| | |
| | """ |
| | 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() |
| |
|
