合并代码

Utils/LogManager.py

@@ -26,7 +26,7 @@ def _force_utf8_everywhere():
     except Exception:
         pass
 
-# _force_utf8_everywhere()
+_force_utf8_everywhere()
 
 class LogManager:
     """

Utils/OCRUtils.py

@@ -0,0 +1,233 @@
+import cv2
+import numpy as np
+from typing import List, Tuple, Union, Optional
+from PIL import Image
+
+ArrayLikeImage = Union[np.ndarray, str, Image.Image]
+
+class OCRUtils:
+    @classmethod
+    def _to_gray(cls, img: ArrayLikeImage) -> np.ndarray:
+        """
+        接受路径/np.ndarray/PIL.Image，统一转为灰度 np.ndarray。
+        """
+        # 路径
+        if isinstance(img, str):
+            arr = cv2.imread(img, cv2.IMREAD_GRAYSCALE)
+            if arr is None:
+                raise FileNotFoundError(f"图像加载失败，请检查路径: {img}")
+            return arr
+
+        # PIL.Image
+        if isinstance(img, Image.Image):
+            return cv2.cvtColor(np.array(img.convert("RGB")), cv2.COLOR_RGB2GRAY)
+
+        # numpy 数组
+        if isinstance(img, np.ndarray):
+            if img.ndim == 2:
+                return img  # 已是灰度
+            if img.ndim == 3:
+                return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            raise ValueError("不支持的图像维度（期望 2D 灰度或 3D BGR/RGB）")
+
+        raise TypeError("large_image 类型必须是 str / np.ndarray / PIL.Image.Image")
+
+    @classmethod
+    def non_max_suppression(
+        cls,
+        boxes: List[List[float]],
+        scores: Optional[np.ndarray] = None,
+        overlapThresh: float = 0.5
+    ) -> np.ndarray:
+        """
+        boxes: [ [x1,y1,x2,y2], ... ]
+        scores: 每个框的置信度（用于“按分数做 NMS”）。若为 None，则退化为按 y2 排序的经典近似。
+        返回: 经过 NMS 保留的 boxes(int) ndarray，形状 (N,4)
+        """
+        if len(boxes) == 0:
+            return np.empty((0, 4), dtype=int)
+
+        boxes = np.asarray(boxes, dtype=np.float32)
+        x1, y1, x2, y2 = boxes.T
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+
+        if scores is None:
+            order = np.argsort(y2)  # 经典写法
+        else:
+            scores = np.asarray(scores, dtype=np.float32)
+            order = np.argsort(scores)[::-1]  # 分数从高到低
+
+        keep = []
+        while order.size > 0:
+            i = order[0] if scores is not None else order[-1]
+            keep.append(i)
+
+            rest = order[1:] if scores is not None else order[:-1]
+
+            xx1 = np.maximum(x1[i], x1[rest])
+            yy1 = np.maximum(y1[i], y1[rest])
+            xx2 = np.minimum(x2[i], x2[rest])
+            yy2 = np.minimum(y2[i], y2[rest])
+
+            w = np.maximum(0, xx2 - xx1 + 1)
+            h = np.maximum(0, yy2 - yy1 + 1)
+            inter = w * h
+            ovr = inter / areas[rest]
+
+            inds = np.where(ovr <= overlapThresh)[0]
+            order = rest[inds]
+
+        return boxes[keep].astype(int)
+
+    # @classmethod
+    # def find_template(
+    #     cls,
+    #     template_path: str,
+    #     large_image: ArrayLikeImage,
+    #     threshold: float = 0.8,
+    #     overlapThresh: float = 0.5,
+    #     return_boxes: bool = False
+    # ) -> Union[List[Tuple[int, int]], Tuple[List[Tuple[int, int]], np.ndarray]]:
+    #     """
+    #     在 large_image 中查找 template_path 模板的位置。
+    #     - large_image 可为文件路径、np.ndarray 或 PIL.Image
+    #     - threshold: 模板匹配阈值（TM_CCOEFF_NORMED）
+    #     - overlapThresh: NMS 重叠阈值
+    #     - return_boxes: True 时同时返回保留的框数组 (N,4)
+    #
+    #     返回:
+    #       centers 或 (centers, boxes)
+    #       centers: [(cx, cy), ...]
+    #       boxes:   [[x1,y1,x2,y2], ...] (np.ndarray, int)
+    #     """
+    #     # 模板（灰度）
+    #     template = cv2.imread(template_path, cv2.IMREAD_GRAYSCALE)
+    #     if template is None:
+    #         raise FileNotFoundError(f"模板图像加载失败，请检查路径: {template_path}")
+    #
+    #     # 大图（灰度）
+    #     gray = cls._to_gray(large_image)
+    #
+    #     # 模板尺寸
+    #     tw, th = template.shape[::-1]
+    #
+    #     # 模板匹配（相关系数归一化）
