def generate_panorama(data_dir, file_prefix, num_images, figsize=(20, 20)):
# The naming convention for a sequence of images is nameN.jpg, where N is a running number 1,2,..
files = [
os.path.join(data_dir, '%s%d.jpg' % (file_prefix, i + 1))
for i in range(num_images)
]
# Read images.
ims = [sol4_utils.read_image(f, 1) for f in files]
# Extract feature point locations and descriptors.
def im_to_points(im):
pyr, _ = sol4_utils.build_gaussian_pyramid(im, 3, 7)
return sol4.find_features(pyr)
p_d = [im_to_points(im) for im in ims]
# Compute homographies between successive pairs of images.
Hs = []
for i in range(num_images - 1):
points1, points2 = p_d[i][0], p_d[i + 1][0]
desc1, desc2 = p_d[i][1], p_d[i + 1][1]
# Find matching feature points.
ind1, ind2 = sol4.match_features(desc1, desc2, .7)
points1, points2 = points1[ind1, :], points2[ind2, :]
# Compute homography using RANSAC.
H12, inliers = sol4.ransac_homography(points1, points2, 10000, 6)
# Display inlier and outlier matches.
sol4.display_matches(ims[i],
ims[i + 1],
points1,
points2,
inliers=inliers)
Hs.append(H12)
# Compute composite homographies from the panorama coordinate system.
Htot = sol4.accumulate_homographies(Hs, (num_images - 1) // 2)
# Final panorama is generated using 3 channels of the RGB images
ims_rgb = [sol4_utils.read_image(f, 2) for f in files]
# Render panorama for each color channel and combine them.
panorama = [
sol4.render_panorama([im[..., i] for im in ims_rgb], Htot)
for i in range(3)
]
panorama = np.dstack(panorama)
#plot the panorama
plt.imsave('external/kitchen.jpg', panorama)
plt.figure(figsize=figsize)
plt.imshow(panorama.clip(0, 1))
plt.show()
def gen_pan_with_pyr_blend(ims_rgb, Htot):
"""
Renders rgb panorama with pyramid blending, for each channel separately
:param ims_rgb: a list of consecutive rgb images
:param Htot: a list of corresponding homographies from image i to the center image
:return: A rgb image with the rendered panorama
"""
# Render panorama for each color channel and combine them.
panorama = [
sol4.render_panorama([im[..., i] for im in ims_rgb], Htot)
for i in range(3)
]
panorama = np.dstack(panorama)
return panorama