import os import cv2 import numpy as np import torch from PIL import Image from albumentations.augmentations.functional import image_compression from facenet_pytorch.models.mtcnn import MTCNN from concurrent.futures import ThreadPoolExecutor from torchvision.transforms import Normalize mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] normalize_transform = Normalize(mean, std) class VideoReader: """Helper class for reading one or more frames from a video file.""" def __init__(self, verbose=True, insets=(0, 0)): """Creates a new VideoReader. Arguments: verbose: whether to print warnings and error messages insets: amount to inset the image by, as a percentage of (width, height). This lets you "zoom in" to an image to remove unimportant content around the borders. Useful for face detection, which may not work if the faces are too small. """ self.verbose = verbose self.insets = insets def read_frames(self, path, num_frames, jitter=0, seed=None): """Reads frames that are always evenly spaced throughout the video. Arguments: path: the video file num_frames: how many frames to read, -1 means the entire video (warning: this will take up a lot of memory!) jitter: if not 0, adds small random offsets to the frame indices; this is useful so we don't always land on even or odd frames seed: random seed for jittering; if you set this to a fixed value, you probably want to set it only on the first video """ assert num_frames > 0 capture = cv2.VideoCapture(path) frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) if frame_count <= 0: return None frame_idxs = np.linspace(0, frame_count - 1, num_frames, endpoint=True, dtype=np.int32) if jitter > 0: np.random.seed(seed) jitter_offsets = np.random.randint(-jitter, jitter, len(frame_idxs)) frame_idxs = np.clip(frame_idxs + jitter_offsets, 0, frame_count - 1) result = self._read_frames_at_indices(path, capture, frame_idxs) capture.release() return result def read_random_frames(self, path, num_frames, seed=None): """Picks the frame indices at random. Arguments: path: the video file num_frames: how many frames to read, -1 means the entire video (warning: this will take up a lot of memory!) """ assert num_frames > 0 np.random.seed(seed) capture = cv2.VideoCapture(path) frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) if frame_count <= 0: return None frame_idxs = sorted(np.random.choice(np.arange(0, frame_count), num_frames)) result = self._read_frames_at_indices(path, capture, frame_idxs) capture.release() return result def read_frames_at_indices(self, path, frame_idxs): """Reads frames from a video and puts them into a NumPy array. Arguments: path: the video file frame_idxs: a list of frame indices. Important: should be sorted from low-to-high! If an index appears multiple times, the frame is still read only once. Returns: - a NumPy array of shape (num_frames, height, width, 3) - a list of the frame indices that were read Reading stops if loading a frame fails, in which case the first dimension returned may actually be less than num_frames. Returns None if an exception is thrown for any reason, or if no frames were read. """ assert len(frame_idxs) > 0 capture = cv2.VideoCapture(path) result = self._read_frames_at_indices(path, capture, frame_idxs) capture.release() return result def _read_frames_at_indices(self, path, capture, frame_idxs): try: frames = [] idxs_read = [] for frame_idx in range(frame_idxs[0], frame_idxs[-1] + 1): # Get the next frame, but don't decode if we're not using it. ret = capture.grab() if not ret: if self.verbose: print("Error grabbing frame %d from movie %s" % (frame_idx, path)) break # Need to look at this frame? current = len(idxs_read) if frame_idx == frame_idxs[current]: ret, frame = capture.retrieve() if not ret or frame is None: if self.verbose: print("Error retrieving frame %d from movie %s" % (frame_idx, path)) break frame = self._postprocess_frame(frame) frames.append(frame) idxs_read.append(frame_idx) if len(frames) > 0: return np.stack(frames), idxs_read if self.verbose: print("No frames read from movie %s" % path) return None except: if self.verbose: print("Exception while reading movie %s" % path) return None def read_middle_frame(self, path): """Reads the frame from the middle of the video.""" capture = cv2.VideoCapture(path) frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) result = self._read_frame_at_index(path, capture, frame_count // 2) capture.release() return result def read_frame_at_index(self, path, frame_idx): """Reads a single frame from a video. If you just want to read a single frame from the video, this is more efficient than scanning through the video to find the frame. However, for reading multiple frames it's not efficient. My guess is that a "streaming" approach is more efficient than a "random access" approach because, unless you happen to grab a keyframe, the decoder still needs to read all the previous frames in order to reconstruct the one you're asking for. Returns a NumPy array of shape (1, H, W, 3) and the index of the frame, or None if reading failed. """ capture = cv2.VideoCapture(path) result = self._read_frame_at_index(path, capture, frame_idx) capture.release() return