩形边框颜色 + color = (0, 255, 0) + + # 捕获指定摄像头的实时视频流 + camera = cv2.VideoCapture(0) + + # 人脸识别分类器本地存储路径 + cascade_path = "/anaconda3/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml" + + # 循环检测识别人脸 + while True: + ret, frame = camera.read() # 读取一帧视频 + + # 图像灰化，降低计算复杂度 + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + # 使用人脸识别分类器，读入分类器 + face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') + + # 利用分类器识别出哪个区域为人脸 + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + if len(faces) > 0: + for (x, y, w, h) in faces: + # 截取脸部图像提交给模型识别这是谁 + image = frame[y: y + h, x: x + w] + faceID = face_predict(image,my_model) + + # 如果是“我” + if faceID == 0: + cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness=2) + + # 文字提示是谁 + cv2.putText(frame, 'me', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) # 字的线宽 + else: + cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness=2) + + # 文字提示是谁 + cv2.putText(frame, 'others', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) + + cv2.imshow("camera", frame) + + # 等待1毫秒看是否有按键输入 + k = cv2.waitKey(1) + # 如果输入q则退出循环 + if k & 0xFF == ord('q'): + break + + # 释放摄像头并销毁所有窗口 + camera.release() + cv2.destroyAllWindows() \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict (3).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict (3).py" new file mode 100644 index 0000000000000000000000000000000000000000..7bd9a167112034b7d32e9dbebe50be9f8fd963be --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict (3).py" @@ -0,0 +1,112 @@ +import tensorflow.keras as keras +import cv2 +IMAGE_SIZE=64 +#裁剪照片得到64*64的尺寸 +def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): + top, bottom, left, right = (0, 0, 0, 0) + + # 获取图像尺寸 + h, w, _ = image.shape + + # 对于长宽不相等的图片，找到最长的一边 + longest_edge = max(h, w) + + # 计算短边需要增加多上像素宽度使其与长边等长 + if h < longest_edge: + dh = longest_edge - h + top = dh // 2 + bottom = dh - top + elif w < longest_edge: + dw = longest_edge - w + left = dw // 2 + right = dw - left + else: + pass + + # RGB颜色 + BLACK = [0, 0, 0] + # 给图像增加边界，是图片长、宽等长，cv2.BORDER_CONSTANT指定边界颜色由value指定 + constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) + # 调整图像大小并返回 + return cv2.resize(constant, (height, width)) +##########预测函数######################## +def face_predict(image,model): + image = resize_image(image) + image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) + image=image.astype('float32') + image/=255 + # 给出输入属于各个类别的概率与误差 + result = model.predict_proba(image) + print('属于该类的概率与误差是:', result) + + # 给出类别预测：0或者1 + result = model.predict_classes(image) + + # 返回类别预测结果 + return result[0] + +####如果带不动就展示这个程序咯 +if __name__ == '__main__': + # 加载模型 + my_model = keras.models.load_model('D:\model\model.h5') + + # 框住人脸的矩形边框颜色 + color = (0, 255, 0) + + # 捕获指定摄像头的实时视频流 + camera = cv2.VideoCapture(0) + + # 人脸识别分类器本地存储路径 + cascade_path = "/anaconda3/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml" + + # 循环检测识别人脸 + while True: + ret, frame = camera.read() # 读取一帧视频 + + # 图像灰化，降低计算复杂度 + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + # 使用人脸识别分类器，读入分类器 + face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') + + # 利用分类器识别出哪个区域为人脸 + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + if len(faces) > 0: + for (x, y, w, h) in faces: + # 截取脸部图像提交给模型识别这是谁 + image = frame[y: y + h, x: x + w] + faceID = face_predict(image,my_model) + + # 如果是“我” + if faceID == 0: + cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness=2) + + # 文字提示是谁 + cv2.putText(frame, 'me', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) # 字的线宽 + else: + cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness=2) + + # 文字提示是谁 + cv2.putText(frame, 'others', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) + + cv2.imshow("camera", frame) + + # 等待1毫秒看是否有按键输入 + k = cv2.waitKey(1) + # 如果输入q则退出循环 + if k & 0xFF == ord('q'): + break + + # 释放摄像头并销毁所有窗口 + camera.release() + cv2.destroyAllWindows() \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict.py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict.py" new file mode 100644 index 0000000000000000000000000000000000000000..7bd9a167112034b7d32e9dbebe50be9f8fd963be --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/predict.py" @@ -0,0 +1,112 @@ +import tensorflow.keras as keras +import cv2 +IMAGE_SIZE=64 +#裁剪照片得到64*64的尺寸 +def resize_image(image, height=IMAGE_SIZE, width=IMAGE_SIZE): + top, bottom, left, right = (0, 0, 0, 0) + + # 获取图像尺寸 + h, w, _ = image.shape + + # 对于长宽不相等的图片，找到最长的一边 + longest_edge = max(h, w) + + # 计算短边需要增加多上像素宽度使其与长边等长 + if h < longest_edge: + dh = longest_edge - h + top = dh // 2 + bottom = dh - top + elif w < longest_edge: + dw = longest_edge - w + left = dw // 2 + right = dw - left + else: + pass + + # RGB颜色 + BLACK = [0, 0, 0] + # 给图像增加边界，是图片长、宽等长，cv2.BORDER_CONSTANT指定边界颜色由value指定 + constant = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=BLACK) + # 调整图像大小并返回 + return cv2.resize(constant, (height, width)) +##########预测函数######################## +def face_predict(image,model): + image = resize_image(image) + image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) + image=image.astype('float32') + image/=255 + # 给出输入属于各个类别的概率与误差 + result = model.predict_proba(image) + print('属于该类的概率与误差是:', result) + + # 给出类别预测：0或者1 + result = model.predict_classes(image) + + # 返回类别预测结果 + return result[0] + +####如果带不动就展示这个程序咯 +if __name__ == '__main__': + # 加载模型 + my_model = keras.models.load_model('D:\model\model.h5') + + # 框住人脸的矩形边框颜色 + color = (0, 255, 0) + + # 捕获指定摄像头的实时视频流 + camera = cv2.VideoCapture(0) + + # 人脸识别分类器本地存储路径 + cascade_path = "/anaconda3/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml" + + # 循环检测识别人脸 + while True: + ret, frame = camera.read() # 读取一帧视频 + + # 图像灰化，降低计算复杂度 + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + + # 使用人脸识别分类器，读入分类器 + face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') + + # 利用分类器识别出哪个区域为人脸 + faces = face_cascade.detectMultiScale(gray, 1.1, 5) + if len(faces) > 0: + for (x, y, w, h) in faces: + # 截取脸部图像提交给模型识别这是谁 + image = frame[y: y + h, x: x + w] + faceID = face_predict(image,my_model) + + # 如果是“我” + if faceID == 0: + cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness=2) + + # 文字提示是谁 + cv2.putText(frame, 'me', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) # 字的线宽 + else: + cv2.rectangle(frame, (x, y), (x + w, y + h), color, thickness=2) + + # 文字提示是谁 + cv2.putText(frame, 'others', + (x + 30, y + 30), # 坐标 + cv2.FONT_HERSHEY_SIMPLEX, # 字体 + 1, # 字号 + (255, 0, 255), # 颜色 + 2) + + cv2.imshow("camera", frame) + + # 等待1毫秒看是否有按键输入 + k = cv2.waitKey(1) + # 如果输入q则退出循环 + if k & 0xFF == ord('q'): + break + + # 释放摄像头并销毁所有窗口 + camera.release() + cv2.destroyAllWindows() \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\345\244\215\344\271\240 (2).pptx" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\345\244\215\344\271\240 (2).pptx" new file mode 100644 index 0000000000000000000000000000000000000000..437150ac45fb1162da2d88f3559e2409514cff6c --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\345\244\215\344\271\240 (2).pptx" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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sha256:1c95bc3952cc9625c0f14bc0fb3d4a1ee76a8327679486e28278485d96cb293e +size 5365457 diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253 (2).