def triangulateWithImagesAndPointFile(filename1, filename2, pointFile, projections_file=None):
'''
Read images and detect features
'''
current_dir = os.path.dirname(os.path.realpath(__file__))
img1 = Image(os.path.join(current_dir, filename1))
# img1.detect_features()
img2 = Image(os.path.join(current_dir, filename2))
# img2.detect_features()
# Match keypoints
# pts1, pts2, matches = CVFuncs.findMatches(img1, img2, filter=True)
pts1, pts2, matches = readPointsFromFile(pointFile)
# CREATE KEYPOINTS
kp1, kp2 = [], []
for point1, point2 in zip(pts1, pts2):
kp1.append(cv2.KeyPoint(point1[0], point1[1], 1))
kp2.append(cv2.KeyPoint(point2[0], point2[1], 1))
img1.kps = kp1
img2.kps = kp2
# CVFuncs.drawMatches(img1, img2, matches, "new.png")
# print pts1
# print pts2
'''
Find K
'''
K = img1.K
'''
Get essential or fundamental matrix
'''
# F, mask = CVFuncs.findFundamentalMat(pts1, pts2)
# test.testFundamentalMat(F, pts1, pts2)
E, mask = CVFuncs.findEssentialMat(pts1, pts2, K)
# E = CVFuncs.EFromF(F, K)
# test.testEssentialMat(E, K, pts1, pts2)
'''
Get R and T (using artificial ones for now)
'''
points, r, t, newMask = CVFuncs.recoverPose(E, pts1, pts2, K)
'''
Draw image projections using R and T
'''
if projections_file:
draw.drawProjections(pts1, pts2, K.matrix, r, t, projections_file)
# draw.drawProjections(pts1, pts2, K.matrix, r, t, "manualprojections.ply")
# draw.drawRandTTransformation(pts1, pts2, K.matrix, r, t, "projections.ply")
'''
Triangulate and draw points
'''
triangulated = CVFuncs.naiveTriangulate(pts1, pts2, K.matrix, r, t)
triangulated = draw.transformPointsToViewingCoordinates(triangulated)
return triangulated, r, t
