216 lines
6.7 KiB
Python
216 lines
6.7 KiB
Python
from globalObject import *
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from math import *
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import math
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from matplotlib import pyplot as plt
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from tkinter.messagebox import showinfo, showwarning, showerror
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#图像取反
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def negative(im,L=255):
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[m,n]=im.shape
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img=im.copy()
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for i in range(m):
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for j in range(n):
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img[i,j]=L-im[i,j]
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return img
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# 全局灰度线性变换（也可对彩色图像进行线性变换）
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# 全局灰度线性变换（也可对彩色图像进行线性变换）
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def global_linear_transmation(im,c=0,d=255):
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img=im.copy()
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maxV = img.max()
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minV = img.min()
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if maxV==minV:
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return np.uint8(img)
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for i in range(img.shape[0]):
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for j in range(img.shape[1]):
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img[i, j] = ((d-c) / (maxV - minV)) * (img[i, j] - minV)+c#img[i,j]代表的是某像素点三通道的值
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return np.uint8(img)
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# 对img_result进行分段线性灰度变换
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def piecewise_linear_transformation(im,lists): #lists存放着各段分段前后的灰度范围
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global img_result,img_empty
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try:
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img=im.copy()
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for list in lists:
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a = int(list[0])
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b = int(list[1])
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c = int(list[2])
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d = int(list[3])
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for i in range(img.shape[0]):
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for j in range(img.shape[1]):
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if(img[i,j]>=a and img[i,j]<=b):
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img[i, j] = ((d- c) / (b-a)) * (im[i, j] - a)+c
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except:
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showerror("错误提示","灰度值设置不合理，起始灰度值不能与终止值相同")
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img = img_empty
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return img
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#位平面分割
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def Bit_Plane_Slicing(im):
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img=im.copy()
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BP=np.zeros([8,img.shape[0],img.shape[1]])
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for n in range(8):
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for i in range(img.shape[0]):
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for j in range(img.shape[1]):
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if(img[i,j]>=2**(7-n)):
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BP[n][i,j]=2**(7-n)
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img[i,j]=img[i,j]-2**(7-n)
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else:
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BP[n][i,j]=0
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return BP
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#对数变换，默认不改变像素点的范围（0-255）
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def logarithmic_transformations(im,c=1):
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img=np.zeros([im.shape[0],im.shape[1]])
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for i in range(im.shape[0]):
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for j in range(im.shape[1]):
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img[i,j]=c*math.log(1+im[i,j])
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return img
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#幂次（伽马）变换
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def power_law_transformations(im,r=0.45,c=1):
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img=np.zeros([im.shape[0],im.shape[1]])
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for i in range(im.shape[0]):
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for j in range(im.shape[1]):
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img[i,j]=c*255.0*(im[i,j]/255.0)**r
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return np.uint8(img)
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if __name__=="__main__":
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im0 = cv2.imread(r'.\img\test.jpg')
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im0=cv2.cvtColor(im0,cv2.COLOR_BGR2RGB)
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im1=cv2.cvtColor(im0,cv2.COLOR_RGB2GRAY)
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im1=piecewise_linear_transformation(im1, [[[0, 100], [0, 80]]])
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plt.subplot(121), plt.imshow(im0, cmap='gray')
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plt.title('Original image'), plt.xticks([]), plt.yticks([])
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plt.subplot(122), plt.imshow(im1, cmap='gray')
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plt.title('Piecewise linear gray enhancementt'), plt.xticks([]), plt.yticks([])
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plt.show()
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'''
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#傅里叶频谱的对数变换
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im0=cv2.imread('2.jpg')
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im0=cv2.cvtColor(im0, cv2.COLOR_BGR2GRAY)
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im0=abs(np.fft.fftshift(np.fft.fft2(im0)))
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im1=Logarithmic_Transformations(im0,1,exp(1))
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plt.subplot(121), plt.imshow(im0, cmap='gray')
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plt.title('Original spectrum'), plt.xticks([]), plt.yticks([])
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plt.subplot(122), plt.imshow(im1, cmap='gray')
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plt.title('log Transformed Spectrum'), plt.xticks([]), plt.yticks([])
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plt.show()
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#加法运算去除“叠加性”随机噪音
