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import ast
import argparse
import os
from pathlib import Path
import easyocr
import numpy as np
import pandas as pd
from accelerate import PartialState
from accelerate.utils import gather_object
from natsort import natsorted
from tqdm import tqdm
from torchvision.datasets.utils import download_url
from utils.logger import logger
from utils.video_utils import extract_frames, get_video_path_list
def init_ocr_reader(root: str = "~/.cache/easyocr", device: str = "gpu"):
root = os.path.expanduser(root)
if not os.path.exists(root):
os.makedirs(root)
download_url(
"https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/video_caption/easyocr/craft_mlt_25k.pth",
root,
filename="craft_mlt_25k.pth",
md5="2f8227d2def4037cdb3b34389dcf9ec1",
)
ocr_reader = easyocr.Reader(
lang_list=["en", "ch_sim"],
gpu=device,
recognizer=False,
verbose=False,
model_storage_directory=root,
)
return ocr_reader
def triangle_area(p1, p2, p3):
"""Compute the triangle area according to its coordinates.
"""
x1, y1 = p1
x2, y2 = p2
x3, y3 = p3
tri_area = 0.5 * np.abs(x1 * y2 + x2 * y3 + x3 * y1 - x2 * y1 - x3 * y2 - x1 * y3)
return tri_area
def compute_text_score(video_path, ocr_reader):
_, images = extract_frames(video_path, sample_method="mid")
images = [np.array(image) for image in images]
frame_ocr_area_ratios = []
for image in images:
# horizontal detected results and free-form detected
horizontal_list, free_list = ocr_reader.detect(np.asarray(image))
width, height = image.shape[0], image.shape[1]
total_area = width * height
# rectangles
rect_area = 0
for xmin, xmax, ymin, ymax in horizontal_list[0]:
if xmax < xmin or ymax < ymin:
continue
rect_area += (xmax - xmin) * (ymax - ymin)
# free-form
quad_area = 0
try:
for points in free_list[0]:
triangle1 = points[:3]
quad_area += triangle_area(*triangle1)
triangle2 = points[3:] + [points[0]]
quad_area += triangle_area(*triangle2)
except:
quad_area = 0
text_area = rect_area + quad_area
frame_ocr_area_ratios.append(text_area / total_area)
video_meta_info = {
"video_path": Path(video_path).name,
"text_score": round(np.mean(frame_ocr_area_ratios), 5),
}
return video_meta_info
def parse_args():
parser = argparse.ArgumentParser(description="Compute the text score of the middle frame in the videos.")
parser.add_argument("--video_folder", type=str, default="", help="The video folder.")
parser.add_argument(
"--video_metadata_path", type=str, default=None, help="The path to the video dataset metadata (csv/jsonl)."
)
parser.add_argument(
"--video_path_column",
type=str,
default="video_path",
help="The column contains the video path (an absolute path or a relative path w.r.t the video_folder).",
)
parser.add_argument("--saved_path", type=str, required=True, help="The save path to the output results (csv/jsonl).")
parser.add_argument("--saved_freq", type=int, default=100, help="The frequency to save the output results.")
parser.add_argument(
"--asethetic_score_metadata_path", type=str, default=None, help="The path to the video quality metadata (csv/jsonl)."
)
parser.add_argument("--asethetic_score_threshold", type=float, default=4.0, help="The asethetic score threshold.")
args = parser.parse_args()
return args
def main():
args = parse_args()
video_path_list = get_video_path_list(
video_folder=args.video_folder,
video_metadata_path=args.video_metadata_path,
video_path_column=args.video_path_column
)
if not (args.saved_path.endswith(".csv") or args.saved_path.endswith(".jsonl")):
raise ValueError("The saved_path must end with .csv or .jsonl.")
if os.path.exists(args.saved_path):
if args.saved_path.endswith(".csv"):
saved_metadata_df = pd.read_csv(args.saved_path)
elif args.saved_path.endswith(".jsonl"):
saved_metadata_df = pd.read_json(args.saved_path, lines=True)
saved_video_path_list = saved_metadata_df[args.video_path_column].tolist()
saved_video_path_list = [os.path.join(args.video_folder, video_path) for video_path in saved_video_path_list]
video_path_list = list(set(video_path_list).difference(set(saved_video_path_list)))
# Sorting to guarantee the same result for each process.
video_path_list = natsorted(video_path_list)
logger.info(f"Resume from {args.saved_path}: {len(saved_video_path_list)} processed and {len(video_path_list)} to be processed.")
if args.asethetic_score_metadata_path is not None:
if args.asethetic_score_metadata_path.endswith(".csv"):
asethetic_score_df = pd.read_csv(args.asethetic_score_metadata_path)
elif args.asethetic_score_metadata_path.endswith(".jsonl"):
asethetic_score_df = pd.read_json(args.asethetic_score_metadata_path, lines=True)
# In pandas, csv will save lists as strings, whereas jsonl will not.
asethetic_score_df["aesthetic_score"] = asethetic_score_df["aesthetic_score"].apply(
lambda x: ast.literal_eval(x) if isinstance(x, str) else x
)
asethetic_score_df["aesthetic_score_mean"] = asethetic_score_df["aesthetic_score"].apply(lambda x: sum(x) / len(x))
filtered_asethetic_score_df = asethetic_score_df[asethetic_score_df["aesthetic_score_mean"] < args.asethetic_score_threshold]
filtered_video_path_list = filtered_asethetic_score_df[args.video_path_column].tolist()
filtered_video_path_list = [os.path.join(args.video_folder, video_path) for video_path in filtered_video_path_list]
video_path_list = list(set(video_path_list).difference(set(filtered_video_path_list)))
# Sorting to guarantee the same result for each process.
video_path_list = natsorted(video_path_list)
logger.info(f"Load {args.asethetic_score_metadata_path} and filter {len(filtered_video_path_list)} videos.")
state = PartialState()
ocr_reader = init_ocr_reader(device=state.device)
# The workaround can be removed after https://github.com/huggingface/accelerate/pull/2781 is released.
index = len(video_path_list) - len(video_path_list) % state.num_processes
logger.info(f"Drop {len(video_path_list) % state.num_processes} videos to avoid duplicates in state.split_between_processes.")
video_path_list = video_path_list[:index]
result_list = []
with state.split_between_processes(video_path_list) as splitted_video_path_list:
for i, video_path in enumerate(tqdm(splitted_video_path_list)):
video_meta_info = compute_text_score(video_path, ocr_reader)
result_list.append(video_meta_info)
if i != 0 and i % args.saved_freq == 0:
state.wait_for_everyone()
gathered_result_list = gather_object(result_list)
if state.is_main_process:
result_df = pd.DataFrame(gathered_result_list)
if args.saved_path.endswith(".csv"):
header = False if os.path.exists(args.saved_path) else True
result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
elif args.saved_path.endswith(".jsonl"):
result_df.to_json(args.saved_path, orient="records", lines=True, mode="a")
logger.info(f"Save result to {args.saved_path}.")
result_list = []
state.wait_for_everyone()
gathered_result_list = gather_object(result_list)
if state.is_main_process:
logger.info(len(gathered_result_list))
if len(gathered_result_list) != 0:
result_df = pd.DataFrame(gathered_result_list)
if args.saved_path.endswith(".csv"):
header = False if os.path.exists(args.saved_path) else True
result_df.to_csv(args.saved_path, header=header, index=False, mode="a")
elif args.saved_path.endswith(".jsonl"):
result_df.to_json(args.saved_path, orient="records", lines=True, mode="a")
logger.info(f"Save the final result to {args.saved_path}.")
if __name__ == "__main__":
main() |