def test_disparity_map(self):
left = cv2.imread('test_data/tsukuba/left.png')
right = cv2.imread('test_data/tsukuba/right.png')
# Load the ground-truth disparity map.
disparity_expected = cv2.imread('test_data/tsukuba/disparity_left.png',
cv2.CV_LOAD_IMAGE_GRAYSCALE)
# Compute disparity using the function under test.
disparity = stereo.disparity_map(left, right)
# Compute the difference between the two. Useful to visualize this!
disparity_diff = cv2.absdiff(disparity, disparity_expected)
# The median difference between the expected and actual disparity
# should be less than the specified threshold.
differences = disparity_diff.flatten().tolist()
median_diff = sorted(differences)[len(differences) / 2]
self.assertLessEqual(median_diff, 5)
def test_disparity_map(self):
left = cv2.imread('test_data/tsukuba/left.png')
right = cv2.imread('test_data/tsukuba/right.png')
# Load the ground-truth disparity map.
disparity_expected = cv2.imread('test_data/tsukuba/disparity_left.png',
cv2.CV_LOAD_IMAGE_GRAYSCALE)
# Compute disparity using the function under test.
disparity = stereo.disparity_map(left, right)
# Compute the difference between the two. Useful to visualize this!
disparity_diff = cv2.absdiff(disparity, disparity_expected)
# The median difference between the expected and actual disparity
# should be less than the specified threshold.
differences = disparity_diff.flatten().tolist()
median_diff = sorted(differences)[len(differences) / 2]
self.assertLessEqual(median_diff, 5)
import stereo
import cv2
left = cv2.imread('set_left.jpg')
right = cv2.imread('set_right.jpg')
disparity = stereo.disparity_map(left, right)
cv2.imwrite("set_disparity.jpg", disparity)
colors = left
focal_length = 5
ply_string = stereo.point_cloud(disparity, colors, focal_length)
with open("set.ply", 'w') as f:
f.write(ply_string)
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)
with open("out.ply", 'w') as f:
f.write(ply_string)
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
ply_string = stereo.point_cloud(disparity, colors, focal_length)
# View me in Meshlab!
with open("our_results.ply", 'w') as f:
f.write(ply_string)
if len(sys.argv) < 3:
print "MUST PASS IN AT LEAST 2 IMAGES TO RECTIFY"
exit()
# Dispose of the first argument (script name)
sys.argv.remove(sys.argv[0])
# Use the last argument as the output filename
# for the PLY file, then remove it from the
# list of arguments
output_filename = sys.argv[len(sys.argv) - 1]
sys.argv.remove(sys.argv[len(sys.argv) - 1])
# Read in the two images, using the only remaining
# arguments as the filenames of the left and right images
image_left = cv2.imread(sys.argv[0])
image_right = cv2.imread(sys.argv[1])
# Calculate the disparity image
disparity_image = stereo.disparity_map(image_left, image_right)
# Construct a point cloud
pc = stereo.point_cloud(disparity_image, image_left, 10)
# Write out the point cloud
with open(output_filename, 'w') as f:
f.write(pc)
# Write out the disparity image
cv2.imwrite('disparity_image.png', disparity_image)