51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
import paddle.inference as paddle_infer
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import numpy as np
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import cv2
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class Person_model_infer:
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def __init__(self, model_dir="./model", target_size=[640, 640]):
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# 初始化 paddle 推理
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self.model_dir = model_dir
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self.config = paddle_infer.Config(model_dir + "/model.pdmodel", model_dir + "/model.pdiparams")
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self.config.disable_glog_info()
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self.config.enable_use_gpu(200, 0)
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self.predictor = paddle_infer.create_predictor(self.config)
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self.input_names = self.predictor.get_input_names()
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self.input_handle = self.predictor.get_input_handle(self.input_names[0])
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self.input_handle1 = self.predictor.get_input_handle(self.input_names[1])
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self.output_names = self.predictor.get_output_names()
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self.output_handle = self.predictor.get_output_handle(self.output_names[0])
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self.target_size = target_size
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origin_shape = (240, 320)
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resize_h, resize_w = self.target_size
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self.im_scale_y = resize_h / float(origin_shape[0])
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self.im_scale_x = resize_w / float(origin_shape[1])
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self.scale_info = np.array([[self.im_scale_y, self.im_scale_x]]).astype('float32')
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def infer(self,src) -> np.ndarray:
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image = self.preprocess(src)
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self.input_handle.copy_from_cpu(image)
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self.input_handle1.copy_from_cpu(self.scale_info)
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self.predictor.run()
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results = self.output_handle.copy_to_cpu()
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return results
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def preprocess(self, src):
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# resize
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# keep_ratio=0
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img = cv2.resize(
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src,
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None,
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None,
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fx=self.im_scale_x,
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fy=self.im_scale_y,
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interpolation=2)
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# Permute
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img = img.astype(np.float32, copy=False)
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img = img.transpose((2, 0, 1))
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img = np.array((img, ))
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return img
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if __name__ == "__main__":
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predictor = Person_model_infer()
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# import time
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# while True:
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# time.sleep(1)
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# print('123') |