def convert(self, video_file, swap_model = False, duration = None, start_time = None, use_gan = False, face_filter = False, photos = True, crop_x = None, width = None, side_by_side = False): # Magic incantation to not have tensorflow blow up with an out of memory error. import tensorflow as tf import keras.backend.tensorflow_backend as K config = tf.ConfigProto() config.gpu_options.allow_growth = True config.gpu_options.visible_device_list="0" K.set_session(tf.Session(config=config)) # Load model model_name = "Original" converter_name = "Masked" if use_gan: model_name = "GAN" converter_name = "GAN" model = PluginLoader.get_model(model_name)(Path(self._model_path(use_gan))) if not model.load(swap_model): print('model Not Found! A valid model must be provided to continue!') exit(1) # Load converter converter = PluginLoader.get_converter(converter_name) converter = converter(model.converter(False), blur_size=8, seamless_clone=True, mask_type="facehullandrect", erosion_kernel_size=None, smooth_mask=True, avg_color_adjust=True) # Load face filter filter_person = self._person_a if swap_model: filter_person = self._person_b filter = FaceFilter(self._people[filter_person]['faces']) # Define conversion method per frame def _convert_frame(frame, convert_colors = True): if convert_colors: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Swap RGB to BGR to work with OpenCV for face in detect_faces(frame, "cnn"): if (not face_filter) or (face_filter and filter.check(face)): frame = converter.patch_image(frame, face) frame = frame.astype(numpy.float32) if convert_colors: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Swap RGB to BGR to work with OpenCV return frame def _convert_helper(get_frame, t): return _convert_frame(get_frame(t)) media_path = self._video_path({ 'name' : video_file }) if not photos: # Process video; start loading the video clip video = VideoFileClip(media_path) # If a duration is set, trim clip if duration: video = video.subclip(start_time, start_time + duration) # Resize clip before processing if width: video = video.resize(width = width) # Crop clip if desired if crop_x: video = video.fx(crop, x2 = video.w / 2) # Kick off convert frames for each frame new_video = video.fl(_convert_helper) # Stack clips side by side if side_by_side: def add_caption(caption, clip): text = (TextClip(caption, font='Amiri-regular', color='white', fontsize=80). margin(40). set_duration(clip.duration). on_color(color=(0,0,0), col_opacity=0.6)) return CompositeVideoClip([clip, text]) video = add_caption("Original", video) new_video = add_caption("Swapped", new_video) final_video = clips_array([[video], [new_video]]) else: final_video = new_video # Resize clip after processing #final_video = final_video.resize(width = (480 * 2)) # Write video output_path = os.path.join(self.OUTPUT_PATH, video_file) final_video.write_videofile(output_path, rewrite_audio = True) # Clean up del video del new_video del final_video else: # Process a directory of photos for face_file in os.listdir(media_path): face_path = os.path.join(media_path, face_file) image = cv2.imread(face_path) image = _convert_frame(image, convert_colors = False) cv2.imwrite(os.path.join(self.OUTPUT_PATH, face_file), image)
def load_filter(self): filter_file = "filter.jpg" # TODO Pass as argument if Path(filter_file).exists(): print('Loading reference image for filtering') return FaceFilter(filter_file)
def load_filter(self): filter_file = self.arguments.filter if Path(filter_file).exists(): print('Loading reference image for filtering') return FaceFilter(filter_file)