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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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'GFPGANv1.4': { |
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'model_path': './pretrain_models/gfpganv14_fp32_bs1_scale.onnx' |
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}, |
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'codeformer': { |
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'model_path':'./pretrain_models/codeformer_fp32_bs1_scale_adain.onnx' |
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}, |
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} |
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class GFPGAN(ModelBase): |
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def __init__(self, model_type='GFPGANv1.4', provider='gpu'): |
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super().__init__(MODEL_ZOO[model_type], provider) |
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self.model_type = model_type |
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self.input_std = self.input_mean = 127.5 |
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self.input_size = (512, 512) |
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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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face_image_h, face_image_w, _ = face_image.shape |
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if face_image_h != 512: |
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face_image = cv2.resize(face_image, (512, 512)) |
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face_image = np.uint8(face_image * 255.0) |
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image_in = CVImage(face_image).set_blob(self.input_std, self.input_mean, self.input_size).blob_in(rgb=False) |
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if 'codeformer' in self.model_type: |
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image_out = self.model.forward([image_in,np.array(1,dtype=np.float32)]) |
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else: |
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image_out = self.model.forward(image_in) |
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output_face = ((image_out[0][0] + 1) / 2).transpose(1, 2, 0).clip(0, 1) |
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if face_image_h != 512: |
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output_face = cv2.resize(output_face, (face_image_w, face_image_h)) |
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output_face = (output_face * 2 - 1.0) |
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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 = GFPGAN(model_type='GFPGANv1.4', provider='gpu') |
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with MyFpsCounter() as mfc: |
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for i in range(10): |
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face = fa.forward(face_img_p) |
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CVImage(face, image_format='cv2').show() |
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