mirror of
https://github.com/hpcaitech/Open-Sora.git
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125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
import argparse
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import os
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import random
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import cv2
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import numpy as np
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import pandas as pd
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from tqdm import tqdm
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from .utils import IMG_EXTENSIONS, extract_frames
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tqdm.pandas()
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try:
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from pandarallel import pandarallel
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pandarallel.initialize(progress_bar=True)
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pandas_has_parallel = True
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except ImportError:
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pandas_has_parallel = False
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def apply(df, func, **kwargs):
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if pandas_has_parallel:
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return df.parallel_apply(func, **kwargs)
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return df.progress_apply(func, **kwargs)
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def get_new_path(path, input_dir, output):
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path_new = os.path.join(output, os.path.relpath(path, input_dir))
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os.makedirs(os.path.dirname(path_new), exist_ok=True)
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return path_new
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def resize(path, length, input_dir, output):
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path_new = get_new_path(path, input_dir, output)
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ext = os.path.splitext(path)[1].lower()
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assert ext in IMG_EXTENSIONS
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img = cv2.imread(path)
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if img is not None:
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h, w = img.shape[:2]
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if min(h, w) > length:
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if h > w:
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new_h = length
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new_w = int(w * new_h / h)
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else:
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new_w = length
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new_h = int(h * new_w / w)
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img = cv2.resize(img, (new_w, new_h))
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cv2.imwrite(path_new, img)
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else:
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path_new = ""
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return path_new
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def rand_crop(path, input_dir, output):
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ext = os.path.splitext(path)[1].lower()
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path_new = get_new_path(path, input_dir, output)
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assert ext in IMG_EXTENSIONS
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img = cv2.imread(path)
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if img is not None:
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h, w = img.shape[:2]
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width, height, _ = img.shape
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pos = random.randint(0, 3)
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if pos == 0:
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img_cropped = img[: width // 2, : height // 2]
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elif pos == 1:
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img_cropped = img[width // 2 :, : height // 2]
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elif pos == 2:
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img_cropped = img[: width // 2, height // 2 :]
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else:
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img_cropped = img[width // 2 :, height // 2 :]
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cv2.imwrite(path_new, img_cropped)
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else:
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path_new = ""
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return path_new
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def main(args):
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data = pd.read_csv(args.input)
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if args.method == "img_rand_crop":
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data["path"] = apply(data["path"], lambda x: rand_crop(x, args.input_dir, args.output))
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output_csv = args.input.replace(".csv", f"_rand_crop.csv")
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elif args.method == "img_resize":
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data["path"] = apply(data["path"], lambda x: resize(x, args.length, args.input_dir, args.output))
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output_csv = args.input.replace(".csv", f"_resized{args.length}.csv")
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elif args.method == "vid_frame_extract":
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points = args.points if args.points is not None else args.points_index
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data = pd.DataFrame(np.repeat(data.values, 3, axis=0), columns=data.columns)
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num_points = len(points)
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data["point"] = np.nan
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for i, point in enumerate(points):
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if isinstance(point, int):
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data.loc[i::num_points, "point"] = point
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else:
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data.loc[i::num_points, "point"] = data.loc[i::num_points, "num_frames"] * point
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data["path"] = apply(data, lambda x: extract_frames(x["path"], args.input_dir, args.output, x["point"]), axis=1)
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output_csv = args.input.replace(".csv", f"_vid_frame_extract.csv")
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data.to_csv(output_csv, index=False)
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print(f"Saved to {output_csv}")
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("method", type=str, choices=["img_resize", "img_rand_crop", "vid_frame_extract"])
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parser.add_argument("input", type=str)
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parser.add_argument("input_dir", type=str)
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parser.add_argument("output", type=str)
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parser.add_argument("--disable-parallel", action="store_true")
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parser.add_argument("--length", type=int, default=2160)
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parser.add_argument("--seed", type=int, default=42, help="seed for random")
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parser.add_argument("--points", nargs="+", type=float, default=None)
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parser.add_argument("--points_index", nargs="+", type=int, default=None)
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args = parser.parse_args()
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return args
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if __name__ == "__main__":
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args = parse_args()
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random.seed(args.seed)
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if args.disable_parallel:
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pandas_has_parallel = False
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main(args)
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