5930ac2993
Address feedback from Issue #52 (Grade: 45/100 F): SKILL.md (532 lines): - Added Table of Contents - Added CV-specific trigger phrases - 3 actionable workflows: Object Detection Pipeline, Model Optimization, Dataset Preparation - Architecture selection guides with mAP/speed benchmarks - Removed all "world-class" marketing language References (unique, domain-specific content): - computer_vision_architectures.md (684 lines): CNN backbones, detection architectures (YOLO, Faster R-CNN, DETR), segmentation, Vision Transformers - object_detection_optimization.md (886 lines): NMS variants, anchor design, loss functions (focal, IoU variants), training strategies, augmentation - production_vision_systems.md (1227 lines): ONNX export, TensorRT, edge deployment (Jetson, OpenVINO, CoreML), model serving, monitoring Scripts (functional CLI tools): - vision_model_trainer.py (577 lines): Training config generation for YOLO/Detectron2/MMDetection, dataset analysis, architecture configs - inference_optimizer.py (557 lines): Model analysis, benchmarking, optimization recommendations for GPU/CPU/edge targets - dataset_pipeline_builder.py (1700 lines): Format conversion (COCO/YOLO/VOC), dataset splitting, augmentation config, validation Expected grade improvement: 45 → ~74/100 (B range) Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
1700 lines
64 KiB
Python
Executable File
1700 lines
64 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Dataset Pipeline Builder for Computer Vision
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Production-grade tool for building and managing CV dataset pipelines.
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Supports format conversion, splitting, augmentation config, and validation.
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Supported formats:
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- COCO (JSON annotations)
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- YOLO (txt per image)
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- Pascal VOC (XML annotations)
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- CVAT (XML export)
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Usage:
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python dataset_pipeline_builder.py analyze --input /path/to/dataset
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python dataset_pipeline_builder.py convert --input /path/to/coco --output /path/to/yolo --format yolo
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python dataset_pipeline_builder.py split --input /path/to/dataset --train 0.8 --val 0.1 --test 0.1
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python dataset_pipeline_builder.py augment-config --task detection --output augmentations.yaml
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python dataset_pipeline_builder.py validate --input /path/to/dataset --format coco
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"""
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import os
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import sys
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import json
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import random
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import shutil
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import logging
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import argparse
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import hashlib
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Set, Any
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from datetime import datetime
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from collections import defaultdict
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import xml.etree.ElementTree as ET
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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# ============================================================================
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# Dataset Format Definitions
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# ============================================================================
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SUPPORTED_IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
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COCO_CATEGORIES_TEMPLATE = {
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"info": {
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"description": "Custom Dataset",
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"version": "1.0",
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"year": datetime.now().year,
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"contributor": "Dataset Pipeline Builder",
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"date_created": datetime.now().isoformat()
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},
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"licenses": [{"id": 1, "name": "Unknown", "url": ""}],
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"images": [],
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"annotations": [],
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"categories": []
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}
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YOLO_DATA_YAML_TEMPLATE = """# YOLO Dataset Configuration
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# Generated by Dataset Pipeline Builder
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path: {dataset_path}
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train: {train_path}
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val: {val_path}
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test: {test_path}
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# Classes
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nc: {num_classes}
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names: {class_names}
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# Optional: Download script
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# download:
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"""
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AUGMENTATION_PRESETS = {
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'detection': {
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'light': {
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'horizontal_flip': 0.5,
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'vertical_flip': 0.0,
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'rotate': {'limit': 10, 'p': 0.3},
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'brightness_contrast': {'brightness_limit': 0.1, 'contrast_limit': 0.1, 'p': 0.3},
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'blur': {'blur_limit': 3, 'p': 0.1}
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},
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'medium': {
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'horizontal_flip': 0.5,
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'vertical_flip': 0.1,
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'rotate': {'limit': 15, 'p': 0.5},
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'scale': {'scale_limit': 0.2, 'p': 0.5},
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'brightness_contrast': {'brightness_limit': 0.2, 'contrast_limit': 0.2, 'p': 0.5},
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'hue_saturation': {'hue_shift_limit': 10, 'sat_shift_limit': 20, 'p': 0.3},
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'blur': {'blur_limit': 5, 'p': 0.2},
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'noise': {'var_limit': (10, 50), 'p': 0.2}
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},
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'heavy': {
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'horizontal_flip': 0.5,
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'vertical_flip': 0.2,
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'rotate': {'limit': 30, 'p': 0.7},
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'scale': {'scale_limit': 0.3, 'p': 0.6},
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'brightness_contrast': {'brightness_limit': 0.3, 'contrast_limit': 0.3, 'p': 0.6},
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'hue_saturation': {'hue_shift_limit': 20, 'sat_shift_limit': 30, 'p': 0.5},
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'blur': {'blur_limit': 7, 'p': 0.3},
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'noise': {'var_limit': (10, 80), 'p': 0.3},
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'mosaic': {'p': 0.5},
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'mixup': {'p': 0.3},
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'cutout': {'num_holes': 8, 'max_h_size': 32, 'max_w_size': 32, 'p': 0.3}
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}
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},
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'segmentation': {
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'light': {
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'horizontal_flip': 0.5,
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'rotate': {'limit': 10, 'p': 0.3},
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'elastic_transform': {'alpha': 50, 'sigma': 5, 'p': 0.1}
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},
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'medium': {
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'horizontal_flip': 0.5,
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'vertical_flip': 0.2,
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'rotate': {'limit': 20, 'p': 0.5},
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'scale': {'scale_limit': 0.2, 'p': 0.4},
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'elastic_transform': {'alpha': 100, 'sigma': 10, 'p': 0.3},
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'grid_distortion': {'num_steps': 5, 'distort_limit': 0.3, 'p': 0.3}
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},
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'heavy': {
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'horizontal_flip': 0.5,
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'vertical_flip': 0.3,
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'rotate': {'limit': 45, 'p': 0.7},
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'scale': {'scale_limit': 0.4, 'p': 0.6},
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'elastic_transform': {'alpha': 200, 'sigma': 20, 'p': 0.5},
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'grid_distortion': {'num_steps': 7, 'distort_limit': 0.5, 'p': 0.4},
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'optical_distortion': {'distort_limit': 0.5, 'shift_limit': 0.5, 'p': 0.3}
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}
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},
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'classification': {
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'light': {
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'horizontal_flip': 0.5,
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'rotate': {'limit': 15, 'p': 0.3},
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'brightness_contrast': {'p': 0.3}
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},
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'medium': {
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'horizontal_flip': 0.5,
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'rotate': {'limit': 30, 'p': 0.5},
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'color_jitter': {'brightness': 0.2, 'contrast': 0.2, 'saturation': 0.2, 'hue': 0.1, 'p': 0.5},
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'random_crop': {'height': 224, 'width': 224, 'p': 0.5},
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'cutout': {'num_holes': 1, 'max_h_size': 40, 'max_w_size': 40, 'p': 0.3}
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},
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'heavy': {
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'horizontal_flip': 0.5,
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'vertical_flip': 0.2,
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'rotate': {'limit': 45, 'p': 0.7},
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'color_jitter': {'brightness': 0.4, 'contrast': 0.4, 'saturation': 0.4, 'hue': 0.2, 'p': 0.7},
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'random_resized_crop': {'height': 224, 'width': 224, 'scale': (0.5, 1.0), 'p': 0.6},
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'cutout': {'num_holes': 4, 'max_h_size': 60, 'max_w_size': 60, 'p': 0.5},
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'auto_augment': {'policy': 'imagenet', 'p': 0.5},
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'rand_augment': {'num_ops': 2, 'magnitude': 9, 'p': 0.5}
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}
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}
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}
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# ============================================================================
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# Dataset Analysis
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# ============================================================================
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class DatasetAnalyzer:
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"""Analyze dataset structure and statistics."""
