def test_rectify_pair(self):
left = cv2.imread('test_data/kitchen_left.jpg')
right = cv2.imread('test_data/kitchen_right.jpg')
F, H_left, H_right = stereo.rectify_pair(left, right)
# Check accuracy against known fundamental matrix and homographies.
max_difference_magnitude = 0.5
F_expected = numpy.array(
[[2.38958660e-07, -2.30078185e-05, -3.82577705e-03],
[-4.57131715e-05, -2.03219010e-06, 1.42189842e-01],
[-5.01095221e-03, -1.09146504e-01, 1.00000000e+00]])
F_diff = self._matrix_diff(F_expected, F, "fundamental matrix")
self.assertLessEqual(F_diff, max_difference_magnitude)
H_left_expected = numpy.array(
[[-9.78273713e-02, 6.04430191e-02, -1.69179548e+01],
[-4.78698812e-03, -1.09671985e-01, 2.31661658e-01],
[4.25159162e-05, -8.17208264e-07, -1.32607389e-01]])
H_left_diff = self._matrix_diff(H_left_expected, H_left,
"left rectifying homography")
H_right_expected = numpy.array(
[[1.04725916e+00, 1.32521331e-01, -7.95404897e+01],
[-2.73716541e-02, 1.00451089e+00, 5.43598999e+00],
[1.96336656e-04, 2.48446573e-05, 9.32407071e-01]])
H_right_diff = self._matrix_diff(H_right_expected, H_right,
"right rectifying homography")
def test_rectify_pair(self):
left = cv2.imread('test_data/kitchen_left.jpg')
right = cv2.imread('test_data/kitchen_right.jpg')
F, H_left, H_right = stereo.rectify_pair(left, right)
# Check accuracy against known fundamental matrix and homographies.
max_difference_magnitude = 0.5
F_expected = numpy.array(
[[2.38958660e-07, -2.30078185e-05, -3.82577705e-03],
[-4.57131715e-05, -2.03219010e-06, 1.42189842e-01],
[-5.01095221e-03, -1.09146504e-01, 1.00000000e+00]])
F_diff = self._matrix_diff(F_expected, F, "fundamental matrix")
self.assertLessEqual(F_diff, max_difference_magnitude)
H_left_expected = numpy.array(
[[-9.78273713e-02, 6.04430191e-02, -1.69179548e+01],
[-4.78698812e-03, -1.09671985e-01, 2.31661658e-01],
[4.25159162e-05, -8.17208264e-07, -1.32607389e-01]])
H_left_diff = self._matrix_diff(H_left_expected, H_left,
"left rectifying homography")
H_right_expected = numpy.array(
[[1.04725916e+00, 1.32521331e-01, -7.95404897e+01],
[-2.73716541e-02, 1.00451089e+00, 5.43598999e+00],
[1.96336656e-04, 2.48446573e-05, 9.32407071e-01]])
H_right_diff = self._matrix_diff(H_right_expected, H_right,
"right rectifying homography")
import argparse
parser = argparse.ArgumentParser(description='TEST DESCRIPTION')
parser.add_argument('-l', '--left', help='Input left image', required=True)
parser.add_argument('-r', '--right', help='Input right image', required=True)
args = parser.parse_args()
print ("Left image: %s" % args.left)
print ("Right image: %s" % args.right)
image_left = cv2.imread(args.left)
image_right = cv2.imread(args.right)
focal_length = 10
F, h_left, h_right = stereo.rectify_pair(image_left, image_right)
r_image_left = cv2.warpPerspective(image_left, h_left,
image_left.shape[:2])
r_image_right = cv2.warpPerspective(image_right, h_right,
image_right.shape[:2])
disp = stereo.disparity_map(image_left, image_right)
ply_string = stereo.point_cloud(disp, image_left, focal_length)
cv2.imwrite("image_left.jpg", r_image_left)
cv2.imwrite("image_right.jpg", r_image_right)
cv2.imwrite("disparity.jpg", disp)
"""Project 2: results script
We used a picture we took of kitchen objects, they can be found under test_data
The script uses right_4.jpn and left_4.jpg
"""
import cv2
import stereo
import numpy
if __name__ == '__main__':
# Read initial images
img_right = cv2.imread("test_data/right_4.jpg")
img_left = cv2.imread("test_data/left_4.jpg")
F, H_left, H_right = stereo.rectify_pair(img_left, img_right)
rectified_left = stereo.warp_image(img_left, H_left)
cv2.imwrite("test_data/rect_left.jpg", rectified_left)
rectified_right = stereo.warp_image(img_right, H_right)
cv2.imwrite("test_data/rect_right.jpg", rectified_right)
disparity = stereo.disparity_map(rectified_left, rectified_right)
cv2.imwrite("test_data/disparity.jpg", disparity)
disparity_image = cv2.imread('test_data/disparity.png',
cv2.CV_LOAD_IMAGE_GRAYSCALE)
colors = cv2.imread('test_data/left_4.jpg')
focal_length = 10
import cv2
import numpy as np
import stereo
left = cv2.imread("my_stereo/mouseleft.JPG")
right = cv2.imread("my_stereo/mouseright.JPG")
f, hleft, hright = stereo.rectify_pair(left, right)
wl, p = stereo.warp_image(left, hleft)
wr, p = stereo.warp_image(right, hright)
# cv2.imwrite("left.png",left)
# cv2.imwrite("right.png",wr)
disparity = stereo.disparity_map(left, right)
cv2.imwrite('disparity.png', disparity)
s = stereo.point_cloud(disparity, left, 3)
with open("my_stereo/mouse.ply", 'w') as f:
f.write(s)
