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# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
"""
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)) # Uses Image.NEAREST by default
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()
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