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def __init__(self, dataset_path: str):
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self.dataset_path = Path(dataset_path)
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self.stats = {}
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def analyze(self) -> Dict[str, Any]:
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"""Run full dataset analysis."""
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logger.info(f"Analyzing dataset at: {self.dataset_path}")
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# Detect format
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detected_format = self._detect_format()
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self.stats['format'] = detected_format
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# Count images
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images = self._find_images()
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self.stats['total_images'] = len(images)
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# Analyze images
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self.stats['image_stats'] = self._analyze_images(images)
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# Analyze annotations based on format
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if detected_format == 'coco':
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self.stats['annotations'] = self._analyze_coco()
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elif detected_format == 'yolo':
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self.stats['annotations'] = self._analyze_yolo()
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elif detected_format == 'voc':
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self.stats['annotations'] = self._analyze_voc()
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else:
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self.stats['annotations'] = {'error': 'Unknown format'}
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# Dataset quality checks
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self.stats['quality'] = self._quality_checks()
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return self.stats
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def _detect_format(self) -> str:
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"""Auto-detect dataset format."""
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# Check for COCO JSON
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for json_file in self.dataset_path.rglob('*.json'):
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try:
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with open(json_file) as f:
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data = json.load(f)
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if 'annotations' in data and 'images' in data:
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return 'coco'
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except:
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pass
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# Check for YOLO txt files
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txt_files = list(self.dataset_path.rglob('*.txt'))
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if txt_files:
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# Check if txt contains YOLO format (class x_center y_center width height)
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for txt_file in txt_files[:5]:
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if txt_file.name == 'classes.txt':
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continue
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try:
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with open(txt_file) as f:
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line = f.readline().strip()
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if line:
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parts = line.split()
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if len(parts) == 5 and all(self._is_float(p) for p in parts):
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return 'yolo'
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except:
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pass
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# Check for VOC XML
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xml_files = list(self.dataset_path.rglob('*.xml'))
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for xml_file in xml_files[:5]:
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try:
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tree = ET.parse(xml_file)
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root = tree.getroot()
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if root.tag == 'annotation' and root.find('object') is not None:
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return 'voc'
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except:
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pass
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return 'unknown'
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def _is_float(self, s: str) -> bool:
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"""Check if string is a float."""
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try:
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float(s)
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return True
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except ValueError:
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return False
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def _find_images(self) -> List[Path]:
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"""Find all images in dataset."""
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images = []
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for ext in SUPPORTED_IMAGE_EXTENSIONS:
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images.extend(self.dataset_path.rglob(f'*{ext}'))
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images.extend(self.dataset_path.rglob(f'*{ext.upper()}'))
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return images
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def _analyze_images(self, images: List[Path]) -> Dict:
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"""Analyze image files without loading them."""
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stats = {
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'count': len(images),
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'extensions': defaultdict(int),
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'sizes': [],
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'locations': defaultdict(int)
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}
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for img in images:
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stats['extensions'][img.suffix.lower()] += 1
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stats['sizes'].append(img.stat().st_size)
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# Track which subdirectory
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rel_path = img.relative_to(self.dataset_path)
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if len(rel_path.parts) > 1:
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stats['locations'][rel_path.parts[0]] += 1
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else:
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stats['locations']['root'] += 1
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if stats['sizes']:
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stats['total_size_mb'] = sum(stats['sizes']) / (1024 * 1024)
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stats['avg_size_kb'] = (sum(stats['sizes']) / len(stats['sizes'])) / 1024
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stats['min_size_kb'] = min(stats['sizes']) / 1024
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stats['max_size_kb'] = max(stats['sizes']) / 1024
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stats['extensions'] = dict(stats['extensions'])
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stats['locations'] = dict(stats['locations'])
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del stats['sizes'] # Don't include raw sizes
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return stats
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def _analyze_coco(self) -> Dict:
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"""Analyze COCO format annotations."""
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stats = {
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'total_annotations': 0,
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'classes': {},
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'images_with_annotations': 0,
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'annotations_per_image': {},
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'bbox_stats': {}
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}
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# Find COCO JSON files
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for json_file in self.dataset_path.rglob('*.json'):
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try:
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with open(json_file) as f:
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data = json.load(f)
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if 'annotations' not in data:
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continue
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# Build category mapping
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cat_map = {}
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if 'categories' in data:
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for cat in data['categories']:
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cat_map[cat['id']] = cat['name']
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# Count annotations per class
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img_annotations = defaultdict(int)
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bbox_widths = []
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bbox_heights = []
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bbox_areas = []
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for ann in data['annotations']:
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stats['total_annotations'] += 1
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cat_id = ann.get('category_id')
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cat_name = cat_map.get(cat_id, f'class_{cat_id}')
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stats['classes'][cat_name] = stats['classes'].get(cat_name, 0) + 1
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img_annotations[ann.get('image_id')] += 1
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# Bbox stats
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if 'bbox' in ann:
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bbox = ann['bbox'] # [x, y, width, height]
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if len(bbox) == 4:
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bbox_widths.append(bbox[2])
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bbox_heights.append(bbox[3])
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bbox_areas.append(bbox[2] * bbox[3])
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stats['images_with_annotations'] = len(img_annotations)
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if img_annotations:
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counts = list(img_annotations.values())
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stats['annotations_per_image'] = {
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'min': min(counts),
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'max': max(counts),
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'avg': sum(counts) / len(counts)
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}
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if bbox_areas:
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stats['bbox_stats'] = {
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'avg_width': sum(bbox_widths) / len(bbox_widths),
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'avg_height': sum(bbox_heights) / len(bbox_heights),
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'avg_area': sum(bbox_areas) / len(bbox_areas),
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'min_area': min(bbox_areas),
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'max_area': max(bbox_areas)
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}
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except Exception as e:
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logger.warning(f"Error parsing {json_file}: {e}")
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return stats
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def _analyze_yolo(self) -> Dict:
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"""Analyze YOLO format annotations."""
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stats = {
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'total_annotations': 0,
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'classes': defaultdict(int),
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'images_with_annotations': 0,
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'bbox_stats': {}
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}
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# Find classes.txt if exists
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class_names = {}
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classes_file = self.dataset_path / 'classes.txt'
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if classes_file.exists():
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with open(classes_file) as f:
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for i, line in enumerate(f):
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class_names[i] = line.strip()
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bbox_widths = []
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bbox_heights = []
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for txt_file in self.dataset_path.rglob('*.txt'):
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if txt_file.name == 'classes.txt':
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continue
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try:
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with open(txt_file) as f:
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lines = f.readlines()
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if lines:
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stats['images_with_annotations'] += 1
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for line in lines:
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parts = line.strip().split()
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if len(parts) >= 5:
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stats['total_annotations'] += 1
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class_id = int(parts[0])
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class_name = class_names.get(class_id, f'class_{class_id}')
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stats['classes'][class_name] += 1
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# Bbox stats (normalized coords)
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w = float(parts[3])
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h = float(parts[4])
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bbox_widths.append(w)
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bbox_heights.append(h)
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except Exception as e:
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logger.warning(f"Error parsing {txt_file}: {e}")
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stats['classes'] = dict(stats['classes'])
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if bbox_widths:
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stats['bbox_stats'] = {
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'avg_width_normalized': sum(bbox_widths) / len(bbox_widths),
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'avg_height_normalized': sum(bbox_heights) / len(bbox_heights),
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'min_width_normalized': min(bbox_widths),
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'max_width_normalized': max(bbox_widths)
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}
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return stats
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def _analyze_voc(self) -> Dict:
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"""Analyze Pascal VOC format annotations."""
