66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
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# 👉 demo1.py ：演示OCR基础功能
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# demo2.py ：演示可视化接口
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# demo3.py ：演示OCR文段后处理（段落合并）接口
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from PPOCR_api import GetOcrApi
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import os
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# 测试图片路径
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TestImagePath = os.path.join(os.path.dirname(os.path.abspath(__file__)), "test.jpg")
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# 初始化识别器对象，传入 PaddleOCR-json 引擎路径。
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# 引擎下载地址： https://github.com/hiroi-sora/PaddleOCR-json/releases
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# Windows： 传入 PaddleOCR-json.exe 的路径。
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# Linux： 传入 run.sh 的路径
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ocr = GetOcrApi(r"Your Path/PaddleOCR-json.exe")
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if ocr.getRunningMode() == "local":
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print(f"初始化OCR成功，进程号为{ocr.ret.pid}")
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elif ocr.getRunningMode() == "remote":
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print(f"连接远程OCR引擎成功，ip：{ocr.ip}，port：{ocr.port}")
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print(f"\n测试图片路径：{TestImagePath}")
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# 示例1：识别本地图片
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res = ocr.run(TestImagePath)
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print(f"\n示例1-图片路径识别结果（原始信息）：\n{res}")
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print(f"\n示例1-图片路径识别结果（格式化输出）：")
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ocr.printResult(res)
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# 示例2：识别图片字节流
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with open(TestImagePath, "rb") as f: # 获取图片字节流
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# 实际使用中，可以联网下载或者截图获取字节流，直接送入OCR，无需保存到本地中转。
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imageBytes = f.read()
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res = ocr.runBytes(imageBytes)
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print(f"\n示例2-字节流识别结果：")
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ocr.printResult(res)
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# 示例3：识别 PIL Image 对象
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try:
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from PIL import Image
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from io import BytesIO
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except Exception:
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print("安装Pillow库后方可测试示例3。")
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Image = None
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if Image:
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# 创建一个PIL Image对象
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pilImage = Image.open(TestImagePath)
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# Image 对象转为 字节流
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buffered = BytesIO()
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pilImage.save(buffered, format="PNG")
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imageBytes = buffered.getvalue()
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# 送入OCR
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res = ocr.runBytes(imageBytes)
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print(f"\n示例3-PIL Image 识别结果：")
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ocr.printResult(res)
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# 以下示例默认禁用
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# 示例4：识别剪贴板图片
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if ocr.isClipboardEnabled():
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res = ocr.runClipboard()
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if res["code"] == 212:
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print(f"\n示例4-当前剪贴板中没有图片。")
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else:
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print(f"\n示例4-剪贴板识别结果：")
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ocr.printResult(res)
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