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