v3.6: 支持白底，初级版本

utils/util.py

@@ -16,11 +16,19 @@ __all__ = [
 
 __StrBase = "qwertyuioplkjhgfdsazxcvbnm1234567890ZXCVBNMLKJHGFDSAQWERTYUIOP"
 __StrBaseLen = len(__StrBase) - 1
-__MAX_IMAGE_SIZE = 3500 * 2000
+__MaxImageSize = 3500 * 2000
+__BorderWidth = 5
 __AcceptExtNames = ["jpg", "jpeg", "bmp", "png", "rgb", "tif", "tiff", "gif"]
 Engine = HuiMvOCR(Args())
 
 
+def __isWhiteBack(img: "np.ndarray") -> "bool":
+    row = np.concatenate([img[:__BorderWidth], img[-__BorderWidth:]], axis=0).reshape((__BorderWidth * 2, -1))
+    col = np.concatenate([img[:, :__BorderWidth], img[:, -__BorderWidth:]], axis=1).reshape((__BorderWidth * 2, -1))
+    full = np.concatenate([row, col], axis=1)
+    return np.average(full) > 127
+
+
 def Response(message: "str" = None, data=None):
     if message is None:
         return jsonify(success=True, message="操作成功", data=data)
@@ -54,7 +62,7 @@ def read_img(content: "str") -> "np.ndarray":
     return cv2.imdecode(np.frombuffer(content, np.uint8), 1)  # noqa
 
 
-def compress_img(img: "np.ndarray", tar_size: "int" = __MAX_IMAGE_SIZE) -> "np.ndarray":
+def compress_img(img: "np.ndarray", tar_size: "int" = __MaxImageSize) -> "np.ndarray":
     cur_size = img.shape[0] * img.shape[1]
     if cur_size <= tar_size:
         return img
@@ -70,7 +78,10 @@ def compress_img(img: "np.ndarray", tar_size: "int" = __MAX_IMAGE_SIZE) -> "np.n
 
 def crop_img(image: "np.ndarray") -> "np.ndarray":
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # noqa 将图像转换为灰度图像
-    _, threshold = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)  # noqa 换为二值图像 => save: [150,255]
+    thresh_type = cv2.THRESH_BINARY  # noqa
+    if __isWhiteBack(gray):
+        thresh_type = cv2.THRESH_BINARY_INV  # noqa
+    _, threshold = cv2.threshold(gray, 155, 255, thresh_type)  # noqa 换为二值图像 => save: [150,255]
     contours, _ = cv2.findContours(threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # noqa 查找轮廓
     if not contours:
         return image