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#!/usr/bin/env python3
"""
Convert CrowdHuman dataset from ODGT format to COCO format.
Dataset structure:
/mnt/archive/person_drone/crowd_human/
├── annotation_train.odgt
├── annotation_val.odgt
├── CrowdHuman_train/
│ └── Images/
├── CrowdHuman_val/
│ └── Images/
└── CrowdHuman_test/
└── Images/
ODGT format: One Dict per line in JSON format with:
- fbox: full body bounding box [x, y, width, height]
- vbox: visible body bounding box [x, y, width, height]
- hbox: head bounding box [x, y, width, height]
We use fbox (full body) for normal person bboxes as requested.
"""
import json
import os
import argparse
from pathlib import Path
from PIL import Image
from typing import Dict, List, Tuple
from datetime import datetime
def parse_odgt_file(odgt_path: str) -> List[Dict]:
"""
Parse ODGT (One Dict per line) annotation file.
Args:
odgt_path: Path to .odgt annotation file
Returns:
List of annotation dictionaries
"""
annotations = []
if not os.path.exists(odgt_path):
print(f"Warning: Annotation file {odgt_path} does not exist")
return annotations
with open(odgt_path, 'r') as f:
for line_num, line in enumerate(f):
line = line.strip()
if not line:
continue
try:
annotation = json.loads(line)
annotations.append(annotation)
except json.JSONDecodeError as e:
print(f"Warning: Could not parse line {line_num + 1} in {odgt_path}: {e}")
continue
return annotations
def convert_split_to_coco(
dataset_root: str,
split_name: str,
annotations_data: List[Dict],
category_mapping: Dict[str, Dict],
start_img_id: int = 1,
start_ann_id: int = 1
) -> Tuple[List[Dict], List[Dict], int, int]:
"""
Convert a CrowdHuman dataset split to COCO format components.
Args:
dataset_root: Root directory of CrowdHuman dataset
split_name: Name of the split (train/val/test)
annotations_data: Parsed ODGT annotations for this split
category_mapping: Mapping of tag to category info
start_img_id: Starting image ID for this split
start_ann_id: Starting annotation ID for this split
Returns:
images_list, annotations_list, next_img_id, next_ann_id
"""
dataset_root = Path(dataset_root)
images_dir = dataset_root / f"CrowdHuman_{split_name}" / "Images"
if not images_dir.exists():
print(f"Warning: Images directory {images_dir} does not exist")
return [], [], start_img_id, start_ann_id
images_list = []
annotations_list = []
img_id = start_img_id
ann_id = start_ann_id
print(f"Processing {len(annotations_data)} images in {split_name} split...")
# Create a mapping from image ID to sequential ID
image_id_mapping = {}
for ann_data in annotations_data:
image_filename = f"{ann_data['ID']}.jpg"
image_path = images_dir / image_filename
# Check if image exists
if not image_path.exists():
print(f"Warning: Image {image_path} not found, skipping...")
continue
# Get image dimensions
try:
with Image.open(image_path) as img:
img_width, img_height = img.size
except Exception as e:
print(f"Error opening image {image_path}: {e}")
continue
# Map original ID to sequential ID
image_id_mapping[ann_data['ID']] = img_id
# Add image info
images_list.append({
"id": img_id,
"file_name": f"CrowdHuman_{split_name}/Images/{image_filename}",
"width": img_width,
"height": img_height,
"license": 1
})
# Process ground truth boxes
if 'gtboxes' in ann_data:
for gt_box in ann_data['gtboxes']:
# Skip if not a person
if gt_box.get('tag') != 'person':
continue
# Skip ignored boxes
if 'head_attr' in gt_box and gt_box['head_attr'].get('ignore', 0) == 1:
continue
# Use fbox (full body box) as requested
if 'fbox' not in gt_box:
continue
fbox = gt_box['fbox']
# fbox format is already [x, y, width, height]
x, y, width, height = fbox
# Ensure coordinates are within image bounds
x = max(0, min(x, img_width - 1))
y = max(0, min(y, img_height - 1))
width = min(width, img_width - x)
height = min(height, img_height - y)
# Calculate area
area = width * height
if area > 0: # Only add valid annotations
# Determine if crowded (occluded)
is_crowd = 0
if 'extra' in gt_box:
# occ: 0 = no occlusion, 1 = partial occlusion, 2 = heavy occlusion
occ_level = gt_box['extra'].get('occ', 0)
# Consider heavy occlusion as crowd
is_crowd = 1 if occ_level >= 2 else 0
annotations_list.append({
"id": ann_id,
"image_id": img_id,
"category_id": category_mapping['person']["id"],
"bbox": [x, y, width, height],
"area": area,
"iscrowd": is_crowd,
"segmentation": []
})
ann_id += 1
img_id += 1
return images_list, annotations_list, img_id, ann_id
def main():
parser = argparse.ArgumentParser(description="Convert CrowdHuman dataset to COCO format")
parser.add_argument(
"--dataset_root",
type=str,
default="/mnt/archive/person_drone/crowd_human",
help="Path to CrowdHuman dataset root directory"
)
parser.add_argument(
"--output_dir",
type=str,
default="/home/svakhreev/projects/DEIM/data/crowd_human_coco",
help="Output directory for COCO format files"
)
parser.add_argument(
"--splits",
type=str,
nargs='+',
default=["train", "val"],
choices=["train", "val", "test"],
help="Splits to convert (default: train and val)"
)
args = parser.parse_args()
dataset_root = Path(args.dataset_root)
output_dir = Path(args.output_dir)
# Create output directory
output_dir.mkdir(parents=True, exist_ok=True)
# Create category mapping for CrowdHuman (single class: person)
category_mapping = {
'person': {
"id": 1, # COCO categories start from 1
"name": "person",
"supercategory": "person"
}
}
print(f"Categories: {category_mapping}")
print(f"Converting splits: {args.splits}")
# Process each split separately
for split in args.splits:
print(f"\n{'='*60}")
print(f"Converting {split} split...")
