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oneao committed 2025-06-11 21:33:35 +08:00
1 parent 6ab34e30b4
commit 3b70d0da3b
10 files changed
+106 -3214

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@@ -1,5 +1,5 @@
{ {
"input_path": "C:/Users/Administrator/.paddlex/predict_input/general_ocr_002.png", "input_path": "C:/Users/ONEAO/.paddlex/predict_input/general_ocr_002.png",
"page_index": null, "page_index": null,
"model_settings": { "model_settings": {
"use_doc_preprocessor": true, "use_doc_preprocessor": true,
@@ -691,39 +691,39 @@
"登机口于起飞前1O分钟关闭GATESCLOSE1OMINUTESBEFORE DEPARTURETIME" "登机口于起飞前1O分钟关闭GATESCLOSE1OMINUTESBEFORE DEPARTURETIME"
], ],
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+62 -62
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@@ -1271,68 +1271,68 @@
"fOrPACIFICINTERNATIONALLINES(PTE)LTDaSCarrier" "fOrPACIFICINTERNATIONALLINES(PTE)LTDaSCarrier"
], ],
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+10 -19
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@@ -1,21 +1,12 @@
# from paddleocr import PaddleOCR
# # 初始化 PaddleOCR 实例
# ocr = PaddleOCR(
# use_doc_orientation_classify=False,
# use_doc_unwarping=False,
# use_textline_orientation=False)
# # 对示例图像执行 OCR 推理
# result = ocr.predict(
# input="test1.png")
# # 可视化结果并保存 json 结果
# for res in result:
# res.print()
# res.save_to_img("output")
# res.save_to_json("output")
from paddleocr import PPChatOCRv4Doc from paddleocr import PPChatOCRv4Doc
import paddle
# 检查 PaddlePaddle 是否安装成功
# 打印是否支持 GPU
print("是否支持 GPU:", paddle.is_compiled_with_cuda())
# 如果支持,再看看当前是否在使用 GPU
if paddle.is_compiled_with_cuda():
print("当前是否使用 GPU:", paddle.device.get_device())
chat_bot_config = { chat_bot_config = {
"module_name": "chat_bot", "module_name": "chat_bot",
@@ -59,7 +50,7 @@ if use_mllm:
mllm_predict_res = pipeline.mllm_pred( mllm_predict_res = pipeline.mllm_pred(
input="test1.png", input="test1.png",
key_list=["Gross Weight"], key_list=["Shipper"],
mllm_chat_bot_config=mllm_chat_bot_config, mllm_chat_bot_config=mllm_chat_bot_config,
) )
mllm_predict_info = mllm_predict_res["mllm_res"] mllm_predict_info = mllm_predict_res["mllm_res"]
@@ -73,7 +64,7 @@ vector_info = pipeline.build_vector(
visual_info_list, flag_save_bytes_vector=True, retriever_config=retriever_config visual_info_list, flag_save_bytes_vector=True, retriever_config=retriever_config
) )
chat_result = pipeline.chat( chat_result = pipeline.chat(
key_list=["Gross Weight"], key_list=["Shipper"],
visual_info=visual_info_list, visual_info=visual_info_list,
vector_info=vector_info, vector_info=vector_info,
mllm_predict_info=mllm_predict_info, mllm_predict_info=mllm_predict_info,