def visualize(image,
masks,
planes,
camera,
postfix,
sceneid,
depthonly=False,
sensor=False):
if sensor == False:
param_map = np.zeros((480, 640, 4))
semantic_map = np.full((480, 640), False, dtype=np.bool)
color_map = np.full((480, 640, 3), 0, dtype=np.uint8)
for planeIndex, plane in enumerate(planes):
m = masks[planeIndex] > 0.5
param_map[m, :] = plane
semantic_map[m] = True
color_map[m, :] = np.random.randint(255, size=3)
points, depth = calc_dp_by_param_map(param_map, camera)
else:
semantic_map = masks > 1e-4 # masks become depth
points = calc_points_by_depth(masks, camera)
depth = np.zeros((480, 640))
if depthonly:
return depth.reshape(480, 640)
pt_colors = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).reshape(
-1, 3
) #(cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + color_map).reshape(-1,3) / 2.0
points = np.concatenate([points, pt_colors], 1)
points = points[semantic_map.reshape(-1), :]
filename = f'{sceneid}{postfix}'
writePointCloud(filename + '.ply', points)
#return depth.reshape(480,640)
m = pyrender.Mesh.from_points(points[:, :3], colors=points[:, 3:6] / 255.0)
scene = pyrender.Scene()
scene.add(m)
v = pyrender.Viewer(scene,
use_raymond_lighting=True,
run_in_thread=True,
record=True,
rotate=True,
rotate_axis=[0, 0, -1],
rotate_rate=np.pi / 4.0,
refresh_rate=10.0)
v.on_mouse_scroll(0, 0, 5, 5)
time.sleep(8)
v.close_external()
v.save_gif(filename + '.gif')
return depth.reshape(480, 640)
def visualize(self, image, masks, planes, camera, postfix, dst_path):
# visualization
param_map = np.zeros((480,640,4))
semantic_map = np.full((480,640),False,dtype=np.bool)
color_map = np.full((480,640,3),0,dtype=np.uint8)
for planeIndex, plane in enumerate(planes):
m = masks[planeIndex]
param_map[m,:] = plane
semantic_map[m] = True
color_map[m,:] = np.random.randint(255, size=3)
points = utils.calc_points_by_param_map(param_map, camera)
#return points, semantic_map
pt_colors = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).reshape(-1,3)#(cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + color_map).reshape(-1,3) / 2.0
points = np.concatenate([points, pt_colors],1)
points = points[semantic_map.reshape(-1),:]
utils.writePointCloud(f'{dst_path}pt-{postfix}.ply',points)
def visualize_mesh(image,
masks,
planes,
camera,
postfix,
sceneid,
line_equs,
line_flags,
split_semantic,
along_line_mask,
depthonly=False):
height, width = 480, 640
param_map = np.zeros((height, width, 4))
semantic_map = np.full((height, width), False, dtype=np.bool)
planeid_map = np.full((height, width), -1, dtype=np.int)
for planeIndex, plane in enumerate(planes):
m = masks[planeIndex] > 0.5
param_map[m, :] = plane
semantic_map[m] = True
planeid_map[m] = planeIndex
#planeid_map[split_semantic<0.5] = -1
points, depth = calc_dp_by_param_map(param_map, camera)
valid_pixel_num = np.sum(semantic_map) # do not write those zeros
id_global = np.zeros((height, width), dtype=np.int)
id_global[semantic_map] = np.arange(valid_pixel_num) + 1
faces, contours = extract_polygon(image, masks, id_global)
urange = np.arange(width, dtype=np.float32).reshape(1, -1).repeat(
height, 0) / width
vrange = np.arange(height, dtype=np.float32).reshape(-1, 1).repeat(
width, 1) / height
urange = urange.reshape(-1, 1)
vrange = 1 - vrange.reshape(-1, 1)
uvrange = np.concatenate([urange, vrange], axis=-1)
normal = param_map[:, :, :3]
normal = normal.reshape(-1, 3)
semantic1d = np.arange(height * width).reshape(height, width)
semantic1d = semantic1d[contours[:, 1], contours[:, 0]]
#semantic1d = semantic_map.reshape(-1)
filename = f'{sceneid}{postfix}'
faces = duplicate_face(faces)
write_obj_file(filename + '.obj', points[semantic1d, :],
normal[semantic1d, :], uvrange[semantic1d, :], faces)
if type(line_equs) == type([]):
return 0
new_points = modify_points(points.copy(), contours, planeid_map, line_equs,
line_flags, along_line_mask, planes)
filename = f'{sceneid}{postfix}'
write_obj_file(filename + '_f.obj', new_points[semantic1d, :],
normal[semantic1d, :], uvrange[semantic1d, :], faces)
exit()
return 0
pt_colors = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).reshape(
-1, 3
) #(cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + color_map).reshape(-1,3) / 2.0
points = np.concatenate([points, pt_colors], 1)
points = points[semantic_map.reshape(-1), :]
writePointCloud(filename + '.ply', points)