'commit'

Apps/XinDianTu/Kit.py

@@ -137,6 +137,110 @@ def is_background_dimmed(image_path, threshold=80):
     return avg_brightness < threshold
 
 
+def detect_rabbit_ad_close(image_path, debug_dir=None):
+    """
+    通过图形学算法检测“新电兔AI”广告的关闭按钮
+    特征：黑色圆，中间有白色X
+    :param image_path: 截图路径
+    :param debug_dir: 调试图保存目录
+    :return: (x, y) 归一化坐标，如果未找到返回 None
+    """
+    if not os.path.exists(image_path):
+        return None
+
+    img = read_image(image_path)
+    if img is None:
+        return None
+
+    h, w = img.shape[:2]
+
+    # ROI: 左侧，中下部 (x: 0-25%, y: 60-90%)
+    roi_x1, roi_x2 = 0, int(w * 0.25)
+    roi_y1, roi_y2 = int(h * 0.6), int(h * 0.9)
+    roi = img[roi_y1:roi_y2, roi_x1:roi_x2]
+    
+    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+    
+    candidates = []
+    # 尝试多个阈值以应对不同的亮度环境
+    for threshold_val in [40, 60, 80, 100]:
+        _, thresh = cv2.threshold(gray, threshold_val, 255, cv2.THRESH_BINARY_INV)
+        
+        # Find contours
+        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        
+        for cnt in contours:
+            area = cv2.contourArea(cnt)
+            # 圆形度检查
+            perimeter = cv2.arcLength(cnt, True)
+            if perimeter == 0: continue
+            circularity = 4 * np.pi * (area / (perimeter * perimeter))
+            
+            # 兔子广告关闭按钮通常很小 (面积在 100-4000 之间)
+            if 100 < area < 4000 and circularity > 0.4:
+                # 获取该候选区域的 bounding box
+                x, y, w_cnt, h_cnt = cv2.boundingRect(cnt)
+                
+                # 在这个黑色圆内部，检查是否有亮色的 'X'
+                padding = 2
+                inner_roi = gray[max(0, y-padding):min(roi.shape[0], y+h_cnt+padding), 
+                                 max(0, x-padding):min(roi.shape[1], x+w_cnt+padding)]
+                
+                # 找亮色物体 (X)
+                _, inner_thresh = cv2.threshold(inner_roi, 180, 255, cv2.THRESH_BINARY)
+                inner_contours, _ = cv2.findContours(inner_thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+                
+                has_x = False
+                for i_cnt in inner_contours:
+                    i_area = cv2.contourArea(i_cnt)
+                    # X 应该比圆小很多
+                    if 10 < i_area < area * 0.5:
+                        has_x = True
+                        break
+                
+                if has_x or circularity > 0.7:
+                    M = cv2.moments(cnt)
+                    if M["m00"] != 0:
+                        cX = int(M["m10"] / M["m00"]) + roi_x1
+                        cY = int(M["m01"] / M["m00"]) + roi_y1
+                        
+                        norm_x = int(cX / w * 1000)
+                        norm_y = int(cY / h * 1000)
+                        
+                        # 避免重复
+                        if not any(abs(cX - c[0]) < 15 and abs(cY - c[1]) < 15 for c in candidates):
+                            candidates.append((cX, cY, area, norm_x, norm_y, has_x))
+
+    if not candidates:
+        return None
+
+    # 评分逻辑
+    def score_candidate(c):
+        # c = (cx, cy, area, nx, ny, has_x)
+        has_x = c[5]
+        # 基础分：如果有 X，大幅加分
+        score = 1000 if has_x else 0
+        # 距离分：越靠近预期的 (93, 830) 分越高
+        dist = np.sqrt((c[3] - 93)**2 + (c[4] - 830)**2)
+        score -= dist * 2
+        # 面积分：理想面积在 500-1500 之间
+        if 500 < c[2] < 1500: score += 200
+        return score
+
+    candidates.sort(key=score_candidate, reverse=True)
+    best = candidates[0]
+    
+    logger.info(f"CV detected rabbit ad close button at Norm({best[3]}, {best[4]}) with score {score_candidate(best):.2f}")
+    
+    if debug_dir:
+        os.makedirs(debug_dir, exist_ok=True)
+        debug_img = img.copy()
+        cv2.circle(debug_img, (best[0], best[1]), 10, (0, 0, 255), -1) # 使用红点表示识别结果
+        save_image(os.path.join(debug_dir, "debug_rabbit_ad_cv.jpg"), debug_img)
+
+    return [best[3], best[4]]
+
+
 def detect_black_agree_button(image_path, debug_dir=None):
     """
     通过计算机图形学检测黑色的"同意"按钮 (Image 1 场景)