Spaces:
Runtime error
Runtime error
File size: 5,507 Bytes
24f9881 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Shariq Farooq Bhat
import numpy as np
from dataclasses import dataclass
from typing import Tuple, List
# dataclass to store the crop parameters
@dataclass
class CropParams:
top: int
bottom: int
left: int
right: int
def get_border_params(rgb_image, tolerance=0.1, cut_off=20, value=0, level_diff_threshold=5, channel_axis=-1, min_border=5) -> CropParams:
gray_image = np.mean(rgb_image, axis=channel_axis)
h, w = gray_image.shape
def num_value_pixels(arr):
return np.sum(np.abs(arr - value) < level_diff_threshold)
def is_above_tolerance(arr, total_pixels):
return (num_value_pixels(arr) / total_pixels) > tolerance
# Crop top border until number of value pixels become below tolerance
top = min_border
while is_above_tolerance(gray_image[top, :], w) and top < h-1:
top += 1
if top > cut_off:
break
# Crop bottom border until number of value pixels become below tolerance
bottom = h - min_border
while is_above_tolerance(gray_image[bottom, :], w) and bottom > 0:
bottom -= 1
if h - bottom > cut_off:
break
# Crop left border until number of value pixels become below tolerance
left = min_border
while is_above_tolerance(gray_image[:, left], h) and left < w-1:
left += 1
if left > cut_off:
break
# Crop right border until number of value pixels become below tolerance
right = w - min_border
while is_above_tolerance(gray_image[:, right], h) and right > 0:
right -= 1
if w - right > cut_off:
break
return CropParams(top, bottom, left, right)
def get_white_border(rgb_image, value=255, **kwargs) -> CropParams:
"""Crops the white border of the RGB.
Args:
rgb: RGB image, shape (H, W, 3).
Returns:
Crop parameters.
"""
if value == 255:
# assert range of values in rgb image is [0, 255]
assert np.max(rgb_image) <= 255 and np.min(rgb_image) >= 0, "RGB image values are not in range [0, 255]."
assert rgb_image.max() > 1, "RGB image values are not in range [0, 255]."
elif value == 1:
# assert range of values in rgb image is [0, 1]
assert np.max(rgb_image) <= 1 and np.min(rgb_image) >= 0, "RGB image values are not in range [0, 1]."
return get_border_params(rgb_image, value=value, **kwargs)
def get_black_border(rgb_image, **kwargs) -> CropParams:
"""Crops the black border of the RGB.
Args:
rgb: RGB image, shape (H, W, 3).
Returns:
Crop parameters.
"""
return get_border_params(rgb_image, value=0, **kwargs)
def crop_image(image: np.ndarray, crop_params: CropParams) -> np.ndarray:
"""Crops the image according to the crop parameters.
Args:
image: RGB or depth image, shape (H, W, 3) or (H, W).
crop_params: Crop parameters.
Returns:
Cropped image.
"""
return image[crop_params.top:crop_params.bottom, crop_params.left:crop_params.right]
def crop_images(*images: np.ndarray, crop_params: CropParams) -> Tuple[np.ndarray]:
"""Crops the images according to the crop parameters.
Args:
images: RGB or depth images, shape (H, W, 3) or (H, W).
crop_params: Crop parameters.
Returns:
Cropped images.
"""
return tuple(crop_image(image, crop_params) for image in images)
def crop_black_or_white_border(rgb_image, *other_images: np.ndarray, tolerance=0.1, cut_off=20, level_diff_threshold=5) -> Tuple[np.ndarray]:
"""Crops the white and black border of the RGB and depth images.
Args:
rgb: RGB image, shape (H, W, 3). This image is used to determine the border.
other_images: The other images to crop according to the border of the RGB image.
Returns:
Cropped RGB and other images.
"""
# crop black border
crop_params = get_black_border(rgb_image, tolerance=tolerance, cut_off=cut_off, level_diff_threshold=level_diff_threshold)
cropped_images = crop_images(rgb_image, *other_images, crop_params=crop_params)
# crop white border
crop_params = get_white_border(cropped_images[0], tolerance=tolerance, cut_off=cut_off, level_diff_threshold=level_diff_threshold)
cropped_images = crop_images(*cropped_images, crop_params=crop_params)
return cropped_images
|