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{
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{
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"input_path": "C:/Users/Administrator/.paddlex/predict_input/general_ocr_002.png",
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"input_path": "C:/Users/ONEAO/.paddlex/predict_input/general_ocr_002.png",
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"page_index": null,
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"page_index": null,
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"model_settings": {
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"model_settings": {
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"use_doc_preprocessor": true,
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"use_doc_preprocessor": true,
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@@ -691,39 +691,39 @@
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"登机口于起飞前1O分钟关闭GATESCLOSE1OMINUTESBEFORE DEPARTURETIME"
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"登机口于起飞前1O分钟关闭GATESCLOSE1OMINUTESBEFORE DEPARTURETIME"
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],
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],
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"rec_scores": [
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@@ -1271,68 +1271,68 @@
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"fOrPACIFICINTERNATIONALLINES(PTE)LTDaSCarrier"
|
"fOrPACIFICINTERNATIONALLINES(PTE)LTDaSCarrier"
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[
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[
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Before Width: | Height: | Size: 202 KiB |
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Before Width: | Height: | Size: 304 KiB |
@@ -1,21 +1,12 @@
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# from paddleocr import PaddleOCR
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# # 初始化 PaddleOCR 实例
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# ocr = PaddleOCR(
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# use_doc_orientation_classify=False,
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# use_doc_unwarping=False,
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# use_textline_orientation=False)
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# # 对示例图像执行 OCR 推理
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# result = ocr.predict(
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# input="test1.png")
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# # 可视化结果并保存 json 结果
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# for res in result:
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# res.print()
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# res.save_to_img("output")
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# res.save_to_json("output")
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from paddleocr import PPChatOCRv4Doc
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from paddleocr import PPChatOCRv4Doc
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import paddle
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# 检查 PaddlePaddle 是否安装成功
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# 打印是否支持 GPU
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print("是否支持 GPU:", paddle.is_compiled_with_cuda())
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# 如果支持,再看看当前是否在使用 GPU
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if paddle.is_compiled_with_cuda():
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print("当前是否使用 GPU:", paddle.device.get_device())
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chat_bot_config = {
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chat_bot_config = {
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"module_name": "chat_bot",
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"module_name": "chat_bot",
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@@ -59,7 +50,7 @@ if use_mllm:
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mllm_predict_res = pipeline.mllm_pred(
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mllm_predict_res = pipeline.mllm_pred(
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input="test1.png",
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input="test1.png",
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key_list=["Gross Weight"],
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key_list=["Shipper"],
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mllm_chat_bot_config=mllm_chat_bot_config,
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mllm_chat_bot_config=mllm_chat_bot_config,
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)
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)
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mllm_predict_info = mllm_predict_res["mllm_res"]
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mllm_predict_info = mllm_predict_res["mllm_res"]
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@@ -73,7 +64,7 @@ vector_info = pipeline.build_vector(
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visual_info_list, flag_save_bytes_vector=True, retriever_config=retriever_config
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visual_info_list, flag_save_bytes_vector=True, retriever_config=retriever_config
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)
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)
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chat_result = pipeline.chat(
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chat_result = pipeline.chat(
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key_list=["Gross Weight"],
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key_list=["Shipper"],
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visual_info=visual_info_list,
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visual_info=visual_info_list,
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vector_info=vector_info,
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vector_info=vector_info,
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mllm_predict_info=mllm_predict_info,
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mllm_predict_info=mllm_predict_info,
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