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epilines.py
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"""
It is for Epipolar geometry
"""
import numpy as np
import cv2
from matplotlib import pyplot as plt
def Epipolar_geometry(leftpath, rightpath):
"""
:param leftpath: The path of left images
:param rightpath: The path of right images
:return:
"""
# objP = np.zeros((6 * 7, 3), np.float32)
# objP[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)
# patternSize = (7, 6)
imgl = cv2.imread(leftpath, 0) # queryimage # left image
imgr = cv2.imread(rightpath, 0) # trainimage # right image
# id = leftpath[16:]
id = leftpath[42:]
# # The origin image is gray
# grayl = cv2.cvtColor(imgl, cv2.COLOR_BGR2GRAY)
# grayr = cv2.cvtColor(imgr,cv2.COLOR_BGR2GRAY)
# criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# retl, cornersl = cv2.findChessboardCorners(grayl, patternSize, None)
# retr, cornersr = cv2.findChessboardCorners(grayr, patternSize, None)
# if not retl or not retr:
# return
# cornersl2 = cv2.cornerSubPix(grayl, cornersl, (11, 11), (-1, -1), criteria)
# cornersr2 = cv2.cornerSubPix(grayr, cornersr, (11, 11), (-1, -1), criteria)
# imgl = cv2.drawChessboardCorners(grayl, patternSize, cornersl2, retl)
# imgr = cv2.drawChessboardCorners(grayr, patternSize, cornersr2, retr)
# FLANN: Fast Libary for Approximate Nearest Neighbors
(pts1, pts2) = findMatches(imgl, imgr, id)
F, pts1, pts2 = findFundamentalMatrix(pts1, pts2)
findEpilines(imgl, imgr, pts1, pts2, F, id)
# # Brute Force
# sift = cv2.xfeatures2d.SIFT_create(100)
# kp1, des1 = sift.detectAndCompute(imgl, None)
# kp2, des2 = sift.detectAndCompute(imgr, None)
# bf = cv2.BFMatcher()
# # bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck = False)
# matches = bf.knnMatch(des1, des2, k=2)
# goodMatches = []
# minRatio = 1/3
# for m,n in matches:
# if m.distance / n.distance < minRatio:
# goodMatches.append([m])
# sorted(goodMatches,key=lambda x:x[0].distance)
# #绘制最优匹配点
# img3 = None
# img3 = cv2.drawMatchesKnn(imgl, kp1, imgr, kp2, matches, img3, flags=cv2.DRAW_MATCHES_FLAGS_DEFAULT)
# img3 = cv2.resize(img3,(1000, 400))
# cv2.imwrite('output/epilines/epilines_' + id, img3)
def findMatches(img1, img2, id):
"""
:param img1: The left image
:param img2: The right image
:param id: The name of image
:return: The list of symmetric point
"""
# vgg = cv2.xfeatures2d.VGG_create()
# brisk = cv2.BRISK_create()
# gms = cv2.xfeatures2d.matchGMS()
# sift = cv2.xfeatures2d.SIFT_create(100)
sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
matchesMask = [[0, 0] for i in range(len(matches))]
good = []
pts1 = []
pts2 = []
# ratio test as per Lowe's paper
for i, (m, n) in enumerate(matches):
if m.distance < 0.8 * n.distance:
good.append(m)
pts2.append(kp2[m.trainIdx].pt)
pts1.append(kp1[m.queryIdx].pt)
matchesMask[i] = [1, 0]
# draw matches
drawParams = dict( # singlePointColor=(255,0,0), matchColor=(0,255,0),
matchesMask=matchesMask,
flags=0)
resultImage = cv2.drawMatchesKnn(img1, kp1, img2, kp2, matches, None, **drawParams)
# # Univariate transformation
# matchesMask = Univariatetrans(good, kp1, kp2, img1, img2)
# # draw matches
# drawParams = dict(matchColor = (0,255,0), # draw matches in green color
# singlePointColor = None, matchesMask = matchesMask, flags = 2)
# resultImage = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **drawParams)
# cv2.imwrite('output/epilines/epilines_' + id, resultImage)
cv2.imwrite('output/calibration_binocular/epilines_' + id, resultImage)
return pts1, pts2
def Univariatetrans(goodMatches, kp1, kp2, img1, img2):
"""
:param goodMatches: The matches points
:param kp1: keypoints 1
:param kp2: keypoints 2
:param img1: image 1
:param img2: image 2
:return: matchesMask
"""
MIN_MATCH_COUNT = 10
if len(goodMatches) > MIN_MATCH_COUNT:
src_pts = np.float32([kp1[m.queryIdx].pt for m in goodMatches]).reshape(-1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in goodMatches]).reshape(-1, 2)
# Get the projection matrix
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
matchesMask = mask.ravel().tolist() # 用来配置匹配图，只绘制单应性图片中关键点的匹配线
h, w = img1.shape[:2]
# four corner
pts = np.float32([[55, 74], [695, 45], [727, 464], [102, 548]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)
# Draw the framework
img2 = cv2.polylines(img2, [np.int32(dst)], True, (0, 255, 0), 2, cv2.LINE_AA)
else:
print("Not enough matches are found - %d/%d" % (len(goodMatches), MIN_MATCH_COUNT))
matchesMask = None
return matchesMask
def findFundamentalMatrix(pts1, pts2):
"""
:param pts1: Symmetric point list 1
:param pts2: Symmetric point list 2
:return: Fundamental matrix and inlier points
"""
pts1 = np.int32(pts1)
pts2 = np.int32(pts2)
F, mask = cv2.findFundamentalMat(pts1, pts2, cv2.FM_LMEDS)
# F, mask = cv2.findFundamentalMat(pts1, pts2, cv2.RANSAC, 5.0)
# We select only inlier points
pts1 = pts1[mask.ravel() == 1]
pts2 = pts2[mask.ravel() == 1]
return F, pts1, pts2
def findEpilines(img1, img2, pts1, pts2, F, id):
"""
:param img1: The left image
:param img2: The right image
:param pts1: Symmetric point 1
:param pts2: Symmetric point 2
:param F: Fundamental matrix
:param id: The id of raw picture
:return:
"""
# Find epilines corresponding to points in right image (second image) [img6] and
# drawing its lines on left image [img5]
lines1 = cv2.computeCorrespondEpilines(pts2.reshape(-1, 1, 2), 2, F)
lines1 = lines1.reshape(-1, 3)
img5, img6 = drawlines(img1, img2, lines1, pts1, pts2)
# Find epilines corresponding to points in left image (first image) [img4] and
# drawing its lines on right image [img3]
lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1, 1, 2), 1, F)
lines2 = lines2.reshape(-1, 3)
img3, img4 = drawlines(img2, img1, lines2, pts2, pts1)
# cv2.imwrite('output/epilines/epilines_left' + id, img5)
# cv2.imwrite('output/epilines/epilines_right' + id, img3)
cv2.imwrite('output/calibration_binocular/epilines_left' + id, img5)
cv2.imwrite('output/calibration_binocular/epilines_right' + id, img3)
# plt.subplot(121), plt.imshow(img5)
# plt.subplot(122), plt.imshow(img3)
plt.subplot(221), plt.imshow(img5)
plt.subplot(222), plt.imshow(img6)
plt.subplot(223), plt.imshow(img3)
plt.subplot(224), plt.imshow(img4)
plt.show()
def drawlines(img1, img2, lines, pts1, pts2):
"""
:param img1: The image on which we draw the epilines for the points in img2
:param img2: The other image
:param lines: corresponding epilines
:param pts1: Inlier point 1
:param pts2: Inlier point 2
:return: The new left and right image
"""
r, c = img1.shape
img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2BGR)
img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2BGR)
for r, pt1, pt2 in zip(lines, pts1, pts2):
color = tuple(np.random.randint(0, 255, 3).tolist())
x0, y0 = map(int, [0, -r[2] / r[1]])
x1, y1 = map(int, [c, -(r[2] + r[0] * c) / r[1]])
img1 = cv2.line(img1, (x0, y0), (x1, y1), color, 1)
img1 = cv2.circle(img1, tuple(pt1), 5, color, -1)
img2 = cv2.circle(img2, tuple(pt2), 5, color, -1)
return img1, img2
def main():
# for id in range(1, 15):
# if id == 10:
# continue
# # leftpath = './data/left/left' + ('0' if (id < 10) else '') + str(id) + '.jpg'
# # rightpath = './data/right/right' + ('0' if (id < 10) else '') + str(id) + '.jpg'
# leftpath = 'output/calibration_binocular/rectifiedleft' + ('0' if (id < 10) else '') + str(id) + '.jpg'
# rightpath = 'output/calibration_binocular/rectifiedright' + ('0' if (id < 10) else '') + str(id) + '.jpg'
# print(leftpath)
# print(rightpath)
# Epipolar_geometry(leftpath, rightpath)
leftpath = 'output/calibration_binocular/rectifiedleft04.jpg'
rightpath = 'output/calibration_binocular/rectifiedright04.jpg'
print(leftpath)
print(rightpath)
Epipolar_geometry(leftpath, rightpath)
if __name__ == '__main__':
main()