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