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"""
This file preprocesses FaceForensics dataset by cropping it
Copied from https://github.com/pfnet-research/tgan2/blob/master/scripts/make_face_forensics.py
"""
import argparse
import os
from typing import List
from multiprocessing import Pool
from PIL import Image
import cv2
# import h5py
import imageio
import numpy as np
import pandas
from tqdm import tqdm
def parse_videos(source_dir, splits: List[str], categories: List[dir]):
results = []
for split in splits:
for category in categories:
target_dir = os.path.join(source_dir, split, category)
filenames = sorted(os.listdir(target_dir))
for filename in filenames:
results.append({
'split': split,
'category': category,
'filename': filename,
'filepath': os.path.join(split, category, filename),
})
return pandas.DataFrame(results)
def crop(img, left, right, top, bottom, margin):
cols = right - left
rows = bottom - top
if cols < rows:
padding = rows - cols
left -= padding // 2
right += (padding // 2) + (padding % 2)
cols = right - left
else:
padding = cols - rows
top -= padding // 2
bottom += (padding // 2) + (padding % 2)
rows = bottom - top
assert(rows == cols)
return img[top:bottom, left:right]
def job_proxy(kwargs):
process_and_save_video(**kwargs)
def process_and_save_video(video_path: os.PathLike, mask_path: os.PathLike, img_size: int, wide_crop: bool, output_dir: os.PathLike):
try:
video = process_video(video_path, mask_path, img_size=img_size, wide_crop=wide_crop)
except KeyboardInterrupt:
raise
except:
print(f'Couldnt process {video_path}')
return
os.makedirs(output_dir, exist_ok=True)
# if os.path.isdir(output_dir) and len(os.listdir(output_dir)) > 0:
# return
for i, frame in enumerate(video):
frame = frame.transpose(1, 2, 0)
Image.fromarray(frame).save(os.path.join(output_dir, f'{i:06d}.jpg'), q=95)
def process_video(video_path, mask_path, img_size, threshold=5, margin=0.02, wide_crop: bool=False):
video_reader = imageio.get_reader(video_path)
mask_reader = imageio.get_reader(mask_path)
assert(video_reader.get_length() == mask_reader.get_length())
# Searching for the widest crop which would work for the whole video
if wide_crop:
left_most = float('inf')
top_most = float('inf')
right_most = float('-inf')
bottom_most = float('-inf')
for img, mask in zip(video_reader, mask_reader):
hist = (255 - mask).astype(np.float64).sum(axis=2)
horiz_hist = np.where(hist.mean(axis=0) > threshold)[0]
vert_hist = np.where(hist.mean(axis=1) > threshold)[0]
left, right = horiz_hist[0], horiz_hist[-1]
top, bottom = vert_hist[0], vert_hist[-1]
left_most = min(left_most, left)
top_most = min(top_most, top)
right_most = max(right_most, right)
bottom_most = max(bottom_most, bottom)
video = []
for img, mask in zip(video_reader, mask_reader):
if wide_crop:
left, right, top, bottom = left_most, right_most, top_most, bottom_most
else:
hist = (255 - mask).astype(np.float64).sum(axis=2)
horiz_hist = np.where(hist.mean(axis=0) > threshold)[0]
vert_hist = np.where(hist.mean(axis=1) > threshold)[0]
left, right = horiz_hist[0], horiz_hist[-1]
top, bottom = vert_hist[0], vert_hist[-1]
dst_img = crop(img, left, right, top, bottom, margin)
try:
dst_img = cv2.resize(
dst_img, (img_size, img_size),
interpolation=cv2.INTER_LANCZOS4).transpose(2, 0, 1)
video.append(dst_img)
except KeyboardInterrupt:
raise
except:
print(img.shape, dst_img.shape, left, right, top, bottom)
T = len(video)
video = np.concatenate(video).reshape(T, 3, img_size, img_size)
return video
# def count_frames(path):
# reader = imageio.get_reader(path)
# n_frames = 0
# while True:
# try:
# img = reader.get_next_data()
# except IndexError as e:
# break
# else:
# n_frames += 1
# return n_frames
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--source_dir', type=str, default='data/FaceForensics_compressed')
parser.add_argument('--output_dir', type=str, default='data/ffs_processed')
parser.add_argument('--img_size', type=int, default=256)
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--wide_crop', action='store_true', help="Should we crop each frame independently (this makes a video shaking)?")
args = parser.parse_args()
# splits = ['train', 'val', 'test']
# categories = ['original', 'mask', 'altered']
splits = ['train']
categories = ['original', 'mask']
df = parse_videos(args.source_dir, splits, categories)
os.makedirs(args.output_dir, exist_ok=True)
for split in splits:
target_frame = df[df['split'] == split]
filenames = target_frame['filename'].unique()
# print('Count # of frames')
# rets = []
# for i, filename in enumerate(filenames):
# fn_frame = target_frame[target_frame['filename'] == filename]
# video_path = os.path.join(
# args.source_dir, fn_frame[fn_frame['category'] == 'original'].iloc[0]['filepath'])
# rets.append(p.apply_async(count_frames, args=(video_path,)))
# n_frames = 0
# for ret in tqdm(rets):
# n_frames += ret.get()
# print('# of frames: {}'.format(n_frames))
# h5file = h5py.File(os.path.join(args.output_dir, '{}.h5'.format(split)), 'w')
# dset = h5file.create_dataset('image', (n_frames, 3, args.img_size, args.img_size), dtype=np.uint8)
# conf = []
# start = 0
pool = Pool(processes=args.num_workers)
job_kwargs_list = []
for i, filename in enumerate(filenames):
fn_frame = target_frame[target_frame['filename'] == filename]
video_path = os.path.join(args.source_dir, fn_frame[fn_frame['category'] == 'original'].iloc[0]['filepath'])
mask_path = os.path.join(args.source_dir, fn_frame[fn_frame['category'] == 'mask'].iloc[0]['filepath'])
job_kwargs_list.append(dict(
video_path=video_path,
mask_path=mask_path,
img_size=args.img_size,
wide_crop=args.wide_crop,
output_dir=os.path.join(args.output_dir, filename[:filename.rfind('.')]),
))
for _ in tqdm(pool.imap_unordered(job_proxy, job_kwargs_list), desc=f'Processing {split}', total=len(job_kwargs_list)):
pass
# T = len(video)
#dset[start:(start + T)] = video
# conf.append({'start': start, 'end': (start + T)})
# start += T
# conf = pandas.DataFrame(conf)
# conf.to_json(os.path.join(args.output_dir, '{}.json'.format(split)), orient='records')
if __name__ == '__main__':
main() |