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			44 lines
		
	
	
		
			1.6 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			44 lines
		
	
	
		
			1.6 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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| #
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| # Licensed under the Apache License, Version 2.0 (the "License");
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| # you may not use this file except in compliance with the License.
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| # You may obtain a copy of the License at
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| #
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| #     http://www.apache.org/licenses/LICENSE-2.0
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| #
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| # Unless required by applicable law or agreed to in writing, software
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| # distributed under the License is distributed on an "AS IS" BASIS,
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| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| # See the License for the specific language governing permissions and
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| # limitations under the License.
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| 
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| import fastdeploy as fd
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| import cv2
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| import os
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| import numpy as np
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| 
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| 
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| def test_facealignment_pipnet():
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|     model_url = "https://bj.bcebos.com/paddlehub/fastdeploy/FaceLandmark1000.onnx"
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|     input_url = "https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png"
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|     output_url = "https://bj.bcebos.com/paddlehub/fastdeploy/tests/facelandmark1000_result_landmarks.npy"
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|     fd.download(model_url, ".")
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|     fd.download(input_url, ".")
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|     fd.download(output_url, ".")
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|     model_path = "FaceLandmark1000.onnx"
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|     # use ORT
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|     runtime_option = fd.RuntimeOption()
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|     runtime_option.use_ort_backend()
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|     model = fd.vision.facealign.FaceLandmark1000(
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|         model_path, runtime_option=runtime_option)
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| 
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|     # compare diff
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|     im = cv2.imread("./facealign_input.png")
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|     result = model.predict(im.copy())
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|     expect = np.load("./facelandmark1000_result_landmarks.npy")
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| 
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|     diff = np.fabs(np.array(result.landmarks) - expect)
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|     thres = 1e-04
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|     assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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|         diff.max(), thres)
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