89 lines
2.0 KiB
Python
89 lines
2.0 KiB
Python
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# 统计各灰度值的像素个数
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def histogram(image):
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(row, col) = image.shape
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hist = [0]*256
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for i in range(row):
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for j in range(col):
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hist[image[i, j]] += 1
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return hist
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#MODE=0自适应阈值（算术平均法）二值化，MODE=ture，自定义阈值二值化
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def thresholding(im,T=128,L=255,MODE=0):
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[m,n]=im.shape
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img=im.copy()
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if MODE==0:
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list = histogram(img)
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for i in range(len(list)):
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list[i]=list[i]/(m*n)
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dT=1
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while dT>=0.5:
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T1=0
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T2=0
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t=T
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for i in range(floor(T)):
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T1+=list[i]*i
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for i in range(floor(T)+1,256,1):
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T2+=list[i]*i
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T=(T1+T2)/2
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dT=abs(T-t)
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for i in range(m):
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for j in range(n):
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if img[i,j]>=T:
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img[i,j]=L
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else:
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img[i,j]=0
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return img
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#多图像加运算并取平均
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def add(list):#list=[im1,im2,im3,...,imn],list为三维列表
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img=np.zeros([list.shape[1],list.shape[2]])
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for i in range(list.shape[0]):
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img+=list[i]
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img=img/list.shape[0]
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return img
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#减运算im1-im2
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def reduce(im1,im2):
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img=im1-im2
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return img
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#乘运算
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def multiply(im1,im2):
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img=im1.copy()
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for i in range(im1.shape[0]):
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for j in range(im1.shape[1]):
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img[i,j]=im1[i,j]*im2[i,j]
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return img
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#除运算，im1/im2
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def divide(im1,im2):
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img=im1/im2
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return img
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#与运算
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def img_and(im1,im2,L=255):
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img=np.zeros([im1.shape[0],im1.shape[1]])
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for i in range(im1.shape[0]):
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for j in range(im1.shape[1]):
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if(im1[i,j] and im2[i,j]):
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img[i,j] = L
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else:
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img[i,j] = 0
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return img
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#或运算
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def img_or(im1,im2,L=255):
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img=np.zeros([im1.shape[0],im1.shape[1]])
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for i in range(im1.shape[0]):
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for j in range(im1.shape[1]):
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if(im1[i,j] or im2[i,j]):
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img[i,j] = L
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else:
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img[i,j] = 0
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return img
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