def filter(self, pts2D):
""" Filter image points based on loaded mask.
Usage: Mask = BaseMask('mask.png')
FilteredPts = Mask.filter(pts)
Input:
pts - 2-by-N numpy array of 2D points
Output:
FilteredPts - 2-by-N numpy array of 2D points. Those that agree
with the mask (i.e., those points [x, y] s.t. mask[x,y] = True)
remain the same, while invalid points are zeroed.
"""
# Find points that are not zero in image space
idxPts = utils.find( np.logical_and(pts2D[0, :] > 0, pts2D[1,:] > 0) )
# Get rounded and flattened (1D) pixel indexes for our 2D points
idxPtsFlat = utils.ravel_index(pts2D[::-1, idxPts].astype(int), \
self.data.shape)
# Find invalid 2D points by comparing them to the mask
idxInvalidPts = utils.find( self.data.flat[idxPtsFlat] == 0 )
# Set invalid points to zero
pts2D[:, idxPts[idxInvalidPts]] = 0
return pts2D
def getClicks3D(self):
""" Display depth image and get click positions.
Usage: pts3D = getClicks3D()
Input:
-NONE-
Output:
pts3D - 3-by-N array of 3d points in 3d map corresponding to clicks
"""
# Get just the depth values
depth = self.cloud[2,:]
depth = depth.reshape(self.img.shape)
temp_ = tempfile.NamedTemporaryFile(suffix='.png', delete = False)
Image.fromarray( (depth*500).astype(np.uint8) ).save(temp_.name)
clicker = ck.Clicker()
clicker.clicker(temp_.name)
self.pcd_clicks = np.array(clicker.coords)
idx = utils.ravel_index(self.pcd_clicks[:, ::-1], self.img.shape)
# Find points that have actual depth and not nans
pts = list()
for i, val in enumerate(idx):
count_x = 0
count_y = 0
counter = 0
while any(np.isnan(self.cloud[:,val+count_x + \
count_y*self.img.shape[1]])):
counter += 1
if counter % 2:
count_x += 1
else:
count_y += 1
idx[i] = val + count_x + count_y * self.img.shape[1]
pts3D = np.zeros((3, idx.size))
for i in range(3):
pts3D[i,:] = self.cloud[i, idx]
return pts3D
def getn(self, idx):
"""
Given an index return a single number n representing the corresponding index on the flattened grid
"""
from utils import ravel_index
return ravel_index(idx, self.shape)