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253 (2).py" new file mode 100644 index 0000000000000000000000000000000000000000..b963ef653a73d6df9f114bd7f495777525ce8f1d --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253 (2).py" @@ -0,0 +1,51 @@ +""" 从视频读取帧保存为图片""" +import cv2 +import numpy as np +#cap = cv2.VideoCapture("C:/Users/lenovo/Videos/1.mp4")#读取文件 +cap = cv2.VideoCapture(0)#读取摄像头 + +#皮肤检测 +def A(img): + + YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB) #转换至YCrCb空间 + (y,cr,cb) = cv2.split(YCrCb) #拆分出Y,Cr,Cb值 + cr1 = cv2.GaussianBlur(cr, (5,5), 0) + _, skin = cv2.threshold(cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) #Ostu处理 + res = cv2.bitwise_and(img,img, mask = skin) + return res + +def B(img): + + #binaryimg = cv2.Canny(Laplacian, 50, 200) #二值化，canny检测 + h = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) #寻找轮廓 + contour = h[0] + contour = sorted(contour, key = cv2.contourArea, reverse=True)#已轮廓区域面积进行排序 + #contourmax = contour[0][:, 0, :]#保留区域面积最大的轮廓点坐标 + bg = np.ones(dst.shape, np.uint8) *255#创建白色幕布 + ret = cv2.drawContours(bg,contour[0],-1,(0,0,0),3) #绘制黑色轮廓 + return ret + + +while(True): + + ret, frame = cap.read() + #下面三行可以根据自己的电脑进行调节 + src = cv2.resize(frame,(400,350), interpolation=cv2.INTER_CUBIC)#窗口大小 + cv2.rectangle(src, (90, 60), (300, 300 ), (0, 255, 0))#框出截取位置 + roi = src[60:300 , 90:300] # 获取手势框图 + + res = A(roi) # 进行肤色检测 + cv2.imshow("0",roi) + + gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY) + dst = cv2.Laplacian(gray, cv2.CV_16S, ksize = 3) + Laplacian = cv2.convertScaleAbs(dst) + + contour = B(Laplacian)#轮廓处理 + cv2.imshow("2",contour) + + key = cv2.waitKey(50) & 0xFF + if key == ord('q'): + break +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253 (3).py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253 (3).py" new file mode 100644 index 0000000000000000000000000000000000000000..b963ef653a73d6df9f114bd7f495777525ce8f1d --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253 (3).py" @@ -0,0 +1,51 @@ +""" 从视频读取帧保存为图片""" +import cv2 +import numpy as np +#cap = cv2.VideoCapture("C:/Users/lenovo/Videos/1.mp4")#读取文件 +cap = cv2.VideoCapture(0)#读取摄像头 + +#皮肤检测 +def A(img): + + YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB) #转换至YCrCb空间 + (y,cr,cb) = cv2.split(YCrCb) #拆分出Y,Cr,Cb值 + cr1 = cv2.GaussianBlur(cr, (5,5), 0) + _, skin = cv2.threshold(cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) #Ostu处理 + res = cv2.bitwise_and(img,img, mask = skin) + return res + +def B(img): + + #binaryimg = cv2.Canny(Laplacian, 50, 200) #二值化，canny检测 + h = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) #寻找轮廓 + contour = h[0] + contour = sorted(contour, key = cv2.contourArea, reverse=True)#已轮廓区域面积进行排序 + #contourmax = contour[0][:, 0, :]#保留区域面积最大的轮廓点坐标 + bg = np.ones(dst.shape, np.uint8) *255#创建白色幕布 + ret = cv2.drawContours(bg,contour[0],-1,(0,0,0),3) #绘制黑色轮廓 + return ret + + +while(True): + + ret, frame = cap.read() + #下面三行可以根据自己的电脑进行调节 + src = cv2.resize(frame,(400,350), interpolation=cv2.INTER_CUBIC)#窗口大小 + cv2.rectangle(src, (90, 60), (300, 300 ), (0, 255, 0))#框出截取位置 + roi = src[60:300 , 90:300] # 获取手势框图 + + res = A(roi) # 进行肤色检测 + cv2.imshow("0",roi) + + gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY) + dst = cv2.Laplacian(gray, cv2.CV_16S, ksize = 3) + Laplacian = cv2.convertScaleAbs(dst) + + contour = B(Laplacian)#轮廓处理 + cv2.imshow("2",contour) + + key = cv2.waitKey(50) & 0xFF + if key == ord('q'): + break +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git "a/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253.py" "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253.py" new file mode 100644 index 0000000000000000000000000000000000000000..b963ef653a73d6df9f114bd7f495777525ce8f1d --- /dev/null +++ "b/\346\210\220\345\223\201\345\244\226\346\217\264/lazy1/\346\211\213\345\212\277\350\257\206\345\210\253.py" @@ -0,0 +1,51 @@ +""" 从视频读取帧保存为图片""" +import cv2 +import numpy as np +#cap = cv2.VideoCapture("C:/Users/lenovo/Videos/1.mp4")#读取文件 +cap = cv2.VideoCapture(0)#读取摄像头 + +#皮肤检测 +def A(img): + + YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB) #转换至YCrCb空间 + (y,cr,cb) = cv2.split(YCrCb) #拆分出Y,Cr,Cb值 + cr1 = cv2.GaussianBlur(cr, (5,5), 0) + _, skin = cv2.threshold(cr1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) #Ostu处理 + res = cv2.bitwise_and(img,img, mask = skin) + return res + +def B(img): + + #binaryimg = cv2.Canny(Laplacian, 50, 200) #二值化，canny检测 + h = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) #寻找轮廓 + contour = h[0] + contour = sorted(contour, key = cv2.contourArea, reverse=True)#已轮廓区域面积进行排序 + #contourmax = contour[0][:, 0, :]#保留区域面积最大的轮廓点坐标 + bg = np.ones(dst.shape, np.uint8) *255#创建白色幕布 + ret = cv2.drawContours(bg,contour[0],-1,(0,0,0),3) #绘制黑色轮廓 + return ret + + +while(True): + + ret, frame = cap.read() + #下面三行可以根据自己的电脑进行调节 + src = cv2.resize(frame,(400,350), interpolation=cv2.INTER_CUBIC)#窗口大小 + cv2.rectangle(src, (90, 60), (300, 300 ), (0, 255, 0))#框出截取位置 + roi = src[60:300 , 90:300] # 获取手势框图 + + res = A(roi) # 进行肤色检测 + cv2.imshow("0",roi) + + gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY) + dst = cv2.Laplacian(gray, cv2.CV_16S, ksize = 3) + Laplacian = cv2.convertScaleAbs(dst) + + contour = B(Laplacian)#轮廓处理 + cv2.imshow("2",contour) + + key = cv2.waitKey(50) & 0xFF + if key == ord('q'): + break +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git "a/\347\254\254\344\270\200\347\253\240/2DAnimation.swf" "b/\347\254\254\344\270\200\347\253\240/2DAnimation.swf" new file mode 100644 index 0000000000000000000000000000000000000000..b3e31524e6db8f7c61d6bd555fb364ae3fb4c2a9 --- /dev/null +++ "b/\347\254\254\344\270\200\347\253\240/2DAnimation.