mirror of
https://github.com/hpcaitech/Open-Sora.git
synced 2026-04-10 21:01:26 +02:00
Merge branch 'dev/v1.1' of github.com:hpcaitech/Open-Sora-dev into dev/v1.1
This commit is contained in:
zhengzangw
commit
e763669eab
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@ -248,7 +248,7 @@ is [here](/docs/datasets.md). We provide tools to process video data. Our data p
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the following steps:
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1. Manage datasets. [[docs](/tools/datasets/README.md)]
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2. Scene detection and video splitting. [[docs](/tools/splitting/README.md)]
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2. Scene detection and video splitting. [[docs](/tools/scene_cut/README.md)]
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3. Score and filter videos. [[docs](/tools/scoring/README.md)]
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4. Generate video captions. [[docs](/tools/caption/README.md)]
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@ -1,15 +1,24 @@
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# Datasets
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For Open-Sora 1.1, we conduct mixed training with both images and videos. The main datasets we use are listed below.
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Please refer to [README](/README.md#data-processing) for data processing.
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## Panda-70M
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[Panda-70M](https://github.com/snap-research/Panda-70M) is a large-scale dataset with 70M video-caption pairs.
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We use the [training-10M subset](https://github.com/snap-research/Panda-70M/tree/main/dataset_dataloading) for training,
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which contains ~10M videos of better quality.
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## Pexels
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[Pexels](https://www.pexels.com/) is a popular online platform that provides high-quality stock photos, videos, and music for free.
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Most videos from this website are of high quality. Thus, we use them for both pre-training and HQ fine-tuning.
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We really appreciate the great platform and the contributors!
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## Inter4K
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[Inter4K](https://github.com/alexandrosstergiou/Inter4K) is a dataset containing 1K video clips with 4K resolution.
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The dataset is proposed for super-resolution tasks. We use the dataset for HQ fine-tuning.
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## HD-VG-130M
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[HD-VG-130M](https://github.com/daooshee/HD-VG-130M?tab=readme-ov-file) comprises 130M text-video pairs. The caption is generated by BLIP-2. We find the cut and the text quality are relatively poor. It contains 20 splits. For OpenSora 1.0, we use the first split (~350K). We plan to use the whole dataset and re-process it.
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You can download the dataset and prepare it for training according to [the dataset repository's instructions](https://github.com/daooshee/HD-VG-130M). There is a README.md file in the Google Drive link that provides instructions on how to download and cut the videos. For this version, we directly use the dataset provided by the authors.
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## Inter4k
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[Inter4k](https://github.com/alexandrosstergiou/Inter4K) is a dataset containing 1k video clips with 4K resolution. The dataset is proposed for super-resolution tasks. We use the dataset for HQ training. The videos are processed as mentioned [here](/README.md#data-processing).
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## Pexels.com
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[Pexels.com](https://www.pexels.com/) is a website that provides free stock photos and videos. We collect 19K video clips from this website for HQ training. The videos are processed as mentioned [here](/README.md#data-processing).
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[HD-VG-130M](https://github.com/daooshee/HD-VG-130M?tab=readme-ov-file) comprises 130M text-video pairs.
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The caption is generated by BLIP-2.
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We find the scene and the text quality are relatively poor. For OpenSora 1.0, we only use ~350K samples from this dataset.
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|
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@ -2,7 +2,7 @@
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In many cases, raw videos contain several scenes and are too long for training. Thus, it is essential to split them into shorter
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clips based on scenes. Here, we provide code for scene detection and video splitting.
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## Formatting
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## Prepare a meta file
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At this step, you should have a raw video dataset prepared. We need a meta file for the dataset. To create a meta file from a folder, run:
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```bash
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@ -15,7 +15,7 @@ If you already have a meta file for the videos and want to keep the information.
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The following command will add a new column `path` to the meta file.
