def part_2a(ps5_obj, save_imgs=True):
part_1c(ps5_obj, False) # sets up arrays object from last part
# Todo: Define size values to be used in ps5.get_keypoints
size = {"trans_a": 10., "trans_b": 10., "sim_a": 10., "sim_b": 10.}
# You can leave these values to 0
octave = {"trans_a": 0, "trans_b": 0, "sim_a": 0, "sim_b": 0}
ps5_obj.compute_angles()
ps5_obj.create_keypoints(size, octave)
keypoints = ps5_obj.get_keypoints()
r_maps = ps5_obj.get_r_maps()
if save_imgs:
trans_a_out = draw_keypoints(keypoints["trans_a"], r_maps["trans_a"])
trans_b_out = draw_keypoints(keypoints["trans_b"], r_maps["trans_b"])
sim_a_out = draw_keypoints(keypoints["sim_a"], r_maps["sim_a"])
sim_b_out = draw_keypoints(keypoints["sim_b"], r_maps["sim_b"])
trans_a_b_out = ps5.make_image_pair(trans_a_out, trans_b_out)
sim_a_b_out = ps5.make_image_pair(sim_a_out, sim_b_out)
imwrite("ps5-2-a-1.png", trans_a_b_out)
imwrite("ps5-2-a-2.png", sim_a_b_out)
def part_1a(ps5_obj, save_imgs=True):
gradients = ps5_obj.get_gradients_a()
trans_a_x, trans_a_y = gradients["t_x"], gradients["t_y"]
sim_a_x, sim_a_y = gradients["s_x"], gradients["s_y"]
if save_imgs:
trans_a_pair = ps5.make_image_pair(trans_a_x, trans_a_y)
imwrite("ps5-1-a-1.png", trans_a_pair)
sim_a_pair = ps5.make_image_pair(sim_a_x, sim_a_y)
imwrite("ps5-1-a-2.png", sim_a_pair)
return {"t_x": trans_a_x, "t_y": trans_a_y, "s_x": sim_a_x, "s_y": sim_a_y}
def part_3a(ps5_obj):
part_2b(ps5_obj, False) # Sets up arrays object from part 2b
images = ps5_obj.get_input_images()
k_pts = ps5_obj.get_keypoints()
matches = ps5_obj.get_matches()
threshold = 15. # Todo: Define a threshold value.
translation, good_matches = ps5.compute_translation_RANSAC(
k_pts["trans_a"], k_pts["trans_b"], matches["trans"], threshold)
print '3a: Translation vector: \n', translation
trans_pair = ps5.make_image_pair(images["trans_a"], images["trans_b"])
trans_pair = draw_consensus_set(good_matches, trans_pair,
images["trans_a"].shape)
cv2.imwrite(os.path.join(output_dir, "ps5-3-a-1.png"), trans_pair)
def part_3c(ps5_obj, save_imgs=True):
part_2b(ps5_obj, False) # Sets up arrays object from part 2b
images = ps5_obj.get_input_images()
k_pts = ps5_obj.get_keypoints()
matches = ps5_obj.get_matches()
threshold = 15. # Todo: Define a threshold value.
similarity_affine, sim_aff_good_matches = ps5.compute_affine_RANSAC(
k_pts["sim_a"], k_pts["sim_b"], matches["sim"], threshold)
if save_imgs:
print '3c: Transform Matrix for the best set: \n', similarity_affine
sim_aff_pair = ps5.make_image_pair(images["sim_a"], images["sim_b"])
sim_aff_pair = draw_consensus_set(sim_aff_good_matches, sim_aff_pair,
images["sim_a"].shape)
cv2.imwrite(os.path.join(output_dir, "ps5-3-c-1.png"), sim_aff_pair)
return similarity_affine