181 lines
7.7 KiB
Python
181 lines
7.7 KiB
Python
import uiautomator2 as u2
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import time
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import os
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import cv2
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import numpy as np
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def test_click():
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d = u2.connect()
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w, h = d.window_size()
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print(f"Device size: {w}x{h}")
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# 1. Take screenshot
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print("Taking screenshot...")
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screenshot_path = r"d:\dsWork\aiData\Output\debug_ad_before.jpg"
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d.screenshot(screenshot_path)
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# 2. Check hierarchy
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print("Dumping hierarchy...")
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try:
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xml = d.dump_hierarchy()
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with open(r"d:\dsWork\aiData\Output\hierarchy.xml", "w", encoding="utf-8") as f:
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f.write(xml)
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print(r"Hierarchy saved to d:\dsWork\aiData\Output\hierarchy.xml")
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except Exception as e:
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print(f"Failed to dump hierarchy: {e}")
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# 3. Visualize the current fixed point (80/1000, 835/1000)
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# Norm: 0.08, 0.835
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# Previous attempt was: (86, 2004) for 1080x2400
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# Let's try to detect the black circle using HSV or thresholding
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img = cv2.imread(screenshot_path)
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if img is None:
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print("Failed to load screenshot")
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return
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# ROI: Left side, bottom half
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# x: 0 - 200, y: 1500 - 2200 (approx for 1080x2400)
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roi_x1, roi_x2 = 0, int(w * 0.25)
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roi_y1, roi_y2 = int(h * 0.6), int(h * 0.9)
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roi = img[roi_y1:roi_y2, roi_x1:roi_x2]
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# Convert to grayscale
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gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
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# The close button is a black circle with a white X.
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# We look for a dark circle, and then check if it contains something bright.
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candidates = []
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# 尝试多个阈值以应对不同的亮度环境
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for threshold_val in [40, 60, 80, 100]:
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_, thresh = cv2.threshold(gray, threshold_val, 255, cv2.THRESH_BINARY_INV)
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# Find contours
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for cnt in contours:
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area = cv2.contourArea(cnt)
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# 圆形度检查
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perimeter = cv2.arcLength(cnt, True)
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if perimeter == 0: continue
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circularity = 4 * np.pi * (area / (perimeter * perimeter))
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# 兔子广告关闭按钮通常很小 (40x40 左右在 1080p 下是 1600 面积)
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if 100 < area < 4000 and circularity > 0.4:
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# 获取该候选区域的 bounding box
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x, y, w_cnt, h_cnt = cv2.boundingRect(cnt)
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# 在这个黑色圆内部，检查是否有亮色的 'X'
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# 我们可以对这个区域做反向阈值，找亮色物体
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padding = 2
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inner_roi = gray[max(0, y-padding):min(roi.shape[0], y+h_cnt+padding),
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max(0, x-padding):min(roi.shape[1], x+w_cnt+padding)]
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# 找亮色物体 (X)
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_, inner_thresh = cv2.threshold(inner_roi, 180, 255, cv2.THRESH_BINARY)
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inner_contours, _ = cv2.findContours(inner_thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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has_x = False
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for i_cnt in inner_contours:
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i_area = cv2.contourArea(i_cnt)
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# X 应该比圆小很多
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if 10 < i_area < area * 0.5:
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has_x = True
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break
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if has_x:
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M = cv2.moments(cnt)
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if M["m00"] != 0:
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cX = int(M["m10"] / M["m00"]) + roi_x1
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cY = int(M["m01"] / M["m00"]) + roi_y1
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norm_x = int(cX / w * 1000)
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norm_y = int(cY / h * 1000)
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# 避免重复
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if not any(abs(cX - c[0]) < 15 and abs(cY - c[1]) < 15 for c in candidates):
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candidates.append((cX, cY, area, norm_x, norm_y, True)) # True means found X
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# 保存候选区域图片以便调试
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cv2.imwrite(rf"d:\dsWork\aiData\Output\cand_{len(candidates)-1}_roi.jpg", inner_roi)
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else:
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# 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
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if circularity > 0.7:
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M = cv2.moments(cnt)
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if M["m00"] != 0:
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cX = int(M["m10"] / M["m00"]) + roi_x1
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cY = int(M["m01"] / M["m00"]) + roi_y1
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norm_x = int(cX / w * 1000)
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norm_y = int(cY / h * 1000)
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if not any(abs(cX - c[0]) < 15 and abs(cY - c[1]) < 15 for c in candidates):
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candidates.append((cX, cY, area, norm_x, norm_y, False))
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cv2.imwrite(rf"d:\dsWork\aiData\Output\cand_{len(candidates)-1}_roi.jpg", inner_roi)
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# Save thresh image for debugging
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cv2.imwrite(r"d:\dsWork\aiData\Output\debug_thresh.jpg", thresh)
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# 评分逻辑
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def score_candidate(c):
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# c = (cx, cy, area, nx, ny, has_x)
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has_x = c[5]
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# 基础分：如果有 X，大幅加分
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score = 1000 if has_x else 0
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# 距离分：越靠近预期的 (93, 830) 分越高
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dist = np.sqrt((c[3] - 93)**2 + (c[4] - 830)**2)
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score -= dist * 2
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# 面积分：理想面积在 500-1500 之间
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if 500 < c[2] < 1500:
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score += 200
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return score
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candidates.sort(key=score_candidate, reverse=True)
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target_x, target_y = int(w * 0.08), int(h * 0.835) # Default fallback
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norm_target_x, norm_target_y = 80, 835
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if candidates:
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print(f"Found {len(candidates)} candidate close buttons via CV:")
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for i, (cx, cy, area, nx, ny, has_x) in enumerate(candidates):
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score = score_candidate((cx, cy, area, nx, ny, has_x))
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print(f" Candidate {i}: ({cx}, {cy}), Area: {area}, Norm: ({nx}, {ny}), HasX: {has_x}, Score: {score:.2f}")
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# Pick the best one
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best_c = candidates[0]
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target_x, target_y = best_c[0], best_c[1]
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norm_target_x, norm_target_y = best_c[3], best_c[4]
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print(f"Selected CV target: ({target_x}, {target_y}), Norm: ({norm_target_x}, {norm_target_y}), HasX: {best_c[5]}")
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# Visualize
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viz_img = cv2.imread(screenshot_path)
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cv2.circle(viz_img, (target_x, target_y), 3, (0, 0, 255), -1)
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debug_path = r"d:\dsWork\aiData\Output\debug_ad_point.jpg"
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red_point_path = r"d:\dsWork\aiData\Output\_redpoint.jpg"
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cv2.imwrite(debug_path, viz_img)
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cv2.imwrite(red_point_path, viz_img)
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print(f"Debug images saved to {debug_path} and {red_point_path}")
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# 4. Perform click
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print(f"Clicking ({target_x}, {target_y}) using single click...")
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d.click(target_x, target_y)
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print("Waiting 3s for user to observe if the ad is closed...")
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time.sleep(3)
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# print("Pressing BACK to return to main page...")
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# d.press("back")
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else:
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print("No CV candidates found. Skipping click to avoid accidental background interaction.")
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time.sleep(2)
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# 5. Take after screenshot
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after_path = r"d:\dsWork\aiData\Output\debug_ad_after.jpg"
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d.screenshot(after_path)
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print(f"After screenshot saved to {after_path}")
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# Check if successful (compare image hash or just file size, though file size varies with compression)
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# Simple check: If the black button is gone, the area should be brighter?
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# Or just let user check.
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if __name__ == "__main__":
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test_click()
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