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```bash
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python tools/scene_cut/process_meta.py --task append_path --meta_path /path/to/meta.csv --folder_path /path/to/video/folder
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python tools/scene_cut/convert_id_to_path.py /path/to/meta.csv --folder_path /path/to/video/folder
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```
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This should output
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- `{prefix}_path-filtered.csv` with column `path` (broken videos filtered)
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@ -28,8 +28,7 @@ We use [`PySceneDetect`](https://github.com/Breakthrough/PySceneDetect) for this
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**Make sure** the input meta file has column `path`, which is the path of a video.
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```bash
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python tools/scene_cut/scene_detect.py --meta_path /path/to/meta.csv
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python tools/scene_cut/scene_detect.py --meta_path /mnt/hdd/data/pexels_new/raw/meta/popular_6_format.csv
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python tools/scene_cut/scene_detect.py /path/to/meta.csv
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```
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The output is `{prefix}_timestamp.csv` with column `timestamp`. Each cell in column `timestamp` is a list of tuples,
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with each tuple indicating the start and end timestamp of a scene
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@ -39,18 +38,13 @@ with each tuple indicating the start and end timestamp of a scene
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After obtaining timestamps for scenes, we conduct video splitting (cutting).
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**Make sure** the meta file contains column `timestamp`.
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TODO: output video size, min_duration, max_duration
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```bash
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python tools/scene_cut/main_cut_pandarallel.py \
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--meta_path /path/to/meta.csv \
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--out_dir /path/to/output/dir
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python tools/scene_cut/main_cut_pandarallel.py \
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--meta_path /mnt/hdd/data/pexels_new/raw/meta/popular_6_format_timestamp.csv \
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--out_dir /mnt/hdd/data/pexels_new/scene_cut/data/popular_6
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python tools/scene_cut/cut.py /path/to/meta.csv --save_dir /path/to/output/dir
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```
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This yields video clips saved in `/path/to/output/dir`. The video clips are named as `{video_id}_scene-{scene_id}.mp4`
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This will save video clips to `/path/to/output/dir`. The video clips are named as `{video_id}_scene-{scene_id}.mp4`
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TODO: meta for video clips
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To create a new meta file for the generated clips, run:
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```bash
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python -m tools.datasets.convert video /path/to/video/folder --output /path/to/save/meta.csv
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```
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|
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@ -1,15 +1,5 @@
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"""
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1. format_raw_meta()
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- only keep intact videos
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- add 'path' column (abs path)
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2. create_meta_for_folder()
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"""
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import os
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# os.chdir('../..')
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print(f"Current working directory: {os.getcwd()}")
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import argparse
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import json
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from functools import partial
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@ -18,7 +8,42 @@ import numpy as np
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import pandas as pd
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from pandarallel import pandarallel
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from tqdm import tqdm
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from utils_video import is_intact_video
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import cv2
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from mmengine.logging import print_log
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from moviepy.editor import VideoFileClip
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def is_intact_video(video_path, mode="moviepy", verbose=False, logger=None):
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if not os.path.exists(video_path):
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if verbose:
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print_log(f"Could not find '{video_path}'", logger=logger)
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return False
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if mode == "moviepy":
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try:
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VideoFileClip(video_path)
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if verbose:
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print_log(f"The video file '{video_path}' is intact.", logger=logger)
