aiData/Test/TestAdClick.py

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 with a white X.
    # We look for a dark circle, and then check if it contains something bright.
    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 (40x40 左右在 1080p 下是 1600 面积)
            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'
                # 我们可以对这个区域做反向阈值，找亮色物体
                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:
                    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, True)) # True means found X
                            # 保存候选区域图片以便调试
                            cv2.imwrite(rf"d:\dsWork\aiData\Output\cand_{len(candidates)-1}_roi.jpg", inner_roi)
                else:
                    # 如果没找到 X，但圆形度很高，也可以作为一个低优先级候选
                    if 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)
                            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, False))
                                cv2.imwrite(rf"d:\dsWork\aiData\Output\cand_{len(candidates)-1}_roi.jpg", inner_roi)

    # Save thresh image for debugging
    cv2.imwrite(r"d:\dsWork\aiData\Output\debug_thresh.jpg", thresh)

    # 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)

    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, has_x) in enumerate(candidates):
            score = score_candidate((cx, cy, area, nx, ny, has_x))
            print(f"  Candidate {i}: ({cx}, {cy}), Area: {area}, Norm: ({nx}, {ny}), HasX: {has_x}, 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}), HasX: {best_c[5]}")
        
        # Visualize
        viz_img = cv2.imread(screenshot_path)
        cv2.circle(viz_img, (target_x, target_y), 3, (0, 0, 255), -1) 
        debug_path = r"d:\dsWork\aiData\Output\debug_ad_point.jpg"
        red_point_path = r"d:\dsWork\aiData\Output\_redpoint.jpg"
        cv2.imwrite(debug_path, viz_img)
        cv2.imwrite(red_point_path, viz_img)
        print(f"Debug images saved to {debug_path} and {red_point_path}")

        # 4. Perform click
        print(f"Clicking ({target_x}, {target_y}) using single click...")
        d.click(target_x, target_y)
        
        print("Waiting 3s for user to observe if the ad is closed...")
        time.sleep(3)
        # print("Pressing BACK to return to main page...")
        # d.press("back")
    else:
        print("No CV candidates found. Skipping click to avoid accidental background interaction.")
    
    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()