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：
- 格子形状的颜色表
- 渐变色带
长的样子分别如下：
格子颜色表
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。
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 | # -*- coding: utf-8 -*-import cv2import numpy as npdef 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 resultdef 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 matrixif __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
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 | # -*- coding: utf-8 -*-import cv2import numpy as npdef 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 resultif __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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