print(f"{'='*60}")
# Initialize COCO data structure for this split
coco_data = {
"info": {
"year": 2024,
"version": "1.0",
"description": f"CrowdHuman Dataset - {split} split in COCO format",
"contributor": "CrowdHuman Dataset",
"url": "https://www.crowdhuman.org/",
"date_created": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
},
"licenses": [
{
"id": 1,
"name": "Academic Use",
"url": ""
}
],
"categories": list(category_mapping.values()),
"images": [],
"annotations": []
}
# Load annotations for this split
if split == "test":
# Test split might not have annotations
print(f"Warning: Test split typically doesn't have annotations")
annotation_file = dataset_root / f"annotation_{split}.odgt"
if not annotation_file.exists():
print(f"No annotation file found for test split, skipping...")
continue
else:
annotation_file = dataset_root / f"annotation_{split}.odgt"
if not annotation_file.exists():
print(f"Warning: Annotation file {annotation_file} not found, skipping {split}")
continue
# Parse ODGT annotations
print(f"Loading annotations from {annotation_file}...")
annotations_data = parse_odgt_file(str(annotation_file))
print(f"Loaded {len(annotations_data)} image annotations")
# Convert to COCO format
images_list, annotations_list, _, _ = convert_split_to_coco(
str(dataset_root),
split,
annotations_data,
category_mapping,
start_img_id=1,
start_ann_id=1
)
# Add to COCO dataset
coco_data["images"] = images_list
coco_data["annotations"] = annotations_list
# Save COCO annotation file for this split
output_file = output_dir / f"annotations_{split}.json"
with open(output_file, 'w') as f:
json.dump(coco_data, f, indent=2)
print(f"\nSplit {split} complete!")
print(f"Total: {len(coco_data['images'])} images and {len(coco_data['annotations'])} annotations")
print(f"Saved COCO format to {output_file}")
# Also create a combined annotation file for all splits
if len(args.splits) > 1:
print(f"\n{'='*60}")
print(f"Creating combined annotation file...")
print(f"{'='*60}")
combined_coco_data = {
"info": {
"year": 2024,
"version": "1.0",
"description": f"CrowdHuman Dataset - Combined splits ({', '.join(args.splits)}) in COCO format",
"contributor": "CrowdHuman Dataset",
"url": "https://www.crowdhuman.org/",
"date_created": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
},
"licenses": [
{
"id": 1,
"name": "Academic Use",
"url": ""
}
],
"categories": list(category_mapping.values()),
"images": [],
"annotations": []
}
img_id = 1
ann_id = 1
for split in args.splits:
annotation_file = dataset_root / f"annotation_{split}.odgt"
if not annotation_file.exists():
continue
print(f"Processing {split} for combined file...")
annotations_data = parse_odgt_file(str(annotation_file))
images_list, annotations_list, next_img_id, next_ann_id = convert_split_to_coco(
str(dataset_root),
split,
annotations_data,
category_mapping,
start_img_id=img_id,
start_ann_id=ann_id
)
combined_coco_data["images"].extend(images_list)
combined_coco_data["annotations"].extend(annotations_list)
img_id = next_img_id
ann_id = next_ann_id
# Save combined annotation file
output_file = output_dir / "annotations_combined.json"
with open(output_file, 'w') as f:
json.dump(combined_coco_data, f, indent=2)
print(f"\nCombined file complete!")
print(f"Total: {len(combined_coco_data['images'])} images and {len(combined_coco_data['annotations'])} annotations")
print(f"Saved combined COCO format to {output_file}")
# Save dataset information for reference
info_file = output_dir / "dataset_info.json"
with open(info_file, 'w') as f:
json.dump({
'splits_processed': args.splits,
'total_images_per_split': {
split: len(json.load(open(output_dir / f"annotations_{split}.json"))['images'])
for split in args.splits
if (output_dir / f"annotations_{split}.json").exists()
},
'total_annotations_per_split': {
split: len(json.load(open(output_dir / f"annotations_{split}.json"))['annotations'])
for split in args.splits
if (output_dir / f"annotations_{split}.json").exists()
},
'categories': category_mapping,
'args': vars(args)
}, f, indent=2)
print(f"\nDataset info saved to {info_file}")
print("\nConversion complete!")
if __name__ == "__main__":
main()
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