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# Copyright (c) 2020 VisualDL Authors. All Rights Reserve. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# ======================================================================= | ||
import math | ||
from functools import reduce | ||
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import numpy as np | ||
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from visualdl.component.base_component import convert_to_HWC | ||
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def padding_image(img, height, width): | ||
height_old, width_old, _ = img.shape | ||
height_before = math.floor((height - height_old) / 2) | ||
height_after = height - height_old - height_before | ||
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width_before = math.floor((width - width_old) / 2) | ||
width_after = width - width_old - width_before | ||
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return np.pad(img, ((height_before, height_after), (width_before, width_after), (0, 0))) | ||
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def merge_images(imgs, dataformats, scale=1.0, rows=-1): | ||
assert rows <= len(imgs), "rows should not greater than numbers of pictures" | ||
channel = imgs[0].shape[2] | ||
# convert format of each image to `hwc` | ||
for i, img in enumerate(imgs): | ||
imgs[i] = convert_to_HWC(img, dataformats) | ||
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height = -1 | ||
width = -1 | ||
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for img in imgs: | ||
height = height if height > img.shape[0] else img.shape[0] | ||
width = width if width > img.shape[1] else img.shape[1] | ||
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# padding every sub-image with height and width | ||
for i, img in enumerate(imgs): | ||
imgs[i] = padding_image(img, height, width) | ||
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# get row and col | ||
len_imgs = len(imgs) | ||
if -1 == rows: | ||
rows = cols = math.floor(math.sqrt(len_imgs)) | ||
while rows*cols < len_imgs: | ||
if rows <= cols: | ||
rows += 1 | ||
else: | ||
cols += 1 | ||
else: | ||
cols = math.ceil(len_imgs/rows) | ||
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# add white sub-image | ||
for i in range(rows*cols-len_imgs): | ||
imgs = np.concatenate((imgs, np.zeros((height, width, channel), dtype=np.uint8)[None, :])) | ||
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imgs = reduce(lambda x, y: np.concatenate((x, y)), [ | ||
reduce(lambda x, y: np.concatenate((x, y), 1), | ||
imgs[i * cols: (i + 1) * cols]) for i in range(rows)]) | ||
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# choose bigger number of rows and cols | ||
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scale = 1.0/scale * rows if rows > cols else 1.0/scale * cols | ||
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dsize = tuple(map(lambda x: math.floor(x/scale), imgs.shape))[-2::-1] | ||
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try: | ||
import cv2 | ||
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imgs = cv2.resize(src=imgs, dsize=dsize) | ||
except ImportError: | ||
from PIL import Image | ||
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imgs = Image.fromarray(imgs) | ||
imgs.resize(dsize) | ||
imgs = np.array(imgs) | ||
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return imgs |
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