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return True
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except Exception as e:
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if verbose:
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print_log(f"Error: {e}", logger=logger)
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print_log(f"The video file '{video_path}' is not intact.", logger=logger)
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return False
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elif mode == "cv2":
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try:
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cap = cv2.VideoCapture(video_path)
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if cap.isOpened():
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if verbose:
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print_log(f"The video file '{video_path}' is intact.", logger=logger)
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return True
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except Exception as e:
|
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if verbose:
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print_log(f"Error: {e}", logger=logger)
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print_log(f"The video file '{video_path}' is not intact.", logger=logger)
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return False
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else:
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raise ValueError
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|
||||
|
||||
def has_downloaded_success(json_path):
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@ -36,12 +61,28 @@ def has_downloaded_success(json_path):
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return True
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def append_format_pandarallel(meta_path, folder_path, mode=".json"):
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def is_intact(row, mode=".json"):
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("meta_path", type=str)
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parser.add_argument("--folder_path", type=str, required=True)
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parser.add_argument("--mode", type=str, default=None)
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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meta_path = args.meta_path
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folder_path = args.folder_path
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mode = args.mode
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def is_intact(row, mode=None):
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video_id = row["id"]
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# video_path = os.path.join(root_raw, f"data/{split}/{video_id}.mp4")
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video_path = os.path.join(folder_path, f"{video_id}.mp4")
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row["path"] = video_path
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if mode == ".mp4":
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if is_intact_video(video_path):
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return True, video_path
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@ -74,7 +115,6 @@ def append_format_pandarallel(meta_path, folder_path, mode=".json"):
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meta.to_csv(out_path, index=False)
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print(f"New meta (shape={meta.shape}) with intact info saved to '{out_path}'")
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# meta_format = meta[meta['intact']]
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meta_format = meta[np.array(intact)]
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meta_format.drop("intact", axis=1, inplace=True)
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out_path = os.path.join(meta_dirpath, f"{wo_ext}_path-filtered.csv")
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@ -82,40 +122,5 @@ def append_format_pandarallel(meta_path, folder_path, mode=".json"):
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print(f"New meta (shape={meta_format.shape}) with format info saved to '{out_path}'")
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def create_subset(meta_path):
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meta = pd.read_csv(meta_path)
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meta_subset = meta.iloc[:100]
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wo_ext, ext = os.path.splitext(meta_path)
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out_path = f"{wo_ext}_head-100{ext}"
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meta_subset.to_csv(out_path, index=False)
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print(f"New meta (shape={meta_subset.shape}) saved to '{out_path}'")
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||||
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--task", default="append_path", required=True)
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parser.add_argument("--meta_path", type=str, required=True)
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parser.add_argument("--folder_path", type=str, required=True)
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parser.add_argument("--mode", type=str, default=None)
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parser.add_argument("--num_workers", default=5, type=int)
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||||
args = parser.parse_args()
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return args
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|
||||
def main():
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args = parse_args()
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meta_path = args.meta_path
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task = args.task
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|
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if task == "append_path":
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append_format_pandarallel(meta_path=meta_path, folder_path=args.folder_path, mode=args.mode)
