264 lines
8.8 KiB
Python
264 lines
8.8 KiB
Python
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#!/usr/bin/env python3
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"""
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Script to prepare fish action video dataset for SlowFast training.
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This script:
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1. Scans the video directory structure (each subfolder = class)
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2. Creates train/val/test splits
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3. Generates CSV files in the format expected by SlowFast
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4. Creates a label mapping file
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IMPORTANT: SlowFast does NOT require pre-split video clips. It automatically
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samples short clips (typically 2-4 seconds) from full-length videos during
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training. You can use your full-length videos as-is.
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Usage:
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python prepare_fish_dataset.py --video_dir ~/data/fish/fish_action_videos --output_dir ./dataset
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"""
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import argparse
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import os
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import random
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from collections import defaultdict
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from pathlib import Path
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def get_video_files(directory, extensions=None):
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"""Get all video files from a directory."""
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if extensions is None:
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extensions = {'.mp4', '.avi', '.mov', '.mkv', '.webm', '.flv', '.m4v'}
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video_files = []
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for root, dirs, files in os.walk(directory):
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for file in files:
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if any(file.lower().endswith(ext) for ext in extensions):
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video_files.append(os.path.join(root, file))
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return sorted(video_files)
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def _canonicalize_class_name(class_name: str) -> str:
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"""
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Map raw folder names to canonical class names.
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Supports either:
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- 3-folder layout: feeding/, normal/, scared/
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- 5-folder layout: feeding/, normal_underwater/, normal_upperwater/,
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scared_underwater/, scared_upperwater/
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"""
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name = class_name.strip().lower()
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if name == "feeding":
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return "feeding"
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if name in {"normal", "normal_underwater", "normal_upperwater"}:
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return "normal"
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if name in {"scared", "scared_underwater", "scared_upperwater"}:
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return "scared"
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return class_name # unknown, handled by caller
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def create_dataset_splits(video_dir, output_dir, train_ratio=0.7, val_ratio=0.15, test_ratio=0.15, seed=42):
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"""
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Create train/val/test splits from video directory structure.
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Args:
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video_dir: Root directory containing class subfolders
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output_dir: Directory to save CSV files
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train_ratio: Ratio of videos for training
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val_ratio: Ratio of videos for validation
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test_ratio: Ratio of videos for testing
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seed: Random seed for reproducibility
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"""
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# Validate ratios
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assert abs(train_ratio + val_ratio + test_ratio - 1.0) < 1e-6, \
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"Ratios must sum to 1.0"
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random.seed(seed)
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# Create output directory
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os.makedirs(output_dir, exist_ok=True)
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# Get all class directories
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video_path = Path(video_dir).expanduser()
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if not video_path.exists():
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raise ValueError(f"Video directory does not exist: {video_dir}")
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# Find all class subdirectories
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class_dirs = [d for d in video_path.iterdir() if d.is_dir()]
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class_dirs = sorted(class_dirs)
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if len(class_dirs) == 0:
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raise ValueError(f"No subdirectories found in {video_dir}")
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print(f"Found {len(class_dirs)} classes:")
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for class_dir in class_dirs:
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print(f" - {class_dir.name}")
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# Canonicalize classes into 3-class setup.
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canonical_classes = []
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unknown_classes = []
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for d in class_dirs:
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canon = _canonicalize_class_name(d.name)
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if canon in {"feeding", "normal", "scared"}:
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canonical_classes.append(canon)
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else:
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unknown_classes.append(d.name)
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if unknown_classes:
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raise ValueError(
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"Unknown class folder(s) found that cannot be mapped to 3 classes: "
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f"{unknown_classes}. Expected folders among: "
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"feeding, normal, scared, normal_underwater, normal_upperwater, "
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"scared_underwater, scared_upperwater."
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)
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canonical_classes = sorted(set(canonical_classes))
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# Ensure stable label ordering.
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desired_order = ["feeding", "normal", "scared"]
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canonical_classes = [c for c in desired_order if c in canonical_classes]
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# Create canonical class->label mapping (3 classes).
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class_to_label = {name: idx for idx, name in enumerate(canonical_classes)}
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label_to_class = {idx: name for name, idx in class_to_label.items()}
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# Collect all videos with their classes
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all_videos = []
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for class_dir in class_dirs:
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raw_class_name = class_dir.name
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class_name = _canonicalize_class_name(raw_class_name)
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label = class_to_label[class_name]
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videos = get_video_files(str(class_dir))
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if len(videos) == 0:
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print(f"Warning: No videos found in {class_dir}")
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continue
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for video_path in videos:
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# Absolute path so training scripts can use path_prefix="" or point CSV paths directly.