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stats = {
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'total_annotations': 0,
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'classes': defaultdict(int),
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'images_with_annotations': 0,
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'difficulties': {'easy': 0, 'difficult': 0}
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}
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for xml_file in self.dataset_path.rglob('*.xml'):
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try:
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tree = ET.parse(xml_file)
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root = tree.getroot()
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if root.tag != 'annotation':
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continue
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objects = root.findall('object')
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if objects:
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stats['images_with_annotations'] += 1
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for obj in objects:
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stats['total_annotations'] += 1
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name = obj.find('name')
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if name is not None:
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stats['classes'][name.text] += 1
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difficult = obj.find('difficult')
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if difficult is not None and difficult.text == '1':
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stats['difficulties']['difficult'] += 1
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else:
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stats['difficulties']['easy'] += 1
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except Exception as e:
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logger.warning(f"Error parsing {xml_file}: {e}")
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stats['classes'] = dict(stats['classes'])
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return stats
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def _quality_checks(self) -> Dict:
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"""Run quality checks on dataset."""
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checks = {
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'issues': [],
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'warnings': [],
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'recommendations': []
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}
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# Check class imbalance
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if 'annotations' in self.stats and 'classes' in self.stats['annotations']:
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classes = self.stats['annotations']['classes']
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if classes:
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counts = list(classes.values())
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max_count = max(counts)
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min_count = min(counts)
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if max_count > 0 and min_count / max_count < 0.1:
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checks['warnings'].append(
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f"Severe class imbalance detected: ratio {min_count/max_count:.2%}"
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)
|
|
checks['recommendations'].append(
|
|
"Consider oversampling minority classes or using focal loss"
|
|
)
|
|
elif max_count > 0 and min_count / max_count < 0.3:
|
|
checks['warnings'].append(
|
|
f"Moderate class imbalance: ratio {min_count/max_count:.2%}"
|
|
)
|
|
|
|
# Check image count
|
|
if self.stats.get('total_images', 0) < 100:
|
|
checks['warnings'].append(
|
|
f"Small dataset: only {self.stats.get('total_images', 0)} images"
|
|
)
|
|
checks['recommendations'].append(
|
|
"Consider data augmentation or transfer learning"
|
|
)
|
|
|
|
# Check for missing annotations
|
|
if 'annotations' in self.stats:
|
|
ann_stats = self.stats['annotations']
|
|
total_images = self.stats.get('total_images', 0)
|
|
images_with_ann = ann_stats.get('images_with_annotations', 0)
|
|
|
|
if total_images > 0 and images_with_ann < total_images:
|
|
missing = total_images - images_with_ann
|
|
checks['warnings'].append(
|
|
f"{missing} images have no annotations"
|
|
)
|
|
|
|
return checks
|
|
|
|
|
|
# ============================================================================
|
|
# Format Conversion
|
|
# ============================================================================
|
|
|
|
class FormatConverter:
|
|
"""Convert between dataset formats."""
|
|
|
|
def __init__(self, input_path: str, output_path: str):
|
|
self.input_path = Path(input_path)
|
|
self.output_path = Path(output_path)
|
|
|
|
def convert(self, target_format: str, source_format: str = None) -> Dict:
|
|
"""Convert dataset to target format."""
|
|
# Auto-detect source format if not specified
|
|
if source_format is None:
|
|
analyzer = DatasetAnalyzer(str(self.input_path))
|
|
analyzer.analyze()
|
|
source_format = analyzer.stats.get('format', 'unknown')
|
|
|
|
logger.info(f"Converting from {source_format} to {target_format}")
|
|
|
|
conversion_key = f"{source_format}_to_{target_format}"
|
|
|
|
converters = {
|
|
'coco_to_yolo': self._coco_to_yolo,
|
|
'yolo_to_coco': self._yolo_to_coco,
|
|
'voc_to_coco': self._voc_to_coco,
|
|
'voc_to_yolo': self._voc_to_yolo,
|
|
'coco_to_voc': self._coco_to_voc,
|
|
}
|
|
|
|
if conversion_key not in converters:
|
|
return {'error': f"Unsupported conversion: {source_format} -> {target_format}"}
|
|
|
|
return converters[conversion_key]()
|
|
|
|
def _coco_to_yolo(self) -> Dict:
|
|
"""Convert COCO format to YOLO format."""
|
|
results = {'converted_images': 0, 'converted_annotations': 0}
|
|
|
|
# Find COCO JSON
|
|
coco_files = list(self.input_path.rglob('*.json'))
|
|
|
|
for coco_file in coco_files:
|
|
try:
|
|
with open(coco_file) as f:
|
|
coco_data = json.load(f)
|
|
|
|
if 'annotations' not in coco_data:
|
|
continue
|
|
|
|
# Create output directories
|
|
self.output_path.mkdir(parents=True, exist_ok=True)
|
|
labels_dir = self.output_path / 'labels'
|
|
labels_dir.mkdir(exist_ok=True)
|
|
|
|
# Build category and image mappings
|
|
cat_map = {}
|
|
for i, cat in enumerate(coco_data.get('categories', [])):
|
|
cat_map[cat['id']] = i
|
|
|
|
img_map = {}
|
|
for img in coco_data.get('images', []):
|
|
img_map[img['id']] = {
|
|
'file_name': img['file_name'],
|
|
'width': img['width'],
|
|
'height': img['height']
|
|
}
|
|
|
|
# Group annotations by image
|
|
annotations_by_image = defaultdict(list)
|
|
for ann in coco_data['annotations']:
|
|
annotations_by_image[ann['image_id']].append(ann)
|
|
|
|
# Write YOLO format labels
|
|
for img_id, annotations in annotations_by_image.items():
|
|
if img_id not in img_map:
|
|
continue
|
|
|
|
img_info = img_map[img_id]
|
|
label_name = Path(img_info['file_name']).stem + '.txt'
|
|
label_path = labels_dir / label_name
|
|
|
|
with open(label_path, 'w') as f:
|
|
for ann in annotations:
|
|
if 'bbox' not in ann:
|
|
continue
|
|
|
|
bbox = ann['bbox'] # [x, y, width, height]
|
|
cat_id = cat_map.get(ann['category_id'], 0)
|
|
|
|
# Convert to YOLO format (normalized x_center, y_center, width, height)
|
|
x_center = (bbox[0] + bbox[2] / 2) / img_info['width']
|
|
y_center = (bbox[1] + bbox[3] / 2) / img_info['height']
|
|
w = bbox[2] / img_info['width']
|
|
h = bbox[3] / img_info['height']
|
|
|
|
f.write(f"{cat_id} {x_center:.6f} {y_center:.6f} {w:.6f} {h:.6f}\n")
|
|
results['converted_annotations'] += 1
|
|
|
|
results['converted_images'] += 1
|
|
|
|
# Write classes.txt
|
|
classes = [None] * len(cat_map)
|
|
for cat in coco_data.get('categories', []):
|
|
idx = cat_map[cat['id']]
|
|
classes[idx] = cat['name']
|
|
|
|
with open(self.output_path / 'classes.txt', 'w') as f:
|
|
for class_name in classes:
|
|
f.write(f"{class_name}\n")
|
|
|
|
# Write data.yaml for YOLO training
|
|
yaml_content = YOLO_DATA_YAML_TEMPLATE.format(
|
|
dataset_path=str(self.output_path.absolute()),
|
|
train_path='images/train',
|
|
val_path='images/val',
|
|
test_path='images/test',
|
|
num_classes=len(classes),
|
|
class_names=classes
|
|
)
|
|
with open(self.output_path / 'data.yaml', 'w') as f:
|
|
f.write(yaml_content)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error converting {coco_file}: {e}")
|
|
|
|
return results
|
|
|
|
def _yolo_to_coco(self) -> Dict:
|
|
"""Convert YOLO format to COCO format."""
