| from config import DatasetName, AffectnetConf, InputDataSize, LearningConfig, ExpressionCodesAffectnet |
| from config import LearningConfig, InputDataSize, DatasetName, FerPlusConf, DatasetType |
|
|
| import numpy as np |
| import os |
| import matplotlib.pyplot as plt |
| import math |
| from datetime import datetime |
| from sklearn.utils import shuffle |
| from sklearn.model_selection import train_test_split |
| from numpy import save, load, asarray, savez_compressed |
| import csv |
| from skimage.io import imread |
| import pickle |
| import csv |
| from tqdm import tqdm |
| from PIL import Image |
| from skimage.transform import resize |
| import tensorflow as tf |
| import random |
| import cv2 |
| from skimage.feature import hog |
| from skimage import data, exposure |
| from matplotlib.path import Path |
| from scipy import ndimage, misc |
| from data_helper import DataHelper |
| from sklearn.metrics import accuracy_score |
| from sklearn.metrics import confusion_matrix |
| from shutil import copyfile |
| from dataset_class import CustomDataset |
| from sklearn.metrics import precision_recall_fscore_support as score |
|
|
|
|
| class FerPlus: |
|
|
| def __init__(self, ds_type): |
| """we set the parameters needed during the whole class: |
| """ |
| self.ds_type = ds_type |
| if ds_type == DatasetType.train: |
| self.img_path = FerPlusConf.no_aug_train_img_path |
| self.anno_path = FerPlusConf.no_aug_train_annotation_path |
| self.img_path_aug = FerPlusConf.aug_train_img_path |
| self.anno_path_aug = FerPlusConf.aug_train_annotation_path |
| self.masked_img_path = FerPlusConf.aug_train_masked_img_path |
| self.orig_image_path = FerPlusConf.orig_image_path_train |
|
|
| elif ds_type == DatasetType.test: |
| self.img_path = FerPlusConf.test_img_path |
| self.anno_path = FerPlusConf.test_annotation_path |
| self.img_path_aug = FerPlusConf.test_img_path |
| self.anno_path_aug = FerPlusConf.test_annotation_path |
| self.masked_img_path = FerPlusConf.test_masked_img_path |
| self.orig_image_path = FerPlusConf.orig_image_path_train |
|
|
| def create_from_orig(self): |
| print('create_from_orig & relabel to affectNetLike--->') |
| """ |
| labels are from 1-7, but we save them from 0 to 6 |
| :param ds_type: |
| :return: |
| """ |
|
|
| '''read the text file, and save exp, and image''' |
| dhl = DataHelper() |
|
|
| exp_affectnet_like_lbls = [6, 5, 4, 1, 0, 2, 3] |
| lbl_affectnet_like_lbls = ['angry/', 'disgust/', 'fear/', 'happy/', 'neutral/', 'sad/', 'surprise/'] |
|
|
| for exp_index in range(len(lbl_affectnet_like_lbls)): |
| exp_prefix = lbl_affectnet_like_lbls[exp_index] |
| for i, file in tqdm(enumerate(os.listdir(self.orig_image_path + exp_prefix))): |
| if file.endswith(".jpg") or file.endswith(".png"): |
| img_source_address = self.orig_image_path + exp_prefix + file |
| img_dest_address = self.img_path + file |
| exp_dest_address = self.anno_path + file[:-4] |
| exp = exp_affectnet_like_lbls[exp_index] |
|
|
| img = np.array(Image.open(img_source_address)) |
| res_img = resize(img, (InputDataSize.image_input_size, InputDataSize.image_input_size, 3), |
| anti_aliasing=True) |
|
|
| im = Image.fromarray(np.round(res_img * 255.0).astype(np.uint8)) |
| '''save image''' |
| im.save(img_dest_address) |
| '''save annotation''' |
| np.save(exp_dest_address + '_exp', exp) |
|
|
| def test_accuracy(self, model, print_samples=False): |
| print('FER: test_accuracy') |
| dhp = DataHelper() |
| '''create batches''' |
| img_filenames, exp_filenames = dhp.create_generator_full_path( |
| img_path=self.img_path, |
| annotation_path=self.anno_path, label=None) |
| print(len(img_filenames)) |
| exp_pr_lbl = [] |
| exp_gt_lbl = [] |
|
|
| cds = CustomDataset() |
| ds = cds.create_dataset(img_filenames=img_filenames, |
| anno_names=exp_filenames, |
| is_validation=True, |
| ds=DatasetName.fer2013) |
|
|
| batch_index = 0 |
| print('FER: loading test ds') |
| for img_batch, exp_gt_b in tqdm(ds): |
| '''predict on batch''' |
| exp_gt_b = exp_gt_b[:, -1] |
| img_batch = img_batch[:, -1, :, :] |
|
|
| pr_data = model.predict_on_batch([img_batch]) |
| probab_exp_pr_b = pr_data[0] |
|
|
| scores_b = np.array([tf.nn.softmax(probab_exp_pr_b[i]) for i in range(len(probab_exp_pr_b))]) |
| exp_pr_b = np.array([np.argmax(scores_b[i]) for i in range(len(probab_exp_pr_b))]) |
|
|
| if print_samples: |
| for i in range(len(exp_pr_b)): |
| dhp.test_image_print_exp(str(i)+str(batch_index+1), np.array(img_batch[i]), |
| np.int8(exp_gt_b[i]), np.int8(exp_pr_b[i])) |
|
|
| exp_pr_lbl += np.array(exp_pr_b).tolist() |
| exp_gt_lbl += np.array(exp_gt_b).tolist() |
| batch_index += 1 |
| exp_pr_lbl = np.float64(np.array(exp_pr_lbl)) |
| exp_gt_lbl = np.float64(np.array(exp_gt_lbl)) |
|
|
| global_accuracy = accuracy_score(exp_gt_lbl, exp_pr_lbl) |
| precision, recall, fscore, support = score(exp_gt_lbl, exp_pr_lbl) |
| conf_mat = confusion_matrix(exp_gt_lbl, exp_pr_lbl, normalize='true') |
| |
| avg_acc = np.mean([conf_mat[i,i] for i in range(7)]) |
|
|
| ds = None |
| face_img_filenames = None |
| eyes_img_filenames = None |
| nose_img_filenames = None |
| mouth_img_filenames = None |
| exp_filenames = None |
|
|
| return global_accuracy, conf_mat, avg_acc, precision, recall, fscore, support |
|
|
