wehub-resource-sync
79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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#
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import os
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import random
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from io import BytesIO
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import numpy as np
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import requests
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import torch
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from PIL import Image
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def preprocess_image(image):
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"""
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image: torch.Tensor
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"""
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h))
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image).contiguous()
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return 2.0 * image - 1.0
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def prepare_mask_and_masked_image(image, mask):
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"""
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image: PIL.Image.Image
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mask: PIL.Image.Image
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"""
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if isinstance(image, Image.Image):
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image = np.array(image.convert("RGB"))
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image).to(dtype=torch.float32).contiguous() / 127.5 - 1.0
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if isinstance(mask, Image.Image):
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mask = np.array(mask.convert("L"))
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mask = mask.astype(np.float32) / 255.0
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mask = mask[None, None]
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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mask = torch.from_numpy(mask).to(dtype=torch.float32).contiguous()
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masked_image = image * (mask < 0.5)
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return mask, masked_image
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def download_image(url):
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response = requests.get(url)
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return Image.open(BytesIO(response.content)).convert("RGB")
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def save_image(images, image_path_dir, image_name_prefix, image_name_suffix):
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"""
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Save the generated images to png files.
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"""
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for i in range(images.shape[0]):
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image_path = os.path.join(
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image_path_dir,
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f"{image_name_prefix}{i + 1}-{random.randint(1000, 9999)}-{image_name_suffix}.png",
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)
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print(f"Saving image {i+1} / {images.shape[0]} to: {image_path}")
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Image.fromarray(images[i]).save(image_path)
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