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2150ef0593c023a9e44596321cb4fb830551c2472a359fb972f108bca1b5aacf +size 10697 diff --git "a/\347\254\254\344\270\200\347\253\240/3DAnimation.avi" "b/\347\254\254\344\270\200\347\253\240/3DAnimation.avi" new file mode 100644 index 0000000000000000000000000000000000000000..44319350e8fbf846eac87273a17f0c095bd83380 --- /dev/null +++ "b/\347\254\254\344\270\200\347\253\240/3DAnimation.avi" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93c3f33f3d8adbed1add81d7e24ec135d667dc55cd4a3ad7a91c27753ec2b504 +size 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https://git-lfs.github.com/spec/v1 +oid sha256:46c070e266545d8c4cd26f9586752fd7694568b6741fd1795d8c2b913253c695 +size 81155 diff --git "a/\347\254\254\344\270\203\347\253\240/clips/700K.wmv" "b/\347\254\254\344\270\203\347\253\240/clips/700K.wmv" new file mode 100644 index 0000000000000000000000000000000000000000..8ff7b53c906691859ae11199e51c75cc4f72210f --- /dev/null +++ "b/\347\254\254\344\270\203\347\253\240/clips/700K.wmv" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:135da1a1b4f3264958ff7df88af1261f49408fba20c6de91edcf67d379555877 +size 1021644 diff --git "a/\347\254\254\344\270\203\347\253\240/windowmedia.swf" "b/\347\254\254\344\270\203\347\253\240/windowmedia.swf" new file mode 100644 index 0000000000000000000000000000000000000000..51a5148b1c29bac354af14f7dc281e59f966f32d --- /dev/null +++ "b/\347\254\254\344\270\203\347\253\240/windowmedia.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:751de5320b5ea73b700c864e262817b5808b5c3cb3139f64379802b86cca22bd +size 12431511 diff --git "a/\347\254\254\344\270\203\347\253\240/\347\254\254\344\270\203\347\253\240 new.ppt" "b/\347\254\254\344\270\203\347\253\240/\347\254\254\344\270\203\347\253\240 new.ppt" new file mode 100644 index 0000000000000000000000000000000000000000..daa019b7ce78dd5f1acd22c072e311837669e402 --- /dev/null +++ "b/\347\254\254\344\270\203\347\253\240/\347\254\254\344\270\203\347\253\240 new.ppt" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6de7cf3a80134a9959bd6eeae690e326f897c46db9494ce4f544fcd5584328ee +size 4547584 diff --git "a/\347\254\254\344\270\203\347\253\240/\347\254\254\344\270\203\347\253\240.ppt" "b/\347\254\254\344\270\203\347\253\240/\347\254\254\344\270\203\347\253\240.ppt" new file mode 100644 index 0000000000000000000000000000000000000000..53c582ea5229b04fc2b378137616ebbbd536b392 --- /dev/null +++ "b/\347\254\254\344\270\203\347\253\240/\347\254\254\344\270\203\347\253\240.ppt" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:781c78fa1119859282b28d86fb16430074d39cb270ec15a5d96b4d902e4de3c0 +size 4627968 diff --git "a/\347\254\254\344\270\203\347\253\240/\347\275\221\344\270\212\347\233\264\346\222\255\345\256\236\344\276\213.swf" "b/\347\254\254\344\270\203\347\253\240/\347\275\221\344\270\212\347\233\264\346\222\255\345\256\236\344\276\213.swf" new file mode 100644 index 0000000000000000000000000000000000000000..515114f7215a53a88cb12fa8064a4195153c8959 --- /dev/null +++ "b/\347\254\254\344\270\203\347\253\240/\347\275\221\344\270\212\347\233\264\346\222\255\345\256\236\344\276\213.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e603fcea007dc699cb985d2208af1a947ea58077ae803f7edbe5f66d5980c76 +size 2840718 diff --git "a/\347\254\254\344\270\211\347\253\240/Dictpad.swf" "b/\347\254\254\344\270\211\347\253\240/Dictpad.swf" new file mode 100644 index 0000000000000000000000000000000000000000..bced4929a7b0b64f960fbb4f1f044aa6928092b8 --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/Dictpad.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8bd56dee5308ba69926009c48598880b8f3d0c133b4a205cf40542f69ea54df0 +size 794726 diff --git "a/\347\254\254\344\270\211\347\253\240/VC6.0+Speech sdk.swf" "b/\347\254\254\344\270\211\347\253\240/VC6.0+Speech sdk.swf" new file mode 100644 index 0000000000000000000000000000000000000000..36b6d1b0d275f0a5313ef786d9d00bcf20ba9297 --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/VC6.0+Speech sdk.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53fb5ee7e95e35f3da01b0d0865ed22d9e40f7f2e2d837c42845695df194f27a +size 490428 diff --git "a/\347\254\254\344\270\211\347\253\240/win7\350\257\255\351\237\263\350\257\206\345\210\253\344\275\277\347\224\250\347\224\250\344\276\213.swf" "b/\347\254\254\344\270\211\347\253\240/win7\350\257\255\351\237\263\350\257\206\345\210\253\344\275\277\347\224\250\347\224\250\344\276\213.swf" new file mode 100644 index 0000000000000000000000000000000000000000..ada7940c2e79d6de94a6f7d95cb74b3435e0a3eb --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/win7\350\257\255\351\237\263\350\257\206\345\210\253\344\275\277\347\224\250\347\224\250\344\276\213.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2761bb240ca91028bf948a3a54216afd133524bf55346cc2e92b661b07af0da +size 1035051 diff --git "a/\347\254\254\344\270\211\347\253\240/\344\277\235\345\255\230\345\243\260\351\237\263.swf" "b/\347\254\254\344\270\211\347\253\240/\344\277\235\345\255\230\345\243\260\351\237\263.swf" new file mode 100644 index 0000000000000000000000000000000000000000..815ec3ef0a490e04a2ea398911ebcb93a19b0bbc --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/\344\277\235\345\255\230\345\243\260\351\237\263.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adc05db62c95e2eb17f0e74c984f522809e7d202dff055cfc66fc2e7bf1b902a +size 407254 diff --git "a/\347\254\254\344\270\211\347\253\240/\345\260\201\351\235\242\347\224\250.swf" "b/\347\254\254\344\270\211\347\253\240/\345\260\201\351\235\242\347\224\250.swf" new file mode 100644 index 0000000000000000000000000000000000000000..6257b81a60c9c4d6efe3b2a5bf48963bbcb95115 --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/\345\260\201\351\235\242\347\224\250.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9fa314ed79447da90399bcc4aa58aa2abd2c7960893e00735b44046c2af3881a +size 2511 diff --git "a/\347\254\254\344\270\211\347\253\240/\345\275\225\345\210\266\345\243\260\351\237\263.swf" "b/\347\254\254\344\270\211\347\253\240/\345\275\225\345\210\266\345\243\260\351\237\263.swf" new file mode 100644 index 0000000000000000000000000000000000000000..d0adc3378c75e79a22f84239e6826ca7bad164ad --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/\345\275\225\345\210\266\345\243\260\351\237\263.