使用HSV对imori.jpg进行掩膜处理，只让蓝色的地方变黑。
像这样通过使用黑白二值图像将对应于黑色部分的原始图像的像素改变为黑色的操作被称为掩膜。
要提取蓝色部分，请先创建这样的二进制图像，使得HSV色彩空间中180\leq H\leq 260的位置的像素值设为1，并将其0和1反转之后与原始图像相乘。
这使得可以在某种程度上将蝾螈（从背景上）分离出来。
python实现：
import cv2
import numpy as np
import matplotlib.pyplot as plt
# BGR -> HSV
def BGR2HSV(_img):
img = _img.copy() / 255.
hsv = np.zeros_like(img, dtype=np.float32)
# get max and min
max_v = np.max(img, axis=2).copy()
min_v = np.min(img, axis=2).copy()
min_arg = np.argmin(img, axis=2)
# H
hsv[..., 0][np.where(max_v == min_v)]= 0
## if min == B
ind = np.where(min_arg == 0)
hsv[..., 0][ind] = 60 * (img[..., 1][ind] - img[..., 2][ind]) / (max_v[ind] - min_v[ind]) + 60
## if min == R
ind = np.where(min_arg == 2)
hsv[..., 0][ind] = 60 * (img[..., 0][ind] - img[..., 1][ind]) / (max_v[ind] - min_v[ind]) + 180
## if min == G
ind = np.where(min_arg == 1)
hsv[..., 0][ind] = 60 * (img[..., 2][ind] - img[..., 0][ind]) / (max_v[ind] - min_v[ind]) + 300
# S
hsv[..., 1] = max_v.copy() - min_v.copy()
# V
hsv[..., 2] = max_v.copy()
return hsv
# make mask
def get_mask(hsv):
mask = np.zeros_like(hsv[..., 0])
#mask[np.where((hsv > 180) & (hsv[0] < 260))] = 255
mask[np.logical_and((hsv[..., 0] > 180), (hsv[..., 0] < 260))] = 1
return mask
# masking
def masking(img, mask):
mask = 1 - mask
out = img.copy()
# mask [h, w] -> [h, w, channel]
mask = np.tile(mask, [3, 1, 1]).transpose([1, 2, 0])
out *= mask
return out
# Read image
img = cv2.imread("imori.jpg").astype(np.float32)
# RGB > HSV
hsv = BGR2HSV(img / 255.)
# color tracking
mask = get_mask(hsv)
# masking
out = masking(img, mask)
out = out.astype(np.uint8)
# Save result
cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()
输入(imori.jpg)：
掩码：
输出：