Is it common for sift.compute to eliminate almost all key points generated by shift.detect? - opencv

Question

I am trying to align multispectral drone images using opencv, and when I try and use homography to align the images I get an error stating I need at least 4 matching points. I went back and broke up the sift.detectandcompute function into two separate lines and printed the number of detected points after each. after sift.detect I had over 100,000 points, when I ran sift.compute, it eliminated that number down to just two. Is there a way to make it less restrictive?
Ive included my code below in case that helps.
import cv2
import numpy as np
import os
def final_align(file_paths):
# Load the images from the file paths
images = [cv2.imread(file_path) for file_path in file_paths]
# Define the calibrated optical centers for each image
calibrated_optical_centers = {
"1": (834.056702, 643.766418),
"2": (836.952271, 631.696899),
"3": (832.183411, 642.485901),
"4": (795.311279, 680.615906),
"5": (807.490295, 685.338379),
}
# Create a list to store the aligned images
aligned_images = []
for file_path in file_paths:
# Get the 5th from last character in the file path
image_id = file_path[-5]
# Get the calibrated optical center for the image
calibrated_optical_center = calibrated_optical_centers[image_id]
# Load the image
image = cv2.imread(file_path)
# Get the shape of the image
height, width = image.shape[:2]
# Calculate the center of the image
center_x = width // 2
center_y = height // 2
# Calculate the shift needed to align the image
shift_x = float(calibrated_optical_center[0] - center_x)
shift_y = float(calibrated_optical_center[1] - center_y)
# Create a translation matrix
M = np.float32([[1, 0, shift_x], [0, 1, shift_y]])
# Apply the translation to the image
aligned_image = cv2.warpAffine(image, M, (width, height))
# Add the aligned image to the list of aligned images
aligned_images.append(aligned_image)
return aligned_images
file_paths = [
"G:\Shared\Mulitband\\flights\\flight_1\\2611_DJI_0011.TIF",
"G:\Shared\Mulitband\\flights\\flight_1\\2612_DJI_0012.TIF",
"G:\Shared\Mulitband\\flights\\flight_1\\2613_DJI_0013.TIF",
"G:\Shared\Mulitband\\flights\\flight_1\\2614_DJI_0014.TIF",
"G:\Shared\Mulitband\\flights\\flight_1\\2615_DJI_0015.TIF",
]
# Call the final_align function
final_aligned_images = final_align(file_paths)
# Get the center of the first image
height, width = final_aligned_images[0].shape[:2]
center_y = height // 2
center_x = width // 2
# Specify the crop size in the y and x direction
crop_y = 1220
crop_x = 1520
#crop function
def crop_images(final_aligned_images, center_y, center_x, crop_y, crop_x):
cropped_images = []
for image in final_aligned_images:
height, width = image.shape[:2]
start_y = center_y - crop_y // 2
end_y = center_y + crop_y // 2 + 1
start_x = center_x - crop_x // 2
end_x = center_x + crop_x // 2 + 1
cropped_image = image[start_y:end_y, start_x:end_x]
cropped_images.append(cropped_image)
return cropped_images
cropped_images = crop_images(final_aligned_images, center_y, center_x, crop_y, crop_x)
#print(cropped_images)
for i, final_complete_image in enumerate(cropped_images):
# Create the Results/aligned directory if it doesn't exist
os.makedirs("G:\Shared\Mulitband\Results\\aligned", exist_ok=True)
# Construct the file path for the aligned image
final_aligned_image_path = "G:\Shared\Mulitband\Results\\aligned\\aligned_{}.tif".format(i)
# Save the final aligned image to the file path
cv2.imwrite(final_aligned_image_path, final_complete_image)
"""
# TEST OF FUNCTION
img = cropped_images[1]
# Call the sift_align function
sift = cv2.xfeatures2d.SIFT_create()
kp = sift.detect(cropped_images[1], None)
img=cv2.drawKeypoints(cropped_images[1] ,
kp ,
img,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imwrite('G:\Shared\Mulitband\Results\\aligned\image-with-keypoints.jpg', img)"""
#Create the SIFT Function
def sift_align(cropped_images):
# Create the SIFT detector and descriptor
sift = cv2.SIFT_create()
# Create a list to store the aligned images
aligned_images = []
# Choose the first image as the reference image
reference_image = cropped_images[0]
# reference_image = cv2.convertScaleAbs(reference_image, alpha=(255.0/65535.0))
# Detect the keypoints and compute the descriptors for the reference image ", reference_descriptors"
reference_keypoints = sift.detect(reference_image, None)
reference_keypoints = sift.compute(reference_image, reference_keypoints)
print("Number of keypoints in reference image:", len(reference_keypoints))
# Iterate over the remaining images
for i, image in enumerate(cropped_images[1:]):
# Detect the keypoints and compute the descriptors for the current image
image_keypoints, = sift.detect(image, None)
# Use the BFMatcher to find the best matches between the reference and current image descriptors
bf = cv2.BFMatcher()
# matches = bf.match(image_descriptors, image_descriptors)
# Sort the matches based on their distances
matches = sorted(matches, key = lambda x:x.distance)
# Use the best matches to estimate the homography between the reference and current image
src_pts = np.float32([reference_keypoints[m.queryIdx].pt for m in matches[:50]]).reshape(-1,1,2)
dst_pts = np.float32([image_keypoints[m.trainIdx].pt for m in matches[:50]]).reshape(-1,1,2)
homography, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
# Use the homography to align the current image with the reference image
aligned_image = cv2.warpPerspective(image, homography, (reference_image.shape[1], reference_image.shape[0]))
# Add the aligned image to the list of aligned images
aligned_images.append(aligned_image)
# Stack the aligned images along the third dimension
aligned_images = np.stack(aligned_images, axis=-1)
return aligned_images
final_complete_images = sift_align(cropped_images)
"""# Save the final aligned images to the Results/aligned directory
for i, final_complete_image in enumerate(final_complete_images):
# Create the Results/aligned directory if it doesn't exist
os.makedirs("G:\Shared\Mulitband\Results\\aligned", exist_ok=True)
# Construct the file path for the aligned image
final_aligned_image_path = "G:\Shared\Mulitband\Results\\aligned\\aligned_{}.tif".format(i)
# Save the final aligned image to the file path
cv2.imwrite(final_aligned_image_path, final_complete_image)"""