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98 lines
3.2 KiB
98 lines
3.2 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 logging |
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import os |
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import numpy as np |
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from PIL import Image |
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import paddle |
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import paddle.vision.transforms as T |
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from paddle.io import Dataset |
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import cv2 |
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from .builder import DATASETS |
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logger = logging.getLogger(__name__) |
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def data_transform(crop_size): |
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transform_list = [T.RandomCrop(crop_size)] |
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return T.Compose(transform_list) |
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@DATASETS.register() |
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class LapStyleDataset(Dataset): |
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""" |
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coco2017 dataset for LapStyle model |
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""" |
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def __init__(self, content_root, style_root, load_size, crop_size): |
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super(LapStyleDataset, self).__init__() |
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self.content_root = content_root |
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self.paths = os.listdir(self.content_root) |
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self.style_root = style_root |
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self.load_size = load_size |
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self.crop_size = crop_size |
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self.transform = data_transform(self.crop_size) |
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def __getitem__(self, index): |
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"""Get training sample |
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return: |
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ci: content image with shape [C,W,H], |
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si: style image with shape [C,W,H], |
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ci_path: str |
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""" |
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path = self.paths[index] |
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content_img = cv2.imread(os.path.join(self.content_root, path)) |
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if content_img.ndim == 2: |
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content_img = cv2.cvtColor(content_img, cv2.COLOR_GRAY2RGB) |
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else: |
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content_img = cv2.cvtColor(content_img, cv2.COLOR_BGR2RGB) |
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content_img = Image.fromarray(content_img) |
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content_img = content_img.resize((self.load_size, self.load_size), |
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Image.BILINEAR) |
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content_img = np.array(content_img) |
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style_img = cv2.imread(self.style_root) |
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style_img = cv2.cvtColor(style_img, cv2.COLOR_BGR2RGB) |
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style_img = Image.fromarray(style_img) |
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style_img = style_img.resize((self.load_size, self.load_size), |
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Image.BILINEAR) |
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style_img = np.array(style_img) |
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content_img = self.transform(content_img) |
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style_img = self.transform(style_img) |
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content_img = self.img(content_img) |
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style_img = self.img(style_img) |
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return {'ci': content_img, 'si': style_img, 'ci_path': path} |
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def img(self, img): |
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"""make image with [0,255] and HWC to [0,1] and CHW |
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return: |
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img: image with shape [3,W,H] and value [0, 1]. |
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""" |
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# [0,255] to [0,1] |
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img = img.astype(np.float32) / 255. |
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# some images have 4 channels |
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if img.shape[2] > 3: |
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img = img[:, :, :3] |
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# HWC to CHW |
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img = np.transpose(img, (2, 0, 1)).astype('float32') |
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return img |
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def __len__(self): |
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return len(self.paths) |
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def name(self): |
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return 'LapStyleDataset'
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