code updated

README.md

@@ -34,7 +34,19 @@ _[Hamidreza Hashempoor](https://hamidreza-hashempoor.github.io/),  Yu Dong Hwang
 ## Dataset Overview
 
 GT format is like (each line):
-`frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, 1.0`
+`frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, 1.0`.
+
+To prepare the dataset, first run `extract_frames.py` to decode frames from each video.  
+In **line 11** of the script, add the video filename and the number of frames you want to extract.  
+```bash
+python extract_frames.py
+```
+
+Then, convert the ground truth into COCO format with:
+```bash
+python convert_to_coco.py
+```
+This will generate annotations/train.json ready for training your detector.
 
 Brief statistics and visualization of FastTracker benchmark and its comparison with other benchmarks.
 

convert_to_coco.py

@@ -0,0 +1,104 @@
+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]  # currently unused
+
+        ann_cnt += 1
+        if track_id != tid_last:
+            tid_curr += 1
+            tid_last = track_id
+
+        ann = {
+            "id": ann_cnt,
+            "category_id": class_id,  # directly use your GT class
+            "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}")

extract_frames.py

@@ -0,0 +1,60 @@
+import os
+import cv2
+import numpy as np
+
+# Paths
+videos_dir = "test_vid"
+frames_dir = "test_frame"
+
+# Define how many frames to extract for each video (should be same in GT file last row)
+frames_to_extract = {
+    "task_day_left_turn-2024_07_31_02_12_05-mot 1.1.mp4": 1965,
+    # add more here...
+}
+
+os.makedirs(frames_dir, exist_ok=True)
+
+for video_file in os.listdir(videos_dir):
+    if video_file.lower().endswith(".mp4"):
+        video_path = os.path.join(videos_dir, video_file)
+
+        # Subdirectory name (remove .mp4)
+        subdir_name = os.path.splitext(video_file)[0]
+        subdir_path = os.path.join(frames_dir, subdir_name)
+        os.makedirs(subdir_path, exist_ok=True)
+
+        cap = cv2.VideoCapture(video_path)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        desired_frames = frames_to_extract.get(video_file, total_frames)
+
+        saved = 0
+        frames = []
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frames.append(frame)
+
+        cap.release()
+
+
+        if desired_frames > len(frames):
+            last_frame = frames[-1]
+            while len(frames) < desired_frames:
+                frames.append(last_frame)
+
+
+        if desired_frames < len(frames):
+            indices = np.linspace(0, len(frames) - 1, desired_frames, dtype=int)
+            frames = [frames[i] for i in indices]
+
+
+        for idx, frame in enumerate(frames):
+            frame_filename = os.path.join(subdir_path, f"frame_{idx:05d}.jpg")
+            cv2.imwrite(frame_filename, frame)
+            saved += 1
+
+        print(f"Extracted {saved} frames from {video_file} into {subdir_name}/")
+
+print(" Done extracting frames for all videos.")