143 lines
4.5 KiB
Python
143 lines
4.5 KiB
Python
import cv2
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from PIL import Image
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import numpy as np
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import math
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#平移
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def move(img,dx=50,dy=50):
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rows,cols= img.shape[:2]
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M0 = np.float32([[1,0,dx],[0,1,dy]])
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dst0 = cv2.warpAffine(img,M0,(cols,rows))
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return dst0
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# def move(img,dx=50,dy=50):
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# rows,cols= img.shape[:2]
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# M0 = np.float32([[1,0,dx],[0,1,dy],[0,0,1]])
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# dst0 = cv2.warpPerspective(img,M0,(cols,rows))
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# return dst0
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#旋转
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def rotate(img,angle=45):
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rows,cols=img.shape[:2]
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M1=cv2.getRotationMatrix2D((cols/2,rows/2),angle ,1)
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# 第三个参数是输出图像的尺寸
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dst1=cv2.warpAffine(img,M1,(cols,rows))
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return dst1
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#镜像
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#垂直镜像
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def reflect_x(img):
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rows,cols=img.shape[:2]
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M2 = np.float32([[1,0,0],[0,-1,rows]])
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dst2 = cv2.warpAffine(img,M2,(cols,rows))
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return dst2
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#水平镜像
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def reflect_y(img):
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rows,cols=img.shape[:2]
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M3 = np.float32([[-1,0,cols],[0,1,0]])
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dst3 = cv2.warpAffine(img,M3,(cols,rows))
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return dst3
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#缩放
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def zoom(img,a,b):
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# 下面的 None 本应该是输出图像的尺寸，但是因为后边我们设置了缩放因子
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# 因此这里为 None
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res=cv2.resize(img,None,fx=a,fy=b,interpolation=cv2.INTER_CUBIC)
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return res
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#仿射变换
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def affine(img,pts1,pts2):
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rows, cols = img.shape[:2]
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# pts1 = np.float32([[50, 50], [200, 50], [50, 200]])
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# pts2 = np.float32([[10, 100], [200, 50], [100, 250]])
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M = cv2.getAffineTransform(pts1, pts2)
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dst = cv2.warpAffine(img, M, (cols, rows))
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return dst
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#透视变换
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def perspective(img,pts1,pts2):
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# pts1 = np.float32([[56, 65], [368, 52], [28, 387], [389, 390]])
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# pts2 = np.float32([[0, 0], [300, 0], [0, 300], [300, 300]])
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M = cv2.getPerspectiveTransform(pts1, pts2)
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dst = cv2.warpPerspective(img, M, (300, 300))
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return dst
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#最近邻插值
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def NN_interpolation(img,dstH,dstW):
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scrH,scrW,_=img.shape
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retimg=np.zeros((dstH,dstW,3),dtype=np.uint8)
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for i in range(dstH):
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for j in range(dstW):
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scrx=round((i+1)*(scrH/dstH))
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scry=round((j+1)*(scrW/dstW))
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retimg[i,j]=img[scrx-1,scry-1]
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return retimg
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#双线性插值
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def BiLinear_interpolation(img,dstH,dstW):
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scrH,scrW,_=img.shape
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img=np.pad(img,((0,1),(0,1),(0,0)),'constant')
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retimg=np.zeros((dstH,dstW,3),dtype=np.uint8)
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for i in range(dstH):
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for j in range(dstW):
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scrx=(i+1)*(scrH/dstH)-1
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scry=(j+1)*(scrW/dstW)-1
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x=math.floor(scrx)
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y=math.floor(scry)
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u=scrx-x
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v=scry-y
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retimg[i,j]=(1-u)*(1-v)*img[x,y]+u*(1-v)*img[x+1,y]+(1-u)*v*img[x,y+1]+u*v*img[x+1,y+1]
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return retimg
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#最佳插法函数
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def BiBubic(x):
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x=abs(x)
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if x<=1:
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return 1-2*(x**2)+(x**3)
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elif x<2:
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return 4-8*x+5*(x**2)-(x**3)
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else:
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return 0
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#三次内插法
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def BiCubic_interpolation(img,dstH,dstW):
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scrH,scrW,_=img.shape
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#img=np.pad(img,((1,3),(1,3),(0,0)),'constant')
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retimg=np.zeros((dstH,dstW,3),dtype=np.uint8)
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for i in range(dstH):
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for j in range(dstW):
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scrx=i*(scrH/dstH)
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scry=j*(scrW/dstW)
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x=math.floor(scrx)
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y=math.floor(scry)
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u=scrx-x
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v=scry-y
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tmp=0
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for ii in range(-1,2):
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for jj in range(-1,2):
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if x+ii<0 or y+jj<0 or x+ii>=scrH or y+jj>=scrW:
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continue
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tmp+=img[x+ii,y+jj]*BiBubic(ii-u)*BiBubic(jj-v)
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retimg[i,j]=np.clip(tmp,0,255)
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return retimg
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if __name__=="__main__":
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img = cv2.imread('Lenna.png')
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rows, cols, ch = img.shape
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cv2.imshow('img', img)
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img0 = move(img, 100, 50)
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img1 = rotate(img,45)
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img2 = reflect_x()
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img3 = reflect_y()
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img4 = zoom(img,2,2)
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img5 = affine(img)
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img6 = toushi()
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cv2.imshow('img0', img0)
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cv2.imshow('img1', img1)
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cv2.imshow('img2', img2)
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cv2.imshow('img3', img3)
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cv2.imshow('img4', img4)
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cv2.imshow('img5', img5)
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cv2.imshow('img6', img6)
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im_path = 'dog.png'
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image = np.array(Image.open(im_path))
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image1 = NN_interpolation(image, image.shape[0] * 2, image.shape[1] * 2)
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image2 = BiLinear_interpolation(image, image.shape[0] * 2, image.shape[1] * 2)
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image3 = BiCubic_interpolation(image, image.shape[0] * 2, image.shape[1] * 2)
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cv2.imshow('BiCubic_interpolation', image3)
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cv2.imshow('BiLinear_interpolation', image2)
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cv2.imshow('NN_interpolation', image1)
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cv2.waitKey(0)
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cv2.destroyAllWindows() |