result def _read_frame_at_index(self, path, capture, frame_idx): capture.set(cv2.CAP_PROP_POS_FRAMES, frame_idx) ret, frame = capture.read() if not ret or frame is None: if self.verbose: print("Error retrieving frame %d from movie %s" % (frame_idx, path)) return None else: frame = self._postprocess_frame(frame) return np.expand_dims(frame, axis=0), [frame_idx] def _postprocess_frame(self, frame): frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if self.insets[0] > 0: W = frame.shape[1] p = int(W * self.insets[0]) frame = frame[:, p:-p, :] if self.insets[1] > 0: H = frame.shape[1] q = int(H * self.insets[1]) frame = frame[q:-q, :, :] return frame class FaceExtractor: def __init__(self, video_read_fn): self.video_read_fn = video_read_fn self.detector = MTCNN(margin=0, thresholds=[0.7, 0.8, 0.8], device="cuda") def process_videos(self, video_path): videos_read = [] frames_read = [] frames = [] results = [] # for video_idx in video_idxs: # Read the full-size frames from this video. # filename = filenames[video_idx] # video_path = os.path.join(input_dir, filename) # result = self.video_read_fn(video_path) result = self.video_read_fn(video_path) # result = video # Error? Then skip this video. # Keep track of the original frames (need them later). my_frames, my_idxs = result frames.append(my_frames) frames_read.append(my_idxs) for i, frame in enumerate(my_frames): h, w = frame.shape[:2] img = Image.fromarray(frame.astype(np.uint8)) img = img.resize(size=[s // 2 for s in img.size]) batch_boxes, probs = self.detector.detect(img, landmarks=False) faces = [] scores = [] if batch_boxes is None: continue for bbox, score in zip(batch_boxes, probs): if bbox is not None: xmin, ymin, xmax, ymax = [int(b * 2) for b in bbox] w = xmax - xmin h = ymax - ymin p_h = h // 3 p_w = w // 3 crop = frame[max(ymin - p_h, 0):ymax + p_h, max(xmin - p_w, 0):xmax + p_w] faces.append(crop) scores.append(score) frame_dict = { #"video_idx": video_idx, "frame_idx": my_idxs[i], "frame_w": w, "frame_h": h, "faces": faces, "scores": scores} results.append(frame_dict) return results def process_video(self, video_path): """Convenience method for doing face extraction on a single video.""" input_dir = os.path.dirname(video_path) filenames = [os.path.basename(video_path)] return self.process_videos(video_path) def confident_strategy(pred, t=0.8): pred = np.array(pred) sz = len(pred) fakes = np.count_nonzero(pred > t) # 11 frames are detected as fakes with high probability if fakes > sz // 2.5 and fakes > 11: return np.mean(pred[pred > t]) elif np.count_nonzero(pred < 0.2) > 0.9 * sz: return np.mean(pred[pred < 0.2]) else: return np.mean(pred) strategy = confident_strategy def put_to_center(img, input_size): img = img[:input_size, :input_size] image = np.zeros((input_size, input_size, 3), dtype=np.uint8) start_w = (input_size - img.shape[1]) // 2 start_h = (input_size - img.shape[0]) // 2 image[start_h:start_h + img.shape[0], start_w: start_w + img.shape[1], :] = img return image def isotropically_resize_image(img, size, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC): h, w = img.shape[:2] if max(w, h) == size: return img if w > h: scale = size / w h = h * scale w = size else: scale = size / h w = w * scale h = size interpolation = interpolation_up if scale > 1 else interpolation_down resized = cv2.resize(img, (int(w), int(h)), interpolation=interpolation) return resized def predict_on_video(face_extractor, video_path, videos, batch_size, input_size, models, strategy=np.mean, apply_compression=False): batch_size *= 4 try: faces = face_extractor.process_video(videos) if len(faces) > 0: x = np.zeros((batch_size, input_size, input_size, 3), dtype=np.uint8) n = 0 for frame_data in faces: for face in frame_data["faces"]: resized_face = isotropically_resize_image(face, input_size) resized_face = put_to_center(resized_face, input_size) if apply_compression: resized_face = image_compression(resized_face, quality=90, image_type=".jpg") if n + 1 < batch_size: x[n] = resized_face n += 1 else: pass if n > 0: x = torch.tensor(x, device="cpu").float() # Preprocess the images. x = x.permute((0, 3, 1, 2)) for i in range(len(x)): x[i] = normalize_transform(x[i] / 255.) # Make a prediction, then take the average. with torch.no_grad(): preds = [] for model in models: y_pred = model(x[:n]) # y_pred = torch.sigmoid(y_pred.squeeze()) bpred = y_pred[:n].cpu().numpy() preds.append(strategy(bpred)) return np.mean(preds) except Exception as e: print("Prediction error on video %s: %s" % (video_path, str(e))) return 0.5 def predict_on_video_set(face_extractor, videos, input_size, num_workers, test_dir, frames_per_video, models, strategy=np.mean, apply_compression=False): def process_file(i): filename = videos y_pred = predict_on_video(face_extractor=face_extractor, video_path=os.path.join(test_dir, filename), videos=videos, input_size=input_size, batch_size=frames_per_video, models=models, strategy=strategy, apply_compression=apply_compression) return y_pred with ThreadPoolExecutor(max_workers=num_workers) as ex: predictions = ex.map(process_file, [1]) return list(predictions)