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elif task == "create_subset":
|
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create_subset(meta_path=meta_path)
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else:
|
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raise ValueError
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|
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|
||||
if __name__ == "__main__":
|
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main()
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|
|
@ -11,7 +11,7 @@ from pandarallel import pandarallel
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from scenedetect import FrameTimecode
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|
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|
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def process_single_row(row, save_dir, log_name=None):
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def process_single_row(row, args, log_name=None):
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video_path = row["path"]
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logger = None
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|
@ -28,7 +28,14 @@ def process_single_row(row, save_dir, log_name=None):
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scene_list = eval(timestamp)
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scene_list = [(FrameTimecode(s, fps=1), FrameTimecode(t, fps=1)) for s, t in scene_list]
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split_video(
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video_path, scene_list, save_dir=save_dir, min_seconds=2, max_seconds=15, shorter_size=720, logger=logger
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video_path,
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scene_list,
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save_dir=args.save_dir,
|
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min_seconds=args.min_seconds,
|
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max_seconds=args.max_seconds,
|
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target_fps=args.target_fps,
|
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shorter_size=args.shorter_size,
|
||||
logger=logger,
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||||
)
|
||||
|
||||
|
||||
|
|
@ -36,10 +43,10 @@ def split_video(
|
|||
video_path,
|
||||
scene_list,
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save_dir,
|
||||
min_seconds=None,
|
||||
max_seconds=None,
|
||||
min_seconds=2.0,
|
||||
max_seconds=15.0,
|
||||
target_fps=30,
|
||||
shorter_size=512,
|
||||
shorter_size=720,
|
||||
verbose=False,
|
||||
logger=None,
|
||||
):
|
||||
|
|
@ -121,9 +128,14 @@ def split_video(
|
|||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--meta_path", default="./data/pexels_new/raw/meta/popular_5_format_timestamp.csv")
|
||||
parser.add_argument("--out_dir", default="./data/pexels_new/scene_cut/data/popular_5")
|
||||
parser.add_argument("--num_workers", default=5, type=int)
|
||||
parser.add_argument("meta_path", type=str)
|
||||
parser.add_argument("--save_dir", type=str)
|
||||
parser.add_argument("--min_seconds", type=float, default=None,
|
||||
help='if not None, clip shorter than min_seconds is ignored')
|
||||
parser.add_argument("--max_seconds", type=float, default=None,
|
||||
help='if not None, clip longer than max_seconds is truncated')
|
||||
parser.add_argument("--target_fps", type=int, default=30, help='target fps of clips')
|
||||
parser.add_argument("--shorter_size", type=int, default=720, help='resize the shorter size by keeping ratio')
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
|
@ -131,25 +143,24 @@ def parse_args():
|
|||
|
||||
def main():
|
||||
args = parse_args()
|
||||
meta_path = args.meta_path
|
||||
out_dir = args.out_dir
|
||||
|
||||
assert os.path.basename(os.path.dirname(out_dir)) == "data"
|
||||
|
||||
os.makedirs(out_dir, exist_ok=True)
|
||||
|
||||
meta = pd.read_csv(meta_path)
|
||||
save_dir = args.save_dir
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
|
||||
# create logger
|
||||
log_dir = os.path.dirname(out_dir)
|
||||
log_name = os.path.basename(out_dir)
|
||||
log_dir = os.path.dirname(save_dir)
|
||||
log_name = os.path.basename(save_dir)
|
||||
timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime(time.time()))
|
||||
log_path = os.path.join(log_dir, f"{log_name}_{timestamp}.log")
|
||||
logger = MMLogger.get_instance(log_name, log_file=log_path)
|
||||
# logger = None
|
||||
|
||||
# initialize pandarallel
|
||||
pandarallel.initialize(progress_bar=True)
|
||||
process_single_row_partial = partial(process_single_row, save_dir=out_dir, log_name=log_name)
|
||||
process_single_row_partial = partial(process_single_row, args=args, log_name=log_name)
|
||||
|
||||
# process
|
||||
meta = pd.read_csv(args.meta_path)
|
||||
meta.parallel_apply(process_single_row_partial, axis=1)
|
||||
|
||||
|
||||
|
|
@ -1,162 +0,0 @@
|
|||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
|
||||
import pandas as pd
|
||||
from imageio_ffmpeg import get_ffmpeg_exe
|
||||
from mmengine.logging import print_log
|
||||
from scenedetect import FrameTimecode
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def single_process(row, save_dir, logger=None):
|
||||
# video_id = row['videoID']
|
||||
# video_path = os.path.join(root_src, f'{video_id}.mp4')
|
||||
video_path = row["path"]
|
||||
|
||||
# check mp4 integrity
|
||||
# if not is_intact_video(video_path, logger=logger):
|
||||
# return False
|
||||
|
||||
timestamp = row["timestamp"]
|
||||
if not (timestamp.startswith("[") and timestamp.endswith("]")):
|
||||
return False
|
||||
scene_list = eval(timestamp)
|
||||
scene_list = [(FrameTimecode(s, fps=1), FrameTimecode(t, fps=1)) for s, t in scene_list]
|
||||
split_video(video_path, scene_list, save_dir=save_dir, logger=logger)
|
||||
return True
|
||||
|
||||
|
||||
def split_video(
|
||||
video_path,
|
||||
scene_list,
|
||||
save_dir,
|
||||
min_seconds=None,
|
||||
max_seconds=None,
|
||||
target_fps=30,
|
||||
shorter_size=512,
|
||||
verbose=False,
|
||||
logger=None,
|
||||
):
|
||||
"""
|
||||
scenes shorter than min_seconds will be ignored;
|
||||
scenes longer than max_seconds will be cut to save the beginning max_seconds.
