1．编写python代码，使用skimag拆分并显示图像RGB空间的三个通道
(我直接用的包中自带的图像)
from skimage import data
from matplotlib import pyplot as plt
import numpy as np
if __name__ == "__main__":
# 载入RGB测试图像
image = data.astronaut()
fig = plt.figure()
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# 两行两列的第一个子图
axis = fig.add_subplot(221)
plt.axis('off') # 不显示坐标轴
plt.imshow(image) # 显示RGB彩色图像
plt.title('RGB图像')
# 第二个子图，显示R通道图像
axis = fig.add_subplot(222)
imageR = image[:, :, 0]
plt.axis('off')
plt.imshow(imageR, cmap='gray')
plt.title('R通道图像')
# 第三个子图，显示G通道图像
axis = fig.add_subplot(223)
imageG = image[:, :, 1]
plt.axis('off')
plt.imshow(imageG, cmap='gray')
plt.title('G通道图像')
# 第四个子图，显示B通道图像
axis = fig.add_subplot(224)
imageB = image[:, :, 2]
plt.axis('off')
plt.imshow(imageB, cmap='gray')
plt.title('B通道图像')
plt.savefig('RGBimage.tif')
plt.show()2.编写python代码实现RGB空间转换为HSI空间（这个是网上随便找的图像）
from skimage import data, io
from matplotlib import pyplot as plt
import math
import numpy as np
import sys
import os
#定义RGB转HSI
def rgb2hsi(r,g,b):
r=r/255
g=g/255
b=b/255
num=0.5*((r-g)+(r-b))
den=((r-g)*(r-g)+(r-b)*(g-b))**0.5
if b<=g:
if den == 0:
den = sys.float_info.min
h=math.acos(num/den)
elif b>g:
if den == 0:
den = sys.float_info.min
h=(2*math.pi)-math.acos(num/den)
s=1-(3*min(r,g,b)/(r+g+b))
i=(r+g+b)/3
return int(h),int(s*100),int(i*255)
if __name__ == "__main__":
print(os.getcwd())
imgPath = os.path.join(os.getcwd(), 'flower.jpg')
image = io.imread(imgPath)
print(image.shape)
hsi_image=np.zeros(image.shape,dtype='uint8')
for ii in range(image.shape[0]):
for jj in range(image.shape[1]):
r,g,b=image[ii,jj,:]
h,s,i=rgb2hsi(r,g,b)
hsi_image[ii,jj,:]=(h,s,i)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.subplot(2,3,1)
plt.axis('off')
plt.imshow(image)
plt.title('RGB原图像')
plt.subplot(2,3,2)
plt.axis('off')
plt.imshow(image[:, :, 0], cmap='gray')
plt.title('R分量')
plt.subplot(2,3,3)
plt.axis('off')
plt.imshow(hsi_image)
plt.title('HSI图像')
plt.subplot(2,3,4)
plt.axis('off')
plt.imshow(hsi_image[:,:,0], cmap='gray')
plt.title('H分量')
plt.subplot(2,3,5)
plt.axis('off')
plt.imshow(hsi_image[:,:,1], cmap='gray')
plt.title('S分量')
plt.subplot(2,3,6)
plt.axis('off')
plt.imshow(hsi_image[:,:,2],cmap='gray')
plt.title('I分量')
plt.savefig('HSIimage.tif')
plt.show()3.编写python代码实现图像的强度分层(这个需要下载opencv-python库)
在终端 路径 -m pip install opencv-python -i 镜像网站(这里我用的清华的镜像网站)
D:\PycharmProjects\projects\venv\Scripts\python.exe -m pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple
import cv2
from skimage import data,color
from matplotlib import pyplot as plt
import numpy as np
# def grayBin(image_matrix):
# initial_conv = np.where((image_matrix < 0.4), 0, 128)
# final_conv = np.where((image_matrix >= 0.8), 255, initial_conv)
# return final_conv
if __name__ == "__main__":
img = data.astronaut()
grayimg = color.rgb2gray(img) #将彩色图像转化为灰度图像
# grayimg = cv2.resize(grayimg, (5000,5000))
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.subplot(1, 2, 1)
plt.imshow(grayimg, cmap = 'gray')
plt.title('灰度图像')
rows, cols = grayimg.shape
labels = np.zeros([rows, cols])
for i in range(rows):
for j in range(cols):
if(grayimg[i, j] < 0.4):
labels[i, j] = 0
elif(grayimg[i, j] < 0.8):
labels[i, j] = 128
else:
labels[i, j] = 255
psdimg = color.label2rgb(labels)#不同的label采用不同的颜色
# labels1 = grayBin(grayimg)
# cv2.imwrite("labels.jpg", labels)
# cv2.imwrite("labels1.jpg", labels1)
# print((labels1 == labels).all())
plt.subplot(1, 2, 2)
plt.imshow(psdimg)
plt.title('强度分层图像')
plt.savefig('灰度分层.tif')
plt.show()4.编写python代码实现灰度值到彩色变换并画出映射函数
from skimage import data, color
from matplotlib import pyplot as plt
import numpy as np
# 定义灰度级到彩色变换
L = 256
def GetR(gray):
if gray < L / 2:
return 0
elif gray > L / 4 * 3:
