| |
| import cv2 |
| import pickle |
| import matplotlib.pyplot as plt |
| from src.preprocess.crop_main_face import get_video_frame_data, get_video_frames |
|
|
|
|
| def get_video_frames(video_path): |
| cap = cv2.VideoCapture(video_path) |
| fps = round(cap.get(cv2.CAP_PROP_FPS)) |
| frames = [] |
| while cap.isOpened(): |
| ret, frame = cap.read() |
| if not ret: |
| break |
| frames.append(frame.copy()) |
| cap.release() |
| return fps, frames |
|
|
|
|
| def get_video_frame_data(fdata_path): |
| with open(fdata_path, "rb") as f: |
| frame_data = pickle.load(f) |
| frame_landmarks = [frame["landmarks"] if not frame is None else [] for frame in frame_data] |
| frame_bboxes = [frame["bboxes"] if not frame is None else [] for frame in frame_data] |
|
|
| assert len(frame_landmarks) == len(frame_bboxes) |
|
|
| if (len(frame_bboxes[0][0].shape) == 1): |
| frame_bboxes = [[bbox.reshape((2, 2)) for bbox in bboxes]for bboxes in frame_bboxes] |
|
|
| return frame_landmarks, frame_bboxes |
|
|
|
|
| video_path = "/home/od/stock/FaceForensicC23/videos/real/950.mp4" |
| fdata_path = "/home/od/stock/FaceForensicC23/frame_data/real/950.pickle" |
|
|
| fps, frames = get_video_frames(video_path) |
| frame_landmarks, frame_bboxes = get_video_frame_data(fdata_path) |
|
|
| import random |
|
|
| idx = random.randrange(0, len(frames)) |
| plt.imshow(cv2.cvtColor(frames[idx], cv2.COLOR_BGR2RGB)) |
| for lm, bbox in zip(frame_landmarks[idx], frame_bboxes[idx]): |
| plt.scatter(lm[:, 0], lm[:, 1], s=1, c="r") |
| plt.scatter(bbox[:, 0], bbox[:, 1], s=1, c="g") |
|
|