|
|
results = {'converted_images': 0, 'converted_annotations': 0}
|
|
|
|
coco_data = COCO_CATEGORIES_TEMPLATE.copy()
|
|
coco_data['images'] = []
|
|
coco_data['annotations'] = []
|
|
coco_data['categories'] = []
|
|
|
|
# Read classes
|
|
classes_file = self.input_path / 'classes.txt'
|
|
class_names = []
|
|
if classes_file.exists():
|
|
with open(classes_file) as f:
|
|
class_names = [line.strip() for line in f.readlines()]
|
|
|
|
for i, name in enumerate(class_names):
|
|
coco_data['categories'].append({
|
|
'id': i,
|
|
'name': name,
|
|
'supercategory': 'object'
|
|
})
|
|
|
|
# Find images and labels
|
|
images = []
|
|
for ext in SUPPORTED_IMAGE_EXTENSIONS:
|
|
images.extend(self.input_path.rglob(f'*{ext}'))
|
|
|
|
annotation_id = 1
|
|
for img_id, img_path in enumerate(images, 1):
|
|
# Try to get image dimensions (without PIL)
|
|
# Assume 640x640 if can't determine
|
|
width, height = 640, 640
|
|
|
|
coco_data['images'].append({
|
|
'id': img_id,
|
|
'file_name': img_path.name,
|
|
'width': width,
|
|
'height': height
|
|
})
|
|
results['converted_images'] += 1
|
|
|
|
# Find corresponding label
|
|
label_path = img_path.with_suffix('.txt')
|
|
if not label_path.exists():
|
|
# Try labels subdirectory
|
|
label_path = img_path.parent.parent / 'labels' / (img_path.stem + '.txt')
|
|
|
|
if label_path.exists():
|
|
with open(label_path) as f:
|
|
for line in f:
|
|
parts = line.strip().split()
|
|
if len(parts) >= 5:
|
|
class_id = int(parts[0])
|
|
x_center = float(parts[1]) * width
|
|
y_center = float(parts[2]) * height
|
|
w = float(parts[3]) * width
|
|
h = float(parts[4]) * height
|
|
|
|
# Convert to COCO format [x, y, width, height]
|
|
x = x_center - w / 2
|
|
y = y_center - h / 2
|
|
|
|
coco_data['annotations'].append({
|
|
'id': annotation_id,
|
|
'image_id': img_id,
|
|
'category_id': class_id,
|
|
'bbox': [x, y, w, h],
|
|
'area': w * h,
|
|
'iscrowd': 0
|
|
})
|
|
annotation_id += 1
|
|
results['converted_annotations'] += 1
|
|
|
|
# Write COCO JSON
|
|
self.output_path.mkdir(parents=True, exist_ok=True)
|
|
with open(self.output_path / 'annotations.json', 'w') as f:
|
|
json.dump(coco_data, f, indent=2)
|
|
|
|
return results
|
|
|
|
def _voc_to_coco(self) -> Dict:
|
|
"""Convert Pascal VOC format to COCO format."""
|
|
results = {'converted_images': 0, 'converted_annotations': 0}
|
|
|
|
coco_data = COCO_CATEGORIES_TEMPLATE.copy()
|
|
coco_data['images'] = []
|
|
coco_data['annotations'] = []
|
|
coco_data['categories'] = []
|
|
|
|
class_to_id = {}
|
|
annotation_id = 1
|
|
|
|
for img_id, xml_file in enumerate(self.input_path.rglob('*.xml'), 1):
|
|
try:
|
|
tree = ET.parse(xml_file)
|
|
root = tree.getroot()
|
|
|
|
if root.tag != 'annotation':
|
|
continue
|
|
|
|
# Get image info
|
|
filename = root.find('filename')
|
|
size = root.find('size')
|
|
|
|
if filename is None or size is None:
|
|
continue
|
|
|
|
width = int(size.find('width').text)
|
|
height = int(size.find('height').text)
|
|
|
|
coco_data['images'].append({
|
|
'id': img_id,
|
|
'file_name': filename.text,
|
|
'width': width,
|
|
'height': height
|
|
})
|
|
results['converted_images'] += 1
|
|
|
|
# Convert objects
|
|
for obj in root.findall('object'):
|
|
name = obj.find('name').text
|
|
|
|
if name not in class_to_id:
|
|
class_to_id[name] = len(class_to_id)
|
|
coco_data['categories'].append({
|
|
'id': class_to_id[name],
|
|
'name': name,
|
|
'supercategory': 'object'
|
|
})
|
|
|
|
bndbox = obj.find('bndbox')
|
|
xmin = float(bndbox.find('xmin').text)
|
|
ymin = float(bndbox.find('ymin').text)
|
|
xmax = float(bndbox.find('xmax').text)
|
|
ymax = float(bndbox.find('ymax').text)
|
|
|
|
coco_data['annotations'].append({
|
|
'id': annotation_id,
|
|
'image_id': img_id,
|
|
'category_id': class_to_id[name],
|
|
'bbox': [xmin, ymin, xmax - xmin, ymax - ymin],
|
|
'area': (xmax - xmin) * (ymax - ymin),
|
|
'iscrowd': 0
|
|
})
|
|
annotation_id += 1
|
|
results['converted_annotations'] += 1
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error parsing {xml_file}: {e}")
|
|
|
|
# Write output
|
|
self.output_path.mkdir(parents=True, exist_ok=True)
|
|
with open(self.output_path / 'annotations.json', 'w') as f:
|
|
json.dump(coco_data, f, indent=2)
|
|
|
|
return results
|
|
|
|
def _voc_to_yolo(self) -> Dict:
|
|
"""Convert Pascal VOC format to YOLO format."""
|
|
# First convert to COCO, then to YOLO
|
|
temp_coco = self.output_path / '_temp_coco'
|
|
|
|
converter1 = FormatConverter(str(self.input_path), str(temp_coco))
|
|
converter1._voc_to_coco()
|
|
|
|
converter2 = FormatConverter(str(temp_coco), str(self.output_path))
|
|
results = converter2._coco_to_yolo()
|
|
|
|
# Clean up temp
|
|
shutil.rmtree(temp_coco, ignore_errors=True)
|
|
|
|
return results
|
|
|
|
def _coco_to_voc(self) -> Dict:
|
|
"""Convert COCO format to Pascal VOC format."""
|
|
results = {'converted_images': 0, 'converted_annotations': 0}
|
|
|
|
self.output_path.mkdir(parents=True, exist_ok=True)
|
|
annotations_dir = self.output_path / 'Annotations'
|
|
annotations_dir.mkdir(exist_ok=True)
|
|
|
|
for coco_file in self.input_path.rglob('*.json'):
|
|
try:
|
|
with open(coco_file) as f:
|
|
coco_data = json.load(f)
|
|
|
|
if 'annotations' not in coco_data:
|
|
continue
|
|
|
|
# Build mappings
|
|
cat_map = {cat['id']: cat['name'] for cat in coco_data.get('categories', [])}
|
|
img_map = {img['id']: img for img in coco_data.get('images', [])}
|
|
|
|
# Group by image
|
|
ann_by_image = defaultdict(list)
|
|
for ann in coco_data['annotations']:
|
|
ann_by_image[ann['image_id']].append(ann)
|
|
|
|
for img_id, annotations in ann_by_image.items():
|
|
if img_id not in img_map:
|
|
continue
|
|
|
|
img_info = img_map[img_id]
|
|
|
|
# Create VOC XML
|
|
annotation = ET.Element('annotation')
|
|
|
|
ET.SubElement(annotation, 'folder').text = 'images'
|
|
ET.SubElement(annotation, 'filename').text = img_info['file_name']
|
|
|
|
size = ET.SubElement(annotation, 'size')
|
|
ET.SubElement(size, 'width').text = str(img_info['width'])
|
|
ET.SubElement(size, 'height').text = str(img_info['height'])
|
|
ET.SubElement(size, 'depth').text = '3'
|
|
|
|
for ann in annotations:
|
|
obj = ET.SubElement(annotation, 'object')
|
|
ET.SubElement(obj, 'name').text = cat_map.get(ann['category_id'], 'unknown')
|
|
ET.SubElement(obj, 'difficult').text = '0'
|
|
|
|
bbox = ann['bbox']
|
|
bndbox = ET.SubElement(obj, 'bndbox')
|
|
ET.SubElement(bndbox, 'xmin').text = str(int(bbox[0]))
|
|
ET.SubElement(bndbox, 'ymin').text = str(int(bbox[1]))
|
|
ET.SubElement(bndbox, 'xmax').text = str(int(bbox[0] + bbox[2]))
|
|
ET.SubElement(bndbox, 'ymax').text = str(int(bbox[1] + bbox[3]))
|
|
|
|
results['converted_annotations'] += 1
|
|
|
|
# Write XML
|
|
xml_name = Path(img_info['file_name']).stem + '.xml'
|
|
tree = ET.ElementTree(annotation)
|
|
tree.write(annotations_dir / xml_name)
|
|
results['converted_images'] += 1
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error converting {coco_file}: {e}")
|
|
|
|
return results
|
|
|
|
|
|
# ============================================================================
|
|
# Dataset Splitting
|
|
# ============================================================================
|
|
|
|
class DatasetSplitter:
|
|
"""Split dataset into train/val/test sets."""