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abs_path = os.path.abspath(video_path)
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all_videos.append((abs_path, label, class_name))
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print(f"\nTotal videos found: {len(all_videos)}")
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# Group videos by class for stratified splitting
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videos_by_class = defaultdict(list)
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for rel_path, label, class_name in all_videos:
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videos_by_class[label].append((rel_path, label, class_name))
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# Create stratified splits
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train_videos = []
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val_videos = []
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test_videos = []
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for label, videos in videos_by_class.items():
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random.shuffle(videos)
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n_total = len(videos)
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n_train = int(n_total * train_ratio)
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n_val = int(n_total * val_ratio)
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train_videos.extend(videos[:n_train])
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val_videos.extend(videos[n_train:n_train + n_val])
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test_videos.extend(videos[n_train + n_val:])
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print(f"\nClass {label_to_class[label]} (label {label}):")
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print(f" Total: {n_total}")
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print(f" Train: {len(videos[:n_train])}")
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print(f" Val: {len(videos[n_train:n_train + n_val])}")
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print(f" Test: {len(videos[n_train + n_val:])}")
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# Shuffle splits
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random.shuffle(train_videos)
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random.shuffle(val_videos)
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random.shuffle(test_videos)
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# Write CSV files
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def write_csv(filename, videos):
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csv_path = os.path.join(output_dir, filename)
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with open(csv_path, 'w') as f:
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for video_path, label, _ in videos:
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f.write(f"{video_path} {label}\n")
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print(f"\nCreated {csv_path} with {len(videos)} videos")
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write_csv("train.csv", train_videos)
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write_csv("val.csv", val_videos)
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write_csv("test.csv", test_videos)
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# Write label mapping file
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label_map_path = os.path.join(output_dir, "label_map.txt")
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with open(label_map_path, 'w') as f:
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for idx in sorted(label_to_class.keys()):
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f.write(f"{idx} {label_to_class[idx]}\n")
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print(f"\nCreated label mapping: {label_map_path}")
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# Print summary
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print("\n" + "="*60)
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print("Dataset Preparation Summary")
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print("="*60)
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print(f"Total classes: {len(class_to_label)}")
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print(f"Total videos: {len(all_videos)}")
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print(f"Train videos: {len(train_videos)}")
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print(f"Val videos: {len(val_videos)}")
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print(f"Test videos: {len(test_videos)}")
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print(f"\nOutput directory: {output_dir}")
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print(f"Video directory: {video_dir}")
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print("\nNext steps:")
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print(f"1. Set DATA.PATH_TO_DATA_DIR to: {os.path.abspath(output_dir)}")
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print(f"2. CSVs list absolute video paths; for PyTorchVideo-style scripts, --path_prefix can be any path")
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print(f" (join ignores prefix when the CSV path is already absolute).")
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print("="*60)
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def main():
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parser = argparse.ArgumentParser(
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description="Prepare fish action video dataset for training (generates 3-class CSVs: feeding/normal/scared)"
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)
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parser.add_argument(
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"--video_dir",
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type=str,
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default="~/data/fish/fish_action_videos/",
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help="Root directory containing class subfolders with videos"
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="~/data/fish/fish_action_videos",
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help="Output directory for CSV files"
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)
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parser.add_argument(
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"--train_ratio",
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type=float,
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default=0.7,
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help="Ratio of videos for training (default: 0.7)"
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)
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parser.add_argument(
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"--val_ratio",
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type=float,
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default=0.15,
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help="Ratio of videos for validation (default: 0.15)"
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)
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parser.add_argument(
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"--test_ratio",
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type=float,
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default=0.15,
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help="Ratio of videos for testing (default: 0.15)"
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)
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parser.add_argument(
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"--seed",
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type=int,
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default=42,
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help="Random seed for reproducibility (default: 42)"
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)
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args = parser.parse_args()
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create_dataset_splits(
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video_dir=args.video_dir,
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output_dir=args.output_dir,
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train_ratio=args.train_ratio,
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val_ratio=args.val_ratio,
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test_ratio=args.test_ratio,
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seed=args.seed
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)
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if __name__ == "__main__":
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main()
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