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Datasets:
Yangzzc
/
FastTracker-Benchmark

Tasks:
Object Detection
Modalities:
Text
Video
Formats:
text
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
Multi-object-tracking
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License:
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1
FastTracker-Benchmark
File size: 2,745 Bytes 
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import os
import json
import cv2
import numpy as np

# Paths
FRAMES_DIR = "test_frame"
GT_DIR = "GT"
OUT_PATH = "annotations"
os.makedirs(OUT_PATH, exist_ok=True)

# Output COCO-style JSON
out_file = os.path.join(OUT_PATH, "train.json")

out = {
    "images": [],
    "annotations": [],
    "videos": [],
    "categories": [
        {"id": 1, "name": "pedestrian"}  # You can expand with more classes if needed
    ]
}

image_cnt = 0
ann_cnt = 0
video_cnt = 0
tid_curr = 0
tid_last = -1

# Loop over sequences (one per video)
for seq in sorted(os.listdir(FRAMES_DIR)):
    seq_path = os.path.join(FRAMES_DIR, seq)
    if not os.path.isdir(seq_path):
        continue

    video_cnt += 1
    out["videos"].append({"id": video_cnt, "file_name": seq})

    # Frames
    images = sorted([f for f in os.listdir(seq_path) if f.endswith(".jpg")])
    num_images = len(images)

    for i, img_name in enumerate(images):
        img_path = os.path.join(seq_path, img_name)
        img = cv2.imread(img_path)
        if img is None:
            continue
        height, width = img.shape[:2]

        image_info = {
            "file_name": f"{seq}/{img_name}",
            "id": image_cnt + i + 1,
            "frame_id": i + 1,
            "prev_image_id": image_cnt + i if i > 0 else -1,
            "next_image_id": image_cnt + i + 2 if i < num_images - 1 else -1,
            "video_id": video_cnt,
            "height": height,
            "width": width
        }
        out["images"].append(image_info)

    # Load GT file
    gt_path = os.path.join(GT_DIR, seq, "gt", "gt.txt")
    if not os.path.exists(gt_path):
        print(f" No GT found for {seq}, skipping annotations.")
        image_cnt += num_images
        continue

    anns = np.loadtxt(gt_path, dtype=np.float32, delimiter=",")

    for i in range(anns.shape[0]):
        frame_id = int(anns[i][0])
        track_id = int(anns[i][1])
        x, y, w, h = anns[i][2:6]
        conf = anns[i][6]
        class_id = int(anns[i][7])
        visibility = anns[i][8] 

        ann_cnt += 1
        if track_id != tid_last:
            tid_curr += 1
            tid_last = track_id

        ann = {
            "id": ann_cnt,
            "category_id": class_id,  
            "image_id": image_cnt + frame_id,
            "track_id": tid_curr,
            "bbox": [float(x), float(y), float(w), float(h)],
            "conf": float(conf),
            "iscrowd": 0,
            "area": float(w * h),
        }
        out["annotations"].append(ann)

    image_cnt += num_images

print(f" Loaded {len(out['images'])} images and {len(out['annotations'])} annotations.")

# Save JSON
with open(out_file, "w") as f:
    json.dump(out, f)

print(f" Saved COCO-style annotations to {out_file}")