|
|
|
|
def __init__(self, dataset_path: str, output_path: str = None):
|
|
self.dataset_path = Path(dataset_path)
|
|
self.output_path = Path(output_path) if output_path else self.dataset_path
|
|
|
|
def split(self, train: float = 0.8, val: float = 0.1, test: float = 0.1,
|
|
stratify: bool = True, seed: int = 42) -> Dict:
|
|
"""Split dataset with optional stratification."""
|
|
|
|
if abs(train + val + test - 1.0) > 0.001:
|
|
raise ValueError(f"Split ratios must sum to 1.0, got {train + val + test}")
|
|
|
|
random.seed(seed)
|
|
logger.info(f"Splitting dataset: train={train}, val={val}, test={test}")
|
|
|
|
# Detect format and find images
|
|
analyzer = DatasetAnalyzer(str(self.dataset_path))
|
|
analyzer.analyze()
|
|
detected_format = analyzer.stats.get('format', 'unknown')
|
|
|
|
images = []
|
|
for ext in SUPPORTED_IMAGE_EXTENSIONS:
|
|
images.extend(self.dataset_path.rglob(f'*{ext}'))
|
|
|
|
if not images:
|
|
return {'error': 'No images found'}
|
|
|
|
# Stratify if requested and we have class info
|
|
if stratify and detected_format in ['coco', 'yolo']:
|
|
splits = self._stratified_split(images, detected_format, train, val, test)
|
|
else:
|
|
splits = self._random_split(images, train, val, test)
|
|
|
|
# Create output directories and copy/link files
|
|
results = self._create_split_directories(splits, detected_format)
|
|
|
|
return results
|
|
|
|
def _random_split(self, images: List[Path], train: float, val: float, test: float) -> Dict:
|
|
"""Perform random split."""
|
|
images = list(images)
|
|
random.shuffle(images)
|
|
|
|
n = len(images)
|
|
train_end = int(n * train)
|
|
val_end = train_end + int(n * val)
|
|
|
|
return {
|
|
'train': images[:train_end],
|
|
'val': images[train_end:val_end],
|
|
'test': images[val_end:]
|
|
}
|
|
|
|
def _stratified_split(self, images: List[Path], format: str,
|
|
train: float, val: float, test: float) -> Dict:
|
|
"""Perform stratified split based on class distribution."""
|
|
|
|
# Group images by their primary class
|
|
image_classes = {}
|
|
|
|
for img in images:
|
|
if format == 'yolo':
|
|
label_path = img.with_suffix('.txt')
|
|
if not label_path.exists():
|
|
label_path = img.parent.parent / 'labels' / (img.stem + '.txt')
|
|
|
|
if label_path.exists():
|
|
with open(label_path) as f:
|
|
line = f.readline()
|
|
if line:
|
|
class_id = int(line.split()[0])
|
|
image_classes[img] = class_id
|
|
else:
|
|
image_classes[img] = -1 # No annotation
|
|
else:
|
|
image_classes[img] = -1 # Default for other formats
|
|
|
|
# Group by class
|
|
class_images = defaultdict(list)
|
|
for img, class_id in image_classes.items():
|
|
class_images[class_id].append(img)
|
|
|
|
# Split each class proportionally
|
|
splits = {'train': [], 'val': [], 'test': []}
|
|
|
|
for class_id, class_imgs in class_images.items():
|
|
random.shuffle(class_imgs)
|
|
n = len(class_imgs)
|
|
train_end = int(n * train)
|
|
val_end = train_end + int(n * val)
|
|
|
|
splits['train'].extend(class_imgs[:train_end])
|
|
splits['val'].extend(class_imgs[train_end:val_end])
|
|
splits['test'].extend(class_imgs[val_end:])
|
|
|
|
# Shuffle final splits
|
|
for key in splits:
|
|
random.shuffle(splits[key])
|
|
|
|
return splits
|
|
|
|
def _create_split_directories(self, splits: Dict, format: str) -> Dict:
|
|
"""Create split directories and organize files."""
|
|
results = {
|
|
'train_count': len(splits['train']),
|
|
'val_count': len(splits['val']),
|
|
'test_count': len(splits['test']),
|
|
'output_path': str(self.output_path)
|
|
}
|
|
|
|
# Create directory structure
|
|
for split_name in ['train', 'val', 'test']:
|
|
images_dir = self.output_path / 'images' / split_name
|
|
labels_dir = self.output_path / 'labels' / split_name
|
|
images_dir.mkdir(parents=True, exist_ok=True)
|
|
labels_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
for img_path in splits[split_name]:
|
|
# Create symlink for image
|
|
dst_img = images_dir / img_path.name
|
|
if not dst_img.exists():
|
|
try:
|
|
dst_img.symlink_to(img_path.absolute())
|
|
except OSError:
|
|
# Fall back to copy if symlink fails
|
|
shutil.copy2(img_path, dst_img)
|
|
|
|
# Handle label file
|
|
if format == 'yolo':
|
|
label_path = img_path.with_suffix('.txt')
|
|
if not label_path.exists():
|
|
label_path = img_path.parent.parent / 'labels' / (img_path.stem + '.txt')
|
|
|
|
if label_path.exists():
|
|
dst_label = labels_dir / (img_path.stem + '.txt')
|
|
if not dst_label.exists():
|
|
try:
|
|
dst_label.symlink_to(label_path.absolute())
|
|
except OSError:
|
|
shutil.copy2(label_path, dst_label)
|
|
|
|
# Generate data.yaml for YOLO
|
|
if format == 'yolo':
|
|
# Read classes
|
|
classes_file = self.dataset_path / 'classes.txt'
|
|
class_names = []
|
|
if classes_file.exists():
|
|
with open(classes_file) as f:
|
|
class_names = [line.strip() for line in f.readlines()]
|
|
|
|
yaml_content = YOLO_DATA_YAML_TEMPLATE.format(
|
|
dataset_path=str(self.output_path.absolute()),
|
|
train_path='images/train',
|
|
val_path='images/val',
|
|
test_path='images/test',
|
|
num_classes=len(class_names),
|
|
class_names=class_names
|
|
)
|
|
with open(self.output_path / 'data.yaml', 'w') as f:
|
|
f.write(yaml_content)
|
|
|
|
return results
|
|
|
|
|
|
# ============================================================================
|
|
# Augmentation Configuration
|
|
# ============================================================================
|
|
|
|
class AugmentationConfigGenerator:
|
|
"""Generate augmentation configurations for different CV tasks."""
|
|
|
|
@staticmethod
|
|
def generate(task: str, intensity: str = 'medium',
|
|
framework: str = 'albumentations') -> Dict:
|
|
"""Generate augmentation config for task and intensity."""
