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from cv2box import CVImage, MyFpsCounter |
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from model_lib import ModelBase |
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import numpy as np |
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import cv2 |
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MODEL_ZOO = { |
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'xseg_0611': { |
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'model_path': './pretrain_models/xseg_230611_16_17.onnx', |
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'input_dynamic_shape': [[1, 256, 256, 3]] |
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}, |
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} |
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class XSEG(ModelBase): |
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def __init__(self, model_type='xseg_0611', provider='cpu'): |
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super().__init__(MODEL_ZOO[model_type], provider) |
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self.model_type = model_type |
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def forward(self, face_image): |
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""" |
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Args: |
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face_image: cv2 image -1~1 RGB |
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Returns: |
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RGB 256x256x3 -1~1 |
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""" |
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face_image = (face_image + 1) / 2 |
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if face_image.shape[-1] >= 4: |
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if len(face_image.shape)>3: |
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face_image = face_image[0] |
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face_image = face_image.transpose(1, 2, 0) |
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face_image_h, face_image_w, _ = face_image.shape |
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if face_image_h != 256: |
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face_image = cv2.resize(face_image, (256, 256)) |
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image_out = self.model.forward(face_image[...,::-1][None].astype(np.float32)) |
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output_face = (image_out[0].squeeze()).clip(0, 1) |
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if face_image_h != 256: |
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output_face = cv2.resize(output_face, (face_image_w, face_image_h)) |
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return output_face |
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if __name__ == '__main__': |
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face_img_p = 'data/source/ym-1.jpeg' |
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fa = XSEG(model_type='xseg_0611', provider='trt16') |
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face_img = (cv2.resize(cv2.imread(face_img_p)/127.5-1,(512,512)))[...,::-1] |
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with MyFpsCounter() as mfc: |
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for i in range(20): |
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face = fa.forward(face_img) |
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