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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--det_model", required=True, help="Path of Detection model of PPOCR."
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)
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parser.add_argument(
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"--cls_model", required=True, help="Path of Classification model of PPOCR."
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)
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parser.add_argument(
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"--rec_model", required=True, help="Path of Recognization model of PPOCR."
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)
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parser.add_argument(
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"--rec_label_file", required=True, help="Path of Recognization label of PPOCR."
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)
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file."
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)
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return parser.parse_args()
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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runtime_option.use_sophgo()
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# Detection模型, 检测文字框
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det_model_file = args.det_model
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det_params_file = ""
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# Classification模型，方向分类，可选
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cls_model_file = args.cls_model
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cls_params_file = ""
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# Recognition模型，文字识别模型
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rec_model_file = args.rec_model
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rec_params_file = ""
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rec_label_file = args.rec_label_file
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# PPOCR的cls和rec模型现在已经支持推理一个Batch的数据
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# 定义下面两个变量后, 可用于设置trt输入shape, 并在PPOCR模型初始化后, 完成Batch推理设置
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cls_batch_size = 1
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rec_batch_size = 1
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# 当使用TRT时，分别给三个模型的runtime设置动态shape,并完成模型的创建.
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# 注意: 需要在检测模型创建完成后，再设置分类模型的动态输入并创建分类模型, 识别模型同理.
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# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
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det_option = runtime_option
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det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640], [1, 3, 960, 960])
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# 用户可以把TRT引擎文件保存至本地
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# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
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det_model = fd.vision.ocr.DBDetector(
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det_model_file,
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det_params_file,
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runtime_option=det_option,
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model_format=fd.ModelFormat.SOPHGO,
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)
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cls_option = runtime_option
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cls_option.set_trt_input_shape(
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"x", [1, 3, 48, 10], [cls_batch_size, 3, 48, 320], [cls_batch_size, 3, 48, 1024]
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)
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# 用户可以把TRT引擎文件保存至本地
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# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
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cls_model = fd.vision.ocr.Classifier(
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cls_model_file,
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cls_params_file,
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runtime_option=cls_option,
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model_format=fd.ModelFormat.SOPHGO,
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)
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rec_option = runtime_option
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rec_option.set_trt_input_shape(
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"x", [1, 3, 48, 10], [rec_batch_size, 3, 48, 320], [rec_batch_size, 3, 48, 2304]
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)
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# 用户可以把TRT引擎文件保存至本地
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# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
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rec_model = fd.vision.ocr.Recognizer(
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rec_model_file,
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rec_params_file,
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rec_label_file,
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runtime_option=rec_option,
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model_format=fd.ModelFormat.SOPHGO,
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)
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# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
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ppocr_v3 = fd.vision.ocr.PPOCRv3(
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det_model=det_model, cls_model=cls_model, rec_model=rec_model
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)
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# 需要使用下行代码, 来启用rec模型的静态shape推理，这里rec模型的静态输入为[3, 48, 584]
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rec_model.preprocessor.static_shape_infer = True
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rec_model.preprocessor.rec_image_shape = [3, 48, 584]
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# 给cls和rec模型设置推理时的batch size
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# 此值能为-1, 和1到正无穷
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# 当此值为-1时, cls和rec模型的batch size将默认和det模型检测出的框的数量相同
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ppocr_v3.cls_batch_size = cls_batch_size
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ppocr_v3.rec_batch_size = rec_batch_size
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# 预测图片准备
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im = cv2.imread(args.image)
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# 预测并打印结果
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result = ppocr_v3.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_ppocr(im, result)
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cv2.imwrite("sophgo_result.jpg", vis_im)
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print("Visualized result save in ./sophgo_result.jpg")
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