|
||||
Currently, the saved file name pattern is f'{fname}_scene-{idx}'.mp4
|
||||
|
||||
Args:
|
||||
scene_list (List[Tuple[FrameTimecode, FrameTimecode]]): each element is (s, t): start and end of a scene.
|
||||
min_seconds (float | None)
|
||||
max_seconds (float | None)
|
||||
target_fps (int | None)
|
||||
shorter_size (int | None)
|
||||
"""
|
||||
FFMPEG_PATH = get_ffmpeg_exe()
|
||||
|
||||
save_path_list = []
|
||||
for idx, scene in enumerate(scene_list):
|
||||
s, t = scene # FrameTimecode
|
||||
if min_seconds is not None:
|
||||
if (t - s).get_seconds() < min_seconds:
|
||||
continue
|
||||
|
||||
duration = t - s
|
||||
if max_seconds is not None:
|
||||
fps = s.framerate
|
||||
max_duration = FrameTimecode(timecode="00:00:00", fps=fps)
|
||||
max_duration.frame_num = round(fps * max_seconds)
|
||||
duration = min(max_duration, duration)
|
||||
|
||||
# save path
|
||||
fname = os.path.basename(video_path)
|
||||
fname_wo_ext = os.path.splitext(fname)[0]
|
||||
# TODO: fname pattern
|
||||
save_path = os.path.join(save_dir, f"{fname_wo_ext}_scene-{idx}.mp4")
|
||||
|
||||
# ffmpeg cmd
|
||||
cmd = [FFMPEG_PATH]
|
||||
|
||||
# Only show ffmpeg output for the first call, which will display any
|
||||
# errors if it fails, and then break the loop. We only show error messages
|
||||
# for the remaining calls.
|
||||
# cmd += ['-v', 'error']
|
||||
|
||||
# input path
|
||||
# cmd += ["-i", video_path]
|
||||
|
||||
# clip to cut
|
||||
cmd += ["-nostdin", "-y", "-ss", str(s.get_seconds()), "-i", video_path, "-t", str(duration.get_seconds())]
|
||||
# cmd += ["-nostdin", "-y", "-ss", str(s.get_seconds()), "-t", str(duration.get_seconds())]
|
||||
|
||||
# target fps
|
||||
# cmd += ['-vf', 'select=mod(n\,2)']
|
||||
if target_fps is not None:
|
||||
cmd += ["-r", f"{target_fps}"]
|
||||
|
||||
# aspect ratio
|
||||
if shorter_size is not None:
|
||||
cmd += ["-vf", f"scale='if(gt(iw,ih),-2,{shorter_size})':'if(gt(iw,ih),{shorter_size},-2)'"]
|
||||
# cmd += ['-vf', f"scale='if(gt(iw,ih),{shorter_size},trunc(ow/a/2)*2)':-2"]
|
||||
|
||||
cmd += ["-map", "0", save_path]
|
||||
|
||||
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
|
||||
stdout, stderr = proc.communicate()
|
||||
if verbose:
|
||||
stdout = stdout.decode("utf-8")
|
||||
print_log(stdout, logger=logger)
|
||||
|
||||
save_path_list.append(video_path)
|
||||
print_log(f"Video clip saved to '{save_path}'", logger=logger)
|
||||
|
||||
return save_path_list
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--root", default="F:/Panda-70M/")
|
||||
parser.add_argument("--split", default="test")
|
||||
parser.add_argument("--num_workers", default=5, type=int)
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
# args = parse_args()
|
||||
# root = args.root
|
||||
# split = args.split
|
||||
|
||||
root = "F:/Panda-70M/"
|
||||
root, split = "F:/pexels_new/", "popular_2"
|
||||
meta_path = os.path.join(root, f"raw/meta/{split}_format_timestamp.csv")
|
||||
root_dst = os.path.join(root, f"scene_cut/data/{split}")
|
||||
|
||||
folder_dst = root_dst
|
||||
# folder_src = os.path.join(root_src, f'data/{split}')
|
||||
# folder_dst = os.path.join(root_dst, os.path.relpath(folder_src, root_src))
|
||||
os.makedirs(folder_dst, exist_ok=True)
|
||||
|
||||
meta = pd.read_csv(meta_path)
|
||||
|
||||
# create logger
|
||||
# folder_path_log = os.path.dirname(root_dst)
|
||||
# log_name = os.path.basename(root_dst)
|
||||
# timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime(time.time()))
|
||||
# log_path = os.path.join(folder_path_log, f"{log_name}_{timestamp}.log")
|
||||
# logger = MMLogger.get_instance(log_name, log_file=log_path)
|
||||
logger = None
|
||||
|
||||
tasks = []
|
||||
pool = ThreadPoolExecutor(max_workers=1)
|
||||
for idx, row in meta.iterrows():
|
||||
task = pool.submit(single_process, row, folder_dst, logger)
|
||||
tasks.append(task)
|
||||
|
||||
for task in tqdm(as_completed(tasks), total=len(meta)):
|
||||
task.result()
|
||||
pool.shutdown()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -29,53 +29,24 @@ def process_single_row(row):
|
|||
return False, ""
|
||||
|
||||
|
||||
def main():
|
||||
meta_path = "F:/pexels_new/raw/meta/popular_1_format.csv"
|
||||
meta = pd.read_csv(meta_path)
|
||||
|
||||
timestamp_list = []
|
||||
for idx, row in tqdm(meta.iterrows()):
|
||||
video_path = row["path"]
|
||||
|
||||
detector = AdaptiveDetector(
|
||||
adaptive_threshold=1.5,
|
||||
luma_only=True,
|
||||
)
|
||||
# detector = ContentDetector()
|
||||
scene_list = detect(video_path, detector, start_in_scene=True)
|
||||
|
||||
timestamp = [(s.get_timecode(), t.get_timecode()) for s, t in scene_list]
|
||||
timestamp_list.append(timestamp)
|
||||
|
||||
meta["timestamp"] = timestamp_list
|
||||
|
||||
wo_ext, ext = os.path.splitext(meta_path)
|
||||
out_path = f"{wo_ext}_timestamp{ext}"
|
||||
meta.to_csv(out_path, index=False)
|
||||
print(f"New meta with timestamp saved to '{out_path}'.")
