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76 lines
2.9 KiB
76 lines
2.9 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. |
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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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import paddle |
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import paddle.nn as nn |
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import math |
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import cv2 |
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import numpy as np |
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from ppgan.utils.download import get_path_from_url |
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from ppgan.models.generators import GPEN |
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from ppgan.faceutils.face_detection.detection.blazeface.utils import * |
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GPEN_weights = 'https://paddlegan.bj.bcebos.com/models/GPEN-512.pdparams' |
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class FaceEnhancement(object): |
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def __init__(self, path_to_enhance=None, size=512, batch_size=1): |
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super(FaceEnhancement, self).__init__() |
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# Initialise the face detector |
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if path_to_enhance is None: |
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model_weights_path = get_path_from_url(GPEN_weights) |
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model_weights = paddle.load(model_weights_path) |
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else: |
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model_weights = paddle.load(path_to_enhance) |
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self.face_enhance = GPEN(size=512, style_dim=512, n_mlp=8) |
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self.face_enhance.load_dict(model_weights) |
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self.face_enhance.eval() |
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self.size = size |
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self.mask = np.zeros((512, 512), np.float32) |
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cv2.rectangle(self.mask, (26, 26), (486, 486), (1, 1, 1), -1, |
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cv2.LINE_AA) |
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self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11) |
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self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11) |
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self.mask = paddle.tile( |
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paddle.to_tensor(self.mask).unsqueeze(0).unsqueeze(-1), |
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repeat_times=[batch_size, 1, 1, 3]).numpy() |
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def enhance_from_image(self, img): |
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if isinstance(img, np.ndarray): |
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img, _ = resize_and_crop_image(img, 512) |
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img = paddle.to_tensor(img).transpose([2, 0, 1]) |
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else: |
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assert img.shape == [3, 512, 512] |
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return self.enhance_from_batch(img.unsqueeze(0))[0] |
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def enhance_from_batch(self, img): |
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if isinstance(img, np.ndarray): |
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img_ori, _ = resize_and_crop_batch(img, 512) |
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img = paddle.to_tensor(img_ori).transpose([0, 3, 1, 2]) |
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else: |
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assert img.shape[1:] == [3, 512, 512] |
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img_ori = img.transpose([0, 2, 3, 1]).numpy() |
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img_t = (img / 255. - 0.5) / 0.5 |
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with paddle.no_grad(): |
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out, __ = self.face_enhance(img_t) |
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image_tensor = out * 0.5 + 0.5 |
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image_tensor = image_tensor.transpose([0, 2, 3, 1]) # RGB |
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image_numpy = paddle.clip(image_tensor, 0, 1) * 255.0 |
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out = image_numpy.astype(np.uint8).cpu().numpy() |
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return out * self.mask + (1 - self.mask) * img_ori
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