def run():
match = Matcher()
img = CVImage('/home/cesar/Documentos/Computer_Vision/01/image_0')
img.read_image()
img.copy_image()
img.acquire()
h = img.new_image.shape[0]
w = img.new_image.shape[1]
n = 2 # Number of roi's
size = np.array([[w / n], [h / n]], np.int32)
start = np.array([[0], [0]], np.int32)
# First roi
correlate_roi(match, img, size, start)
# Second roi
start = np.array([[w / n], [0]])
correlate_roi(match, img, size, start)
# Third roi
start = np.array([[0], [h / n]])
correlate_roi(match, img, size, start)
# Last roi
start = np.array([[w / n], [h / n]])
correlate_roi(match, img, size, start)
# We have stored two times every original keypoint (curr_kp, prev_kp)
match.curr_kp = match.curr_kp[::2]
match.prev_kp = match.prev_kp[::2]
# The same applies for the descriptors
match.curr_dsc = match.curr_dsc[::2]
match.prev_dsc = match.prev_dsc[::2]
print match.curr_kp[0].pt
print match.global_kpts1[0]
# Print the total number of keypoints encountered
print("Total number of keypoints encountered: \
{}".format(get_number_keypoints(match)))
# Test the plot_same_figure function
# plot_same_figure(match, img)
# plot_one(match, img)
# plot_save(match, img)
# Get Fundamental Matrix
vo = VisualOdometry()
print "Type of match.global_kpts1: ", type(match.global_kpts1)
match.global_kpts1, match.global_kpts2 = \
vo.EstimateF_multiprocessing(match.global_kpts2, match.global_kpts1)
# plot_one(match, img)
# plot_one_np(vo.outlier_points_new, img)
# plot_together_np(match.global_kpts1, vo.outlier_points_new, img)
print("Total number of keypoints encountered: \
{}".format(get_number_keypoints(match)))
# Triangulate. To get the actual movement of the camera we are "swapping"
# the scene. The first camera is cam1.P, the first keypoints are
# global_kpts1. On the other hand, the second camera is cam2.P and the
# second keypoints are global_kpts2
scene = get_structure(match, img, vo)
print "ESCENA", scene[:, :20]
print "PROYECCION EN SEGUNDA", vo.cam1.project(scene[:, :20])
print "SEGUNDA", match.global_kpts1[:20]
print "CORREGIDOS SEGUNDA", vo.correctedkpts1[:, :20]
print "PROYECCION EN PRIMERA", vo.cam2.project(scene[:, :20])
print "PRIMERA", match.global_kpts2[:20]
print "CORREGIDOS EN PRIMERA", vo.correctedkpts2[:, :20]
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(scene[0], scene[1], scene[2], 'ko')
plt.axis('equal')
plt.show()
