[OpenCV] Personal memo

Conclusion

Find the median value of HSV

A style that does not dare to use intensional expressions

import cv2
import numpy as np

image = cv2.imread('./testimages/test.jpg') #File reading

#Color extraction in HSV
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV_FULL) #Convert image to HSV
hue = hsv[:, :, 0]#Hue Hue
sat = hsv[:, :, 1]#Saturation Saturation
val = hsv[:, :, 2]#Brightness Value
print(hue, sat, val)
print(hue.shape, sat.shape, val.shape)
print(hue[0], sat[0], val[0])

##Find the intermediate value of hue
h_num = []
for h in hue:
    for hn in h:
        h_num.append(hn)
ave_h = sum(h_num)/len(h_num)
print(ave_h)

##Find the median saturation
s_num = []
for s in sat:
    for sn in s:
        s_num.append(sn)
ave_s = sum(s_num)/len(s_num)
print(ave_s)

##Find the median of lightness
v_num = []
for v in val:
    for vl in v:
        v_num.append(vl)
ave_v = sum(v_num)/len(v_num)
print(ave_v)

Edge acquisition process

import numpy as np
import cv2

img_BGR = cv2.imread("./testimages/test.jpg ")

img_Lab = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2Lab)
img_Lab_L, img_Lab_a, img_Lab_b = cv2.split(img_Lab)

cv2.imshow("pic", img_Lab_a)
cv2.waitKey(0)
cv2.destroyAllWindows()

min_val = 30
max_val = 70
img_canny = cv2.Canny(img_Lab_a, min_val, max_val)
cv2.imshow("pic", img_canny)
cv2.waitKey(0)
cv2.destroyAllWindows()
#cv2.imwrite('out.jpg', img_canny)

Convert from photo to cartoon style

Preparation of screen.jpg

import cv2
import time
import numpy as np

#cap = cv2.VideoCapture(0)

#Tribalization threshold * Adjust the value yourself
th1, th2 = 60, 60

#r, img = cap.read()
img = cv2.imread("./testdir/test.jpg ")

#Get input image in grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#Screen image loading
screen = cv2.imread("screen.jpg ", 0)
#Screen image resizing
screen = cv2.resize(screen, (gray.shape[1], gray.shape[0]))
#Edge detection and color inversion
edge = 255 - cv2.Canny(gray, 80, 120)
#Tribalization of images
gray[gray <= th1] = 0
gray[gray >= th2] = 255
gray[np.where((gray > th1) & (gray < th2))] = screen[np.where((gray > th1) & (gray < th2))]

#Match the binary image and the edge image
gray = cv2.bitwise_and(gray, edge)

#Save image
cv2.imwrite("Sample.jpg ", gray)