def render_plot(dmap: Depthmap) -> np.ndarray:
# detect floor and child
floor: float = dmap.get_floor_level()
mask = dmap.segment_child(floor) # dmap.detect_floor(floor)
# prepare plots
output_plots = [
render_depth(dmap),
render_normal(dmap),
render_segmentation(floor, mask, dmap),
render_confidence(dmap),
]
if dmap.has_rgb:
highest_point: np.ndarray = dmap.get_highest_point(mask)
output_rgb = render_rgb(dmap)
output_rgb = blur_face(output_rgb, highest_point, dmap,
CHILD_HEAD_HEIGHT_IN_METERS)
output_plots.append(output_rgb)
return np.concatenate(output_plots, axis=1)
def __init__(self, dmap: Depthmap, rgb_fpath: str):
"""Create object from depthmap and rgb file path"""
self.floor = dmap.get_floor_level()
self.rgb = cv2.imread(str(rgb_fpath))
dim = (640, int(self.rgb.shape[0] / self.rgb.shape[1] * 640.0))
self.rgb = cv2.resize(self.rgb, dim, cv2.INTER_AREA)
dmap.resize(self.rgb.shape[1], self.rgb.shape[0])
self.dmap = dmap
self.rgb_fpath = rgb_fpath
cache_fpath = f'{rgb_fpath}-hrnet.json'
try:
with open(cache_fpath) as json_file:
self.persons_coordinates = json.load(json_file)
except OSError:
self.persons_coordinates = HRNET_MODEL.result_on_artifact_level_from_image(
self.rgb, rgb_fpath, '0')
with open(cache_fpath, 'w') as json_file:
json.dump(self.persons_coordinates,
json_file,
cls=NumpyEncoder)