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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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import random
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from .builder import DATASETS
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logger = logging.getLogger(__name__)
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def data_transform(img,
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resize_w,
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resize_h,
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load_size=286,
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pos=[0, 0, 256, 256],
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flip=True,
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is_image=True):
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if is_image:
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resized = img.resize((resize_w, resize_h), Image.BICUBIC)
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else:
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resized = img.resize((resize_w, resize_h), Image.NEAREST)
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croped = resized.crop((pos[0], pos[1], pos[2], pos[3]))
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fliped = ImageOps.mirror(croped) if flip else croped
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fliped = np.array(fliped) # transform to numpy array
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expanded = np.expand_dims(fliped, 2) if len(fliped.shape) < 3 else fliped
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transposed = np.transpose(expanded, (2, 0, 1)).astype('float32')
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if is_image:
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normalized = transposed / 255. * 2. - 1.
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else:
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normalized = transposed
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return normalized
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@DATASETS.register()
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class PhotoPenDataset(Dataset):
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def __init__(self, content_root, load_size, crop_size):
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super(PhotoPenDataset, self).__init__()
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inst_dir = os.path.join(content_root, 'train_inst')
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_, _, inst_list = next(os.walk(inst_dir))
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self.inst_list = np.sort(inst_list)
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self.content_root = content_root
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self.load_size = load_size
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self.crop_size = crop_size
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def __getitem__(self, idx):
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ins = Image.open(
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os.path.join(self.content_root, 'train_inst', self.inst_list[idx]))
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img = Image.open(
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os.path.join(self.content_root, 'train_img', self.inst_list[idx]
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.replace(".png", ".jpg")))
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img = img.convert('RGB')
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w, h = img.size
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resize_w, resize_h = 0, 0
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if w < h:
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resize_w, resize_h = self.load_size, int(h * self.load_size / w)
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else:
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resize_w, resize_h = int(w * self.load_size / h), self.load_size
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left = random.randint(0, resize_w - self.crop_size)
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top = random.randint(0, resize_h - self.crop_size)
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flip = False
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img = data_transform(
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img,
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resize_w,
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resize_h,
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load_size=self.load_size,
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pos=[left, top, left + self.crop_size, top + self.crop_size],
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flip=flip,
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is_image=True)
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ins = data_transform(
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ins,
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resize_w,
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resize_h,
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load_size=self.load_size,
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pos=[left, top, left + self.crop_size, top + self.crop_size],
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flip=flip,
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is_image=False)
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return {'img': img, 'ins': ins, 'img_path': self.inst_list[idx]}
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def __len__(self):
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return len(self.inst_list)
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def name(self):
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return 'PhotoPenDataset'
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@DATASETS.register()
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class PhotoPenDataset_test(Dataset):
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def __init__(self, content_root, load_size, crop_size):
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super(PhotoPenDataset_test, self).__init__()
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inst_dir = os.path.join(content_root, 'test_inst')
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_, _, inst_list = next(os.walk(inst_dir))
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self.inst_list = np.sort(inst_list)
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self.content_root = content_root
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self.load_size = load_size
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self.crop_size = crop_size
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def __getitem__(self, idx):
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ins = Image.open(
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os.path.join(self.content_root, 'test_inst', self.inst_list[idx]))
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w, h = ins.size
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resize_w, resize_h = 0, 0
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if w < h:
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resize_w, resize_h = self.load_size, int(h * self.load_size / w)
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else:
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resize_w, resize_h = int(w * self.load_size / h), self.load_size
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left = random.randint(0, resize_w - self.crop_size)
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top = random.randint(0, resize_h - self.crop_size)
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flip = False
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ins = data_transform(
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ins,
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resize_w,
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resize_h,
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load_size=self.load_size,
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pos=[left, top, left + self.crop_size, top + self.crop_size],
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flip=flip,
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is_image=False)
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return {'ins': ins, 'img_path': self.inst_list[idx]}
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def __len__(self):
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return len(self.inst_list)
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def name(self):
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return 'PhotoPenDataset'
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