def main():
logger.info('Load InceptionV3 model')
inceptionv3 = AgenderNetInceptionV3()
inceptionv3.setWeight('trainweight/inceptionv3_2/model.16-3.7887-0.9004-6.6744.h5')
logger.info('Load MobileNetV2 model')
mobilenetv2 = AgenderNetMobileNetV2()
mobilenetv2.setWeight('trainweight/mobilenetv2/model.10-3.8290-0.8965-6.9498.h5')
logger.info('Load SSRNet model')
ssrnet = SSRNet(64, [3, 3, 3], 1.0, 1.0)
ssrnet.setWeight('trainweight/ssrnet/model.37-7.3318-0.8643-7.1952.h5')
logger.info('Load pretrain imdb model')
imdb_model = SSR_net(64, [3, 3, 3], 1.0, 1.0)()
imdb_model.load_weights("tes_ssrnet/imdb_age_ssrnet_3_3_3_64_1.0_1.0.h5")
imdb_model_gender = SSR_net_general(64, [3, 3, 3], 1.0, 1.0)()
imdb_model_gender.load_weights("tes_ssrnet/imdb_gender_ssrnet_3_3_3_64_1.0_1.0.h5")
logger.info('Load pretrain wiki model')
wiki_model = SSR_net(64, [3, 3, 3], 1.0, 1.0)()
wiki_model.load_weights("tes_ssrnet/wiki_age_ssrnet_3_3_3_64_1.0_1.0.h5")
wiki_model_gender = SSR_net_general(64, [3, 3, 3], 1.0, 1.0)()
wiki_model_gender.load_weights("tes_ssrnet/wiki_gender_ssrnet_3_3_3_64_1.0_1.0.h5")
logger.info('Load pretrain morph model')
morph_model = SSR_net(64, [3, 3, 3], 1.0, 1.0)()
morph_model.load_weights("tes_ssrnet/morph_age_ssrnet_3_3_3_64_1.0_1.0.h5")
morph_model_gender = SSR_net_general(64, [3, 3, 3], 1.0, 1.0)()
morph_model_gender.load_weights("tes_ssrnet/morph_gender_ssrnet_3_3_3_64_1.0_1.0.h5")
utk = pd.read_csv('dataset/UTKface.csv')
fgnet = pd.read_csv('dataset/FGNET.csv')
utk_paths = utk['full_path'].values
fgnet_paths = fgnet['full_path'].values
logger.info('Read UTKface aligned images')
utk_images = [cv2.imread('UTKface_aligned/'+path) for path in tqdm(utk_paths)]
logger.info('Read FGNET aligned images')
fgnet_images = [cv2.imread('FGNET_aligned/'+path) for path in tqdm(fgnet_paths)]
utk_X = np.array(utk_images)
fgnet_X = np.array(fgnet_images)
utk_pred_age = dict()
utk_pred_gender = dict()
fgnet_pred_age = dict()
logger.info('Predict with InceptionV3')
start = time.time()
utk_pred_gender['inceptionv3'], utk_pred_age['inceptionv3'] = get_result(inceptionv3, utk_X)
_, fgnet_pred_age['inceptionv3'] = get_result(inceptionv3, fgnet_X)
elapsed = time.time() - start
logger.info('Time elapsed {:.2f} sec'.format(elapsed))
del utk_X, fgnet_X
logger.info('Resize image to 96 for MobileNetV2')
utk_images = [cv2.resize(image, (96, 96), interpolation = cv2.INTER_CUBIC) for image in tqdm(utk_images)]
fgnet_images = [cv2.resize(image, (96, 96), interpolation = cv2.INTER_CUBIC) for image in tqdm(fgnet_images)]
utk_X = np.array(utk_images)
fgnet_X = np.array(fgnet_images)
logger.info('Predict with MobileNetV2')
start = time.time()
utk_pred_gender['mobilenetv2'], utk_pred_age['mobilenetv2'] = get_result(mobilenetv2, utk_X)
_, fgnet_pred_age['mobilenetv2'] = get_result(mobilenetv2, fgnet_X)
elapsed = time.time() - start
logger.info('Time elapsed {:.2f} sec'.format(elapsed))