|
|
|
|
if task not in AUGMENTATION_PRESETS:
|
|
return {'error': f"Unknown task: {task}. Use: detection, segmentation, classification"}
|
|
|
|
if intensity not in AUGMENTATION_PRESETS[task]:
|
|
return {'error': f"Unknown intensity: {intensity}. Use: light, medium, heavy"}
|
|
|
|
base_config = AUGMENTATION_PRESETS[task][intensity]
|
|
|
|
if framework == 'albumentations':
|
|
return AugmentationConfigGenerator._to_albumentations(base_config, task)
|
|
elif framework == 'torchvision':
|
|
return AugmentationConfigGenerator._to_torchvision(base_config, task)
|
|
elif framework == 'ultralytics':
|
|
return AugmentationConfigGenerator._to_ultralytics(base_config, task)
|
|
else:
|
|
return base_config
|
|
|
|
@staticmethod
|
|
def _to_albumentations(config: Dict, task: str) -> Dict:
|
|
"""Convert to Albumentations format."""
|
|
transforms = []
|
|
|
|
for aug_name, params in config.items():
|
|
if aug_name == 'horizontal_flip':
|
|
transforms.append({
|
|
'type': 'HorizontalFlip',
|
|
'p': params
|
|
})
|
|
elif aug_name == 'vertical_flip':
|
|
transforms.append({
|
|
'type': 'VerticalFlip',
|
|
'p': params
|
|
})
|
|
elif aug_name == 'rotate':
|
|
transforms.append({
|
|
'type': 'Rotate',
|
|
'limit': params.get('limit', 15),
|
|
'p': params.get('p', 0.5)
|
|
})
|
|
elif aug_name == 'scale':
|
|
transforms.append({
|
|
'type': 'RandomScale',
|
|
'scale_limit': params.get('scale_limit', 0.2),
|
|
'p': params.get('p', 0.5)
|
|
})
|
|
elif aug_name == 'brightness_contrast':
|
|
transforms.append({
|
|
'type': 'RandomBrightnessContrast',
|
|
'brightness_limit': params.get('brightness_limit', 0.2),
|
|
'contrast_limit': params.get('contrast_limit', 0.2),
|
|
'p': params.get('p', 0.5)
|
|
})
|
|
elif aug_name == 'hue_saturation':
|
|
transforms.append({
|
|
'type': 'HueSaturationValue',
|
|
'hue_shift_limit': params.get('hue_shift_limit', 20),
|
|
'sat_shift_limit': params.get('sat_shift_limit', 30),
|
|
'p': params.get('p', 0.5)
|
|
})
|
|
elif aug_name == 'blur':
|
|
transforms.append({
|
|
'type': 'Blur',
|
|
'blur_limit': params.get('blur_limit', 5),
|
|
'p': params.get('p', 0.3)
|
|
})
|
|
elif aug_name == 'noise':
|
|
transforms.append({
|
|
'type': 'GaussNoise',
|
|
'var_limit': params.get('var_limit', (10, 50)),
|
|
'p': params.get('p', 0.3)
|
|
})
|
|
elif aug_name == 'elastic_transform':
|
|
transforms.append({
|
|
'type': 'ElasticTransform',
|
|
'alpha': params.get('alpha', 100),
|
|
'sigma': params.get('sigma', 10),
|
|
'p': params.get('p', 0.3)
|
|
})
|
|
elif aug_name == 'cutout':
|
|
transforms.append({
|
|
'type': 'CoarseDropout',
|
|
'max_holes': params.get('num_holes', 8),
|
|
'max_height': params.get('max_h_size', 32),
|
|
'max_width': params.get('max_w_size', 32),
|
|
'p': params.get('p', 0.3)
|
|
})
|
|
|
|
# Add bbox format for detection
|
|
bbox_params = None
|
|
if task == 'detection':
|
|
bbox_params = {
|
|
'format': 'pascal_voc',
|
|
'label_fields': ['class_labels'],
|
|
'min_visibility': 0.3
|
|
}
|
|
|
|
return {
|
|
'framework': 'albumentations',
|
|
'task': task,
|
|
'transforms': transforms,
|
|
'bbox_params': bbox_params,
|
|
'code_example': AugmentationConfigGenerator._albumentations_code(transforms, task)
|
|
}
|
|
|
|
@staticmethod
|
|
def _albumentations_code(transforms: List, task: str) -> str:
|
|
"""Generate Albumentations code example."""
|
|
code = """import albumentations as A
|
|
from albumentations.pytorch import ToTensorV2
|
|
|
|
transform = A.Compose([
|
|
"""
|
|
for t in transforms:
|
|
params = ', '.join(f"{k}={v}" for k, v in t.items() if k != 'type')
|
|
code += f" A.{t['type']}({params}),\n"
|
|
|
|
code += " A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"
|
|
code += " ToTensorV2(),\n"
|
|
code += "]"
|
|
|
|
if task == 'detection':
|
|
code += ", bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']))"
|
|
else:
|
|
code += ")"
|
|
|
|
return code
|
|
|
|
@staticmethod
|
|
def _to_torchvision(config: Dict, task: str) -> Dict:
|
|
"""Convert to torchvision transforms format."""
|
|
transforms = []
|
|
|
|
for aug_name, params in config.items():
|
|
if aug_name == 'horizontal_flip':
|
|
transforms.append({
|
|
'type': 'RandomHorizontalFlip',
|
|
'p': params
|
|
})
|
|
elif aug_name == 'vertical_flip':
|
|
transforms.append({
|
|
'type': 'RandomVerticalFlip',
|
|
'p': params
|
|
})
|
|
elif aug_name == 'rotate':
|
|
transforms.append({
|
|
'type': 'RandomRotation',
|
|
'degrees': params.get('limit', 15)
|
|
})
|
|
elif aug_name == 'color_jitter':
|
|
transforms.append({
|
|
'type': 'ColorJitter',
|
|
'brightness': params.get('brightness', 0.2),
|
|
'contrast': params.get('contrast', 0.2),
|
|
'saturation': params.get('saturation', 0.2),
|
|
'hue': params.get('hue', 0.1)
|
|
})
|
|
|
|
return {
|
|
'framework': 'torchvision',
|
|
'task': task,
|
|
'transforms': transforms
|
|
}
|
|
|
|
@staticmethod
|
|
def _to_ultralytics(config: Dict, task: str) -> Dict:
|
|
"""Convert to Ultralytics YOLO format."""
|
|
yolo_config = {
|
|
'hsv_h': 0.015,
|
|
'hsv_s': 0.7,
|
|
'hsv_v': 0.4,
|
|
'degrees': config.get('rotate', {}).get('limit', 0.0),
|
|
'translate': 0.1,
|
|
'scale': config.get('scale', {}).get('scale_limit', 0.5),
|
|
'shear': 0.0,
|
|
'perspective': 0.0,
|
|
'flipud': config.get('vertical_flip', 0.0),
|
|
'fliplr': config.get('horizontal_flip', 0.5),
|
|
'mosaic': config.get('mosaic', {}).get('p', 1.0) if 'mosaic' in config else 0.0,
|
|
'mixup': config.get('mixup', {}).get('p', 0.0) if 'mixup' in config else 0.0,
|
|
'copy_paste': 0.0
|
|
}
|
|
|
|
return {
|
|
'framework': 'ultralytics',
|
|
'task': task,
|
|
'config': yolo_config,
|
|
'usage': "# Add to data.yaml or pass to Trainer\nmodel.train(data='data.yaml', augment=True, **aug_config)"
|
|
}
|
|
|
|
|
|
# ============================================================================
|
|
# Dataset Validation
|
|
# ============================================================================
|
|
|
|
class DatasetValidator:
|
|
"""Validate dataset integrity and quality."""
|
|
|
|
def __init__(self, dataset_path: str, format: str = None):
|
|
self.dataset_path = Path(dataset_path)
|
|
self.format = format
|
|
|
|
def validate(self) -> Dict:
|
|
"""Run all validation checks."""
