'commit'

Apps/AiTeJiYiChong/Crawler.py

@@ -73,8 +73,24 @@ async def get_station_list(d, service, max_scrolls=MAX_SCROLLS):
         logger.info("检查是否存在广告弹窗...")
         ad_res = await ReadImageKit.detect_ad_popup(screenshot_path, device_info=device_info)
         if ad_res:
-            logger.info(f"检测到广告弹窗，准备关闭: {ad_res}")
+            ad_type = ad_res.get("ad_type")
-            d.click(ad_res['x'], ad_res['y'])
+            logger.info(f"检测到广告弹窗，准备关闭: {ad_res} [Type: {ad_type}]")
+            
+            x, y = ad_res['x'], ad_res['y']
+            
+            if ad_type == "rabbit":
+                logger.info(">>> 检测到顽固的兔子广告，执行强力双击策略...")
+                # 使用 u2 的 double_click，它比两次 click + sleep 更快
+                d.double_click(x, y, duration=0.05)
+                
+                # 处理“误触背景卡片”的逻辑
+                logger.info(">>> 考虑到可能误触背景卡片，等待 2s 后执行返回操作...")
+                await asyncio.sleep(2.0)
+                d.press("back")
+                await asyncio.sleep(1.0)
+                logger.info(">>> 已尝试从误触页面返回。")
+            else:
+                d.click(x, y)
                 await asyncio.sleep(1.5)
             # 重新截图
             if os.path.exists(screenshot_path): os.remove(screenshot_path)

Apps/AiTeJiYiChong/ReadImageKit.py

@@ -133,9 +133,14 @@ class ReadImageKit:
             if result.get("has_ad") and result.get("close_point"):
                 if result.get("ad_type") == "rabbit":
                     logger.info("大模型找到了兔子广告！")
+                    # 强制使用固定坐标 (94, 830) - 归一化 0-1000
+                    # 根据多次 CV 测试结果 (94, 828) 和 (93, 830) 的综合评估
+                    logger.info(">>> 采用强制固定坐标策略 (94, 830) 以确保点击准确性")
+                    norm_point = [94, 830]
+                else:
+                    norm_point = result["close_point"]
 
                 # 转换坐标
-                norm_point = result["close_point"]
                 if len(norm_point) == 2:
                     if not device_info:
                         device_info = cls._FALLBACK_DEVICE_INFO
@@ -147,7 +152,11 @@ class ReadImageKit:
                     x = int(norm_point[0] / 1000 * w)
                     y = int(norm_point[1] / 1000 * h)
                     
-                    return {"x": x, "y": y}
+                    return {
+                        "x": x, 
+                        "y": y,
+                        "ad_type": result.get("ad_type", "unknown")
+                    }
             
             return None
 

Apps/XinDianTu/Crawler.py

@@ -168,8 +168,24 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
         logger.info("检查是否存在广告弹窗...")
         ad_res = await ReadImageKit.detect_ad_popup(screenshot_path, device_info=device_info)
         if ad_res:
-            logger.info(f"检测到广告弹窗，准备关闭: {ad_res}")
+            ad_type = ad_res.get("ad_type")
-            d.click(ad_res['x'], ad_res['y'])
+            logger.info(f"检测到广告弹窗，准备关闭: {ad_res} [Type: {ad_type}]")
+            
+            x, y = ad_res['x'], ad_res['y']
+            
+            if ad_type == "rabbit":
+                logger.info(">>> 检测到顽固的兔子广告，执行强力双击策略...")
+                # 使用 u2 的 double_click，它比两次 click + sleep 更快
+                d.double_click(x, y, duration=0.05)
+                
+                # 处理“误触背景卡片”的逻辑：既然可能进去了，就等一会儿再退出来
+                logger.info(">>> 考虑到可能误触背景卡片，等待 2s 后执行返回操作...")
+                await asyncio.sleep(2.0)
+                d.press("back")
+                await asyncio.sleep(1.0)
+                logger.info(">>> 已尝试从误触页面返回。")
+            else:
+                d.click(x, y)
                 await asyncio.sleep(1.5)
             # 重新截图
             if os.path.exists(screenshot_path): os.remove(screenshot_path)