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a50161bb510b15d07e9e0a4a4d6651b938b42dadb053a239010cf68afe808e03 +size 855476 diff --git "a/\347\254\254\344\270\211\347\253\240/\347\211\271\346\256\212\351\237\263\346\225\210.swf" "b/\347\254\254\344\270\211\347\253\240/\347\211\271\346\256\212\351\237\263\346\225\210.swf" new file mode 100644 index 0000000000000000000000000000000000000000..12befac8968bccc6c5ee7b2f4d550041511c905d --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/\347\211\271\346\256\212\351\237\263\346\225\210.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64e62ccc0890032e541ff5dfe098c969c15a22d359c9e7465c2eea6ac719d00c +size 504621 diff --git "a/\347\254\254\344\270\211\347\253\240/\347\254\254\344\270\211\347\253\240.ppt" "b/\347\254\254\344\270\211\347\253\240/\347\254\254\344\270\211\347\253\240.ppt" new file mode 100644 index 0000000000000000000000000000000000000000..6f7839fc85d9e8d4bed87f165ed2f4048afc5265 --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/\347\254\254\344\270\211\347\253\240.ppt" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7fbf6a5df31f54fcc5868da6aab34b4b8132af55bd7d579cef06a186b8c6077 +size 18746880 diff --git "a/\347\254\254\344\270\211\347\253\240/\347\274\226\350\276\221\345\243\260\351\237\263.swf" "b/\347\254\254\344\270\211\347\253\240/\347\274\226\350\276\221\345\243\260\351\237\263.swf" new file mode 100644 index 0000000000000000000000000000000000000000..130417b66e6be3d9c58b874b112af6d703b917a4 --- /dev/null +++ "b/\347\254\254\344\270\211\347\253\240/\347\274\226\350\276\221\345\243\260\351\237\263.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:60aaab87717bc742a08d430b235f3a178393a3a64a1518de6e10e6a2a08e685a +size 3589989 diff --git "a/\347\254\254\344\272\214\347\253\240/Matlab\345\233\276\345\203\217\345\244\204\347\220\206\346\272\220\347\240\201.txt" "b/\347\254\254\344\272\214\347\253\240/Matlab\345\233\276\345\203\217\345\244\204\347\220\206\346\272\220\347\240\201.txt" new file mode 100644 index 0000000000000000000000000000000000000000..c75800659236b571b590b4982a414aef8adb81db --- /dev/null +++ "b/\347\254\254\344\272\214\347\253\240/Matlab\345\233\276\345\203\217\345\244\204\347\220\206\346\272\220\347\240\201.txt" @@ -0,0 +1,334 @@ +Ե(룬س +I = imread('rice.png'); %ȡrice.pngͼ񲢸ֵI +imshow(I);%ʾIеͼ +BW1=edge(I,'Roberts'); %RobertsӼ⣬ԵͼֵBW1 +figure;imshow(BW1);%ʾͼBW1 +BW2=edge(I,'Sobel'); %SobelӼ⣬ԵͼֵBW2 +figure;imshow(BW2); %ʾͼBW2 +BW3=edge(I,'Canny'); %CannyӼ⣬ԵͼֵBW3 +figure;imshow(BW3); %ʾͼBW3 + + + + + +1huffman.m(ʱس򿪣 +clear all +fprintf('Reading data...') +data = imread('C:\Users\lotus\Desktop\ý弼\MATLAB\ͼƬ\lena.jpg'); +data = uint8(data); %,Ϊuint8 +%save D:\a.dat +%%%%%%%%%%%%%%%%%%%ʾԭͼƬ%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +subplot(121) +imshow(data); +xlabel('\fontsize{16}ԭʼͼ'); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +fprintf('Done!\n') +%ѹ +fprintf('compressing data ...'); +[zipped,info] = norm2huff(data); +fprintf('Done!\n') +%ѹ +fprintf('Decompresing data ...'); +unzipped = huff2norm(zipped,info); +fprintf('Done!\n') +%Ƿʧ +isOK = isequal(data(:),unzipped(:)) +if(isOK==1) + unzipped=data; +end +%ʾѹЧ +whos data zipped unzipped +%%%%ʾѹͼƬ%%%%%% +subplot(122) +imshow(unzipped);xlabel('\fontsize{16}ͼ'); + + +2norm2huff.m +%%%%%%%%%% norm2huff %%%%%%%%%% +function [zipped,info] = norm2huff(vector) +if ~isa(vector,'uint8'), + error('input argument must be a uint8 vector') +end +vector = vector(:)'; +%תΪ +f = frequency(vector); +%Ԫسֵĸ +simbols = find(f~=0); +f = f(simbols); %Ԫذճֵĸ +[f,sortindex] = sort(f); +simbols = simbols(sortindex); +%generate the codeword as the 52 bits of a double +len = length(simbols); +simbols_index = num2cell(1:len); +codeword_tmp = cell(len,1); +while length(f)>1, + index1 = simbols_index{1}; + index2 = simbols_index{2}; + codeword_tmp(index1) = addnode(codeword_tmp(index1),uint8(0)); + codeword_tmp(index2) = addnode(codeword_tmp(index2),uint8(1)); + f = [sum(f(1:2)) f(3:end)]; + simbols_index = [{[index1 index2]} simbols_index(3:end)]; +%,ʹڵƵʾǰһڵƵ൱ +%resort data in order to have the two nodes with lower frequency as + % first to + [f,sortindex] = sort(f); + simbols_index = simbols_index(sortindex); +end +%ӦӦԪ +codeword = cell(256:1); +codeword(simbols) = codeword_tmp; +%ַܵ +len = 0; +for index=1:length(vector), + len = len+length(codeword{double(vector(index))+1}); +end +%01 +string = repmat(uint8(0),1,len); +pointer = 1; +for index=1:length(vector), + code = codeword{double(vector(index))+1}; + len = length(code); + string(pointer+(0:len-1)) = code; + pointer =pointer+len; +end + %Ҫ, +len = length(string); +pad = 8-mod(len,8); +if pad>0, + string =[string uint8(zeros(1,pad))]; +end +%ʵõ +codeword =codeword(simbols); +codelen =zeros(size(codeword)); +weights=2.^(0:23); +maxcodelen = 0; +for index=1:length(codeword), + len = length(codeword{index}); + if len>maxcodelen, + maxcodelen = len; + end + if len>0, + code = sum(weights(codeword{index}==1)); + code =bitset(code,len+1); + codeword{index} = code; + codelen(index) = len; + end +end +codeword = [codeword{:}]; +%ѹ +cols = length(string)/8; +string = reshape(string,8,cols); +weights = 2.^(0:7); +zipped = uint8(weights*double(string)); +%洢һϡ +huffcodes = sparse(1,1); % init sparse matrix +for index = 1:numel(codeword), + huffcodes(codeword(index),1) = simbols(index); +end +%Ϣṹ +info.pad = pad; +info.ratio = cols./length(vector); +info.length = length(vector); +info.maxcodelen = maxcodelen; +info.huffcodes=huffcodes; + + +(3)huff2norm.m +%%%%%%%% huff2norm %%%%%%%%%%%% +function vector =huff2norm (zipped,info) +% HUFF2NORM huffman +% HUFF2NORM(X,INFO)ϢṹinfozippedĽ% +%X(:)ʽ +if ~isa(zipped,'uint8'), + error('input argument must be a unit8 vector') +end +%01 +len = length(zipped); +string =repmat(uint8(0),1,len.*8); +bitindex = 1:8; +for index = 1:len, + string(bitindex+8.*(index-1)) = uint8(bitget(zipped(index),bitindex)); +end +%ַ +string = logical(string(:)'); % make a row of it +len = length(string); +string ((len-info.pad+1):end) = []; % remove 0 padding +len = length(string); +% +weights = 2.