|
|
results = {
|
|
'valid': True,
|
|
'errors': [],
|
|
'warnings': [],
|
|
'stats': {}
|
|
}
|
|
|
|
# Auto-detect format if not specified
|
|
if self.format is None:
|
|
analyzer = DatasetAnalyzer(str(self.dataset_path))
|
|
analyzer.analyze()
|
|
self.format = analyzer.stats.get('format', 'unknown')
|
|
|
|
results['format'] = self.format
|
|
|
|
# Run format-specific validation
|
|
if self.format == 'coco':
|
|
self._validate_coco(results)
|
|
elif self.format == 'yolo':
|
|
self._validate_yolo(results)
|
|
elif self.format == 'voc':
|
|
self._validate_voc(results)
|
|
else:
|
|
results['warnings'].append(f"Unknown format: {self.format}")
|
|
|
|
# General checks
|
|
self._validate_images(results)
|
|
self._check_duplicates(results)
|
|
|
|
# Set overall validity
|
|
results['valid'] = len(results['errors']) == 0
|
|
|
|
return results
|
|
|
|
def _validate_coco(self, results: Dict):
|
|
"""Validate COCO format dataset."""
|
|
for json_file in self.dataset_path.rglob('*.json'):
|
|
try:
|
|
with open(json_file) as f:
|
|
data = json.load(f)
|
|
|
|
if 'annotations' not in data:
|
|
continue
|
|
|
|
# Check required fields
|
|
if 'images' not in data:
|
|
results['errors'].append(f"{json_file}: Missing 'images' field")
|
|
if 'categories' not in data:
|
|
results['warnings'].append(f"{json_file}: Missing 'categories' field")
|
|
|
|
# Validate annotations
|
|
image_ids = {img['id'] for img in data.get('images', [])}
|
|
category_ids = {cat['id'] for cat in data.get('categories', [])}
|
|
|
|
for ann in data['annotations']:
|
|
if ann.get('image_id') not in image_ids:
|
|
results['errors'].append(
|
|
f"Annotation {ann.get('id')} references non-existent image {ann.get('image_id')}"
|
|
)
|
|
if ann.get('category_id') not in category_ids:
|
|
results['warnings'].append(
|
|
f"Annotation {ann.get('id')} references unknown category {ann.get('category_id')}"
|
|
)
|
|
|
|
# Validate bbox
|
|
if 'bbox' in ann:
|
|
bbox = ann['bbox']
|
|
if len(bbox) != 4:
|
|
results['errors'].append(
|
|
f"Annotation {ann.get('id')}: Invalid bbox format"
|
|
)
|
|
elif any(v < 0 for v in bbox[:2]) or any(v <= 0 for v in bbox[2:]):
|
|
results['warnings'].append(
|
|
f"Annotation {ann.get('id')}: Suspicious bbox values {bbox}"
|
|
)
|
|
|
|
results['stats']['coco_images'] = len(data.get('images', []))
|
|
results['stats']['coco_annotations'] = len(data['annotations'])
|
|
results['stats']['coco_categories'] = len(data.get('categories', []))
|
|
|
|
except json.JSONDecodeError as e:
|
|
results['errors'].append(f"{json_file}: Invalid JSON - {e}")
|
|
except Exception as e:
|
|
results['errors'].append(f"{json_file}: Error - {e}")
|
|
|
|
def _validate_yolo(self, results: Dict):
|
|
"""Validate YOLO format dataset."""
|
|
label_files = list(self.dataset_path.rglob('*.txt'))
|
|
valid_labels = 0
|
|
invalid_labels = 0
|
|
|
|
for txt_file in label_files:
|
|
if txt_file.name == 'classes.txt':
|
|
continue
|
|
|
|
try:
|
|
with open(txt_file) as f:
|
|
lines = f.readlines()
|
|
|
|
for line_num, line in enumerate(lines, 1):
|
|
parts = line.strip().split()
|
|
if not parts:
|
|
continue
|
|
|
|
if len(parts) < 5:
|
|
results['errors'].append(
|
|
f"{txt_file}:{line_num}: Expected 5 values, got {len(parts)}"
|
|
)
|
|
invalid_labels += 1
|
|
continue
|
|
|
|
try:
|
|
class_id = int(parts[0])
|
|
x, y, w, h = map(float, parts[1:5])
|
|
|
|
# Check normalized coordinates
|
|
if not (0 <= x <= 1 and 0 <= y <= 1):
|
|
results['warnings'].append(
|
|
f"{txt_file}:{line_num}: Center coords outside [0,1]: ({x}, {y})"
|
|
)
|
|
if not (0 < w <= 1 and 0 < h <= 1):
|
|
results['warnings'].append(
|
|
f"{txt_file}:{line_num}: Size outside (0,1]: ({w}, {h})"
|
|
)
|
|
|
|
valid_labels += 1
|
|
|
|
except ValueError as e:
|
|
results['errors'].append(
|
|
f"{txt_file}:{line_num}: Invalid values - {e}"
|
|
)
|
|
invalid_labels += 1
|
|
|
|
except Exception as e:
|
|
results['errors'].append(f"{txt_file}: Error - {e}")
|
|
|
|
results['stats']['yolo_valid_labels'] = valid_labels
|
|
results['stats']['yolo_invalid_labels'] = invalid_labels
|
|
|
|
def _validate_voc(self, results: Dict):
|
|
"""Validate Pascal VOC format dataset."""
|
|
xml_files = list(self.dataset_path.rglob('*.xml'))
|
|
valid_annotations = 0
|
|
|
|
for xml_file in xml_files:
|
|
try:
|
|
tree = ET.parse(xml_file)
|
|
root = tree.getroot()
|
|
|
|
if root.tag != 'annotation':
|
|
continue
|
|
|
|
# Check required fields
|
|
filename = root.find('filename')
|
|
if filename is None:
|
|
results['warnings'].append(f"{xml_file}: Missing filename")
|
|
|
|
size = root.find('size')
|
|
if size is None:
|
|
results['warnings'].append(f"{xml_file}: Missing size")
|
|
else:
|
|
for dim in ['width', 'height']:
|
|
if size.find(dim) is None:
|
|
results['errors'].append(f"{xml_file}: Missing {dim}")
|
|
|
|
# Validate objects
|
|
for obj in root.findall('object'):
|
|
name = obj.find('name')
|
|
if name is None or not name.text:
|
|
results['errors'].append(f"{xml_file}: Object missing name")
|
|
|
|
bndbox = obj.find('bndbox')
|
|
if bndbox is None:
|
|
results['errors'].append(f"{xml_file}: Object missing bndbox")
|
|
else:
|
|
for coord in ['xmin', 'ymin', 'xmax', 'ymax']:
|
|
elem = bndbox.find(coord)
|
|
if elem is None:
|
|
results['errors'].append(f"{xml_file}: Missing {coord}")
|
|
|
|
valid_annotations += 1
|
|
|
|
except ET.ParseError as e:
|
|
results['errors'].append(f"{xml_file}: XML parse error - {e}")
|
|
except Exception as e:
|
|
results['errors'].append(f"{xml_file}: Error - {e}")
|
|
|
|
results['stats']['voc_annotations'] = valid_annotations
|
|
|
|
def _validate_images(self, results: Dict):
|
|
"""Check for image file issues."""
|
|
images = []
|
|
for ext in SUPPORTED_IMAGE_EXTENSIONS:
|
|
images.extend(self.dataset_path.rglob(f'*{ext}'))
|
|
|
|
results['stats']['total_images'] = len(images)
|
|
|
|
# Check for empty images
|
|
empty_images = [img for img in images if img.stat().st_size == 0]
|
|
if empty_images:
|
|
results['errors'].append(f"Found {len(empty_images)} empty image files")
|
|
|
|
# Check for very small images
|
|
small_images = [img for img in images if img.stat().st_size < 1000]
|
|
if small_images:
|
|
results['warnings'].append(f"Found {len(small_images)} very small images (<1KB)")
|
|
|
|
def _check_duplicates(self, results: Dict):
|
|
"""Check for duplicate images by hash."""
