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""" |
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This file extracts the frames for the frame datasets in SA-CO/Gold and Silver. |
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Call like: |
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> python extract_frames.py <dataset_name> |
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""" |
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import json |
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import os |
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import shutil |
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import sys |
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from multiprocessing import Pool |
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from PIL import Image |
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from tqdm import tqdm |
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from utils import ( |
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annotation_files, |
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config, |
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get_frame_from_video, |
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is_valid_image, |
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update_annotations, |
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) |
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def extract_frame(path_video, global_frame_idx, path_frame, image_size, file_name): |
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frame = get_frame_from_video(path_video, global_frame_idx) |
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os.makedirs(os.path.dirname(path_frame), exist_ok=True) |
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img = Image.fromarray(frame) |
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if frame.shape[:2] != image_size: |
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print(f"Resizing image {file_name} from {frame.shape[:2]} to {image_size}") |
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height, width = image_size |
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img = img.resize((width, height)) |
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img.save(path_frame) |
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def process_image(args): |
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image, dataset_name, config = args |
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original_video, global_frame_idx, file_name, image_size = image |
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extra_subpath = "" |
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if dataset_name == "ego4d": |
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extra_subpath = "v1/clips" |
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elif dataset_name == "yt1b": |
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original_video = f"video_{original_video}.mp4" |
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elif dataset_name == "sav": |
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extra_subpath = "videos_fps_6" |
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path_video = os.path.join( |
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config[f"{dataset_name}_path"], |
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"downloaded_videos", |
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extra_subpath, |
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original_video, |
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) |
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path_frame = os.path.join(config[f"{dataset_name}_path"], "frames", file_name) |
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to_return = file_name |
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try: |
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extract_frame(path_video, global_frame_idx, path_frame, image_size, file_name) |
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if not is_valid_image(path_frame): |
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print(f"Invalid image in {path_frame}") |
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to_return = None |
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except: |
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print(f"Invalid image in {path_frame}") |
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to_return = None |
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return to_return |
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def main(): |
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assert len(sys.argv) > 1, "You have to provide the name of the dataset" |
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dataset_name = sys.argv[1] |
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assert ( |
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dataset_name in annotation_files |
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), f"The dataset can be one of {list(annotation_files.keys())}" |
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all_outputs = [] |
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for file in annotation_files[dataset_name]: |
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with open(os.path.join(config["path_annotations"], file), "r") as f: |
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annotation = json.load(f) |
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images = annotation["images"] |
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images = set( |
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( |
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image["original_video"], |
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image["global_frame_idx"], |
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image["file_name"], |
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tuple(image["image_size"]), |
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) |
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for image in images |
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) |
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args_list = [(image, dataset_name, config) for image in images] |
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with Pool(os.cpu_count()) as pool: |
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outputs = list( |
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tqdm(pool.imap_unordered(process_image, args_list), total=len(images)) |
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) |
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all_outputs.extend(outputs) |
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if any(out is None for out in outputs): |
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update_annotations(dataset_name, all_outputs, key="file_name") |
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if config[f"remove_downloaded_videos_{dataset_name}"]: |
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shutil.rmtree(os.path.join(config[f"{dataset_name}_path"], "downloaded_videos")) |
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if __name__ == "__main__": |
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main() |
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