^(0:51); +vector =repmat(uint8(0),1,info.length); +vectorindex = 1; +codeindex = 1; +code = 0; +for index = 1:len, + code = bitset(code,codeindex,string(index)); + codeindex = codeindex+1; + byte = decode(bitset(code,codeindex),info); + if byte>0, % + vector(vectorindex) = byte-1; + codeindex = 1; + code = 0; + vectorindex = vectorindex+1; + end +end + + +(4)frequency.m(ڲú +%%%%%%%%%%%%%% frequency %%%%%%%%%% +function f=frequency(vector) +%FREQUENCYԪسָ +if ~isa(vector,'uint8'), + error('input argument must be a uint8 vector') +end +f = repmat(0,1,256); +%ɨ +len = length(vector); +for index =0:256, % + f(index+1) = sum(vector==uint8(index)); +end +%һ +f = f./len; + + +5decode.m(ڲú +%%%%%%%%%%%%% decode %%%%%%%%%% +function byte = decode(code,info) +byte = info.huffcodes(code); + + +6addnode.m(ڲú +%%%%%%%%%% addnode %%%%%%%%%% +function codeword_new = addnode(codeword_old,item) +codeword_new = cell(size(codeword_old)); +for index =1:length(codeword_old), + codeword_new{index} = [item codeword_old{index}]; +end + + + + +DCT +1DCTQ.m +clear all; +clc; +I = imread('cameraman.tif'); +I=double(uint8(I)); +I_old=uint8(I); +[mr,mc]=size(I_old); +T = dctmtx(8); +dct = @(block_struct) T * block_struct.data * T'; +B_dct= blockproc(I,[8 8],dct); %DCT任 + mask1=[16 11 10 16 24 40 51 61 + 12 12 14 19 26 58 60 55 + 14 13 16 24 40 57 69 56 + 14 17 22 29 51 87 80 62 + 18 22 37 56 68 109 103 77 + 24 35 55 64 81 104 113 92 + 49 64 78 87 103 121 120 101 + 72 92 95 98 112 100 103 99 ]; + mask2=[16 11 10 200 200 200 200 200 + 12 12 200 200 200 200 200 200 + 200 200 200 200 200 200 200 200 + 200 200 200 200 200 200 200 200 + 200 200 200 200 200 200 200 200 + 200 200 200 200 200 200 200 200 + 200 200 200 200 200 200 200 200 + 200 200 200 200 200 200 200 200]; +%1 +fun1=@(block_struct)round(block_struct.data./mask1); +B_quant1= blockproc(B_dct,[8 8],fun1); +%ͼƬֳ8*8Ŀ +k=0; +for i=1:8:249 + for j=1:8:249 + k=k+1; + model(:,:,k)=B_quant1(i:i+7,j:j+7); + end +end +%г̱ѹ,ѹռֽ +total=0; +for m=1:k + total=total+RLE(model(:,:,m)); +end +%ѹ + scale1=mr*mc/total; + disp(['1ͼг̱ѹ: ', num2str(scale1)]); +% +fun2=@(block_struct)block_struct.data.*mask1; +B_abquant1=blockproc(B_quant1,[8 8],fun2); +invdct = @(block_struct) T' * block_struct.data * T; +%DCT任 +B_abdct1= blockproc(B_abquant1,[8 8],invdct); +I_new1=uint8(B_abdct1); +%2 +fun3=@(block_struct)round(block_struct.data./mask2); +B_quant2= blockproc(B_dct,[8 8],fun3); +%ͼƬֳ8*8Ŀ +k=0; +for i=1:8:249 + for j=1:8:249 + k=k+1; + model(:,:,k)=B_quant2(i:i+7,j:j+7); + end +end +%г̱ѹ,ѹռֽ +total=0; +for m=1:k + total=total+RLE(model(:,:,m)); +end +%ѹ + scale2=mr*mc/total; + disp(['2ͼг̱ѹ: ', num2str(scale2)]); +% +fun4=@(block_struct)block_struct.data.*mask2; +B_abquant2=blockproc(B_quant2,[8 8],fun4); +invdct = @(block_struct) T' * block_struct.data * T; + %DCT任 +B_abdct2= blockproc(B_abquant2,[8 8],invdct); +I_new2=uint8(B_abdct2); +%ʾЧȽͼ +figure(1),imshow(I_old); xlabel('ԭͼ'); +figure(2), imshow(I_new1); xlabel('1任ͼ'); +figure(3), imshow(I_new2); xlabel('2任ͼ'); + +(2)ӳRLE.m +%8*8ĿZɨг̱ +function P=RLE(I) +% I=[20,43,109,153,162,165,175,171; +% 117,166,184,187,193,180,170,171; +% 186,185,185,189,181,158,115,135; +% 178,174,155,118,90,77,44,28; +% 174,150,102,78,56,35,19,14; +% 179,175,139,104,47,25,36,90; +% 185,189,188,158,95,68,172,198; +% 188,192,198,193,164,154,201,209;]; +X=[1,1,2,3,2,1,1,2,3,4,5,4,3,2,1,1,2,3,4,5,6,7,6,5,4,3,2,1,1,2,3,4,5,6,7,8,8,7,6,5,4,3,2,3,4,5,6,7,8,8,7,6,5,4,5,6,7,8,8,7,6,7,8,8]; 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ͼ

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+ ִͼ + + diff --git "a/\347\254\254\345\205\255\347\253\240/Bigfile.jpg" "b/\347\254\254\345\205\255\347\253\240/Bigfile.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..88fb1e159dd489f436e6a0a83808c558675165e0 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/Bigfile.jpg" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27b7030ad47b50cbcab965759d1dbffebb35f2595fb78a5c9fd57700d4a120f9 +size 317934 diff --git "a/\347\254\254\345\205\255\347\253\240/Smallfile.jpg" "b/\347\254\254\345\205\255\347\253\240/Smallfile.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..921bb1b551fc4e252a1bd8113b2fe4f67d765714 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/Smallfile.jpg" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:98ec344f4a14cd22cfb1cc6010a608e80bb944b8fc7980ae5e733f98744a3662 +size 53040 diff --git "a/\347\254\254\345\205\255\347\253\240/a.jpg" 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0000000000000000000000000000000000000000..58df32f4900a559f14765c5a60cf2be15629987d --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/background.htm" @@ -0,0 +1,7 @@ + + + ͼΪҳı + + + + diff --git "a/\347\254\254\345\205\255\347\253\240/background.jpg" "b/\347\254\254\345\205\255\347\253\240/background.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..f243f4dae38edb3dee134ac166e7160bdbc1452c --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/background.jpg" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4bfb76d6d7fa6b0ba3b46d6904b0c6d7fa4ee18f93cf4192502b563377dd3987 +size 91694 diff --git "a/\347\254\254\345\205\255\347\253\240/c.jpg" "b/\347\254\254\345\205\255\347\253\240/c.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..6af7374970bdeff012f42533acab01d6ea433bf4 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/c.jpg" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1cfea1dd1574afff3afe429cf112f9376cbdcbe9f61f2e7689a9dc752d65ae29 +size 30086 diff --git "a/\347\254\254\345\205\255\347\253\240/clips/100K.wmv" "b/\347\254\254\345\205\255\347\253\240/clips/100K.wmv" new file mode 100644 index 0000000000000000000000000000000000000000..2a58dabd5805e64c8795fcbaed8fbb08baf4dcb4 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/clips/100K.wmv" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:464900d2fe810065289ed8b90be96f02cb112b725457d476194fb3c45d53b4bd +size 166447 diff --git "a/\347\254\254\345\205\255\347\253\240/clips/28K.wmv" "b/\347\254\254\345\205\255\347\253\240/clips/28K.wmv" new file mode 100644 index 0000000000000000000000000000000000000000..9da8945b84ecbc35d8309cd6233ef78728b1a3f4 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/clips/28K.wmv" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2b1374e21a8bee3d0818042e65a25fb74da3855e0a95028d79524cb7d05ff4d +size 81155 diff --git "a/\347\254\254\345\205\255\347\253\240/clips/350K.wmv" "b/\347\254\254\345\205\255\347\253\240/clips/350K.wmv" new file mode 100644 index 0000000000000000000000000000000000000000..1b67256f0d311b73f496c6fc5e13ddb5a78acdc8 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/clips/350K.wmv" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3bb412147f4682a452c948fbe70023793a44d8940fb5c47887091f0965fdead +size 487958 diff --git "a/\347\254\254\345\205\255\347\253\240/clips/56k.wmv" "b/\347\254\254\345\205\255\347\253\240/clips/56k.wmv" new file mode 100644 index 0000000000000000000000000000000000000000..c4789389c478c3b1048b952d3b56458c9911e979 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/clips/56k.wmv" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46c070e266545d8c4cd26f9586752fd7694568b6741fd1795d8c2b913253c695 +size 81155 diff --git "a/\347\254\254\345\205\255\347\253\240/clips/700K.wmv" "b/\347\254\254\345\205\255\347\253\240/clips/700K.wmv" new file mode 100644 index 0000000000000000000000000000000000000000..8ff7b53c906691859ae11199e51c75cc4f72210f --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/clips/700K.wmv" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:135da1a1b4f3264958ff7df88af1261f49408fba20c6de91edcf67d379555877 +size 1021644 diff --git "a/\347\254\254\345\205\255\347\253\240/d.jpg" "b/\347\254\254\345\205\255\347\253\240/d.jpg" new file mode 100644 index 0000000000000000000000000000000000000000..b07119c536cbe411e52ca1fbbe6d6aab24afdb8b --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/d.jpg" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11296a63dca97ba661387736bbf252fa2507107596f16ab054ed739be7a2254a +size 31080 diff --git "a/\347\254\254\345\205\255\347\253\240/demo.asp" "b/\347\254\254\345\205\255\347\253\240/demo.asp" new file mode 100644 index 0000000000000000000000000000000000000000..05f31ac0e9d35427c86a0551fb673473e27884d9 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/demo.asp" @@ -0,0 +1,8 @@ + + + + + Ϊ<%=request.form("Name")%>
+ ԱΪ<%=request.form("sex")%> + + diff --git "a/\347\254\254\345\205\255\347\253\240/ex.xml" "b/\347\254\254\345\205\255\347\253\240/ex.xml" new file mode 100644 index 0000000000000000000000000000000000000000..b11c818f130ba225224d48a2860779c0fcb4aa15 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/ex.xml" @@ -0,0 +1,37 @@ + + + + + <> + <Ա>Ů + <>19 + <רҵ>赸 + <༶> + <Ƭ>a.jpg + + + <> + <Ա> + <>18 + <רҵ> + <༶> + <Ƭ>b.jpg + + + <> + <Ա>Ů + <>18 + <רҵ> + <༶>İ + <Ƭ>c.jpg + + + <> + <Ա> + <>19 + <רҵ> + <༶>һ + <Ƭ>d.jpg + + + diff --git "a/\347\254\254\345\205\255\347\253\240/ex.xsl" "b/\347\254\254\345\205\255\347\253\240/ex.xsl" new file mode 100644 index 0000000000000000000000000000000000000000..be89f4913dd0ec4246ff004322de7fd364a10452 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/ex.xsl" @@ -0,0 +1,38 @@ + + + + + + + + + + + + + + + + + + + + + + + + +
Աרҵ༶Ƭ
+ + + + + +
+ + + + + + + diff --git "a/\347\254\254\345\205\255\347\253\240/first.htm" "b/\347\254\254\345\205\255\347\253\240/first.htm" new file mode 100644 index 0000000000000000000000000000000000000000..733caad35ec70e3cd7d5717dad5b3d6e0d720991 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/first.htm" @@ -0,0 +1,31 @@ + + + + + +

Ա

+
+
+ + + + + + + + + + + + + +
Ա + Ů + +
+ + + +
+
+ \ No newline at end of file diff --git "a/\347\254\254\345\205\255\347\253\240/flash.swf" "b/\347\254\254\345\205\255\347\253\240/flash.swf" new file mode 100644 index 0000000000000000000000000000000000000000..b3e31524e6db8f7c61d6bd555fb364ae3fb4c2a9 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/flash.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2150ef0593c023a9e44596321cb4fb830551c2472a359fb972f108bca1b5aacf +size 10697 diff --git "a/\347\254\254\345\205\255\347\253\240/sound.mid" "b/\347\254\254\345\205\255\347\253\240/sound.mid" new file mode 100644 index 0000000000000000000000000000000000000000..8d95cf76c72bd16e76d7ff8972bbfa1239704a60 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/sound.mid" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4ef9a1c7c9c8665c85ddb5054286ea898cad2528d2e92b8ba38868a2f114bea +size 8589 diff --git "a/\347\254\254\345\205\255\347\253\240/sound.mp3" "b/\347\254\254\345\205\255\347\253\240/sound.mp3" new file mode 100644 index 0000000000000000000000000000000000000000..e7da5174cad796d37e89f014892348b9d244bef3 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/sound.mp3" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc831f8cc2fb7dc16d2eaae774c563fdfa91de0c37be960e1b1ff77d947ddd26 +size 3285577 diff --git "a/\347\254\254\345\205\255\347\253\240/video.wmv" "b/\347\254\254\345\205\255\347\253\240/video.wmv" new file mode 100644 index 0000000000000000000000000000000000000000..9b55e7ecdf1556ab32adfef240f5d27e0e5f391f --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/video.wmv" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:063c1e37afc29f16a4f88d7ce7726ae84c5e7eea7e930fc24ac692a5af3d2343 +size 4317768 diff --git "a/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245FLASH.htm" "b/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245FLASH.htm" new file mode 100644 index 0000000000000000000000000000000000000000..2c6efa5a8a0267ce0b8d7726bff9673ded87018b --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245FLASH.htm" @@ -0,0 +1,8 @@ + + +ǶFlash + + + + + \ No newline at end of file diff --git "a/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245\350\203\214\346\231\257\351\237\263\344\271\220.htm" "b/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245\350\203\214\346\231\257\351\237\263\344\271\220.htm" new file mode 100644 index 0000000000000000000000000000000000000000..670d3de5b0038168b4960d5af7f4d4beb4f4cd1a --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245\350\203\214\346\231\257\351\237\263\344\271\220.htm" @@ -0,0 +1,9 @@ + + + ĵǶ뱳 + +

Ƕ˱ֵĵ

+ + + + \ No newline at end of file diff --git "a/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245\350\247\206\351\242\221.html" "b/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245\350\247\206\351\242\221.html" new file mode 100644 index 0000000000000000000000000000000000000000..e0328d2c237e43db50aaa0dce11789eacd34ee06 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\345\265\214\345\205\245\350\247\206\351\242\221.html" @@ -0,0 +1,19 @@ + + + ǶƵ + + + + + + + + + + + +
+
+Ƶļ + + diff --git "a/\347\254\254\345\205\255\347\253\240/\346\217\222\345\205\245\345\243\260\351\237\263\345\222\214\350\247\206\351\242\221.htm" "b/\347\254\254\345\205\255\347\253\240/\346\217\222\345\205\245\345\243\260\351\237\263\345\222\214\350\247\206\351\242\221.htm" new file mode 100644 index 0000000000000000000000000000000000000000..35603a5c108cae8f6d010ab14485b5adf5f53463 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\346\217\222\345\205\245\345\243\260\351\237\263\345\222\214\350\247\206\351\242\221.htm" @@ -0,0 +1,11 @@ + + + Ƶ + + + ļ +
