def generatePanorama(im1, im2):
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
pano_im_no_clip = imageStitching_noClip(im1, im2, H2to1)
return pano_im_no_clip
def generatePanaroma(im1, im2):
'''
Generate and save panorama of im1 and im2.
INPUT
im1 and im2 - two images for stitching
OUTPUT
Blends img1 and warped img2 (with no clipping)
and saves the panorama image.
'''
######################################
# TO DO ...
t = time.time()
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
print('Time to compute Brieflite: ' + str(time.time() - t))
t = time.time()
matches = briefMatch(desc1, desc2)
print('Time to compute BriefMatch: ' + str(time.time() - t))
t = time.time()
H = ransacH(matches, locs1, locs2, num_iter=2000, tol=2)
print('Time to compute Ransac: ' + str(time.time() - t))
np.save('../results/q6_1.npy', H)
return imageStitching_noClip(im1, im2, H)
def generatePanaroma(im1, im2):
'''
Generate and save panorama of im1 and im2.
INPUT
im1 and im2 - two images for stitching
OUTPUT
Blends img1 and warped img2 (with no clipping)
and saves the panorama image.
'''
######################################
# TO DO ...
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
locs1[:, [0, 1]] = locs1[:, [1, 0]]
locs2[:, [0, 1]] = locs2[:, [1, 0]]
bestH = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
#print(bestH)
output = imageStitching(im1, im2, bestH)
#output=imageStitching_noClip(im1,im2,bestH)
return output
def generatePanorama(im1, im2):
'''
Accepts two images as input, computes keypoints and descriptors for
both the images, finds putative feature correspondences by matching keypoint
descriptors, estimates a homography using RANSAC and then warps one of the
images with the homography so that they are aligned and then overlays them
[input]
* im1 - Input image 1
* im2 - Input image 2
[output]
* pano_im - Output panorama image
'''
# Compute keypoints and descriptors
print('Computing feature descriptors for im1...')
locs1, desc1 = briefLite(im1)
print('Computing feature descriptors for im2...')
locs2, desc2 = briefLite(im2)
# Match keypoint descriptors
matches = briefMatch(desc1, desc2)
# Estimate homography
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
# Align and blend the images to form the panorama
pano_im = imageStitching_noClip(im1, im2, H2to1)
return pano_im
def generatePanorama(im1, im2):
'''
Returns a panorama of im1 and im2 without cliping.
'''
######################################
# TO DO ...
locs1, desc1 = briefLite(im1)
print(f'No of desc1 is {desc1.shape[0]}')
locs2, desc2 = briefLite(im2)
print(f'No of desc2 is {desc2.shape[0]}')
matches = briefMatch(desc1, desc2)
print(f'No of matches is {matches.shape[0]}')
H2to1 = ransacH(matches, locs1, locs2, num_iter=10000, tol=1)
#Save result
np.save('../results/q6_1.npy', H2to1)
#pano_im = imageStitching_mask(im1, im2, H2to1)
pano_im = imageStitching(im1, im2, H2to1)
pano_im = imageStitching_noClip(im1, im2, H2to1)
cv2.imwrite('../results/q6_3.jpg', pano_im)
return pano_im
def generatePanaroma(img1, img2):
im1 = cv2.imread(img1)
im2 = cv2.imread(img2)
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
H2to1 = ransacH(matches, locs1, locs2, num_iter=10000, tol=2)
return imageStitching_noClip(im1, im2, H2to1)
def generatePanorama(im1, im2):
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
pano_im = imageStitching_noClip(im1, im2, H2to1)
cv2.imwrite('../results/q6_3.jpg', pano_im)
def generatePanorama(im1, im2):
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches, m = briefMatch(desc1, desc2)
m, n = matches.shape
H2to1 = ransacH(matches, m, locs1, locs2, num_iter=5000, tol=2)
# To get the M - Translation and scalling matrix
first_1 = np.array([[1064], [576], [1]])
first_2 = np.array([[0], [0], [1]])
first_3 = np.array([[0], [576], [1]])
first_4 = np.array([[1064], [0], [1]])
second_1 = np.matmul(H2to1, first_1)
n4 = second_1[2, 0]
second_1 = second_1 / n4
print(second_1)
second_2 = np.matmul(H2to1, first_2)
n4 = second_2[2, 0]
second_2 = second_2 / n4
print(second_2)
second_3 = np.matmul(H2to1, first_3)
n4 = second_3[2, 0]
second_3 = second_3 / n4
print(second_3)
second_4 = np.matmul(H2to1, first_4)
n4 = second_4[2, 0]
second_4 = second_4 / n4
print(second_4)
# Through geometry
image_width = second_1[1] - second_4[1]
image_height = second_4[0] - second_3[0]
# 600 and 1500 are previously defined distances in the function (Random values taken)
translation_height = image_height - 600
translation_width = 1500 - image_width
# im1 = cv2.warpPerspective(im1,np.eye(3),(3000,600))
M = np.eye(3) + np.array([[.85, 0, translation_height], [0, .85, 400],
[0, 0, 1]])
warp_im_1 = cv2.warpPerspective(im1, M, (1714, 815))
warp_im_2 = cv2.warpPerspective(im2, np.matmul(M, H2to1), (1714, 815))
im3 = np.maximum(warp_im_1, warp_im_2)
return im3
def generatePanorama(im1, im2):
'''
Returns a panorama of im1 and im2 without cliping.
