Upload 196 files

Initial upload of the entire dataset

.gitattributes

@@ -57,3 +57,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+train/coco_annotations/train_with_additional_data.json filter=lfs diff=lfs merge=lfs -text
+train/coco_annotations/train.json filter=lfs diff=lfs merge=lfs -text
+val/coco_annotations/val.json filter=lfs diff=lfs merge=lfs -text

compute_coco_ap.py

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+"""
+Computes COCO-style evaluation metrics (Average Precision & Average Recall)
+"""
+
+# --- Configure here ---
+GROUND_TRUTH_JSON = "ground_truth.json"
+PREDICTIONS_JSON = "predictions.json"
+IOU_TYPE = "segm"  # "segm", "bbox", or "keypoints"
+OUTPUT_PATH = "results_ap.json"  # set to None to skip saving
+# ----------------------
+
+import json
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+def _load_predictions_for_coco(gt_coco: COCO, predictions_json_path: str):
+    """
+    Loads predictions into COCO's result format.
+
+    Args:
+        gt_coco (COCO): COCO object initialized with ground truth annotations.
+        predictions_json_path (str): Path to predictions JSON file.
+
+    Returns:
+        COCO: A COCO results object that can be passed into COCOeval.
+    """
+    with open(predictions_json_path, "r") as f:
+        data = json.load(f)
+
+    # Normalize predictions into a list of annotations
+    if isinstance(data, list):
+        anns = data
+    elif isinstance(data, dict) and "annotations" in data:
+        anns = data["annotations"]
+    else:
+        raise ValueError("Predictions must be a list or a dict with an 'annotations' key.")
+
+    # Ensure every annotation has a 'score' field (required for COCOeval)
+    for ann in anns:
+        if "score" not in ann:
+            ann["score"] = 1.0  # Assign default score if missing
+
+    # Load predictions into COCO format
+    return gt_coco.loadRes(anns)
+
+
+def compute_ap_map(ground_truth_json: str, predictions_json: str, iou_type: str = "segm"):
+    """
+    Computes COCO-style AP/mAP and AR metrics.
+
+    Args:
+        ground_truth_json (str): Path to COCO-format ground truth file.
+        predictions_json (str): Path to predictions file.
+        iou_type (str): Type of evaluation ("segm", "bbox", or "keypoints").
+
+    Returns:
+        dict: Dictionary containing AP and AR values across IoU thresholds,
+              object sizes, and max detections.
+    """
+    # Load ground truth
+    gt_coco = COCO(ground_truth_json)
+
+    # Load predictions into COCO result format
+    pred_coco = _load_predictions_for_coco(gt_coco, predictions_json)
+
+    # Run COCO evaluation
+    coco_eval = COCOeval(gt_coco, pred_coco, iou_type)
+    coco_eval.evaluate()
+    coco_eval.accumulate()
+    coco_eval.summarize()
+
+    # Collect results from coco_eval.stats (12 values for bbox/segm)
+    stats = coco_eval.stats
+    results = {
+        "AP[0.50:0.95]": float(stats[0]),  # mean AP over IoU thresholds .50:.95
+        "[email protected]": float(stats[1]),        # AP at IoU=0.50
+        "[email protected]": float(stats[2]),        # AP at IoU=0.75
+        "AP_small": float(stats[3]),       # AP for small objects
+        "AP_medium": float(stats[4]),      # AP for medium objects
+        "AP_large": float(stats[5]),       # AP for large objects
+        "AR@1": float(stats[6]),           # AR given max 1 detection per image
+        "AR@10": float(stats[7]),          # AR given max 10 detections per image
+        "AR@100": float(stats[8]),         # AR given max 100 detections per image
+        "AR_small": float(stats[9]),       # AR for small objects
+        "AR_medium": float(stats[10]),     # AR for medium objects
+        "AR_large": float(stats[11]),      # AR for large objects
+    }
+    return results
+
+
+if __name__ == "__main__":
+    scores = compute_ap_map(GROUND_TRUTH_JSON, PREDICTIONS_JSON, IOU_TYPE)
+
+    # Optionally save results to JSON
+    if OUTPUT_PATH:
+        with open(OUTPUT_PATH, "w") as f:
+            json.dump(scores, f, indent=2)

