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import cv2
import numpy as np
import matplotlib.pyplot as plt
import pdb
def rotate_to_horizontal(image):
# Rotate the image to horizontal (90 degrees counterclockwise)
rotated_image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)
return rotated_image
def rotate_to_vertical(image):
# Rotate the image back to vertical (90 degrees clockwise)
rotated_image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)
return rotated_image
def plot_images(images):
num_images = len(images)
for i, image in enumerate(images):
plt.figure(figsize=(6, 6))
plt.imshow(image)
plt.title(f"Image {i+1}/{num_images}")
plt.axis('off')
plt.show()
def image_stitching(image_paths):
images = [cv2.imread(path) for path in image_paths]
images = [image.astype(np.uint8) for image in images]
images = [rotate_to_horizontal(image) for image in images]
images = list(reversed(images))
if len(images) == 15:
images = images[2:12]
#plot_images(images)
# Create a list to store the stitched images
stitched_images = []
# Accumulated homography matrix for stitching
accumulated_homography = np.eye(3)
# Iterate through pairs of adjacent images and stitch them together
for i in range(len(images) - 1):
# Perform keypoint and feature descriptor extraction
orb = cv2.ORB_create()
keypoints_and_descriptors = [orb.detectAndCompute(image, None) for image in [images[i], images[i + 1]]]
# Match the keypoints using Brute-Force Matcher
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(keypoints_and_descriptors[0][1], keypoints_and_descriptors[1][1])
# Filter the matches to remove outliers using RANSAC
src_pts = np.float32([keypoints_and_descriptors[0][0][m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
dst_pts = np.float32([keypoints_and_descriptors[1][0][m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
M, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
# Accumulate the homography matrices
accumulated_homography = np.dot(M, accumulated_homography)
# Warp perspective and stitch the images
stitched_image = cv2.warpPerspective(images[i + 1], accumulated_homography,
(images[i].shape[1] + images[i + 1].shape[1], images[i].shape[0]))
stitched_image[0:images[i].shape[0], 0:images[i].shape[1]] = images[i]
# Remove the empty pixels and retain maximum image information
# stitched_image = remove_empty_pixels(stitched_image)
stitched_images.append(stitched_image)
# Combine all stitched images into a final panorama
final_panorama = stitched_images[0]
for i in range(1, len(stitched_images)):
final_panorama = cv2.warpPerspective(stitched_images[i], np.eye(3),
(final_panorama.shape[1] + stitched_images[i].shape[1], final_panorama.shape[0]))
final_panorama[0:stitched_images[i].shape[0], 0:stitched_images[i].shape[1]] = stitched_images[i]
gray_images = [cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) for image in images[:2]]
# Draw the keypoints on the images
keypoints_drawn = [cv2.drawKeypoints(gray_image, kp[0], None, color=(0, 255, 0),
flags=cv2.DrawMatchesFlags_DRAW_RICH_KEYPOINTS) for gray_image, kp in
zip(gray_images, keypoints_and_descriptors[:2])]
# Draw the matches on the images
matches_drawn = cv2.drawMatches(images[0], keypoints_and_descriptors[0][0], images[1],
keypoints_and_descriptors[1][0], matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
# Crop the final image to 512x512 centered around the middle
cropped_final_panorama = final_panorama[:512, :512]
rotated_final_panorama = rotate_to_vertical(cropped_final_panorama)
return rotated_final_panorama |