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