Apps/XinDianTu/Opener.py

@@ -61,8 +61,25 @@ async def check_and_close_ad(d):
         ad_result = await ReadImageKit.detect_ad_popup(screenshot_path, device_info=device_info)
         if ad_result:
             x, y = ad_result["x"], ad_result["y"]
-            logger.info(f"通过视觉大模型检测到广告关闭按钮: ({x}, {y})")
+            ad_type = ad_result.get("ad_type")
+            
+            logger.info(f"通过视觉大模型检测到广告关闭按钮: ({x}, {y}) [Type: {ad_type}]")
+            
+            # 针对“新电兔AI”兔子广告的强力关闭策略
+            if ad_type == "rabbit":
+                logger.info(">>> 检测到顽固的兔子广告，执行强力双击策略...")
+                # 使用 u2 的 double_click，它比两次 click + sleep 更快
+                d.double_click(x, y, duration=0.05)
+                
+                # 处理“误触背景卡片”的逻辑
+                logger.info(">>> 考虑到可能误触背景卡片，等待 2s 后执行返回操作...")
+                await asyncio.sleep(2.0)
+                d.press("back")
+                await asyncio.sleep(1.0)
+                logger.info(">>> 已尝试从误触页面返回。")
+            else:
                 d.click(x, y)
+                
             if os.path.exists(screenshot_path):
                 os.remove(screenshot_path)
             return True

Apps/XinDianTu/ReadImageKit.py

@@ -129,9 +129,14 @@ class ReadImageKit:
             if result.get("has_ad") and result.get("close_point"):
                 if result.get("ad_type") == "rabbit":
                     logger.info("大模型找到了兔子广告！")
+                    # 强制使用固定坐标 (94, 830) - 归一化 0-1000
+                    # 根据多次 CV 测试结果 (94, 828) 和 (93, 830) 的综合评估
+                    logger.info(">>> 采用强制固定坐标策略 (94, 830) 以确保点击准确性")
+                    norm_point = [94, 830]
+                else:
+                    norm_point = result["close_point"]
 
                 # 转换坐标
-                norm_point = result["close_point"]
                 if len(norm_point) == 2:
                     if not device_info:
                         device_info = cls._FALLBACK_DEVICE_INFO
@@ -143,7 +148,11 @@ class ReadImageKit:
                     x = int(norm_point[0] / 1000 * w)
                     y = int(norm_point[1] / 1000 * h)
                     
-                    return {"x": x, "y": y}
+                    return {
+                        "x": x, 
+                        "y": y,
+                        "ad_type": result.get("ad_type", "unknown")
+                    }
             