return L
else:
return 4 * gray - 2 * L
def GetG(gray):
if gray < L / 4:
return 4 * gray
elif gray > L / 4 * 3:
return 4 * L - 4 * gray
else:
return L
def GetB(gray):
if gray < L / 4:
return L
elif gray > L / 2:
return 0
else:
return 2 * L - 4 * gray
if __name__ == "__main__":
img = data.astronaut()
grayimg = color.rgb2gray(img) * 255 # 将彩色图像转化为灰度图像
colorimg = np.zeros(img.shape, dtype='uint8')
for ii in range(img.shape[0]):
for jj in range(img.shape[1]):
a = grayimg[ii, jj]
a0 = GetR(a)
a1 = GetG(a)
a2 = GetB(a)
# 确保值在 0 到 255 之间
r = np.clip(a0, 0, 255)
g = np.clip(a1, 0, 255)
b = np.clip(a2, 0, 255)
colorimg[ii, jj, :] = (r, g, b)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.subplot(1, 2, 1)
plt.axis('off')
plt.imshow(grayimg, cmap='gray')
plt.title('灰度图像')
plt.subplot(1, 2, 2)
plt.axis('off')
plt.imshow(colorimg)
plt.title('伪彩色图像')
plt.savefig('Intensity2Color.tif')
plt.show()映射
from matplotlib import pyplot as plt
#定义灰度级到彩色变换
L = 255
def GetR(gray):
if gray < L / 2:
return 0
elif gray > L / 4 * 3:
return L
else:
return 4 * gray - 2 * L
def GetG(gray):
if gray < L / 4:
return 4 * gray
elif gray > L / 4 * 3:
return 4 * L - 4 * gray
else:
return L
def GetB(gray):
if gray < L / 4:
return L
elif gray > L / 2:
return 0
else:
return 2 * L - 4 * gray
if __name__ == "__main__":
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
L = 255
x = [0, 64, 127, 191, 255]
#绘制灰度图像到R通道的映射关系
plt.subplot(2,2,1)
R = []
for i in x:
R.append(GetR(i))
plt.plot(x, R, 'r--', label = '红色变换')
plt.legend(loc = 'best')
#绘制灰度图像到G通道的映射关系
plt.subplot(2,2,2)
G = []
for i in x:
G.append(GetG(i))
plt.plot(x, G, 'g', label = '绿色变换')
plt.legend(loc = 'best')
#绘制灰度图像到B通道的映射关系
plt.subplot(2,2,3)
B = []
for i in x:
B.append(GetB(i))
plt.plot(x, B, 'b', marker = 'o', markersize = 5, label = '蓝色变换')
plt.legend(loc = 'best')
#绘制灰度图像到RGB的映射关系
plt.subplot(2,2,4)
plt.plot(x, R, 'r--')
plt.plot(x, G, 'g')
plt.plot(x, B, 'b', marker = 'o', markersize = 5)
plt.savefig('灰度到彩色的映射关系.tif')
plt.show()5.编写python代码，实现颜色空间分割(这个花是2里的花，我懒得在找图片了)
import cv2
from skimage import data, io
from matplotlib import pyplot as plt
import math
import numpy as np
import sys
# 定义RGB转HSI
def rgb2hsi(r, g, b):
r = r / 255
g = g / 255
b = b / 255
num = 0.5 * ((r - g) + (r - b))
den = ((r - g) ** 2 + (r - b) * (g - b)) ** 0.5
if b <= g:
if den == 0:
den = sys.float_info.min
h = math.acos(num / den)
elif b > g:
if den == 0:
den = sys.float_info.min
h = (2 * math.pi) - math.acos(num / den)
s = 1 - 3 * min(r, g, b) / (r + g + b)
i = (r + g + b) / 3
return int(h), int(s * 100), int(i * 255)
if __name__ == "__main__":
image = io.imread('flower.jpg')
hsi_image = np.zeros(image.shape, dtype='uint8')
for ii in range(image.shape[0]):
for jj in range(image.shape[1]):
r, g, b = image[ii, jj, :]
h, s, i = rgb2hsi(r, g, b)
hsi_image[ii, jj, :] = (h, s, i)
H = hsi_image[:, :, 0]
S = hsi_image[:, :, 1]
I = hsi_image[:, :, 2]
# 生成二值饱和度模板
S_template = S > 0.3 * S.max()
# 色调图像与二值饱和度模板相乘可得到分割结果F
F = np.multiply(H, S_template)
plt.figure() # 创建一个新的图形窗口
plt.imshow(F, cmap='gray')
# 显示结果
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# 在创建第一个子图之前，显式移除当前的轴
plt.gca().remove()
plt.subplot(2, 3, 1)
plt.axis('off')
plt.imshow(image)
plt.title('原始RGB图像')
plt.subplot(2, 3, 2)
plt.axis('off')
plt.imshow(H, cmap='gray')
plt.title('H分量')
plt.subplot(2, 3, 3)
plt.axis('off')
plt.imshow(S, cmap='gray')
plt.title('S分量')
plt.subplot(2, 3, 4)
plt.axis('off')
plt.imshow(I, cmap='gray')
plt.title('I分量')
plt.subplot(2, 3, 5)
plt.axis('off')
plt.imshow(S_template, cmap='gray')
plt.title('二值饱和度模板')
plt.subplot(2, 3, 6)
plt.axis('off')
plt.imshow(F, cmap='gray')
plt.title('分割结果')
plt.savefig('HSI彩色分割.tif')
plt.show()
