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https://github.com/PaddlePaddle/FastDeploy.git
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c6480de736
* bind success * bind success fix * FDMat pybind, ResizeByShort pybind * FDMat pybind, ResizeByShort pybind, remove initialized_ * override BindProcessorManager::Run in python is available * PyProcessorManager done * vision_pybind fix * manager.py fix * add tutorials * remove Apply() bind * remove Apply() bind and fix * fix reviewed problem * fix reviewed problem * fix reviewed problem readme * fix reviewed problem readme etc * apply return outputs * nits * update readme * fix FDMatbatch * add op pybind: CenterCrop, Pad * add op overload for pass FDMatBatch --------- Co-authored-by: Wang Xinyu <shaywxy@gmail.com>
90 lines
2.3 KiB
Python
90 lines
2.3 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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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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hw = [500, 500]
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self.resize_op = fd.C.vision.processors.ResizeByShort(100, 1, True, hw)
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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.normalize_permute_op = fd.C.vision.processors.NormalizeAndPermute(
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mean, std, is_scale, min, max, swap_rb)
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width = 50
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height = 50
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self.centercrop_op = fd.C.vision.processors.CenterCrop(width, height)
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top = 5
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bottom = 5
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left = 5
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right = 5
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pad_value = [225, 225, 225]
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self.pad_op = fd.C.vision.processors.Pad(top, bottom, left, right,
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pad_value)
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def apply(self, image_batch):
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outputs = []
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self.resize_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.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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