del utk_X, fgnet_X
logger.info('Resize image to 64 for SSR-Net')
utk_images = [cv2.resize(image, (64, 64), interpolation = cv2.INTER_CUBIC) for image in tqdm(utk_images)]
fgnet_images = [cv2.resize(image, (64, 64), interpolation = cv2.INTER_CUBIC) for image in tqdm(fgnet_images)]
utk_X = np.array(utk_images)
fgnet_X = np.array(fgnet_images)
logger.info('Predict with SSR-Net')
start = time.time()
utk_pred_gender['ssrnet'], utk_pred_age['ssrnet'] = get_result(ssrnet, utk_X)
_, fgnet_pred_age['ssrnet'] = get_result(ssrnet, fgnet_X)
elapsed = time.time() - start
logger.info('Time elapsed {:.2f} sec'.format(elapsed))
logger.info('Predict with IMDB_SSR-Net')
start = time.time()
utk_pred_gender['ssrnet-imdb'] = np.around(imdb_model_gender.predict(utk_X).squeeze()).astype('int')
utk_pred_age['ssrnet-imdb'] = imdb_model.predict(utk_X).squeeze()
fgnet_pred_age['ssrnet-imdb'] = imdb_model.predict(fgnet_X).squeeze()
elapsed = time.time() - start
logger.info('Time elapsed {:.2f} sec'.format(elapsed))
logger.info('Predict with Wiki_SSR-Net')
start = time.time()
utk_pred_gender['ssrnet-wiki'] = np.around(wiki_model_gender.predict(utk_X).squeeze()).astype('int')
utk_pred_age['ssrnet-wiki'] = wiki_model.predict(utk_X).squeeze()
fgnet_pred_age['ssrnet-wiki'] = wiki_model.predict(fgnet_X).squeeze()
elapsed = time.time() - start
logger.info('Time elapsed {:.2f} sec'.format(elapsed))
logger.info('Predict with Morph_SSR-Net')
start = time.time()
utk_pred_gender['ssrnet-morph'] = np.around(morph_model_gender.predict(utk_X).squeeze()).astype('int')
utk_pred_age['ssrnet-morph'] = morph_model.predict(utk_X).squeeze()
fgnet_pred_age['ssrnet-morph'] = morph_model.predict(fgnet_X).squeeze()
elapsed = time.time() - start
logger.info('Time elapsed {:.2f} sec'.format(elapsed))
utk_pred_age = pd.DataFrame.from_dict(utk_pred_age)
utk_pred_gender = pd.DataFrame.from_dict(utk_pred_gender)
fgnet_pred_age = pd.DataFrame.from_dict(fgnet_pred_age)
utk_pred_age = pd.concat([utk['age'], utk_pred_age], axis=1)
utk_pred_gender = pd.concat([utk['gender'], utk_pred_gender], axis=1)
fgnet_pred_age = pd.concat([fgnet['age'], fgnet_pred_age], axis=1)
utk_pred_age.to_csv('result/utk_age_prediction.csv', index=False)
utk_pred_age.to_csv('result/utk_gender_prediction.csv', index=False)
fgnet_pred_age.to_csv('result/fgnet_age_prediction.csv', index=False)
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
faces_2 = np.empty((1, img_size, img_size, 3))
faces_2[0, :, :, :] = cv2.resize(rgb_image[y1:y2 + 1, x1:x2 + 1, :],
(img_size, img_size))
faces_2[0, :, :, :] = cv2.normalize(faces_2[0, :, :, :],
None,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX)
predicted_ages = model.predict(faces_2)
predicted_genders = model_gender.predict(faces_2)
print(str(predicted_ages))
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > frame_window:
emotion_window.pop(0)
try:
emotion_mode = mode(emotion_window)