if config.DATASET == 'IOG':
# load Label Encoder and mean files
print('[INFO] loading label encoders and mean files...')
age_le = pickle.loads(open(deploy.AGE_LABEL_ENCODER, 'rb').read())
gender_le = pickle.loads(open(deploy.GENDER_LABEL_ENCODER, 'rb').read())
age_means = json.loads(open(deploy.AGE_MEAN).read())
gender_means = json.loads(open(deploy.GENDER_MEAN).read())
# initialize image preprocessors
age_mp = MeanPreprocessor(age_means['R'], age_means['G'], age_means['B'])
gender_mp = MeanPreprocessor(gender_means['R'], gender_means['G'], gender_means['B'])
age_preds, gender_preds = predict(face, sp, age_mp, gender_mp, cp, iap, deploy.AGE_NETWORK_PATH, deploy.GENDER_NETWORK_PATH, age_le, gender_le)
# visualize age and gender predictions
age_canvas = AgeGenderHelper.visualize_age(age_preds, age_le)
gender_canvas = AgeGenderHelper.visualize_gender(gender_preds, gender_le)
elif config.DATASET == 'ADIENCE':
# age_preds_cross, gender_preds_cross = [], []
i = 0
# load Label Encoder and mean files
print(f'[INFO] loading label encoders and mean files for cross validation {i}...')
age_le = pickle.loads(open(deploy.AGE_LABEL_ENCODERS[i], 'rb').read())
gender_le = pickle.loads(open(deploy.GENDER_LABEL_ENCODERS[i], 'rb').read())
age_means = json.loads(open(deploy.AGE_MEANS[i]).read())
gender_means = json.loads(open(deploy.GENDER_MEANS[i]).read())
# initialize image preprocessors
age_mp = MeanPreprocessor(age_means['R'], age_means['G'], age_means['B'])
# detect faces in grayscale image
rects = detector(gray, 1)
# loop over face detections
for rect in rects:
# determine facial landmarks for face region, then align face
shape = predictor(gray, rect)
face = fa.align(image, gray, rect)
age_preds, gender_preds = prediction(config, face, sp, age_mp,
gender_mp, cp, age_model,
gender_model)
# visualize age and gender predictions
age_canvas = agh.visualize_age(age_preds, age_le)
gender_canvas = agh.visualize_gender(gender_preds, gender_le)
# draw bounding box around face
clone = image.copy()
x, y, w, h = face_utils.rect_to_bb(rect)
cv2.rectangle(clone, (x, y), (x + w, y + h), (0, 255, 0), 2)
# show output iamge
cv2.imshow('Input', clone)
cv2.imshow('Face', face)
cv2.imshow('Age Probabilities', age_canvas)
cv2.imshow('Gender Probabilities', gender_canvas)
cv2.waitKey(0)
elif 'ADIENCE' in config.DATASET:
