1

京东搜索_自动版.py

@@ -30,9 +30,9 @@ mysql_zdzs_proxy_host = '59.110.219.171'
 mysql_zdzs_proxy_user = 'zgcindex'
 mysql_zdzs_proxy_password = 'zgcprice2019'
 mysql_zdzs_proxy_database = 'zdzs_proxy'
-username = 'xxx'
+username = '13311252087'
 # 登陆密码
-password = 'xxx'
+password = 'zaijian666'
 
 # mysql_zdzs_proxy_host = '59.110.219.171'
 # mysql_zdzs_proxy_user = 'zgcindex'
@@ -220,11 +220,43 @@ def find_best_match_angle(background_img_path, rotating_img_path):
 
         # 将旋转后的验证码图片放置到背景图的指定位置
         new_background.paste(rotated_captcha, insert_position, rotated_captcha)
+        # 将PIL图像转换为NumPy数组
+        numpy_image = np.array(new_background)
 
+        # 将RGB格式的图像转换为BGR格式，因为OpenCV默认使用BGR格式
+        bgr_image = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR)
         # 保存合成后的图片
+        gray = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
 
-        new_background.save(f"captcha_{abs(angle)}.png")
-        distortion_level = detect_circular_distortion(f"captcha_{abs(angle)}.png")
+        # 确定圆形区域的中心和半径
+        circle_center = (gray.shape[1] // 2, gray.shape[0] // 2)
+
+        # 创建一个遮罩，只包含圆形区域
+        mask = np.zeros_like(gray)
+        cv2.circle(mask, circle_center, 65, 255, thickness=-1)
+
+        # 应用遮罩到图像
+        masked_img = cv2.bitwise_and(gray, gray, mask=mask)
+
+        # 初始化ORB检测器
+        orb = cv2.ORB_create(500)
+
+        # 检测并计算圆形区域和整个图像的关键点和描述符
+        kp1, des1 = orb.detectAndCompute(gray, None)
+        kp2, des2 = orb.detectAndCompute(masked_img, None)
+
+        # 匹配描述符
+        bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+        matches = bf.match(des1, des2)
+
+        # 计算匹配点的平均距离，作为扭曲程度的一个简单估计
+        if matches:
+            distances = [match.distance for match in matches]
+            average_distance = sum(distances) / len(distances)
+        else:
+            average_distance = float('inf')
+        # new_background.save(f"captcha_{abs(angle)}.png")
+        distortion_level = average_distance
         if angle == -360:
             best_score = distortion_level
         print(f'中心区域扭曲程度估计（数值越小表示越接近未扭曲）: {distortion_level}')