compute_pr_f1_f2.py

@@ -0,0 +1,164 @@
+"""
+Computes Precision, Recall, F1, F2. For this script, a confidence threshold variable is required to discard low-score predictions from the model
+"""
+
+
+# --- Configure here ---
+GROUND_TRUTH_JSON = "ground_truth.json"
+PREDICTIONS_JSON = "predictions.json"
+IOU_THRESHOLD = 0.5       # IoU threshold for a TP
+CONFIDENCE_THRESHOLD = 0.1  # ENSURE THAT YOU ADJUST IT ACCORDING TO THE MODEL YOU CHOOSE: predictions with score < this are ignored
+OUTPUT_PATH = "resuts.json"  # set to None to skip saving
+# ----------------------
+
+import json
+import numpy as np
+from pycocotools.coco import COCO
+from pycocotools import mask as maskUtils
+from sklearn.metrics import precision_score, recall_score, f1_score
+
+def _load_and_filter_predictions(predictions_json_path: str, conf_thr: float):
+    """
+    Loads predictions and applies confidence filtering.
+
+    Args:
+        predictions_json_path (str): Path to predictions file.
+        conf_thr (float): Minimum confidence score required to keep a prediction.
+
+    Returns:
+        list: Filtered list of prediction annotations.
+    """
+    with open(predictions_json_path, "r") as f:
+        data = json.load(f)
+
+    # Normalize to list of annotations
+    if isinstance(data, list):
+        anns = data
+    elif isinstance(data, dict) and "annotations" in data:
+        anns = data["annotations"]
+    else:
+        raise ValueError("Predictions must be a list or a dict with an 'annotations' key.")
+
+    # Keep only predictions above confidence threshold
+    filtered = []
+    for ann in anns:
+        score = ann.get("score", 1.0)  # Default score if missing
+        if score >= conf_thr:
+            if "score" not in ann:
+                ann = {**ann, "score": float(score)}
+            filtered.append(ann)
+    return filtered
+
+
+def compute_pr_f1_f2(ground_truth_json: str, predictions_json: str, iou_thr: float, conf_thr: float):
+    """
+    Computes precision, recall, F1, and F2 scores.
+
+    Steps:
+    - Load ground truth annotations.
+    - Load predictions and filter by confidence.
+    - For each image, compute IoU between GT and predicted masks.
+    - Match predictions to GT with highest IoU >= threshold.
+    - Count TP, FP, FN to derive metrics.
+
+    Args:
+        ground_truth_json (str): Path to COCO-format ground truth file.
+        predictions_json (str): Path to predictions file.
+        iou_thr (float): IoU threshold to accept a prediction as True Positive.
+        conf_thr (float): Confidence threshold for filtering predictions.
+
+    Returns:
+        dict: Metrics including precision, recall, F1, F2, and counts of TP, FP, FN.
+    """
+    # Load ground truth
+    gt_coco = COCO(ground_truth_json)
+
+    # Load and filter predictions, then convert to COCO results
+    filtered_preds = _load_and_filter_predictions(predictions_json, conf_thr)
+    pred_coco = gt_coco.loadRes(filtered_preds)
+
+    gt_img_ids = gt_coco.getImgIds()
+    y_true = []
+    y_pred = []
+
+    # Evaluate image by image
+    for img_id in gt_img_ids:
+        gt_ann_ids = gt_coco.getAnnIds(imgIds=img_id)
+        pred_ann_ids = pred_coco.getAnnIds(imgIds=img_id)
+
+        gt_anns = gt_coco.loadAnns(gt_ann_ids)
+        pred_anns = pred_coco.loadAnns(pred_ann_ids)
+
+        # Convert GT and predictions to binary masks
+        gt_masks = [maskUtils.decode(gt_coco.annToRLE(ann)) for ann in gt_anns]
+        pred_masks = [maskUtils.decode(pred_coco.annToRLE(ann)) for ann in pred_anns]
+
+        matched_gt = set()
+        for pred_mask in pred_masks:
+            best_iou = 0.0
+            best_gt_idx = None
+            # Find best IoU match with ground truth masks
+            for i, gt_mask in enumerate(gt_masks):
+                intersection = np.logical_and(gt_mask, pred_mask).sum()
+                union = np.logical_or(gt_mask, pred_mask).sum()
+                iou = (intersection / union) if union > 0 else 0.0
+                if iou > best_iou:
+                    best_iou = iou
+                    best_gt_idx = i
+
+            if best_iou >= iou_thr and best_gt_idx not in matched_gt:
+                # True Positive
+                y_true.append(1)
+                y_pred.append(1)
+                matched_gt.add(best_gt_idx)
+            else:
+                # False Positive
+                y_true.append(0)
+                y_pred.append(1)
+
+        # Unmatched ground truth = False Negatives
+        for i in range(len(gt_masks)):
+            if i not in matched_gt:
+                y_true.append(1)
+                y_pred.append(0)
+
+    # Compute metrics
+    precision = precision_score(y_true, y_pred, zero_division=1)
+    recall = recall_score(y_true, y_pred, zero_division=1)
+    f1 = f1_score(y_true, y_pred, zero_division=1)
+    f2 = (5 * precision * recall) / (4 * precision + recall) if (precision + recall) > 0 else 0.0
+
+    # Count TP, FP, FN 
+    tp = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 1)
+    fp = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 1)
+    fn = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 0)
+
+    results = {
+        "precision": float(precision),
+        "recall": float(recall),
+        "f1": float(f1),
+        "f2": float(f2),
+
+        # Uncomment to print the number of fp, tp, fn as well
+
+        # "tp": int(tp),
+        # "fp": int(fp),
+        # "fn": int(fn),
+    }
+    return results
+
+
+if __name__ == "__main__":
+    # Run evaluation and print results
+    scores = compute_pr_f1_f2(
+        GROUND_TRUTH_JSON,
+        PREDICTIONS_JSON,
+        IOU_THRESHOLD,
+        CONFIDENCE_THRESHOLD,
+    )
+    print(json.dumps(scores, indent=2))
+
+    # Optionally save results to JSON
+    if OUTPUT_PATH:
+        with open(OUTPUT_PATH, "w") as f:
+            json.dump(scores, f, indent=2)

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