Make one Mario using Numpy and OpenCV

Work from the side! Data frame that gets angry

But it has a purpose. OpenCV uses Numpy's ndarray array to handle the data.

That means you can't reproduce it by throwing a one-dimensional array for gray and a three-dimensional array for color! I did a metamorphosis challenge that completely ignored the manners and rules, and broke each time. This time, the work has been completed successfully I will upload a code that is of no use to the world.

I drew an uncle plumber in a 12x12 array.

mario.py

import cv2
import numpy as np

val = 0
#Create an empty list(12*12 for all elements 0)
rchannel = [[val] * 12 for i in range(12)]
rchannel = np.array(rchannel)
gchannel = [[val] * 12 for i in range(12)]
gchannel = np.array(gchannel)
bchannel = [[val] * 12 for i in range(12)]
bchannel = np.array(bchannel)

#Hell's hand-made zone
rchannel[3,3:8] = 255
rchannel[4,2:11] = 255
rchannel[5,2:5] = 146
gchannel[5,2:5] = 41
bchannel[5,2:5] = 6
rchannel[5,5:7] = 248
gchannel[5,5:7] = 178
bchannel[5,5:7] = 108
rchannel[5,7] = 146
gchannel[5,7] = 41
bchannel[5,7] = 6
rchannel[5,8] = 248
gchannel[5,8] = 178
bchannel[5,8] = 108
rchannel[6,1] = 146
gchannel[6,1] = 41
bchannel[6,1] = 6
rchannel[6,2] = 248
gchannel[6,2] = 178
bchannel[6,2] = 108
rchannel[6,3] = 146
gchannel[6,3] = 41
bchannel[6,3] = 6
rchannel[6,4:7] = 248
gchannel[6,4:7] = 178
bchannel[6,4:7] = 108
rchannel[6,7] = 146
gchannel[6,7] = 41
bchannel[6,7] = 6
rchannel[6,8:11] = 248
gchannel[6,8:11] = 178
bchannel[6,8:11] = 108
rchannel[7,1] = 146
gchannel[7,1] = 41
bchannel[7,1] = 6
rchannel[7,2] = 248
gchannel[7,2] = 178
bchannel[7,2] = 108
rchannel[7,3:5] = 146
gchannel[7,3:5] = 41
bchannel[7,3:5] = 6
rchannel[7,5:8] = 248
gchannel[7,5:8] = 178
bchannel[7,5:8] = 108
rchannel[7,8] = 146
gchannel[7,8] = 41
bchannel[7,8] = 6
rchannel[7,9:11] = 248
gchannel[7,9:11] = 178
bchannel[7,9:11] = 108
rchannel[7,11] = 146
gchannel[7,11] = 41
bchannel[7,11] = 6
rchannel[8,1:3] = 146
gchannel[8,1:3] = 41
bchannel[8,1:3] = 6
rchannel[8,3:7] = 248
gchannel[8,3:7] = 178
bchannel[8,3:7] = 108
rchannel[8,7:11] = 146
gchannel[8,7:11] = 41
bchannel[8,7:11] = 6
rchannel[9,3:10] = 248
gchannel[9,3:10] = 178
bchannel[9,3:10] = 108
rchannel[10,2:4] = 255
bchannel[10,4] = 255
rchannel[10,5:9] = 255
rchannel[11,1:4] = 255
bchannel[11,4] = 255
rchannel[11,5:11] = 255

#Combine channels
data_map = np.stack((bchannel,gchannel,rchannel),-1)
#Change data type → Now it can be handled by OpenCV
data_map = np.array(data_map, dtype="uint8")
#Enlarged for easy visualization
data_map = cv2.resize(data_map, (400, 400),interpolation=cv2.INTER_NEAREST)

#display
cv2.imshow("show",data_map)
cv2.waitKey(0)
cv2.destroyAllWindows()

May 2020 be a good year.