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