OpenCV-Python 彩色图像均衡化与规定化

彩色图像均衡化

在HSI空间对亮度分量进行均衡化

def method_one():
img = cv2.imread('../assets/Fig6.png')
his = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
cv2.imshow('img', img)
his_i = his[:, :, 1]
equal_i = cv2.equalizeHist(hsl_i)
his[:, :, 1] = equal_i
dst = cv2.cvtColor(his, cv2.COLOR_HLS2BGR)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

对彩色图像的R，G，B三通道进行均衡化

def method_two():
img = cv2.imread('../assets/Fig6.png')
(b, g, r) = cv2.split(img)
equal_b = cv2.equalizeHist(b)
equal_g = cv2.equalizeHist(g)
equal_r = cv2.equalizeHist(r)
dst = cv2.merge((equal_b, equal_g, equal_r))
cv2.imshow('img', img)
cv2.imshow('dst', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

对彩色图像进行规定话

现在有两幅图，FigA 和 FigB，现在要对FigA进行规定化，使FigA 图像的直方图规范化最接近FigB的情况。

def method_three():
img = cv2.imread('../assets/Fig6A.jpg')
dst = cv2.imread('../assets/Fig6B.jpg')
def_img = cv2.imread('../assets/Fig6A.jpg')
color = ('b', 'g', 'r')
for i, col in enumerate(color):
hist1, bins = np.histogram(img[:, :, i].ravel(), 256, [0, 256])
hist2, bins = np.histogram(dst[:, :, i].ravel(), 256, [0, 256])
# 获得累计直方图
cdf1 = hist1.cumsum()
cdf2 = hist2.cumsum()
# 归一化处理
cdf1_hist = hist1.cumsum() / cdf1.max()
cdf2_hist = hist2.cumsum() / cdf2.max()

# diff_cdf 里是每2个灰度值比率间的差值
diff_cdf = [0]*256
for j in range(256):
for k in range(256):
diff_cdf[j][k] = abs(cdf1_hist[j] - cdf2_hist[k])
# FigA 中的灰度级与目标灰度级的对应表
lut = np.zeros(256, dtype=np.int)
for j in range(256):
squ_min = diff_cdf[j][0]
index = 0
for k in range(256):
if squ_min > diff_cdf[j][k]:
squ_min = diff_cdf[j][k]
index = k
lut[j] = ([j, index])

h = int(img.shape[0])
w = int(dst.shape[1])
# 对原图像进行灰度值的映射
for j in range(h):
for k in range(w):
def_img[j, k, i] = lut[img[j, k, i]][1]

cv2.namedWindow('Fig6A', 0)
cv2.resizeWindow('Fig6A', 400, 520)
cv2.namedWindow('Fig6B', 0)
cv2.resizeWindow('Fig6B', 400, 520)
cv2.namedWindow('def', 0)
cv2.resizeWindow('def', 400, 520)
cv2.imshow('Fig6A', img)
cv2.imshow('Fig6B', dst)
cv2.imshow('def', def_img)

cv2.waitKey(0)
cv2.destroyAllWindows()