imread Mac下基于opencv做单应性矩阵求解和几何校正

这段时间做了些工作，在Mac下基于opencv做了单应性矩阵的求解，下面的文档中给出了循环读取文件的代码，读出文件以后两两做siftImageAlignment，求出了几何校正后影像result和单应性矩阵H。imreadimread

import numpy

import os

import math

import cv2

def image_file(file_path):

if os.path.exists(file_path):

print(“files exists”)

else:

print(“no files exists”)

image_dir = []

files = os.listdir(file_path)

for fi in files:

fi_d = os.path.join(file_path, fi)

if os.path.isdir(fi_d):

print(os.path.join(file_path, fi_d))

image_file(fi_d)

else:

if fi.endswith(‘JPG’):

print(os.path.join(file_path, fi_d))

image_dir.append(fi_d)

else:

print(“error file”)

print len(image_dir)

return image_dir

def save_file(img_dir,save_image,save_path):

[dirname,filename] = os.path.split(img_dir)

save_image_ = os.path.join(save_path,filename)

cv2.imwrite(save_image_, save_image)

return save_image_

def sift_kp(image):

print ‘sift_kp’

gray_image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

sift = cv2.xfeatures2d_SIFT.create()

kp,des = sift.detectAndCompute(image,None)

kp_image = cv2.drawKeypoints(gray_image,kp,None)

return kp_image,kp,des

def get_good_match(des1,des2):

print ‘get_good_match’

bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append(m)
return good

def siftImageAlignment(img1,img2):

print ‘siftImageAlignment’

_,kp1,des1 = sift_kp(img1)

_,kp2,des2 = sift_kp(img2)

goodMatch = get_good_match(des1,des2)

print ‘get_good_match_end’

if len(goodMatch) > 4:

ptsA= numpy.float32([kp1[m.queryIdx].pt for m in goodMatch]).reshape(-1, 1, 2)

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