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# ------------------------------------------------------------------------
# Copyright (c) 2023-present, BAAI. All Rights Reserved.
#
# 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.
# ------------------------------------------------------------------------
# pyre-unsafe
"""Image utilities."""
import numpy as np
import PIL.Image
import torch
def im_resize(img, size=None, scale=None, mode="linear"):
"""Resize image by the scale or size."""
if size is None:
if not isinstance(scale, (tuple, list)):
scale = (scale, scale)
h, w = img.shape[:2]
size = int(h * scale[0] + 0.5), int(w * scale[1] + 0.5)
else:
if not isinstance(size, (tuple, list)):
size = (size, size)
resize_modes = {"linear": PIL.Image.BILINEAR}
from torchvision.transforms import ToPILImage
to_pil = ToPILImage()
img = to_pil(img.to(torch.float32).cpu())
# img = PIL.Image.fromarray(img)
return np.array(img.resize(size[::-1], resize_modes[mode]))
def im_rescale(img, scales, max_size=0):
"""Rescale image to match the detecting scales."""
im_shape = img.shape
img_list, img_scales = [], []
size_min = np.min(im_shape[:2])
size_max = np.max(im_shape[:2])
for target_size in scales:
im_scale = float(target_size) / float(size_min)
target_size_max = max_size if max_size > 0 else target_size
if np.round(im_scale * size_max) > target_size_max:
im_scale = float(target_size_max) / float(size_max)
img_list.append(im_resize(img, scale=im_scale))
img_scales.append((im_scale, im_scale))
return img_list, img_scales
def im_vstack(arrays, fill_value=None, dtype=None, size=None, align=None):
"""Stack image arrays in sequence vertically."""
if fill_value is None:
return np.vstack(arrays)
# Compute the max stack shape.
max_shape = np.max(np.stack([arr.shape for arr in arrays]), 0)
if size is not None and min(size) > 0:
max_shape[: len(size)] = size
if align is not None and min(align) > 0:
align_size = np.ceil(max_shape[: len(align)] / align)
max_shape[: len(align)] = align_size.astype("int64") * align
# Fill output with the given value.
output_dtype = dtype or arrays[0].dtype
output_shape = [len(arrays)] + list(max_shape)
output = np.empty(output_shape, output_dtype)
output[:] = fill_value
# Copy arrays.
for i, arr in enumerate(arrays):
copy_slices = (slice(0, d) for d in arr.shape)
output[(i,) + tuple(copy_slices)] = arr
return output
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