Color Matrix¶
Computes the average R, G, B values for each region in the RGB image denoted by the gray-scale mask and saves them in a matrix of n x 4, where n = the number of color chips represented in the mask.
plantcv.transform.get_color_matrix(rgb_img, mask)
returns headers, color_matrix
-
Parameters
- rgb_img = RGB image with color chips visualized
- mask = a gray-scale img with unique values for each segmented space, representing unique, discrete color chips.
-
Returns
- color_matrix = a n x 4 matrix containing the average red value, average green value, and average blue value for each color chip.
- headers = a list of 4 headers corresponding to the 4 columns of color_matrix respectively
-
Example use:
from plantcv import plantcv as pcv
rgb_img, imgpath, imgname = pcv.readimage(filename="target_img.png")
mask, maskpath, maskname = pcv.readimage(filename="mask_img.png")
headers, color_matrix = pcv.transform.get_color_matrix(rgb_img, mask)
print(headers)
print(color_matrix)
['chip_number', 'r_avg', 'g_avg', 'b_avg']
[[ 10. 20.7332 33.672 92.7748]
[ 20. 203.508 79.774 25.77 ]
[ 30. 54.6916 34.0924 26.0352]
[ 40. 193.2972 203.198 199.4544]
[ 50. 40.2052 92.0536 37.222 ]
[ 60. 36.9256 52.4976 123.6224]
[ 70. 177.7984 103.3772 85.1672]
[ 80. 119.4276 128.4068 126.6948]
[ 90. 141.9036 34.0584 22.5056]
[ 100. 160.9764 50.3872 47.6984]
[ 110. 51.994 73.584 107.734 ]
[ 120. 65.9104 69.5172 68.482 ]
[ 130. 227.1652 183.1696 30.1332]
[ 140. 35.9472 25.4984 47.3424]
[ 150. 39.51 52.9624 26.3956]
[ 160. 32.8148 34.8512 35.5284]
[ 170. 146.522 55.7016 94.7452]
[ 180. 114.6672 155.3968 42.5688]
[ 190. 84.2172 88.8424 134.2356]
[ 200. 34.5308 90.4592 132.9108]
[ 210. 207.1596 128.736 28.7744]
[ 220. 74.632 158.8224 144.3724]]