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https://github.com/PaddlePaddle/FastDeploy.git
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ab38c9110f
* add Cast, HWC2CHW, Normalize, PadToSize, StridePad * add comments * fix comments * fix manager.cc
103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
import fastdeploy as fd
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import cv2
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from fastdeploy.vision.common.manager import PyProcessorManager
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from fastdeploy.vision.common.processors import *
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def parse_arguments():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--use_cvcuda",
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required=False,
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type=bool,
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help="Use CV-CUDA in preprocess")
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return parser.parse_args()
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# define CustomProcessor
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class CustomProcessor(PyProcessorManager):
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def __init__(self) -> None:
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super().__init__()
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# create op
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self.resize_short_op = ResizeByShort(
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target_size=100, interp=1, use_scale=True, max_hw=[500, 500])
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self.normalize_permute_op = NormalizeAndPermute(
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225],
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is_scale=True,
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min=[],
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max=[],
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swap_rb=False)
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self.centercrop_op = CenterCrop(width=50, height=50)
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self.pad_op = Pad(top=5,
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bottom=5,
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left=5,
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right=5,
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value=[225, 225, 225])
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self.cast_op = Cast(dtype="float")
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self.hwc2chw_op = HWC2CHW()
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self.normalize_op = Normalize(
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225],
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is_scale=True,
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min=[],
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max=[],
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swap_rb=False)
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self.pad_to_size_op = PadToSize(
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height=160, width=160, value=[225, 225, 225])
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self.resize_op = Resize(
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width=50,
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height=50,
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scale_w=-1.0,
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scale_h=-1.0,
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interp=1,
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use_scale=False)
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self.stride_pad_op = StridePad(stride=3, value=[225, 225, 225])
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def apply(self, image_batch):
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outputs = []
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self.resize_short_op(image_batch)
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self.centercrop_op(image_batch)
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self.pad_op(image_batch)
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self.pad_to_size_op(image_batch)
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self.normalize_permute_op(image_batch)
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for i in range(len(image_batch.mats)):
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outputs.append(image_batch.mats[i])
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return outputs
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if __name__ == "__main__":
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# read jpg
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im1 = cv2.imread('ILSVRC2012_val_00000010.jpeg')
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im2 = cv2.imread('ILSVRC2012_val_00000010.jpeg')
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mat1 = fd.C.vision.FDMat()
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mat1.from_numpy(im1)
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mat2 = fd.C.vision.FDMat()
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mat2.from_numpy(im2)
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images = [mat1, mat2]
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args = parse_arguments()
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# creae processor
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preprocessor = CustomProcessor()
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# use CV-CUDA
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if args.use_cvcuda:
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preprocessor.use_cuda(True, -1)
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# show input
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for i in range(len(images)):
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images[i].print_info('images' + str(i) + ': ')
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# run the Processer with CVCUDA
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outputs = preprocessor(images)
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# show output
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for i in range(len(outputs)):
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outputs[i].print_info('outputs' + str(i) + ': ')
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