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--meta_path", default="F:/pexels_new/raw/meta/popular_1_format.csv")
|
||||
parser.add_argument("--num_workers", default=5, type=int)
|
||||
parser.add_argument("meta_path", type=str)
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def main_pandarallel():
|
||||
def main():
|
||||
args = parse_args()
|
||||
meta_path = args.meta_path
|
||||
|
||||
# meta_path = 'F:/pexels_new/raw/meta/popular_1_format.csv'
|
||||
meta = pd.read_csv(meta_path)
|
||||
|
||||
pandarallel.initialize(progress_bar=True)
|
||||
|
||||
meta = pd.read_csv(meta_path)
|
||||
ret = meta.parallel_apply(process_single_row, axis=1)
|
||||
|
||||
succ, timestamps = list(zip(*ret))
|
||||
|
||||
meta["timestamp"] = timestamps
|
||||
meta = meta[np.array(succ)]
|
||||
|
||||
|
|
@ -86,4 +57,4 @@ def main_pandarallel():
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main_pandarallel()
|
||||
main()
|
||||
|
|
|
|||
|
|
@ -1,97 +0,0 @@
|
|||
import os
|
||||
|
||||
import cv2
|
||||
from mmengine.logging import print_log
|
||||
from moviepy.editor import VideoFileClip
|
||||
|
||||
|
||||
def iterate_files(folder_path):
|
||||
for root, dirs, files in os.walk(folder_path):
|
||||
# root contains the current directory path
|
||||
# dirs contains the list of subdirectories in the current directory
|
||||
# files contains the list of files in the current directory
|
||||
|
||||
# Process files in the current directory
|
||||
for file in files:
|
||||
file_path = os.path.join(root, file)
|
||||
# print("File:", file_path)
|
||||
yield file_path
|
||||
|
||||
# Process subdirectories and recursively call the function
|
||||
for subdir in dirs:
|
||||
subdir_path = os.path.join(root, subdir)
|
||||
# print("Subdirectory:", subdir_path)
|
||||
iterate_files(subdir_path)
|
||||
|
||||
|
||||
def iterate_folders(folder_path):
|
||||
for root, dirs, files in os.walk(folder_path):
|
||||
for subdir in dirs:
|
||||
subdir_path = os.path.join(root, subdir)
|
||||
yield subdir_path
|
||||
# print("Subdirectory:", subdir_path)
|
||||
iterate_folders(subdir_path)
|
||||
|
||||
|
||||
def clone_folder_structure(root_src, root_dst, verbose=False):
|
||||
src_path_list = iterate_folders(root_src)
|
||||
src_relpath_list = [os.path.relpath(x, root_src) for x in src_path_list]
|
||||
|
||||
os.makedirs(root_dst, exist_ok=True)
|
||||
dst_path_list = [os.path.join(root_dst, x) for x in src_relpath_list]
|
||||
for folder_path in dst_path_list:
|
||||
os.makedirs(folder_path, exist_ok=True)
|
||||
if verbose:
|
||||
print(f"Create folder: '{folder_path}'")
|
||||
|
||||
|
||||
def is_intact_video(video_path, mode="moviepy", verbose=False, logger=None):
|
||||
if not os.path.exists(video_path):
|
||||
if verbose:
|
||||
print_log(f"Could not find '{video_path}'", logger=logger)
|
||||
return False
|
||||
|
||||
if mode == "moviepy":
|
||||
try:
|
||||
VideoFileClip(video_path)
|
||||
if verbose:
|
||||
print_log(f"The video file '{video_path}' is intact.", logger=logger)
|
||||
return True
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
print_log(f"Error: {e}", logger=logger)
|
||||
print_log(f"The video file '{video_path}' is not intact.", logger=logger)
|
||||
return False
|
||||
elif mode == "cv2":
|
||||
try:
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
if cap.isOpened():
|
||||
if verbose:
|
||||
print_log(f"The video file '{video_path}' is intact.", logger=logger)
|
||||
return True
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
print_log(f"Error: {e}", logger=logger)
|
||||
print_log(f"The video file '{video_path}' is not intact.", logger=logger)
|
||||
return False
|
||||
else:
|
||||
raise ValueError
|
||||
|
||||
|
||||
def count_frames(video_path, logger=None):
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
|
||||
if not cap.isOpened():
|
||||
print_log(f"Error: Could not open video file '{video_path}'", logger=logger)
|
||||
return
|
||||
|
||||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
print_log(f"Total frames in the video '{video_path}': {total_frames}", logger=logger)
|
||||
|
||||
cap.release()
|
||||
|
||||
|
||||
def count_files(root, suffix=".mp4"):
|
||||
files_list = iterate_files(root)
|
||||
cnt = len([x for x in files_list if x.endswith(suffix)])
|
||||
return cnt
|
||||
Loading…
Reference in a new issue