x1, x2, y1, y2 = apply_offsets(face_coordinates, offsets)
gray_face = gray_image[y1:y2, x1:x2]
# processing input
try:
gray_face = cv2.resize(gray_face, (target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
# prediction
predicted_class = np.argmax(model.predict(gray_face))
label_text = labels[predicted_class]
gradient_function = compile_gradient_function(model,
predicted_class, 'conv2d_7')
register_gradient()
guided_model = modify_backprop(model, 'GuidedBackProp', task)
saliency_function = compile_saliency_function(guided_model, 'conv2d_7')
guided_gradCAM = calculate_guided_gradient_CAM(gray_face,
gradient_function, saliency_function)
guided_gradCAM = cv2.resize(guided_gradCAM, (x2-x1, y2-y1))
rgb_guided_gradCAM = np.repeat(guided_gradCAM[:, :, np.newaxis], 3, axis=2)
rgb_image[y1:y2, x1:x2, :] = rgb_guided_gradCAM
draw_bounding_box((x1, y1, x2 - x1, y2 - y1), rgb_image, color)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imwrite('../images/guided_gradCAM.png', bgr_image)
# predicted_class = class_to_arg[class_name]
predicted_class = 0
offsets = (0, 0)
elif task == 'emotion':
model_filename = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
# model_filename = '../trained_models/fer2013_big_XCEPTION.54-0.66.hdf5'
class_to_arg = get_class_to_arg('fer2013')
# predicted_class = class_to_arg[class_name]
predicted_class = 0
offsets = (0, 0)
model = load_model(model_filename, compile=False)
gradient_function = compile_gradient_function(model, predicted_class, 'conv2d_7')
register_gradient()
guided_model = modify_backprop(model, 'GuidedBackProp', task)
saliency_function = compile_saliency_function(guided_model, 'conv2d_7')
# parameters for loading data and images
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
face_detection = load_detection_model(detection_model_path)
color = (0, 255, 0)
# getting input model shapes for inference
target_size = model.input_shape[1:3]
# starting lists for calculating modes
emotion_window = []
# starting video streaming
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)