import numpy as np def compute_iou(box1, box2): “”” 2つのBounding BoxのIoUを計算 Args: box1: [x1, y1, x2, y2] 形式のBox box2: [x1, y1, x2, y2] 形式のBox Returns: iou: IoU値（0〜1） “”” # 交差部分の座標を計算 # 左上座標: 両方の左上のmax x1_inter = max(box1[0], box2[0]) y1_inter = max(box1[1], box2[1]) # 右下座標: 両方の右下のmin x2_inter = min(box1[2], box2[2]) y2_inter = min(box1[3], box2[3]) # 交差部分の面積を計算 # 幅または高さが負の場合は交差なし（0） inter_width = max(0, x2_inter – x1_inter) inter_height = max(0, y2_inter – y1_inter) inter_area = inter_width * inter_height # 各Boxの面積を計算 box1_area = (box1[2] – box1[0]) * (box1[3] – box1[1]) box2_area = (box2[2] – box2[0]) * (box2[3] – box2[1]) # 和集合の面積 = Box1 + Box2 – 交差部分 union_area = box1_area + box2_area – inter_area # IoUを計算（ゼロ除算を防ぐ） iou = inter_area / union_area if union_area > 0 else 0 return iou # テスト box1 = [50, 30, 200, 150] # 正解Box box2 = [55, 35, 195, 145] # 予測Box（少しずれている） iou = compute_iou(box1, box2) print(f”IoU: {iou:.4f}”)

def compute_ap(recalls, precisions): “”” Precision-Recall曲線からAverage Precision (AP)を計算 11点補間法を使用 Args: recalls: Recall値のリスト（昇順） precisions: 対応するPrecision値のリスト Returns: ap: Average Precision（0〜1） “”” # NumPy配列に変換 recalls = np.array(recalls) precisions = np.array(precisions) # 11点補間: Recall = 0.0, 0.1, 0.2, …, 1.0 recall_levels = np.linspace(0, 1, 11) # 各Recall点での補間Precisionを計算 interpolated_precisions = [] for r in recall_levels: # Recall ≥ r となる最大のPrecisionを取得 # そのようなRecallがない場合は0 mask = recalls >= r if np.any(mask): p_interp = np.max(precisions[mask]) else: p_interp = 0 interpolated_precisions.append(p_interp) # 平均を計算 ap = np.mean(interpolated_precisions) return ap # テスト recalls = [0.25, 0.50, 0.50, 0.75, 0.75, 1.00] precisions = [1.00, 1.00, 0.67, 0.75, 0.60, 0.67] ap = compute_ap(recalls, precisions) print(f”AP: {ap:.4f}”)

def evaluate_detection(predictions, ground_truths, iou_threshold=0.5, num_classes=None): “”” 物体検出の評価（mAP計算） Args: predictions: 予測結果のリスト [{‘box’: [x1,y1,x2,y2], ‘score’: 信頼度, ‘class’: クラスID}, …] ground_truths: 正解のリスト [{‘box’: [x1,y1,x2,y2], ‘class’: クラスID}, …] iou_threshold: IoU閾値（デフォルト0.5） num_classes: クラス数（Noneの場合は自動検出） Returns: mAP: mean Average Precision class_aps: 各クラスのAP辞書 “”” # クラスを取得 if num_classes is None: classes = set([gt[‘class’] for gt in ground_truths]) else: classes = range(num_classes) class_aps = {} for cls in classes: # このクラスの予測と正解を抽出 cls_preds = [p for p in predictions if p[‘class’] == cls] cls_gts = [gt for gt in ground_truths if gt[‘class’] == cls] # 正解が0の場合はスキップ if len(cls_gts) == 0: continue # 予測を信頼度でソート（降順） cls_preds = sorted(cls_preds, key=lambda x: x[‘score’], reverse=True) # 各正解が検出されたかを追跡 detected = [False] * len(cls_gts) # TP/FPを記録 tp_list = [] fp_list = [] for pred in cls_preds: # 最もIoUが高い正解を探す best_iou = 0 best_gt_idx = -1 for gt_idx, gt in enumerate(cls_gts): # すでに検出済みの正解はスキップ if detected[gt_idx]: continue iou = compute_iou(pred[‘box’], gt[‘box’]) if iou > best_iou: best_iou = iou best_gt_idx = gt_idx # IoU閾値を超えているか判定 if best_iou >= iou_threshold and best_gt_idx != -1: # True Positive detected[best_gt_idx] = True tp_list.append(1) fp_list.append(0) else: # False Positive tp_list.append(0) fp_list.append(1) # 累積和を計算 tp_cumsum = np.cumsum(tp_list) fp_cumsum = np.cumsum(fp_list) # PrecisionとRecallを計算 num_gts = len(cls_gts) recalls = tp_cumsum / num_gts precisions = tp_cumsum / (tp_cumsum + fp_cumsum) # APを計算 ap = compute_ap(recalls, precisions) class_aps[cls] = ap print(f”Class {cls}: AP = {ap:.4f} (GT: {num_gts}, Predictions: {len(cls_preds)})”) # mAPを計算 if len(class_aps) > 0: mAP = np.mean(list(class_aps.values())) else: mAP = 0.0 return mAP, class_aps # ======================================== # 使用例 # ======================================== # 予測結果 predictions = [ {‘box’: [50, 30, 200, 150], ‘score’: 0.95, ‘class’: 0}, # cat – TP {‘box’: [300, 100, 450, 250], ‘score’: 0.92, ‘class’: 1}, # dog – TP {‘box’: [55, 35, 205, 155], ‘score’: 0.88, ‘class’: 0}, # cat – FP（重複） {‘box’: [100, 200, 150, 300], ‘score’: 0.75, ‘class’: 0}, # cat – FP（誤検出） {‘box’: [310, 110, 460, 260], ‘score’: 0.70, ‘class’: 1}, # dog – FP（重複） ] # 正解（Ground Truth） ground_truths = [ {‘box’: [52, 32, 198, 148], ‘class’: 0}, # cat {‘box’: [305, 105, 448, 248], ‘class’: 1}, # dog ] # 評価を実行 print(“=” * 50) print(“物体検出の評価結果”) print(“=” * 50) mAP, class_aps = evaluate_detection(predictions, ground_truths, iou_threshold=0.5) print(“=” * 50) print(f”mAP@0.5: {mAP:.4f}”)