|
|
images = []
|
|
for ext in SUPPORTED_IMAGE_EXTENSIONS:
|
|
images.extend(self.dataset_path.rglob(f'*{ext}'))
|
|
|
|
hashes = {}
|
|
duplicates = []
|
|
|
|
for img in images:
|
|
try:
|
|
with open(img, 'rb') as f:
|
|
file_hash = hashlib.md5(f.read()).hexdigest()
|
|
|
|
if file_hash in hashes:
|
|
duplicates.append((img, hashes[file_hash]))
|
|
else:
|
|
hashes[file_hash] = img
|
|
except:
|
|
pass
|
|
|
|
if duplicates:
|
|
results['warnings'].append(f"Found {len(duplicates)} duplicate images")
|
|
results['stats']['duplicate_images'] = len(duplicates)
|
|
|
|
|
|
# ============================================================================
|
|
# Main CLI
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# ============================================================================
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def main():
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parser = argparse.ArgumentParser(
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description="Dataset Pipeline Builder for Computer Vision",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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Analyze dataset:
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python dataset_pipeline_builder.py analyze --input /path/to/dataset
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Convert COCO to YOLO:
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python dataset_pipeline_builder.py convert --input /path/to/coco --output /path/to/yolo --format yolo
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Split dataset:
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python dataset_pipeline_builder.py split --input /path/to/dataset --train 0.8 --val 0.1 --test 0.1
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Generate augmentation config:
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python dataset_pipeline_builder.py augment-config --task detection --intensity heavy
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Validate dataset:
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python dataset_pipeline_builder.py validate --input /path/to/dataset --format coco
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"""
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)
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subparsers = parser.add_subparsers(dest='command', help='Command to run')
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# Analyze command
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analyze_parser = subparsers.add_parser('analyze', help='Analyze dataset structure and statistics')
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analyze_parser.add_argument('--input', '-i', required=True, help='Path to dataset')
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analyze_parser.add_argument('--json', action='store_true', help='Output as JSON')
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# Convert command
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convert_parser = subparsers.add_parser('convert', help='Convert between annotation formats')
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convert_parser.add_argument('--input', '-i', required=True, help='Input dataset path')
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convert_parser.add_argument('--output', '-o', required=True, help='Output dataset path')
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convert_parser.add_argument('--format', '-f', required=True,
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choices=['yolo', 'coco', 'voc'],
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help='Target format')
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convert_parser.add_argument('--source-format', '-s',
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choices=['yolo', 'coco', 'voc'],
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help='Source format (auto-detected if not specified)')
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# Split command
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split_parser = subparsers.add_parser('split', help='Split dataset into train/val/test')
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split_parser.add_argument('--input', '-i', required=True, help='Input dataset path')
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split_parser.add_argument('--output', '-o', help='Output path (default: same as input)')
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split_parser.add_argument('--train', type=float, default=0.8, help='Train split ratio')
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split_parser.add_argument('--val', type=float, default=0.1, help='Validation split ratio')
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split_parser.add_argument('--test', type=float, default=0.1, help='Test split ratio')
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split_parser.add_argument('--stratify', action='store_true', help='Stratify by class')
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split_parser.add_argument('--seed', type=int, default=42, help='Random seed')
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# Augmentation config command
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aug_parser = subparsers.add_parser('augment-config', help='Generate augmentation configuration')
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aug_parser.add_argument('--task', '-t', required=True,
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choices=['detection', 'segmentation', 'classification'],
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help='CV task type')
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aug_parser.add_argument('--intensity', '-n', default='medium',
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choices=['light', 'medium', 'heavy'],
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help='Augmentation intensity')
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aug_parser.add_argument('--framework', '-f', default='albumentations',
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choices=['albumentations', 'torchvision', 'ultralytics'],
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help='Target framework')
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aug_parser.add_argument('--output', '-o', help='Output file path')
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# Validate command
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validate_parser = subparsers.add_parser('validate', help='Validate dataset integrity')
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validate_parser.add_argument('--input', '-i', required=True, help='Path to dataset')
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validate_parser.add_argument('--format', '-f',
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choices=['yolo', 'coco', 'voc'],
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help='Dataset format (auto-detected if not specified)')
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validate_parser.add_argument('--json', action='store_true', help='Output as JSON')
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args = parser.parse_args()
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if args.command is None:
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parser.print_help()
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sys.exit(1)
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try:
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if args.command == 'analyze':
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analyzer = DatasetAnalyzer(args.input)
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results = analyzer.analyze()
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if args.json:
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print(json.dumps(results, indent=2, default=str))
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else:
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print("\n" + "="*60)
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print("DATASET ANALYSIS REPORT")
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print("="*60)
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print(f"\nFormat: {results.get('format', 'unknown')}")
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print(f"Total Images: {results.get('total_images', 0)}")
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if 'image_stats' in results:
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stats = results['image_stats']
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print(f"\nImage Statistics:")
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print(f" Total Size: {stats.get('total_size_mb', 0):.2f} MB")
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print(f" Extensions: {stats.get('extensions', {})}")
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print(f" Locations: {stats.get('locations', {})}")
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if 'annotations' in results:
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ann = results['annotations']
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print(f"\nAnnotations:")
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print(f" Total: {ann.get('total_annotations', 0)}")
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print(f" Images with annotations: {ann.get('images_with_annotations', 0)}")
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if 'classes' in ann:
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print(f" Classes: {len(ann['classes'])}")
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for cls, count in sorted(ann['classes'].items(), key=lambda x: -x[1])[:10]:
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print(f" - {cls}: {count}")
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if 'quality' in results:
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q = results['quality']
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if q.get('warnings'):
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print(f"\nWarnings:")
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for w in q['warnings']:
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print(f" ⚠ {w}")
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if q.get('recommendations'):
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print(f"\nRecommendations:")
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for r in q['recommendations']:
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print(f" → {r}")
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elif args.command == 'convert':
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converter = FormatConverter(args.input, args.output)
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results = converter.convert(args.format, args.source_format)
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print(json.dumps(results, indent=2))
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elif args.command == 'split':
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output = args.output if args.output else args.input
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splitter = DatasetSplitter(args.input, output)
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results = splitter.split(
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train=args.train,
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val=args.val,
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test=args.test,
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stratify=args.stratify,
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seed=args.seed
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)
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print(json.dumps(results, indent=2))
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elif args.command == 'augment-config':
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config = AugmentationConfigGenerator.generate(
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args.task,
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args.intensity,
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args.framework
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)
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output = json.dumps(config, indent=2)
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if args.output:
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with open(args.output, 'w') as f:
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f.write(output)
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print(f"Configuration saved to {args.output}")
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else:
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print(output)
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elif args.command == 'validate':
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validator = DatasetValidator(args.input, args.format)
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results = validator.validate()
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if args.json:
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print(json.dumps(results, indent=2))
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else:
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print("\n" + "="*60)
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print("DATASET VALIDATION REPORT")
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print("="*60)
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print(f"\nFormat: {results.get('format', 'unknown')}")
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print(f"Valid: {'✓' if results['valid'] else '✗'}")
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if results.get('errors'):
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print(f"\nErrors ({len(results['errors'])}):")
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for err in results['errors'][:10]:
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print(f" ✗ {err}")
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if len(results['errors']) > 10:
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print(f" ... and {len(results['errors']) - 10} more")
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if results.get('warnings'):
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print(f"\nWarnings ({len(results['warnings'])}):")
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for warn in results['warnings'][:10]:
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print(f" ⚠ {warn}")
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if len(results['warnings']) > 10:
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print(f" ... and {len(results['warnings']) - 10} more")
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if results.get('stats'):
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print(f"\nStatistics:")
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for key, value in results['stats'].items():
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print(f" {key}: {value}")
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sys.exit(0)
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except Exception as e:
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logger.error(f"Error: {e}")
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sys.exit(1)
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if __name__ == '__main__':
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main()
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