pip install opencv-python
pip install matplotlib
- Stitching several images to get panorama image.
- For this, we need to implement Warping and Stitching.
Rembrandt, <The Nightwatch>, 1642
Instead of SVD, I used the LinearAlgebra concept to obtain H. When using SVD, there are problems such as having to select the smallest value, but when calculating by focusing on the Algebra concept, an h matrix(8x1) can be obtained without selecting a certain value.
To find h, we should find matrix A and matrix B
Shape of A is (2N,8) and is volatile, so it creates a frame with A = np.empty((0,8)) and adds values corresponding to the matrix whenever a point is added.
Get matrix A
N = len(src_points)
xy_1 = src_points
xy_2 = dst_points
# test for n(len) = 8 -> (2n = 16)
len_of_pair = len(xy_1)
# 1. make A
A = np.empty((0, 8))
for idx_a in range(len_of_pair):
A = np.append(A, np.array([[xy_1[idx_a][0], xy_1[idx_a][1], 1, 0, 0, 0, -
xy_1[idx_a][0] * xy_2[idx_a][0], -xy_1[idx_a][1] * xy_2[idx_a][0]]]), axis=0)
A = np.append(A, np.array([[0, 0, 0, xy_1[idx_a][0], xy_1[idx_a][1], 1, -
xy_1[idx_a][0] * xy_2[idx_a][1], -xy_1[idx_a][1] * xy_2[idx_a][1]]]), axis=0)
A = A.astype(int)
Get matrix B same as A. Then, H is obtained from A and B by referring to the above equation. At this time, since H is a 3x3 matrix, 1 is added to h33 by definition.
Get matrix B and calculate h by A and B
# 2. make B
B = np.empty((0, 1))
for idx_b in range(len_of_pair):
B = np.append(B, np.array([[xy_2[idx_b][0]]]), axis=0)
B = np.append(B, np.array([[xy_2[idx_b][1]]]), axis=0)
# 3. caculate H by A, B
At = np.transpose(A)
part_A = At.dot(A)
part_B = At.dot(B)
H = np.linalg.inv(part_A).dot(part_B)
H = np.transpose(H)[0]
H = np.append(H, 1)
H = np.reshape(H, (3, 3))
return HImplement a function that warps according to the target plane using Bilinar interpolation. By applying a Homography matrix to the image, src_img is warped to fit the coordinates of the target(dst_img).
Divide the coordinate scale into trans_x and trans_y and store it as a variable. Calculate the Villinar interpolation by dividing the water purification part and the decimal part into tx, ty / a, and b. Finally, it is trimmed into int form and returned.
Bilinear Interpolation
warped[y][x] = ((((1.0 - a) * (1.0 - b)) * src_img[ty][tx])
+ ((a * (1.0 - b)) * src_img[ty][tx + 1])
+ ((a * b) * src_img[ty + 1][tx + 1])
+ (((1.0 - a) * b) * src_img[ty + 1][tx]))Stitch only needs to add warped images and dst_img to the process of calculating get_coord, get_homography, and warp in order.
The task of attaching the image was carried out simply by allocating dst_img value after the right-hand coordinate of warped(result)
src_points, dst_points = get_coord(src_img, dst_img)
H = get_homography(src_points, dst_points)
Hi = np.linalg.inv(H)
target_h = int((src_img.shape[0] + dst_img.shape[0]) * 1)
target_w = int((src_img.shape[1] + dst_img.shape[1]) * 1)
warped = warp(src_img, Hi, target_h, target_w)
result_h, result_w = dst_img.shape[:2]
result = np.copy(warped)
result[0:result_h, 0:result_w, :] = dst_img- Hi(H_inverse) should be applied to calculate (because it is the principle that A is warp by calculating Hi on B)
Some slides are from SeonJoo Kim, Yonsei University
how-to-perform-bilinear-interpolation-in-python
OS: Mac Ventura
Language: Python(3.9.12)