| import os | |
| from torchvision.io import read_video | |
| import torchvision.transforms.functional as TF | |
| import json | |
| import pdb | |
| from PIL import Image, ImageChops | |
| import numpy as np, hashlib, io, os, math | |
| def md5(path): | |
| h = hashlib.md5() | |
| with open(path, "rb") as f: | |
| for chunk in iter(lambda: f.read(8192), b""): | |
| h.update(chunk) | |
| return h.hexdigest() | |
| metadata_path = "metadata" | |
| # json_list = os.listdir(metadata_path) | |
| json_list = ["426eb9d9-d4cf-4acc-9efb-979f61a5be91.json"] | |
| for json_file in json_list: | |
| with open(os.path.join("metadata", json_file), "r") as f: | |
| metadata = json.load(f) | |
| # load metadata | |
| video_id = metadata["video_id"] | |
| video_name = metadata["video_name"] | |
| start_pt = metadata["start_sec"] | |
| end_pt = metadata["end_sec"] | |
| num_output_frames = metadata["num_output_frames"] | |
| offset = metadata["offset"] | |
| stride = metadata["stride"] | |
| # save paths | |
| video_path = f"ego4d_full_videos/v2/full_scale/{video_id}.mp4" | |
| save_dir = f"frames/{video_name}" | |
| print("\n PROCESSING VIDEO ", video_name) | |
| video, _, info = read_video( | |
| video_path, start_pts=start_pt, end_pts=end_pt, pts_unit="sec" | |
| ) | |
| video = video.permute(0, 3, 1, 2).float() / 255.0 # [T, C, H, W] | |
| os.makedirs(save_dir, exist_ok=True) | |
| cropped_vid = video[offset::stride, :][:num_output_frames] | |
| # Save each frame | |
| for idx, frame_tensor in enumerate(cropped_vid): | |
| frame = TF.to_pil_image(frame_tensor) # Convert to PIL image | |
| # rgb_path = os.path.join(save_dir, "rgb_frames") | |
| os.makedirs(save_dir, exist_ok=True) | |
| frame.save( | |
| os.path.join(save_dir, f"{idx:06d}.jpg") | |
| ) # Zero-padded filenames | |
| print(f"Saved {len(cropped_vid)} frames to {save_dir}") | |
| # # TODO(ilona) - remove this later | |
| # # checks that current dataset aligns with the files that we have !! | |
| # for frame_number in [f"{i:06d}" for i in range(0, num_output_frames)]: | |
| # paths = [ | |
| # f"/data/ilona/datasets/ego4d/ego4d_chosen_videos/{video_name}/rgb_frames/{frame_number}.jpg", | |
| # # f"/data/ilona/datasets/go4d/frames/{video_name}/rgb_frames/{frame_number}.jpg", | |
| # f"/data/ilona/datasets/itto_release/itto/ego4d/frames/{video_name}/{frame_number}.jpg", | |
| # ] | |
| # # Load images | |
| # imgs = [Image.open(p).convert("RGB") for p in paths] | |
| # # Basic metadata & hashes | |
| # meta = [ | |
| # (os.path.basename(p), im.size, md5(p)) for p, im in zip(paths, imgs) | |
| # ] | |
| # # Ensure same size | |
| # same_size = imgs[0].size == imgs[1].size | |
| # # Pixel-wise difference | |
| # diff_img = ImageChops.difference(imgs[0], imgs[1]) | |
| # diff_array = np.asarray(diff_img) | |
| # nonzero_pixels = int(np.count_nonzero(diff_array)) | |
| # # Mean absolute error per channel | |
| # arr0 = np.asarray(imgs[0], dtype=np.int16) | |
| # arr1 = np.asarray(imgs[1], dtype=np.int16) | |
| # mae = float(np.mean(np.abs(arr0 - arr1))) | |
| # # Peak Signal-to-Noise Ratio (PSNR) | |
| # mse = float(np.mean((arr0 - arr1) ** 2)) | |
| # if mse == 0: | |
| # psnr = float("inf") | |
| # else: | |
| # PIXEL_MAX = 255.0 | |
| # psnr = 20 * math.log10(PIXEL_MAX / math.sqrt(mse)) | |
| # if mae > 0.01: | |
| # print("file 1: ", meta[0][0]) | |
| # print("file 2: ", meta[1][0]) | |
| # print("stride: ", stride) | |
| # print("mean abs error: ", mae) | |
| # print("psnr: ", psnr) | |