Archive original 多媒体技术 course files

ck/README.md

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+### 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

lazy1/face_dataset.py

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+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')

lazy1/find_path.py

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+import cv2
+print(cv2.__file__)

lazy1/get_data.py

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+#!/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')

lazy1/main.py

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+#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()

lazy1/make_cnn.py

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+#!/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)

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()

lazy1/复习.pptx

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lazy1/多媒体大作业2220193626李子毅.pdf

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lazy1/多媒体答辩.pptx

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lazy1/大作业选题.docx

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lazy1/手势识别.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()

复习/~$复习.pptx

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+ͼƬ����    �̢������̢���˧��
+��Ƶ��д    �̢���˼Դ
+����ת����  �̢��������̲���
+�������    �̢�ս����������
+ͼ��ȥ��    �̢�������̢�л��
+ͼ����    �̢�����Ц�������֡���˼��
+����ʶ��    ������Ȼ��������
+����ʶ��    �̢ٶ���
+��Ե��ȡ    �̢��Ŵ�
+ͼ����    �̢�Ϳ־¡
+
+
+��ý�����
+�ҵļ���    �̢�����Դ������̢���Խ
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+�ҵ�ż��    �̢��������̢ڹ��򶫢۷�־ǿ
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+ȼ�����ֶ������  �̢�������
+�ҵ����    �̢�������̢���ܿ���������㡢������
+���ҽ���    �̢�����Ȼ������΢�����Ј�
+�ҵİ���    �̢ٹ����������̢ھ���������ǡ�����Ԫ
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+΢��Ӱ��������   �̢�����ΰ��������
+��������    �̢������á�������
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+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';'*.*'},'ѡ������ͼƬ');
+str=[pathname_img filename_img];%ƴ��pathname��filename���γ�����·����
+img=imread(str);
+set(handles.pushbutton1,'userdata',img);
+axes(handles.axes1);%��axes1�ľ�������о������
+imshow(img);
+function pushbutton2_Callback(hObject, eventdata, handles)
+axis off;
+[filename_hide,pathname_hide] = uigetfile({'*.jpg';'*.bmp';'*.*'},'ѡ��ˮӡͼƬ');
+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
+
+%Ƕ����ɣ���ʼ����
+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������״��������ȡ��������ˮӡ,
+%��Կ����ˮӡͼ��ĸߺͿ��������346����350����Կ����346350��
+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);
+% ��Ϣ��ȡ
+%ֻ��֪��ͼƬԭ������״��������ȡ��������ˮӡ��
+
+%׼������
+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
+
+%��ȡ��ϣ���ʼ����
+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));%��һ������0��λ��
+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
+
+
+%Ƕ����ɣ���ʼ����
+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������״��������ȡ��������ˮӡ,
+%��Կ����ˮӡͼ��ĸߺͿ��������346����350����Կ����346350��
+str1=get(findobj('tag','edit1'),'userdata');
+key=str1
+
+mhide=str2double([str1(1),str1(2),str1(3)]);%��
+nhide=str2double([str1(4),str1(5),str1(6)]);%��
+%***************************************************
+%�ж��ܲ���װ�����ɫͼ��������ԵĻ���Ҫ�������ռ�
+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(:);
+
+% ��Ϣ��ȡ
+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
+%��ȡ��ϣ���ʼ����
+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
+%***********************************************
+%�ж��Ƿ����װ�²�ɫͼ��
+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');
+%�ж�ά��
+imghide_size=size(imhide);
+dimension=numel(imghide_size);
+
+[m,n,h]=size(img);
+[mhide,nhide]=size(imhide);
+%��ȡ������Կ
+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));%��һ������0��λ��
+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
+
+
+%Ƕ����ɣ���ʼ����
+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������״��������ȡ��������ˮӡ,
+%��Կ����ˮӡͼ��ĸߺͿ��������346����350����Կ����346350��
+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);
+%***************************************************
+%�ж��ܲ���װ�����ɫͼ��������ԵĻ���Ҫ�������ռ�
+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(:);
+
+% ��Ϣ��ȡ
+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
+%��ȡ��ϣ���ʼ����
+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
+%***********************************************
+%�ж��Ƿ����װ�²�ɫͼ��
+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_book�����¼�ˣ���i���¼�˶�������ɫ
+%����ɫ��ȡ
+for i=1:m
+    for j=1:n
+        for k=1:16
+            if(zm(i,j)<16*k)%����������ɫ�ķ�����
+                zm_book(k)=zm_book(k)+1;%��k������+1
+                %zm_hide(i,j)�ṹ�������¼�˵�i����ɫ�ĵ�j������ɫ������λ��
+                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
+
+%Ƕ����ɣ���ʼ����
+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������״��������ȡ��������ˮӡ,
+%��Կ����ˮӡͼ��ĸߺͿ��������346����350����Կ����346350��
+str1=get(findobj('tag','edit1'),'userdata');
+key=str1
+
+mhide=str2double([str1(1),str1(2),str1(3)]);%��
+nhide=str2double([str1(4),str1(5),str1(6)]);%��
+%***************************************************
+%�ж��ܲ���װ�����ɫͼ��������ԵĻ���Ҫ�������ռ�
+judge=0;
+if(m*n>mhide*nhide*3)
+    judge=1;
+end
+if(judge==1)
+    nhide=nhide*3;
+end
+%***************************************************
+%��ȡ��ɫ������Ϣ
+
+%��������ͼ��
+zm=Img;
+zm_book=zeros(16,1);%zm_book�����¼�ˣ���i���¼�˶�������ɫ
+%����ɫ��ȡ����ͼ�����Ϣ
+for i=1:m
+    for j=1:n
+        for k=1:16
+            if(zm(i,j)<16*k)%����������ɫ�ķ�����
+                zm_book(k)=zm_book(k)+1;%��k������+1
+                %zm_hide(i,j)�ṹ�������¼�˵�i����ɫ�ĵ�j������ɫ������λ��
+                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
+%��ȡ��ϣ���ʼ����
+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
+%***********************************************
+%�ж��Ƿ����װ�²�ɫͼ��
+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);

多媒体大作业/王晨_2220171938/实验源码/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

多媒体大作业/王晨_2220171938/实验源码/dec2bin_z.m

@@ -0,0 +1,12 @@
+%function dec2bin_z
+%ʮ����ת������
+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

多媒体大作业/王晨_2220171938/实验源码/firstNzreo.m

@@ -0,0 +1,13 @@
+%function firstNzreo
+%��һ���������λ��
+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