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Merged
merged 2 commits into from
Feb 13, 2019
Merged

Simpler MapMask #31

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804fdb2
deleted distance_mask, binary_mask. Created dilate_mask().
sourabh-nutonomy Feb 13, 2019
4cdfad6
cleanup docstring
Alex-nutonomy Feb 13, 2019
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78 changes: 16 additions & 62 deletions python-sdk/nuscenes/utils/map_mask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,26 +14,17 @@


class MapMask:
def __init__(self, img_file: str, precision: float=0.1, foreground: int=255, background: int=0,
dist_thresh: float=2.0):
def __init__(self, img_file: str):
"""
Init a map mask object that contains the semantic prior (drivable surface and sidewalks) mask.
:param img_file: File path to map png file.
:param precision: Precision in meters.
:param foreground: Foreground value.
:param background: Background value.
:param dist_thresh: This parameter specifies the threshold on the distance from the semantic prior mask.
The semantic prior mask is dilated to include points which are within this distance from itself.
"""
assert osp.exists(img_file), 'map mask {} does not exist'.format(img_file)
self.img_file = img_file
self.precision = precision
self.foreground = foreground
self.background = background
self.dist_thresh = dist_thresh
self._mask = None
self._distance_mask = None # Distance to semantic_prior (lazy load).
self._binary_mask = None # Binary mask of semantic prior + dilation of dist_thresh (lazy load)
self.precision = 0.1 # Precision in meters.
self.foreground = 255
self.background = 0
self._mask = None # Binary map mask (lazy load).
self._transf_matrix = None # Transformation matrix from global coords to map coords (lazy load).

@property
Expand Down Expand Up @@ -64,29 +55,17 @@ def transform_matrix(self) -> np.ndarray:
[0, 0, 1, 0], [0, 0, 0, 1]])
return self._transf_matrix

@property
def distance_mask(self) -> np.ndarray:
"""
Generate distance mask from self.mask which is the original mask from the png file.
:return: <np.float32: image.height, image.width>. The distance mask.
"""
if self._distance_mask is None:
# Distance to nearest foreground in mask.
self._distance_mask = cv2.distanceTransform((self.foreground - self.mask).astype(np.uint8), cv2.DIST_L2, 5)
self._distance_mask = (self._distance_mask * self.precision).astype(np.float32)

return self._distance_mask

@property
def binary_mask(self) -> np.array:
def dilate_mask(self, dist_thresh: float = 2.0) -> None:
"""
Create binary mask of semantic prior plus dilation.
:return: <np.uint8: image.height, image.width>. The binary mask.
Dilates the mask by a distance threshold.
:param dist_thresh: This parameter specifies the threshold on the distance from the semantic prior mask.
The semantic prior mask is dilated to include points which are within this distance from itself.
"""
if self._binary_mask is None:
self._binary_mask = (self.distance_mask < self.dist_thresh).astype(np.uint8) * self.foreground
# Distance to nearest foreground in mask.
distance_mask = cv2.distanceTransform((self.foreground - self.mask).astype(np.uint8), cv2.DIST_L2, 5)
distance_mask = (distance_mask * self.precision).astype(np.float32)

return self._binary_mask
self._mask = (distance_mask < dist_thresh).astype(np.uint8) * self.foreground

def export_to_png(self, filename: str='mask.png') -> None:
"""
Expand Down Expand Up @@ -122,28 +101,14 @@ def is_on_mask(self, x, y) -> np.array:
on_mask = np.ones(x.size, dtype=np.bool)

on_mask[px < 0] = False
on_mask[px >= self.binary_mask.shape[1]] = False
on_mask[px >= self.mask.shape[1]] = False
on_mask[py < 0] = False
on_mask[py >= self.binary_mask.shape[0]] = False
on_mask[py >= self.mask.shape[0]] = False

on_mask[on_mask] = (self.binary_mask[py[on_mask], px[on_mask]] == self.foreground)
on_mask[on_mask] = (self.mask[py[on_mask], px[on_mask]] == self.foreground)

return on_mask

def dist_to_mask(self, x: float, y: float) -> float:
"""
Get the distance of a point to the nearest foreground in perception GT semantic prior mask (dilated).
:param x: Global x.
:param y: Global y.
:return: Distance to nearest foreground, if not in distance mask, return -1.
"""
px, py = self.get_pixel(x, y)

if px < 0 or px >= self.distance_mask.shape[1] or py < 0 or py >= self.distance_mask.shape[0]:
return -1

return self.distance_mask[py, px]

def get_pixel(self, x: np.array, y: np.array) -> Tuple[np.ndarray, np.ndarray]:
"""
Get the image coordinates given the x-y coordinates of points.
Expand All @@ -155,14 +120,3 @@ def get_pixel(self, x: np.array, y: np.array) -> Tuple[np.ndarray, np.ndarray]:

return px, py

def set_dist_thresh(self, dist_thresh: float) -> None:
"""
This function sets self.dist_thresh to a new value. This method can be used to change the threshold multiple
times during an experiment without creating new NuScenes objects.
:param dist_thresh: The semantic prior mask is dilated to include points which are within this distance from
itself.
"""
self.dist_thresh = dist_thresh

# Update the binary mask to None since the distance threshold was changed.
self._binary_mask = None