feat generate 修改中心点判断

app/service/generate_image/utils/image_processing.py

@@ -153,11 +153,38 @@ def stain_detection(image, spot_size=100):
             logger.info(f"第{index + 1}发现了污点")
             return False, None
     # 中心区域检测
-    center_x, center_y = width // 2, height // 2
-    center_image = image[center_y - spot_size // 2:center_y + spot_size // 2, center_x - spot_size // 2:center_x + spot_size // 2]
-    gray_center_image = cv2.cvtColor(center_image, cv2.COLOR_BGR2GRAY)
-    white_threshold = 200  # 设定的接近最大值的阈值
-    is_pure_white = (gray_center_image >= white_threshold).all()
+    # 将图像转换为灰度图像
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # 获取图像的中心点坐标
+    center_x, center_y = image.shape[1] // 2, image.shape[0] // 2
+    # 定义中心区域的大小
+    patch_size = 100
+    half_patch = patch_size // 2
+    # 提取中心区域
+    center_patch = gray[center_y - half_patch:center_y + half_patch, center_x - half_patch:center_x + half_patch]
+    # 设置阈值来检测纯白区域
+    _, thresh = cv2.threshold(center_patch, 254, 255, cv2.THRESH_BINARY)
+    # 寻找轮廓
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # 过滤非连续的纯白区域
+    filtered_contours = [cnt for cnt in contours if cv2.contourArea(cnt) >= 50]  # 根据面积进行过滤，这里假设面积大于50的为连续区域
+    # 如果有连续的纯白区域存在
+    if filtered_contours:
+        # 将纯白区域替换为灰色
+        if DEBUG:
+            for cnt in filtered_contours:
+                x, y, w, h = cv2.boundingRect(cnt)
+                # 在原始图像上进行替换
+                image[y + center_y - half_patch:y + center_y - half_patch + h, x + center_x - half_patch:x + center_x - half_patch + w][thresh[y:y + h, x:x + w] == 255] = (128, 128, 128)
+            # 显示图像
+            cv2.imshow('Marked Image', image)
+            cv2.waitKey(0)
+        logger.info("中心区域存在连续的纯白区域")
+        is_pure_white = True
+    else:
+        logger.info("中心区域不存在连续的纯白区域")
+        is_pure_white = False
+
     if is_pure_white:
         return False, None
     if DEBUG: