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reconstruction.py
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Sun, Feb 23, 11:38

reconstruction.py
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import seg_and_disp as sad
from scipy.misc import imread
import matplotlib.pyplot as plt
import numpy as np
from fullpipe import points_from_mask, warp
from misc import load_swc, gaussianSmooth3D
from mpl_toolkits.mplot3d import Axes3D
from scipy.signal import medfilt
from experimentation import compute_matches, backerror, find_cameras, computeFundamental, to_camera
from ransac import ransac
from tomasikanade import anglesBetweenCameras
import argparse
from os.path import expanduser
from keras import backend
def disparity4points(points, disparity):
disp = []
for i in range(len(points)):
disp.append(disparity[points[i, 0], points[i, 1]])
return np.array(disp)
def paint_path(shape, points):
image = np.zeros(shape)
for i, point in enumerate(points):
image[point[0], point[1]] = 255
return image
def main():
backend.set_image_dim_ordering('th')
cmap = 'plasma'
parser = argparse.ArgumentParser(description='Test model on image.')
parser.add_argument('w', metavar='w', type=str, nargs=1,
help='weights')
parser.add_argument('i', metavar='i', type=str, nargs=1,
help='image')
parser.add_argument('--j', type=str, nargs=1, help="second image")
parser.add_argument('--d', type=str, nargs=1, help="delineation image 1")
parser.add_argument('--e', type=str, nargs=1, help="delineation image 2")
parser.add_argument("--full", action="store_true", help="The filters are applied once on the whole image")
args = parser.parse_args()
weights_path = expanduser(args.w[0])
image_paths = [expanduser(args.i[0]),
expanduser(args.j[0])]
line_path = [expanduser(args.d[0]),
expanduser(args.e[0])]
lines = [load_swc(lp) for lp in line_path]
images = [imread(p) for p in image_paths]
images = [paint_path(images[0].shape, line) for line in lines]
flow_x, flow_y = sad.disparity(*images, weights_path=weights_path, full=True)
potential_matches = compute_matches((flow_x, flow_y), lines[0])
best_fit, largest_inliers, final_error = ransac(data=potential_matches, min_samples=6,
max_iter=200, max_error=0.1, min_inliers=4, find_model=find_cameras,
error=backerror)
M, S, T = best_fit
F = computeFundamental(to_camera(M[:2, :], T[0]), to_camera(M[2:, :], T[1]))
# disp = disparity4points(lines[0], disparity)
# disp = medfilt(disp, 15)
S = gaussianSmooth3D(S.T, 2).T
np.savetxt(expanduser(args.i[0]+"_reconstruction.txt"), S)
# print("Cameras {}".format(anglesBetweenCameras(M, [0, 1])))
#
# fig, axes = plt.subplots(2, 2)
# fig3d = plt.figure()
# ax3d = fig3d.add_subplot(111, projection='3d')
#
# disparity = np.sqrt(np.square(flow_x) + 0 * np.square(flow_y))
#
# axes[0, 0].imshow((images[0] > 0) + np.array(images[1] > 0).astype(float), cmap)
# axes[0, 1].imshow(disparity, cmap)
# axes[1, 0].imshow(flow_x * (images[0] > 0), cmap)
# axes[1, 1].imshow(flow_y * (images[0] > 0), cmap)
#
# # points = points_from_mask(images[0] > 0)
#
# ax3d.plot(lines[0][:, 1], lines[0][:, 0], disp[:])
# ax3d.plot(S[0, :], S[1, :], S[2, :], color='r')
#
# plt.show()
main()

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