def test_findDistanceToMask(self):
image_1 = self.images[0]
image_2 = self.images[1]
num_matches = 10
kp1, kp2, matches = pano.findMatchesBetweenImages(
image_1, image_2, num_matches)
homography = pano.findHomography(kp1, kp2, matches)
corners_1 = pano.getImageCorners(image_1)
corners_2 = pano.getImageCorners(image_2)
min_xy, max_xy = pano.getBoundingCorners(corners_1, corners_2,
homography)
left_image = pano.warpCanvas(image_1, homography, min_xy, max_xy)
right_image = np.zeros_like(left_image)
min_xy = min_xy.astype(np.int)
right_image[-min_xy[1]:-min_xy[1] + image_2.shape[0],
-min_xy[0]:-min_xy[0] + image_2.shape[1]] = image_2
left_mask = pano.createImageMask(left_image)
right_mask = pano.createImageMask(right_image)
left_only_mask, overlap_mask, right_only_mask = pano.createRegionMasks(
left_mask, right_mask)
left_distance_mask = pano.findDistanceToMask(left_mask)
left_only_distance_mask = pano.findDistanceToMask(left_only_mask)
write_mask_image(left_only_distance_mask,
"left_only_distance_mask.png")
overlap_distance_mask = pano.findDistanceToMask(overlap_mask)
right_distance_mask = pano.findDistanceToMask(right_mask)
right_only_distance_mask = pano.findDistanceToMask(right_only_mask)
write_mask_image(right_only_distance_mask,
"right_only_distance_mask.png")
def test_getImageCorners(self):
# getCorners_cv2 = []
# cv2.getPerspectiveTransform(self.images[0], getCorners_cv2)
# print(getCorners_cv2)
print(self.images[0].shape)
getCorners_my = pano.getImageCorners(self.images[0])
print(getCorners_my)
from os import path
#import assignment8 as a8
import panorama as a8
SRC_FOLDER = "images/source"
num_matches = 10
from matplotlib import pyplot as plt
image1 = plt.imread('images/source/sample/1.jpg')
image2 = plt.imread('images/source/sample/2.jpg')
image3 = plt.imread('images/source/sample/3.jpg')
corner_1 = a8.getImageCorners(image1)
corner_2 = a8.getImageCorners(image2)
corner_3 = a8.getImageCorners(image3)
image_1_kp, image_2_kp, matches = a8.findMatchesBetweenImages(
image1, image2, num_matches)
#print image_1_kp
homography = a8.findHomography(image_1_kp, image_2_kp, matches)
print homography
min_xy, max_xy = a8.getBoundingCorners(corner_1, corner_2, homography)
print min_xy, max_xy
warped_image = a8.warpCanvas(image1, homography, min_xy, max_xy)
print warped_image