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Spagnolo Gasper 2022-11-27 12:58:38 +01:00
parent ecf7355b64
commit 6af7bbb4b8
2 changed files with 95 additions and 7 deletions
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57
assignment4/solution.py
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@ -93,21 +93,72 @@ def two_b() -> None:
"""
jjjjj
"""
graph_a_small = uz_image.imread_gray("datam/img1.jpg", uz_image.ImageType.float64)
graph_b_small = uz_image.imread_gray("datam/img2.jpg", uz_image.ImageType.float64)
#graph_a_small = uz_image.imread_gray("datam/img1.jpg", uz_image.ImageType.float64)
#graph_b_small = uz_image.imread_gray("datam/img2.jpg", uz_image.ImageType.float64)
graph_a_small = uz_image.imread_gray("data/graf/graf_a_small.jpg", uz_image.ImageType.float64)
graph_b_small = uz_image.imread_gray("data/graf/graf_b_small.jpg", uz_image.ImageType.float64)
a, b = uz_image.find_matches(graph_a_small, graph_b_small)
print(a)
print(b)
uz_image.display_matches(graph_a_small, a, graph_b_small, b)
def ex3():
three_a()
def three_a() -> None:
"""
hello
"""
keypoints_path = ["data/newyork/newyork.txt", "data/graf/graf.txt"]
images_a_path = ["data/newyork/newyork_a.jpg", "data/graf/graf_a.jpg"]
images_b_path = ["data/newyork/newyork_b.jpg", "data/graf/graf_b.jpg"]
def map_keypoints(keypoints):
# Map the keypoints
a_points =[]
b_points = []
for row in keypoints:
a_points.append((row[0], row[1]))
b_points.append((row[2], row[3]))
return np.array(a_points), np.array(b_points)
fig, axs = plt.subplots(4, 2)
fig.suptitle("Transformation and rotation using homography")
for i in range(len(keypoints_path)):
keypoints = np.loadtxt(keypoints_path[i], dtype=np.float64)
image_a = uz_image.imread_gray(images_a_path[i], uz_image.ImageType.float64)
image_b = uz_image.imread_gray(images_b_path[i], uz_image.ImageType.float64)
axs[i*2, 0].imshow(image_a, cmap="gray")
axs[i*2, 1].imshow(image_b, cmap="gray")
axs[i*2, 0].set_title("A")
axs[i*2, 1].set_title("B")
homography_matrix = uz_image.estimate_homography(image_a, image_b, keypoints)
img_output = cv2.warpPerspective(image_a, homography_matrix, (image_a.shape[1], image_a.shape[0]))
axs[i*2+1, 0].imshow(img_output, cmap="gray")
axs[i*2+1, 0].set_title("A transformed")
# invert keypoints
keypoints[:,[0, 1, 2, 3]] = keypoints[:,[2, 3, 0, 1]]
homography_matrix = uz_image.estimate_homography(image_a, image_b, keypoints)
img_output = cv2.warpPerspective(image_b, homography_matrix, (image_b.shape[1], image_b.shape[0]))
axs[i*2+1, 1].imshow(img_output, cmap="gray")
axs[i*2+1, 1].set_title("B transformed")
plt.show()
# ######## #
# SOLUTION #
# ######## #
def main():
#ex1()
ex2()
#ex2()
ex3()
if __name__ == '__main__':
main()

41
assignment4/uz_framework/image.py
View File

@ -1065,17 +1065,23 @@ def find_matches(image_a: npt.NDArray[np.float64],
"""
# Get the keypoints
_, image_a_keypoints = harris_detector(image_a, 6, treshold=1e-6)
_, image_b_keypoints = harris_detector(image_b, 6, treshold=1e-6)
_, image_a_keypoints = harris_detector(image_a, 3, treshold=1e-6)
_, image_b_keypoints = harris_detector(image_b, 3, treshold=1e-6)
print("[+] Keypoints detected")
# Get the descriptors
image_a_descriptors = simple_descriptors(image_a, image_a_keypoints[:, 0], image_a_keypoints[:, 1])
image_b_descriptors = simple_descriptors(image_b, image_b_keypoints[:, 0], image_b_keypoints[:, 1])
print("[+] Descriptors computed")
# Find correspondences
correspondences_a = find_correspondences(image_a_descriptors, image_b_descriptors)
correspondences_b = find_correspondences(image_b_descriptors, image_a_descriptors)
print("[+] Correspondences found")
def select_best_correspondences(c_a, d_a, c_b, d_b):
#Find correspondances that map into same index a->b (b)
unique, counts = np.unique(c_a[:, 1], return_counts=True)
@ -1108,6 +1114,7 @@ def find_matches(image_a: npt.NDArray[np.float64],
correspondences_a = select_best_correspondences(correspondences_a, image_a_descriptors, correspondences_b, image_b_descriptors)
correspondences_b = select_best_correspondences(correspondences_b, image_b_descriptors, correspondences_a, image_a_descriptors)
print("[+] Correspondences filtered")
def check_repciporacvbillity(c_a, c_b):
for _, match in enumerate(c_a):
if np.flip(match) not in c_b:
@ -1119,8 +1126,38 @@ def find_matches(image_a: npt.NDArray[np.float64],
correspondences_a = check_repciporacvbillity(correspondences_a, correspondences_b)
correspondences_b = check_repciporacvbillity(correspondences_b, correspondences_a)
print("[+] Correspondences reciprocated")
# Map correspondences to keypoints
image_a_keypoints = np.flip(image_a_keypoints[correspondences_a[:, 0]])
image_b_keypoints = np.flip(image_b_keypoints[correspondences_b[:, 0]])
return image_a_keypoints, image_b_keypoints
def estimate_homography(image_a: npt.NDArray[np.float64],
image_b: npt.NDArray[np.float64],
keypoints: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:
"""
[x_r1 yr_1 1 0 0 0 -x_t1*x_r1 -x_t1*yr_1 -x_t1]
[0 0 0 x_r1 yr_1 1 -y_t1*x_r1 -y_t1*yr_1 -y_t1]
....
"""
# Construct the A matrix
A = np.zeros((2 * keypoints.shape[0], 9))
for ix, pair in enumerate(keypoints):
x_r, y_r, x_t, y_t = pair
A[ix*2] = [x_r, y_r, 1, 0, 0, 0, -x_t*x_r, -x_t*y_r, -x_t]
A[ix*2+1] = [0, 0, 0, x_r, y_r, 1, -y_t*x_r, -y_t*y_r, -y_t]
# Perform SVD
_, _, V = np.linalg.svd(A)
# Compute vector h
h = V[-1] / V[-1][-1]
# Reshape to 3x3
H = h.reshape(3, 3)
return H