+    #     result = cv2.matchTemplate(gray, template, cv2.TM_CCOEFF_NORMED)
+    #
+    #     # 阈值筛选
+    #     ys, xs = np.where(result >= threshold)
+    #     if len(xs) == 0:
+    #         return ([], np.empty((0, 4), dtype=int)) if return_boxes else []
+    #
+    #     # 收集候选框与分数
+    #     boxes = []
+    #     scores = []
+    #     for (x, y) in zip(xs, ys):
+    #         boxes.append([x, y, x + tw, y + th])
+    #         scores.append(result[y, x])
+    #
+    #     # 按分数做 NMS
+    #     boxes_nms = cls.non_max_suppression(boxes, scores=np.array(scores), overlapThresh=overlapThresh)
+    #
+    #     # 计算中心点
+    #     centers = [((x1 + x2) // 2, (y1 + y2) // 2) for (x1, y1, x2, y2) in boxes_nms]
+    #
+    #
+    #
+    #     if return_boxes:
+    #         return centers, boxes_nms
+    #
+    #
+    #     return centers
+
+    @classmethod
+    def find_template(
+            cls,
+            template_path: str,
+            large_image: ArrayLikeImage,
+            threshold: float = 0.8,
+            overlapThresh: float = 0.5,
+            return_boxes: bool = False
+    ) -> Union[List[Tuple[int, int]], Tuple[List[Tuple[int, int]], np.ndarray]]:
+        """
+        在 large_image 中查找 template_path 模板的位置。
+        - large_image 可为文件路径、np.ndarray 或 PIL.Image
+        - threshold: 模板匹配阈值（TM_CCOEFF_NORMED）
+        - overlapThresh: NMS 重叠阈值
+        - return_boxes: True 时同时返回保留的框数组 (N,4)
+
+        若检测结果为空，则在相同阈值下最多重试三次（共 3 次尝试）。
+        返回:
+          centers 或 (centers, boxes)
+          centers: [(cx, cy), ...]
+          boxes:   [[x1,y1,x2,y2], ...] (np.ndarray, int)
+        """
+        # 模板（灰度）
+        template = cv2.imread(template_path, cv2.IMREAD_GRAYSCALE)
+        if template is None:
+            raise FileNotFoundError(f"模板图像加载失败，请检查路径: {template_path}")
+
+        # 大图（灰度）
+        gray = cls._to_gray(large_image)
+
+        # 模板尺寸
+        tw, th = template.shape[::-1]
+
+        # 内部：执行一次匹配并返回 (centers, boxes_nms)
+        def _match_once(cur_threshold: float):
+            # 模板匹配（相关系数归一化）
+            result = cv2.matchTemplate(gray, template, cv2.TM_CCOEFF_NORMED)
+
+            # 阈值筛选
+            ys, xs = np.where(result >= cur_threshold)
+            if len(xs) == 0:
+                return [], np.empty((0, 4), dtype=int)
+
+            # 收集候选框与分数
+            boxes = []
+            scores = []
+            for (x, y) in zip(xs, ys):
+                boxes.append([int(x), int(y), int(x + tw), int(y + th)])
+                scores.append(float(result[y, x]))
+
+            # 按分数做 NMS
+            boxes_nms = cls.non_max_suppression(
+                boxes,
+                scores=np.asarray(scores, dtype=np.float32),
+                overlapThresh=overlapThresh
+            )
+
+            # 计算中心点（转为 Python int）
+            centers = [(int((x1 + x2) // 2), int((y1 + y2) // 2))
+                       for (x1, y1, x2, y2) in boxes_nms]
+
+            # 统一为 np.ndarray[int]
+            boxes_nms = np.asarray(boxes_nms, dtype=int)
+            return centers, boxes_nms
+
+        # ===== 重试控制（最多 3 次）=====
+        MAX_RETRIES = 3
+        THRESHOLD_DECAY = 0.0  # 如需越试越宽松，可改为 0.02~0.05；不需要则保持 0
+        MIN_THRESHOLD = 0.6
+
+        cur_threshold = float(threshold)
+        last_centers, last_boxes = [], np.empty((0, 4), dtype=int)
+
+        for attempt in range(MAX_RETRIES):
+            centers, boxes_nms = _match_once(cur_threshold)
+            if centers:
+                if return_boxes:
+                    return centers, boxes_nms
+                return centers
+
+            # 记录最后一次（若最终失败按规范返回空）
+            last_centers, last_boxes = centers, boxes_nms
+
+            # 为下一次尝试准备（这里默认不衰减阈值；如需可打开 THRESHOLD_DECAY）
+            if attempt < MAX_RETRIES - 1 and THRESHOLD_DECAY > 0.0:
+                cur_threshold = max(MIN_THRESHOLD, cur_threshold - THRESHOLD_DECAY)
+
+        # 全部尝试失败
+        if return_boxes:
+            return last_centers, last_boxes
+        return last_centers