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99 lines
3.2 KiB
99 lines
3.2 KiB
2 years ago
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# 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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