目录 前言 格子颜色表 渐变色带 前言 在做色彩相关的算法分析时候，经常需要使用规则的颜色表来进行辅助。下面用python（numpy和opencv）来生成颜色表并保存为图片。 有两种类型： 格
- 前言
- 格子颜色表
- 渐变色带
在做色彩相关的算法分析时候，经常需要使用规则的颜色表来进行辅助。下面用python（numpy和opencv）来生成颜色表并保存为图片。
有两种类型：
- 格子形状的颜色表
- 渐变色带
长的样子分别如下：
这里需要注意，当划分的颜色数量比较少时，最好把一个颜色像素扩展成为一个格子，不然的话整个图看起来就太小了。
# -*- coding: utf-8 -*-
import cv2
import numpy as np
def generate_color_chart(block_num=18,
block_columns=6,
grid_width=32,
grid_height=None):
"""
Generate color chart by uniformly distributed color indexes, only support
8 bit (uint8).
Parameters
----------
block_num: Block number of color chart, also the number of color indexes.
block_columns: Column number of color chart. Row number is computed by
block_num / block_columns
grid_width: Width of color grid
grid_height: Height of color grid. If not set, it will equal to grid_width.
"""
color_index = np.linspace(0, 255, block_num)
color_index = np.uint8(np.round(color_index))
if grid_height is None:
grid_height = grid_width
# compute sizes
block_rows = np.int_(np.ceil(block_num / block_columns))
block_width = grid_width * block_num
block_height = grid_height * block_num
width = block_width * block_columns
height = block_height * block_rows
result = np.zeros((height, width, 3), dtype=np.uint8)
# compute red-green block, (blue will be combined afterward)
red_block, green_block = np.meshgrid(color_index, color_index)
red_block = expand_pixel_to_grid(red_block, grid_width, grid_height)
green_block = expand_pixel_to_grid(green_block, grid_width, grid_height)
rg_block = np.concatenate([red_block, green_block], axis=2)
# combine blue channel
for i in range(block_num):
blue = np.ones_like(rg_block[..., 0], dtype=np.uint8) * color_index[i]
color_block = np.concatenate([rg_block, blue[..., np.newaxis]], axis=2)
# compute block index
block_row = i // block_columns
block_column = i % block_columns
xmin = block_column * block_width
ymin = block_row * block_height
xmax = xmin + block_width
ymax = ymin + block_height
result[ymin:ymax, xmin:xmax, :] = color_block
result = result[..., ::-1] # convert from rgb to bgr
return result
def expand_pixel_to_grid(matrix, grid_width, grid_height):
"""
Expand a pixel to a grid. Inside the grid, every pixel have the same value
as the source pixel.
Parameters
----------
matrix: 2D numpy array
grid_width: width of grid
grid_height: height of grid
"""
height, width = matrix.shape[:2]
new_heigt = height * grid_height
new_width = width * grid_width
repeat_num = grid_width * grid_height
matrix = np.expand_dims(matrix, axis=2).repeat(repeat_num, axis=2)
matrix = np.reshape(matrix, (height, width, grid_height, grid_width))
# put `height` and `grid_height` axes together;
# put `width` and `grid_width` axes together.
matrix = np.transpose(matrix, (0, 2, 1, 3))
matrix = np.reshape(matrix, (new_heigt, new_width, 1))
return matrix
if __name__ == '__main__':
color_chart16 = generate_color_chart(block_num=16,
grid_width=32,
block_columns=4)
color_chart18 = generate_color_chart(block_num=18,
grid_width=32,
block_columns=6)
color_chart36 = generate_color_chart(block_num=36,
grid_width=16,
block_columns=6)
color_chart52 = generate_color_chart(block_num=52,
grid_width=8,
block_columns=13)
color_chart256 = generate_color_chart(block_num=256,
grid_width=1,
block_columns=16)
cv2.imwrite('color_chart16.png', color_chart16)
cv2.imwrite('color_chart18.png', color_chart18)
cv2.imwrite('color_chart36.png', color_chart36)
cv2.imwrite('color_chart52.png', color_chart52)
cv2.imwrite('color_chart256.png', color_chart256)
渐变色带
# -*- coding: utf-8 -*-
import cv2
import numpy as np
def generate_color_band(left_colors, right_colors, grade=256, height=32):
"""
Generate color bands by uniformly changing from left colors to right
colors. Note that there might be multiple bands.
Parameters
----------
left_colors: Left colors of the color bands.
right_colors: Right colors of the color bands.
grade: how many colors are contained in one color band.
height: height of one color band.
"""
# check and process color parameters, which should be 2D list
# after processing
if not isinstance(left_colors, (tuple, list)):
left_colors = [left_colors]
if not isinstance(right_colors, (tuple, list)):
right_colors = [right_colors]
if not isinstance(left_colors[0], (tuple, list)):
left_colors = [left_colors]
if not isinstance(right_colors[0], (tuple, list)):
right_colors = [right_colors]
# initialize channel, and all other colors should have the same channel
channel = len(left_colors[0])
band_num = len(left_colors)
result = []
for i in range(band_num):
left_color = left_colors[i]
right_color = right_colors[i]
if len(left_color) != channel or len(right_color) != channel:
raise ValueError("All colors should have same channel number")
color_band = np.linspace(left_color, right_color, grade)
color_band = np.expand_dims(color_band, axis=0)
color_band = np.repeat(color_band, repeats=height, axis=0)
color_band = np.clip(np.round(color_band), 0, 255).astype(np.uint8)
result.append(color_band)
result = np.concatenate(result, axis=0)
result = np.squeeze(result)
return result
if __name__ == '__main__':
black = [0, 0, 0]
white = [255, 255, 255]
red = [0, 0, 255]
green = [0, 255, 0]
blue = [255, 0, 0]
gray_band = generate_color_band([[0], [255]], [[255], [0]])
color_band8 = generate_color_band(
[black, white, red, green, blue, black, black, black],
[white, black, white, white, white, red, green, blue]
)
cv2.imwrite('gray_band.png', gray_band)
cv2.imwrite('color_band8.png', color_band8)
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