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def SaltAndPepper(src, percentage):
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#NoiseImg = src #使用此语句传递的是地址，程序会出错
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NoiseImg = src.copy() #在此要使用copy函数，否则src和主程序中的img都会跟着改变
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NoiseNum = int(percentage * src.shape[0] * src.shape[1])
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for i in range(NoiseNum):
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randX = random.randint(0, src.shape[0] - 1) #产生[0, src.shape[0] - 1]之间随机整数
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randY = random.randint(0, src.shape[1] - 1)
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if random.randint(0, 1) == 0:
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NoiseImg[randX, randY] = 0
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else:
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NoiseImg[randX, randY] = 255
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return NoiseImg
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im0=cv2.imread('2.jpg')
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im0=cv2.cvtColor(im0, cv2.COLOR_BGR2GRAY)
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[m,n]=im0.shape
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im=np.zeros([100,m,n])
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for i in range(100):
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im[i]=SaltAndPepper(im0,0.1)
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im2=add(im)
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plt.subplot(131), plt.imshow(im0, cmap='gray')
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plt.title('Original image'), plt.xticks([]), plt.yticks([])
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plt.subplot(132), plt.imshow(im[0], cmap='gray')
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plt.title('0.1 Salt and pepper noise'), plt.xticks([]), plt.yticks([])
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plt.subplot(133), plt.imshow(im2, cmap='gray')
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plt.title('Original image'), plt.xticks([]), plt.yticks([])
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plt.show()
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#差影法的应用
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im0=cv2.imread('2.jpg')
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im0=cv2.cvtColor(im0, cv2.COLOR_BGR2GRAY)
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im1=cv2.imread('2.jpg')
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im1=cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
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im2=im0-im1
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plt.subplot(131), plt.imshow(im0, cmap='gray')
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plt.title('image before'), plt.xticks([]), plt.yticks([])
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plt.subplot(132), plt.imshow(im1, cmap='gray')
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plt.title('image after'), plt.xticks([]), plt.yticks([])
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plt.subplot(133), plt.imshow(im2, cmap='gray')
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plt.title('image difference'), plt.xticks([]), plt.yticks([])
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plt.show()
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#用逻辑运算提取子图像
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img=cv2.imread('2.jpg')
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im0=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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im0=Thresholding(im0)
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im=Bit_Plane_Slicing(im0)
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im1=im[0]
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im1=Negative(Thresholding(im1,1,1,1),1)
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(10, 10))
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im1 = cv2.morphologyEx(im1, cv2.MORPH_CLOSE, kernel)
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im1 = cv2.morphologyEx(im1, cv2.MORPH_OPEN, kernel)
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im2=img_and(im0,im1)
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plt.subplot(131), plt.imshow(im0,cmap='gray')
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plt.title('Original Two valued image'), plt.xticks([]), plt.yticks([])
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plt.subplot(132), plt.imshow(im1,cmap='gray')
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plt.title('intersection Two valued image'), plt.xticks([]), plt.yticks([])
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plt.subplot(133), plt.imshow(im2,cmap='gray')
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plt.title('Sub image'), plt.xticks([]), plt.yticks([])
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plt.show()
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#用乘法运算提取局部图像
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def GetStructuringElement(path):
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global img, im1, im2
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img = cv2.imread(path)
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b, g, r = cv2.split(img)
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img = cv2.merge([r, g, b])
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im0 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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im = Bit_Plane_Slicing(im0)
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im1 = im[0]
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im1 = Negative(Thresholding(im1, 1, 1, 1), 1)
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
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im1 = cv2.morphologyEx(im1, cv2.MORPH_CLOSE, kernel)
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im1 = cv2.morphologyEx(im1, cv2.MORPH_OPEN, kernel)
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r = multiply(r, im1)
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g = multiply(g, im1)
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b = multiply(b, im1)
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im2 = cv2.merge([r, g, b])
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return im2
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if __name__=="__main__":
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GetStructuringElement('2.jpg')
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plt.subplot(131), plt.imshow(img)
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plt.title('Original image'), plt.xticks([]), plt.yticks([])
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plt.subplot(132), plt.imshow(im1, cmap='gray')
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plt.title('Two valued image'), plt.xticks([]), plt.yticks([])
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plt.subplot(133), plt.imshow(im2)
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plt.title('Local image'), plt.xticks([]), plt.yticks([])
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plt.show()
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'''
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