代码语言：javascript复制
# -*- coding: utf-8 -*-
# python version 3.6.4
import cv2
import numpy as np
import copy
def RGB2HSI(rgb_img):
"""
这是将RGB彩色图像转化为HSI图像的函数
:param rgm_img: RGB彩色图像
:return: HSI图像
"""
# 保存原始图像的行列数
row = rgb_img.shape[0]
col = rgb_img.shape[1]
# 对原始图像进行复制
hsi_img = rgb_img.copy()
# 对图像进行通道拆分
B, G, R = cv2.split(rgb_img)
# 把通道归一化到[0,1]
[B, G, R] = [i / 255.0 for i in ([B, G, R])]
H = np.zeros((row, col)) # 定义H通道
I = (R G B) / 3.0 # 计算I通道
S = np.zeros((row, col)) # 定义S通道
for i in range(row):
den = np.sqrt((R[i] - G[i]) ** 2 (R[i] - B[i]) * (G[i] - B[i]))
thetha = np.arccos(0.5 * (R[i] - B[i] R[i] - G[i]) / den) # 计算夹角
h = np.zeros(col) # 定义临时数组
# den>0且G>=B的元素h赋值为thetha
h[B[i] <= G[i]] = thetha[B[i] <= G[i]]
# den>0且G<=B的元素h赋值为thetha
h[G[i] < B[i]] = 2 * np.pi - thetha[G[i] < B[i]]
# den<0的元素h赋值为0
h[den == 0] = 0
H[i] = h / (2 * np.pi) # 弧度化后赋值给H通道
# 计算S通道
for i in range(row):
min = []
# 找出每组RGB值的最小值
for j in range(col):
arr = [B[i][j], G[i][j], R[i][j]]
min.append(np.min(arr))
min = np.array(min)
# 计算S通道
S[i] = 1 - min * 3 / (R[i] B[i] G[i])
# I为0的值直接赋值0
S[i][R[i] B[i] G[i] == 0] = 0
# 扩充到255以方便显示，一般H分量在[0,2pi]之间，S和I在[0,1]之间
hsi_img[:, :, 0] = H * 255
hsi_img[:, :, 1] = S * 255
hsi_img[:, :, 2] = I * 255
return hsi_img
def HSI2RGB(hsi_img):
"""
这是将HSI图像转化为RGB图像的函数
:param hsi_img: HSI彩色图像
:return: RGB图像
"""
# 保存原始图像的行列数
row = np.shape(hsi_img)[0]
col = np.shape(hsi_img)[1]
# 对原始图像进行复制
rgb_img = hsi_img.copy()
# 对图像进行通道拆分
H, S, I = cv2.split(hsi_img)
# 把通道归一化到[0,1]
[H, S, I] = [i / 255.0 for i in ([H, S, I])]
R, G, B = H, S, I
for i in range(row):
h = H[i] * 2 * np.pi
# H大于等于0小于120度时
a1 = h >= 0
a2 = h < 2 * np.pi / 3
a = a1 & a2 # 第一种情况的花式索引
tmp = np.cos(np.pi / 3 - h)
b = I[i] * (1 - S[i])
r = I[i] * (1 S[i] * np.cos(h) / tmp)
g = 3 * I[i] - r - b
B[i][a] = b[a]
R[i][a] = r[a]
G[i][a] = g[a]
# H大于等于120度小于240度
a1 = h >= 2 * np.pi / 3
a2 = h < 4 * np.pi / 3
a = a1 & a2 # 第二种情况的花式索引
tmp = np.cos(np.pi - h)
r = I[i] * (1 - S[i])
g = I[i] * (1 S[i] * np.cos(h - 2 * np.pi / 3) / tmp)
b = 3 * I[i] - r - g
R[i][a] = r[a]
G[i][a] = g[a]
B[i][a] = b[a]
# H大于等于240度小于360度
a1 = h >= 4 * np.pi / 3
a2 = h < 2 * np.pi
a = a1 & a2 # 第三种情况的花式索引
tmp = np.cos(5 * np.pi / 3 - h)
g = I[i] * (1 - S[i])
b = I[i] * (1 S[i] * np.cos(h - 4 * np.pi / 3) / tmp)
r = 3 * I[i] - g - b
B[i][a] = b[a]
G[i][a] = g[a]
R[i][a] = r[a]
rgb_img[:, :, 0] = B * 255
rgb_img[:, :, 1] = G * 255
rgb_img[:, :, 2] = R * 255
return rgb_img
def classify(ll, distance):
ll.sort()
new = list()
j = 0
i = 1
while i < len(ll):
if ll[i] - ll[j] <= distance:
i = i 1
if i == len(ll):
new.append(ll[j:])
else:
new.append(ll[j:i])
j = copy.deepcopy(i)
i = 1
return new
def eraseline(rgb_img, near_pixel=3):
# 转换成HSI模式后，H维度的数字表示人眼所见的颜色，对此维度做聚类
hsi_img = RGB2HSI(rgb_img)
d = dict()
s = set()
for x in range(hsi_img.shape[0]):
for y in range(hsi_img.shape[1]):
s.add(hsi_img[x, y, 0]) # 得到所有颜色的分类
for each in s:
d[each] = list()
for x in range(hsi_img.shape[0]):
for y in range(hsi_img.shape[1]):
d[hsi_img[x, y, 0]].append((x, y)) # 得到同一颜色所在的所有的坐标
fenlei_list = classify(list(d.keys()), near_pixel)
fenlei_cord_dict = dict()
fenlei_len_list = list()
# 同一类颜色，坐标点数 填进列表
for i, colorlist in enumerate(fenlei_list):
fenlei_cord_dict[i] = list()
for color in colorlist:
fenlei_cord_dict[i] = d[color]
fenlei_len_list.append((i, len(fenlei_cord_dict[i])))
fenlei_len_list = sorted(fenlei_len_list, key=lambda x: x[1])
newimg = np.full(rgb_img.shape, 255, dtype='uint8')
for cctuple in fenlei_len_list[-5:-1]:
for cccord in fenlei_cord_dict[cctuple[0]]:
newimg[cccord[0], cccord[1], 0] = rgb_img[cccord[0], cccord[1], 0]
newimg[cccord[0], cccord[1], 1] = rgb_img[cccord[0], cccord[1], 1]
newimg[cccord[0], cccord[1], 2] = rgb_img[cccord[0], cccord[1], 2]
return newimg
if __name__ == '__main__':
# 利用opencv读入图片
file = 'img_1.png'
rgb_img = cv2.imread(file, cv2.IMREAD_COLOR)
# 去除连线
rgb_img_no_line = eraseline(rgb_img)
cv2.imwrite('after.jpg', rgb_img_no_line)```