'''
######################################
# TO DO ...
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
pano_im = imageStitching_noClip(im1, im2, H2to1)
return pano_im
def generatePanorama(im1, im2):
'''
Returns a panorama of im1 and im2 without cliping.
'''
######################################
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
bestH = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
blended_image = imageStitching(im1, im2, bestH)
panorama = imageStitching_noClip(im1, im2, bestH)
cv2.imwrite('../results/q6_3.jpg', panorama)
cv2.imshow('6.3p', panorama)
def generatePanorama(im1, im2):
""" This is to generate the panorama given im1 and im2 by detecting and
matching keypoints, calculating homography with RANSAC.
Args:
im1: input image1 in numpy.array with size [H, W, 3]
im2: input image2 in numpy.array with size [H, W, 3]
Returns:
im3: stitched panorama in numpy.array.
"""
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
im3 = imageStitching_noClip(im1, im2, H2to1)
return im3
def main(args):
im1 = cv2.imread(args.im1)
im2 = cv2.imread(args.im2)
# Compute BREIF descriptors
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
# Match descriptors
matches = briefMatch(desc1, desc2)
# Estimate best Homogrpahy H
H2to1, _ = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
# Stitch images together
pano_im = imageStitching_noClip(im1, im2, H2to1)
# Save and show
cv2.imwrite('../results/panoImg.jpg', pano_im)
def generatePanorama(im1, im2):
# computes keypoints and descriptors for both the images
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
# finds putative feature correspondences by matching keypoint descriptors
matches = briefMatch(desc1, desc2)
# estimates a homography using RANSAC
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
# warps one of the images with the homography so that they are aligned and then overlays them.
pano_im = imageStitching_noClip(im1, im2, H2to1)
# save and display panaroma
cv2.imwrite('../results/panoImg.jpg', pano_im)
cv2.imshow('panoramas', pano_im)
cv2.waitKey(0)
cv2.destroyAllWindows()
def generatePanaroma(im1, im2):
'''
Generate and save panorama of im1 and im2.
INPUT
im1 and im2 - two images for stitching
OUTPUT
Blends img1 and warped img2 (with no clipping)
and saves the panorama image.
'''
######################################
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
pano_im = imageStitching_noClip(im1, im2, H2to1)
cv2.imwrite('../results/6_3_stitched.jpg', pano_im)
return pano_im
def generatePanorama(im1, im2):
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
pano_im_no_clip = imageStitching_noClip(im1, im2, H2to1)
return pano_im_no_clip
if __name__ == '__main__':
im1 = cv2.imread('../data/incline_L.png')
im2 = cv2.imread('../data/incline_R.png')
print(im1.shape)
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
# plotMatches(im1,im2,matches,locs1,locs2)
H2to1 = ransacH(matches, locs1, locs2, num_iter=5000, tol=2)
# pano_im = imageStitching(im1, im2, H2to1)
print(H2to1)
# np.save('../results/q6_1.npy', H2to1)
# cv2.imwrite('../results/6_1.jpg', pano_im)
# cv2.imshow('panoramas', pano_im)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# pano_im_no_clip = imageStitching_noClip(im1, im2, H2to1)
pano_im_no_clip = generatePanorama(im1, im2)
cv2.imwrite('../results/q6_2_pan.jpg', pano_im_no_clip)
cv2.imshow('panoramas_no_clip', pano_im_no_clip)
cv2.waitKey(0)
cv2.destroyAllWindows()
# im1 = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
# im2 = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
# im3 = cv2.cvtColor(im3, cv2.COLOR_BGR2GRAY)
# to resize im3 is better.
im3 = cv2.resize(im3, (im1.shape[1], im1.shape[0]))
# im1 = cv2.resize(im1, (im3.shape[1], im3.shape[0]))
locs1, desc1 = briefLite(im1)
locs2, desc2 = briefLite(im2)
matches = briefMatch(desc1, desc2)
print(im1.shape)
print(im2.shape)
print(im3.shape)
# plotMatches(im1,im2,matches,locs1,locs2)
num_iter = 5e3
tol = 3
bestH = ransacH(matches, locs1, locs2, num_iter=num_iter, tol=tol)
print('H:', bestH)
final_img = compositeH(bestH, im2, im3)
# final_img = cv2.warpPerspective(im3, bestH, (im2.shape[1],im2.shape[0]))
res = final_img
cv2.imshow("iter=%d_tol=%d" % (num_iter, tol), res)
cv2.waitKey(0)
cv2.destroyAllWindows()
