def calibrate_from_initialization(img,
mask,
A_init,
R_init,
T_init,
edge_sfactor=0.5,
visualize=False):
h, w = img.shape[:2]
edges = image_utils.robust_edge_detection(
cv2.resize(img, None, fx=edge_sfactor, fy=edge_sfactor))
edges = cv2.resize(edges, None, fx=1. / edge_sfactor, fy=1. / edge_sfactor)
edges = cv2.Canny(edges.astype(np.uint8) * 255, 100,
200) / 255.0 # find edge
mask = cv2.dilate(mask, np.ones((25, 25), dtype=np.uint8))
edges = edges * (1 - mask)
dist_transf = cv2.distanceTransform((1 - edges).astype(np.uint8),
cv2.DIST_L2, 0) # distance to the edge
cam_init = cam_utils.Camera('tmp', A_init, R_init, T_init, h, w)
template, field_mask = draw_utils.draw_field(cam_init)
II, JJ = (template > 0).nonzero()
synth_field2d = np.array([[JJ, II]]).T[:, :, 0]
field3d = cam_utils.plane_points_to_3d(synth_field2d, cam_init)
A, R, T = _calibrate_camera_dist_transf(A_init, R_init, T_init,
dist_transf, field3d)
if visualize:
cam_res = cam_utils.Camera('tmp', A, R, T, h, w)
field2d, __ = cam_res.project(field3d)
io.imshow(img, points=field2d)
return A, R, T, field3d
def calibrate_by_click(img1,
img2,
mask1,
mask2,
edge_sfactor=0.5,
field_img_path='./demo/data/field.png'):
h, w = img1.shape[0:2]
points2d_img1, points2d_img2, points3d = _set_correspondences(
img1, img2, field_img_path=field_img_path)
points2d_list = [points2d_img1, points2d_img2]
img_list = [img1, img2]
mask_list = [mask1, mask2]
# For save the calibration
A_out, R_out, T_out = [], [], []
for i, points2d in enumerate(points2d_list):
# ------------------------------------------------------------------------------------------------------------------
# OpenCV initial calibration
fx, fy = cam_utils.grid_search_focal_length(points3d,
points2d,
h,
w,
same_f=True)
A = cam_utils.intrinsic_matrix_from_focal_length(fx, fy, h, w)
points_3d_cv = points3d[:, np.newaxis, :].astype(np.float32)
points_2d_cv = points2d[:, np.newaxis, :].astype(np.float32)
print(points2d)
_, rvec, tvec, _ = cv2.solvePnPRansac(points_3d_cv, points_2d_cv, A,
None)
rvec, tvec = np.squeeze(rvec), np.squeeze(tvec)
R, _ = cv2.Rodrigues(rvec)
T = np.array([tvec]).T
cam_cv = cam_utils.Camera('tmp', A, R, T, h, w)
# ------------------------------------------------------------------------------------------------------------------
# Photometric refinement
A__, R__, T__, field3d = calibrate_from_initialization(
img_list[i], mask_list[i], A, R, T, edge_sfactor)
cam = cam_utils.Camera('tmp', A__, R__, T__, h, w)
# Sanity check, project the soccer field points
p2_cv, _ = cam_cv.project(field3d)
p2_cv, _ = cam_utils.inside_frame(p2_cv, cam.height, cam.width)
p2_opt, _ = cam_cv.project(field3d)
p2_opt, valid_id = cam_utils.inside_frame(p2_opt, cam_cv.height,
cam_cv.width)
class Index(object):
def save_opt(self, event):
A_out.append(cam.A)
R_out.append(cam.R)
T_out.append(cam.T)
plt.close()
def save_pnp(self, event):
A_out.append(cam_cv.A)
R_out.append(cam_cv.R)
T_out.append(cam_cv.T)
plt.close()
def discard(self, event):
A_out.append()
R_out.append()
T_out.append()
plt.close()
fig, ax = plt.subplots(1, 2)
io_utils.imshow(img_list[i], ax=ax[0], points=p2_opt)
io_utils.imshow(img_list[i], ax=ax[1], points=p2_cv)
callback = Index()
axdisc = plt.axes([0.6, 0.05, 0.1, 0.075])
axcv = plt.axes([0.7, 0.05, 0.1, 0.075])
axopt = plt.axes([0.81, 0.05, 0.1, 0.075])
bnext = Button(axopt, 'Save opt')
bnext.on_clicked(callback.save_opt)
bprev = Button(axdisc, 'Discard')
bprev.on_clicked(callback.discard)
bpcv = Button(axcv, 'Save cv')
bpcv.on_clicked(callback.save_pnp)
plt.show()
return A_out, R_out, T_out, [points2d_img1, points2d_img2, points3d]
def calibrate_from_initialization(img,
mask,
A_init,
R_init,
T_init,
edge_sfactor=0.5,
visualize=False):
# set to 1 to use the improvd calibration propagation
improved_method = 0
validate_pixels = 0 # check if segmented line pixels are on a white line, slow
h, w = img.shape[:2]
if improved_method:
G_mag, G_direction = get_image_gradients(img)
field_green = get_field_green(img)
edges = get_field_markings(G_mag, field_green, mask)
if validate_pixels:
edges = validate_white_line(img, G_mag, G_direction, edges,
field_green)
dist_transf = cv2.distanceTransform((1 - edges).astype(np.uint8),
cv2.DIST_L2, 0)
cam_init = cam_utils.Camera('tmp', A_init, R_init, T_init, h, w)
template, field_mask = draw_utils.draw_field(cam_init)
II, JJ = (template > 0).nonzero()
synth_field2d = np.array([[JJ, II]]).T[:, :, 0]
field3d = cam_utils.plane_points_to_3d(synth_field2d, cam_init)
A, R, T = _calibrate_camera_dist_transf(A_init, R_init, T_init,
dist_transf, field3d)
if visualize:
cam_res = cam_utils.Camera('tmp', A, R, T, h, w)
field2d, __ = cam_res.project(field3d)
io.imshow(img, points=field2d)
else:
edges = image_utils.robust_edge_detection(
cv2.resize(img, None, fx=edge_sfactor, fy=edge_sfactor))
edges = cv2.resize(edges,
None,
fx=1. / edge_sfactor,
fy=1. / edge_sfactor)
edges = cv2.Canny(edges.astype(np.uint8) * 255, 100, 200) / 255.0
mask = cv2.dilate(mask, np.ones((25, 25), dtype=np.uint8))
edges = edges * (1 - mask)
dist_transf = cv2.distanceTransform((1 - edges).astype(np.uint8),
cv2.DIST_L2, 0)
cam_init = cam_utils.Camera('tmp', A_init, R_init, T_init, h, w)
template, field_mask = draw_utils.draw_field(cam_init)
II, JJ = (template > 0).nonzero()
synth_field2d = np.array([[JJ, II]]).T[:, :, 0]
field3d = cam_utils.plane_points_to_3d(synth_field2d, cam_init)
A, R, T = _calibrate_camera_dist_transf(A_init, R_init, T_init,
dist_transf, field3d)
if visualize:
cam_res = cam_utils.Camera('tmp', A, R, T, h, w)
field2d, __ = cam_res.project(field3d)
io.imshow(img, points=field2d)
return A, R, T, field3d