             return None
 

Tools/TestAdClick.py

@@ -0,0 +1,147 @@
+import uiautomator2 as u2
+import time
+import os
+import cv2
+import numpy as np
+
+def test_click():
+    d = u2.connect()
+    w, h = d.window_size()
+    print(f"Device size: {w}x{h}")
+
+    # 1. Take screenshot
+    print("Taking screenshot...")
+    screenshot_path = r"d:\dsWork\aiData\Output\debug_ad_before.jpg"
+    d.screenshot(screenshot_path)
+    
+    # 2. Check hierarchy
+    print("Dumping hierarchy...")
+    try:
+        xml = d.dump_hierarchy()
+        with open(r"d:\dsWork\aiData\Output\hierarchy.xml", "w", encoding="utf-8") as f:
+            f.write(xml)
+        print(r"Hierarchy saved to d:\dsWork\aiData\Output\hierarchy.xml")
+    except Exception as e:
+        print(f"Failed to dump hierarchy: {e}")
+
+    # 3. Visualize the current fixed point (80/1000, 835/1000)
+    # Norm: 0.08, 0.835
+    # Previous attempt was: (86, 2004) for 1080x2400
+    
+    # Let's try to detect the black circle using HSV or thresholding
+    img = cv2.imread(screenshot_path)
+    if img is None:
+        print("Failed to load screenshot")
+        return
+
+    # ROI: Left side, bottom half
+    # x: 0 - 200, y: 1500 - 2200 (approx for 1080x2400)
+    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]
+    
+    # Convert to grayscale
+    gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+    
+    # The close button is a black circle. So it should be very dark.
+    # Try multiple thresholds to find the best candidates
+    candidates = []
+    for threshold_val in [50, 70, 90, 110]:
+        _, 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)
+            # Approximate circle check
+            perimeter = cv2.arcLength(cnt, True)
+            if perimeter == 0: continue
+            circularity = 4 * np.pi * (area / (perimeter * perimeter))
+            
+            # Filter by size and circularity
+            # Size: it's a small button
+            if 50 < area < 6000 and circularity > 0.35:
+                M = cv2.moments(cnt)
+                if M["m00"] != 0:
+                    cX = int(M["m10"] / M["m00"]) + roi_x1
+                    cY = int(M["m01"] / M["m00"]) + roi_y1
+                    
+                    # Calculate normalized coordinates
+                    norm_x = int(cX / w * 1000)
+                    norm_y = int(cY / h * 1000)
+                    
+                    # Avoid duplicates
+                    if not any(abs(cX - c[0]) < 10 and abs(cY - c[1]) < 10 for c in candidates):
+                        candidates.append((cX, cY, area, norm_x, norm_y))
+
+    # Save thresh image for debugging
+    _, debug_thresh = cv2.threshold(gray, 70, 255, cv2.THRESH_BINARY_INV)
+    cv2.imwrite(r"d:\dsWork\aiData\Output\debug_thresh.jpg", debug_thresh)
+
+    # Sort by proximity to expected location (Left side, Middle-Bottom)
+    # Expected norm: (93, 830)
+    def score_candidate(c):
+        # Weighting: X proximity is important, Y proximity is also important, Area around 400-500 is ideal
+        dist_x = abs(c[3] - 93)
+        dist_y = abs(c[4] - 830)
+        area_diff = abs(c[2] - 450) / 450.0
+        return dist_x * 2 + dist_y + area_diff * 100
+
+    candidates.sort(key=score_candidate)
+
+    target_x, target_y = int(w * 0.08), int(h * 0.835) # Default fallback
+    norm_target_x, norm_target_y = 80, 835
+
+    if candidates:
+        print(f"Found {len(candidates)} candidate close buttons via CV:")
+        for i, (cx, cy, area, nx, ny) in enumerate(candidates):
+            score = score_candidate((cx, cy, area, nx, ny))
+            print(f"  Candidate {i}: ({cx}, {cy}), Area: {area}, Norm: ({nx}, {ny}), Score: {score:.2f}")
+        
+        # Pick the best one
+        best_c = candidates[0]
+        target_x, target_y = best_c[0], best_c[1]
+        norm_target_x, norm_target_y = best_c[3], best_c[4]
+        print(f"Selected CV target: ({target_x}, {target_y}), Norm: ({norm_target_x}, {norm_target_y})")
+    else:
+        print("No CV candidates found. Using fixed coordinates.")
+
+    # Visualize
+    cv2.circle(img, (target_x, target_y), 20, (0, 255, 0), -1)
+    cv2.putText(img, f"Click ({target_x}, {target_y})", (target_x + 30, target_y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+    
+    # Also draw the fixed point for comparison (Blue)
+    fixed_x = int(w * 0.08)
+    fixed_y = int(h * 0.835)
+    cv2.circle(img, (fixed_x, fixed_y), 15, (255, 0, 0), -1)
+    
+    debug_path = r"d:\dsWork\aiData\Output\debug_ad_point.jpg"
+    cv2.imwrite(debug_path, img)
+    print(f"Debug image saved to {debug_path}")
+
+    # 4. Perform click
+    print(f"Clicking ({target_x}, {target_y}) using double_click...")
+    # Use d.double_click for a faster interaction
+    d.double_click(target_x, target_y, duration=0.05)
+    
+    # NEW: Handle potential background click as per user's latest request
+    print("Waiting 2s for potential background card to load...")
+    time.sleep(2)
+    print("Pressing BACK to return to main page...")
+    d.press("back")
+    
+    time.sleep(2)
+    
+    # 5. Take after screenshot
+    after_path = r"d:\dsWork\aiData\Output\debug_ad_after.jpg"
+    d.screenshot(after_path)
+    print(f"After screenshot saved to {after_path}")
+    
+    # Check if successful (compare image hash or just file size, though file size varies with compression)
+    # Simple check: If the black button is gone, the area should be brighter?
+    # Or just let user check.
+
+if __name__ == "__main__":
+    test_click()