'commit'

Apps/XinDianTu/Kit.py

@@ -2,7 +2,7 @@ import logging
 import os
 import cv2
 import numpy as np
-import time
+import time
 import hashlib
 from Apps.XinDianTu.Config.Setting import BOTTOM_SAFE_EXCLUDE_RATIO
 from Config.Config import TEMP_IMAGE_DIR
@@ -96,82 +96,7 @@ def clear_temp_dir(save_dir=TEMP_IMAGE_DIR):
             logger.error(f"无法删除文件 {file_path}: {e}")
 
 
-def click_image_template(d, template_path, timeout=5.0, threshold=0.8):
-    """
-    使用 OpenCV 模板匹配查找并点击图片
-    :param d: uiautomator2 设备对象
-    :param template_path: 模板图片路径
-    :param timeout: 超时时间（秒）
-    :param threshold: 匹配阈值 (0.0 - 1.0)
-    :return: 是否点击成功
-    """
-    if not os.path.exists(template_path):
-        logger.info(f"Template file not found: {template_path}")
-        return False
 
-    template = read_image(template_path)
-    if template is None:
-        logger.info(f"Failed to load template: {template_path}")
-        return False
-
-    t_h, t_w = template.shape[:2]
-
-    start_time = time.time()
-    best_val_overall = 0.0
-
-    while time.time() - start_time < timeout:
-        # 临时截图
-        temp_uuid = "temp_click_check"
-        screenshot_path = take_screenshot(d, temp_uuid, save_dir=TEMP_IMAGE_DIR)
-
-        target = read_image(screenshot_path)
-        if target is None:
-            time.sleep(0.5)
-            continue
-
-        # 多尺度匹配
-        found = None
-        # 缩放比例从 0.5 到 1.5，步长 0.1
-        for scale in np.linspace(0.5, 1.5, 11):
-            # 调整模板大小
-            resized_template = cv2.resize(template, (int(t_w * scale), int(t_h * scale)))
-            r_h, r_w = resized_template.shape[:2]
-
-            # 如果模板比目标还大，跳过
-            if r_h > target.shape[0] or r_w > target.shape[1]:
-                continue
-
-            result = cv2.matchTemplate(target, resized_template, cv2.TM_CCOEFF_NORMED)
-            min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
-
-            if max_val > best_val_overall:
-                best_val_overall = max_val
-
-            if max_val >= threshold:
-                found = (max_val, max_loc, r_w, r_h)
-                break  # 找到满足阈值的即停止当前截图的搜索
-
-        # 清理临时文件
-        try:
-            os.remove(screenshot_path)
-        except:
-            pass
-
-        if found:
-            max_val, max_loc, r_w, r_h = found
-            # 计算中心点
-            top_left = max_loc
-            center_x = top_left[0] + r_w // 2
-            center_y = top_left[1] + r_h // 2
-
-            logger.info(f"Found image at ({center_x}, {center_y}) with confidence {max_val:.2f}")
-            d.click(center_x, center_y)
-            return True
-
-        time.sleep(1.0)
-
-    logger.info(f"Image not found after {timeout}s (Best confidence: {best_val_overall:.2f})")
-    return False
 
 
 def is_background_dimmed(image_path, threshold=80):
@@ -711,29 +636,29 @@ def get_row_stats(gray):
     return row_means, row_stds, edge_means
 
 
-def clean_station_name(name):
-    """
-    清理场站名称，移除结尾的省略号及多余空格，方便比对
-    """
-    if not name: return ""
-    name = name.strip()
-    # 移除结尾的 . (通常是截断标识)
-    while name.endswith("."):
-        name = name[:-1]
-    return name.strip()
-
-
-def get_file_md5(path):
-    """计算文件的 MD5 值"""
-    if not os.path.exists(path):
-        return ""
-    hash_md5 = hashlib.md5()
-    with open(path, "rb") as f:
-        for chunk in iter(lambda: f.read(4096), b""):
-            hash_md5.update(chunk)
-    return hash_md5.hexdigest()
-
-
+def clean_station_name(name):
+    """
+    清理场站名称，移除结尾的省略号及多余空格，方便比对
+    """
+    if not name: return ""
+    name = name.strip()
+    # 移除结尾的 . (通常是截断标识)
+    while name.endswith("."):
+        name = name[:-1]
+    return name.strip()
+
+
+def get_file_md5(path):
+    """计算文件的 MD5 值"""
+    if not os.path.exists(path):
+        return ""
+    hash_md5 = hashlib.md5()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+
 def crop_cards_from_image(img_path, output_dir=None, save_debug=True):
     """
     从图片中裁剪场站卡片