+
+ Ƶļ + + diff --git "a/\347\254\254\345\205\255\347\253\240/\346\222\255\346\224\276\345\212\250\347\224\273.swf" "b/\347\254\254\345\205\255\347\253\240/\346\222\255\346\224\276\345\212\250\347\224\273.swf" new file mode 100644 index 0000000000000000000000000000000000000000..bb1ace3aec2cb301fdd0499e53898a47a93e7242 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\346\222\255\346\224\276\345\212\250\347\224\273.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5bac1b5488dc5963ea3de82235eb93d4b41ef71232cf183391a4d3946b6d0768 +size 4125842 diff --git "a/\347\254\254\345\205\255\347\253\240/\346\222\255\346\224\276\345\243\260\351\237\263\345\222\214\350\247\206\351\242\221\346\226\207\344\273\266.swf" "b/\347\254\254\345\205\255\347\253\240/\346\222\255\346\224\276\345\243\260\351\237\263\345\222\214\350\247\206\351\242\221\346\226\207\344\273\266.swf" new file mode 100644 index 0000000000000000000000000000000000000000..3ef4a1d275ed7734e191d6e40388ed99a9330914 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\346\222\255\346\224\276\345\243\260\351\237\263\345\222\214\350\247\206\351\242\221\346\226\207\344\273\266.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:80dc339e11220fe29a715d6568ebfe232cf056ffd9f994d68830386d9a7f65e2 +size 4511245 diff --git "a/\347\254\254\345\205\255\347\253\240/\347\225\214\351\235\242\344\273\213\347\273\215.swf" "b/\347\254\254\345\205\255\347\253\240/\347\225\214\351\235\242\344\273\213\347\273\215.swf" new file mode 100644 index 0000000000000000000000000000000000000000..763fca736bd040fab937b7aac7bbd44ba7b44bc7 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\225\214\351\235\242\344\273\213\347\273\215.swf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c40b680c1f8e07869a7a72b8a2513f051d5b9be8ce51d50a88ac736a6de54220 +size 680647 diff --git "a/\347\254\254\345\205\255\347\253\240/\347\254\254\345\205\255\347\253\240.ppt" "b/\347\254\254\345\205\255\347\253\240/\347\254\254\345\205\255\347\253\240.ppt" new file mode 100644 index 0000000000000000000000000000000000000000..7b6ffcecdb98e2f97c412fc124cd7d48e8663ea4 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\254\254\345\205\255\347\253\240.ppt" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d6e188a9cd0bd66af70e79db4af57d6a72da219379007490c4d8d2f82bc3117 +size 1852416 diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/DB/news.mdb" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/DB/news.mdb" new file mode 100644 index 0000000000000000000000000000000000000000..6696e4093fb9db82cb032ffe2f86c10ee5f039b0 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/DB/news.mdb" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0068afe56cb94e2ec3429e7972a2d4031bdba79ac7600bdaea21fb87d3f1fe0 +size 770048 diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/Web.Config" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/Web.Config" new file mode 100644 index 0000000000000000000000000000000000000000..b79eca34b2390fe4e26f1f8349958834b18b5680 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/Web.Config" @@ -0,0 +1,92 @@ + + + + + + +
+ +
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/footer.ascx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/footer.ascx" new file mode 100644 index 0000000000000000000000000000000000000000..290b01bc61b0b9afabc15c3725591bda6a718713 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/footer.ascx" @@ -0,0 +1,19 @@ +
+ + + + + + + + + + +
+
+
+  
+Copyright © 2010, All Rights Reserved +
+ + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/main.ascx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/main.ascx" new file mode 100644 index 0000000000000000000000000000000000000000..13b2f3c1c6f0693585b8314cc15bfb5179995f26 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/main.ascx" @@ -0,0 +1,13 @@ +<%@ Register TagPrefix="mynews" TagName="top" src="top.ascx"%> + + + + + + +
+   +Ź   +   +˳   +
\ No newline at end of file diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/newslist.ascx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/newslist.ascx" new file mode 100644 index 0000000000000000000000000000000000000000..ac6e3c2334d5303d6a667d5ebf2408bc68d410e3 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/newslist.ascx" @@ -0,0 +1,45 @@ +<%@ Import Namespace="System.Data" %> +<%@ Import Namespace="System.Data.OleDb" %> + + + + + + + + + +
+  :::::::: +
+ + + + + +
+ + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/review.ascx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/review.ascx" new file mode 100644 index 0000000000000000000000000000000000000000..4115c90691a41f0b7cf41f1dabc2703c9e0db7b0 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/review.ascx" @@ -0,0 +1,132 @@ +<%@ Import Namespace="System.Data" %> +<%@ Import Namespace="System.Data.OleDb" %> + + + + + + + + + + +
+  ۣ˹۵ο +
+ + + + + +
+
+ + + + + + + + + + +
+  ۣ +
+ + + + + + + + + + + + + + + + + +
Ŀ +* +
ߣ +* +
E-mail +* +
ݣ +*
+
+ +
+ diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/search.ascx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/search.ascx" new file mode 100644 index 0000000000000000000000000000000000000000..5a45265b5da8f992b3abf145d20f81d0142b602d --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/search.ascx" @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + +
  ::::::::
ؼʣ
+ + +
+ + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/top.ascx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/top.ascx" new file mode 100644 index 0000000000000000000000000000000000000000..3e1a85d9e724ee321b2d503835130455e5b86d30 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/ascx/top.ascx" @@ -0,0 +1,57 @@ +<%@ Import Namespace="System.Data" %> +<%@ Import Namespace="System.Data.OleDb" %> + + + + + + + + +
+ ŷϵͳ +̨ +
+ +ҳ + + + + "> +<%# DataBinder.Eval(Container.DataItem,"Classesname")%> + + + + +
+ + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/classmanager.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/classmanager.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..f4f5077226805552dc226e3a4b3cbd5171c6f11c --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/classmanager.aspx" @@ -0,0 +1,141 @@ +<%@ Page Language="VB" ContentType="text/html" debug="true"%> +<%@ Import Namespace="System.Data"%> +<%@ Import Namespace="System.Data.OleDb"%> +<%@ Register TagPrefix="mynews" TagName="main" src="ascx/main.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> + + + + +ŷ + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + +

ƣ + + +

+ +
+ +
+ + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/classnews.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/classnews.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..2e364f7bbd3e98e218e4d9ec72faafc09d40e2cf --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/classnews.aspx" @@ -0,0 +1,96 @@ +<%@ Page Language="VB"ContentType="text/html" Debug="true"%> +<%@ Import Namespace="System.Data"%> +<%@ Import Namespace="System.Data.OleDb"%> +<%@ Register TagPrefix="mynews" TagName="top" src="ascx/top.ascx"%> +<%@ Register TagPrefix="mynews" TagName="search" src="ascx/search.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> + + + + + +ŷб + + + + + + + + + + + +
+ + + + + + + +
  ::::<%=Classes%>::::
+
+ + + + + +
+
+ 		+ +
+ + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/index.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/index.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..803ae76f674222118762f5b72b6de0d8418ca8ce --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/index.aspx" @@ -0,0 +1,33 @@ +<%@ Page Language="VB" Debug="true" %> +<%@ Import Namespace="System.Data" %> +<%@ Import Namespace="System.Data.OleDb" %> +<%@ Register TagPrefix="mynews" TagName="top" src="ascx/top.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> +<%@ Register TagPrefix="mynews" TagName="search" src="ascx/search.ascx"%> +<%@ Register Src="ascx/newslist.ascx" TagName="newslist" TagPrefix="mynews" %> + + + +ŷϵͳ + + + + + + + + + + + +
+ + + +
+ + + + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/login.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/login.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..34814cfc952a4cb659000714aa4080d0c81e5e7e --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/login.aspx" @@ -0,0 +1,112 @@ +<%@ Page Language="VB" ContentType="text/html"%> +<%@ Import Namespace="System.Data"%> +<%@ Import Namespace="System.Data.OleDb"%> +<%@ Register TagPrefix="mynews" TagName="top" src="ascx/top.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> + + + + + +Ա½ + + + + + +
+ + + + + + + +
+ + + + + + + + + + + + +
û + +
룺 + +
+ +
+
+ + +
+
+ + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsadd.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsadd.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..12dee8c727dfddc55fdd482ce0aed9a02980c266 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsadd.aspx" @@ -0,0 +1,226 @@ +<%@ Page Language="VB" Debug="true"%> +<%@ Import Namespace="System.Data" %> +<%@ Import Namespace="System.Data.OleDb" %> +<%@ Register TagPrefix="mynews" TagName="main" src="ascx/main.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + + + + +
+ + + + + + + + + + + + + + + + + + + + + +
Ŀ + +
ߣ + +
ࣺ +
ݣ + + + + + + + +
+
+ + +
+
+
+ +
+ + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsedit.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsedit.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..11b6e56d171f5cfe2822ab3ea4082ee49e70c9ca --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsedit.aspx" @@ -0,0 +1,252 @@ +<%@ Page Language="VB" Debug="true"%> +<%@ Import Namespace="System.Data" %> +<%@ Import Namespace="System.Data.OleDb" %> +<%@ Register TagPrefix="mynews" TagName="main" src="ascx/main.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> + + + + + +޸ + + + + + + + + + + + + + + + + + + +
+
"> + + + + + + + +
+ + + + + + + + + + + + + + + + + + + + + +
Ŀ +
ߣ
ࣺ + +
ݣ + + + + + + + + +
+ + +
+
+
+ +
+ + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newshow.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newshow.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..f80da36bd9edd161feb2cadd19fe2cffb9b3876a --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newshow.aspx" @@ -0,0 +1,127 @@ +<%@ Page Language="VB" ContentType="text/html" debug="true"%> +<%@ Import Namespace="System.Data" %> +<%@ Import Namespace="System.Data.OleDb" %> +<%@ Register TagPrefix="mynews" TagName="top" src="ascx/top.ascx"%> +<%@ Register TagPrefix="mynews" TagName="review" src="ascx/review.ascx"%> +<%@ Register TagPrefix="mynews" TagName="newslist" src="ascx/newslist.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> + + + + + +ţ<%=session("newstitle")%> + + + + + + +
+ + + + + +
+ + + + + + + + + + + + + + + + +
+ +
+ߣ +ʱ䣺 +
+ +
+Դԣ +
+ +
+ 		+ +
+
+ + + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsmanager.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsmanager.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..8a85cc82d70a778950b850b77b2298128139e426 --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newsmanager.aspx" @@ -0,0 +1,126 @@ +<%@ Page Language="VB" debug="true"%> +<%@ Import Namespace="System.Data"%> +<%@ Import Namespace="System.Data.OleDb"%> +<%@ Import Namespace="System.IO"%> +<%@ Register TagPrefix="mynews" TagName="main" src="ascx/main.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> + + + + + +Ź + + + + + + + + + +
+
+ + + + + + + + +
+
+ + + + + + diff --git "a/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newssearch.aspx" "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newssearch.aspx" new file mode 100644 index 0000000000000000000000000000000000000000..52c4a041655455722b303b8df68722111d4c2d7a --- /dev/null +++ "b/\347\254\254\345\205\255\347\253\240/\347\275\221\347\253\231\345\256\236\344\276\213\346\272\220\344\273\243\347\240\201/VBNETnews\345\244\232\345\252\222\344\275\223\347\275\221\347\253\231/newssearch.aspx" @@ -0,0 +1,78 @@ +<%@ Page Language="VB" %> +<%@ Import Namespace="System.Data"%> +<%@ Import Namespace="System.Data.OleDb"%> +<%@ Register TagPrefix="mynews" TagName="top" src="ascx/top.ascx"%> +<%@ Register TagPrefix="mynews" TagName="special" src="ascx/newslist.ascx"%> +<%@ Register TagPrefix="mynews" TagName="footer" src="ascx/footer.ascx"%> + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + +
+Ĺؼǣ +
+" target="_blank"><%# container.DataItem("Title")%><%# container.DataItem("UpdateTime")%> +
+ 		+ +
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