'commit'

Tools/Test_EasyOCR_Price.py

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+# coding=utf-8
+# pip install easyocr
+# python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 --force-reinstall
+import os
+import sys
+import cv2
+import numpy as np
+# 设置项目根目录
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if project_root not in sys.path:
+    sys.path.append(project_root)
+
+from Util.EasyOcrKit import get_easyocr_reader
+
+def test_easyocr_price():
+    # 待测试的图片路径
+    test_image_path = os.path.join(project_root, "Output", "Screenshot_20260115_083521.jpg")
+    output_image_path = os.path.join(project_root, "Output", "Test_EasyOCR_Result.jpg")
+    
+    print(f"--- 开始测试 EasyOCR 识别 ---")
+    print(f"输入图片: {test_image_path}")
+    
+    if not os.path.exists(test_image_path):
+        print(f"错误: 测试图片不存在！")
+        return
+
+    # 1. 初始化 OCR (使用封装类)
+    reader = get_easyocr_reader(gpu=True)
+    
+    # 2. 读取图片
+    img = cv2.imread(test_image_path)
+    if img is None:
+        print("错误: 无法读取图片。")
+        return
+        
+    # 3. 识别
+    target = '全部时段'
+    found = reader.find_text_position(img, target)
+    
+    h, w = img.shape[:2]
+    
+    if found:
+        text, quad, prob = found
+        pts = np.array(quad).astype(int)
+        cv2.polylines(img, [pts], isClosed=True, color=(0, 255, 0), thickness=2)
+        
+        # 使用封装后的方法获取归一化中心点 (演示 get_normalized_rect)
+        rect = reader.get_normalized_rect(quad, w, h)
+        norm_x = (rect[0] + rect[2]) // 2
+        norm_y = (rect[1] + rect[3]) // 2
+        
+        print(f'找到“{text}” (目标: {target}) 置信度={prob:.2f}')
+        print(f'归一化坐标: [{norm_x}, {norm_y}]')
+        
+        cv2.putText(img, f"OCR Found: {text}", (pts[0][0], pts[0][1] - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+    else:
+        print(f"未能在图片中找到包含“{target}”的文本。")
+        # 打印出所有识别到的文本，方便调试
+        print("识别到的所有文本:")
+        results = reader.read_text(img)
+        for (_, text, _) in results:
+            print(f" - {text}")
+
+    # 4. 保存结果图
+    cv2.imwrite(output_image_path, img)
+    print(f"--- 测试完成 ---")
+    print(f"结果已保存至: {output_image_path}")
+
+if __name__ == "__main__":
+    test_easyocr_price()

Tools/Test_PriceBox_Detection.py

@@ -0,0 +1,67 @@
+# coding=utf-8
+import os
+import sys
+import cv2
+import numpy as np
+
+# 设置项目根目录
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if project_root not in sys.path:
+    sys.path.append(project_root)
+
+from Apps.TeLaiDian.Kit import detect_price_info_container_cv, read_image, save_image
+
+def test_price_box_detection():
+    # 待测试的图片路径
+    test_image_path = os.path.join(project_root, "Output", "Screenshot_20260115_083521.jpg")
+    output_image_path = os.path.join(project_root, "Output", "Test_PriceBox_Result_V2.jpg")
+    
+    print(f"--- 开始测试价格容器识别 ---")
+    print(f"输入图片: {test_image_path}")
+    
+    if not os.path.exists(test_image_path):
+        print(f"错误: 测试图片不存在！")
+        return
+
+    # 1. 调用刚才实现的 CV 识别函数
+    container_norm = detect_price_info_container_cv(test_image_path)
+    
+    if not container_norm:
+        print("识别失败: 未能检测到‘价格信息’容器矩形。")
+        return
+
+    print(f"识别成功! 归一化坐标: {container_norm}")
+    
+    # 2. 读取图片并绘制绿框进行可视化验证
+    img = read_image(test_image_path)
+    if img is None:
+        print("错误: 无法读取图片。")
+        return
+        
+    h, w = img.shape[:2]
+    x1 = int(container_norm[0] * w / 1000)
+    y1 = int(container_norm[1] * h / 1000)
+    x2 = int(container_norm[2] * w / 1000)
+    y2 = int(container_norm[3] * h / 1000)
+    
+    # 绘制外框 (绿框)
+    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 4)
+    
+    # 绘制中间分割线 (区分左侧当前价和右侧会员价)
+    mid_x = x1 + (x2 - x1) // 2
+    cv2.line(img, (mid_x, y1), (mid_x, y2), (0, 255, 0), 2)
+    
+    # 加上文字标注
+    cv2.putText(img, "Target Area (Left Half)", (x1 + 10, y1 + 40), 
+                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+
+    # 3. 保存结果图
+    if save_image(output_image_path, img):
+        print(f"--- 测试完成 ---")
+        print(f"结果已保存至: {output_image_path}")
+        print(f"请检查该图片中的绿框是否准确圈定了‘价格信息’板块及其左侧区域。")
+    else:
+        print("错误: 结果图保存失败。")
+
+if __name__ == "__main__":
+    test_price_box_detection()