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ycb_generate_point_cloud.py
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import os
import numpy as np
import h5py as h5
from scipy.misc import imread
import IPython
import math
import scipy.io
data = scipy.io.loadmat ('C:\\Users\\hp\\Documents\\MATLAB\\ObjectRecognition_YCB\\ObjectProperties.mat')
# Parameters
ycb_data_folder = data ['InfoFolder'] [0] # Folder that contains the ycb data.
target_object = data ['InfoObject'] [0] # Full name of the target object.
viewpoint_camera = data['InfoCamera'] [0] # Camera which the viewpoint will be generated.
viewpoint_angle = data ['InfoAngle'] [0] # Relative angle of the object w.r.t the camera (angle of the turntable).
def im2col(im, psize):
n_channels = 1 if len(im.shape) == 2 else im.shape[0]
(n_channels, rows, cols) = (1,) * (3 - len(im.shape)) + im.shape
im_pad = np.zeros((n_channels,
int(math.ceil(1.0 * rows / psize) * psize),
int(math.ceil(1.0 * cols / psize) * psize)))
im_pad[:, 0:rows, 0:cols] = im
final = np.zeros((im_pad.shape[1], im_pad.shape[2], n_channels,
psize, psize))
for c in xrange(n_channels):
for x in xrange(psize):
for y in xrange(psize):
im_shift = np.vstack(
(im_pad[c, x:], im_pad[c, :x]))
im_shift = np.column_stack(
(im_shift[:, y:], im_shift[:, :y]))
final[x::psize, y::psize, c] = np.swapaxes(
im_shift.reshape(im_pad.shape[1] / psize, psize,
im_pad.shape[2] / psize, psize), 1, 2)
return np.squeeze(final[0:rows - psize + 1, 0:cols - psize + 1])
def filterDiscontinuities(depthMap):
filt_size = 7
thresh = 1000
# Ensure that filter sizes are okay
assert filt_size % 2 == 1, "Can only use odd filter sizes."
# Compute discontinuities
offset = (filt_size - 1) / 2
patches = 1.0 * im2col(depthMap, filt_size)
mids = patches[:, :, offset, offset]
mins = np.min(patches, axis=(2, 3))
maxes = np.max(patches, axis=(2, 3))
discont = np.maximum(np.abs(mins - mids),
np.abs(maxes - mids))
mark = discont > thresh
# Account for offsets
final_mark = np.zeros((480, 640), dtype=np.uint16)
final_mark[offset:offset + mark.shape[0],
offset:offset + mark.shape[1]] = mark
return depthMap * (1 - final_mark)
def registerDepthMap(unregisteredDepthMap,
rgbImage,
depthK,
rgbK,
H_RGBFromDepth):
unregisteredHeight = unregisteredDepthMap.shape[0]
unregisteredWidth = unregisteredDepthMap.shape[1]
registeredHeight = rgbImage.shape[0]
registeredWidth = rgbImage.shape[1]
registeredDepthMap = np.zeros((registeredHeight, registeredWidth))
xyzDepth = np.empty((4,1))
xyzRGB = np.empty((4,1))
# Ensure that the last value is 1 (homogeneous coordinates)
xyzDepth[3] = 1
invDepthFx = 1.0 / depthK[0,0]
invDepthFy = 1.0 / depthK[1,1]
depthCx = depthK[0,2]
depthCy = depthK[1,2]
rgbFx = rgbK[0,0]
rgbFy = rgbK[1,1]
rgbCx = rgbK[0,2]
rgbCy = rgbK[1,2]
undistorted = np.empty(2)
for v in range(unregisteredHeight):
for u in range(unregisteredWidth):
depth = unregisteredDepthMap[v,u]
if depth == 0:
continue
xyzDepth[0] = ((u - depthCx) * depth) * invDepthFx
xyzDepth[1] = ((v - depthCy) * depth) * invDepthFy
xyzDepth[2] = depth
xyzRGB[0] = (H_RGBFromDepth[0,0] * xyzDepth[0] +
H_RGBFromDepth[0,1] * xyzDepth[1] +
H_RGBFromDepth[0,2] * xyzDepth[2] +
H_RGBFromDepth[0,3])
xyzRGB[1] = (H_RGBFromDepth[1,0] * xyzDepth[0] +
H_RGBFromDepth[1,1] * xyzDepth[1] +
H_RGBFromDepth[1,2] * xyzDepth[2] +
H_RGBFromDepth[1,3])
xyzRGB[2] = (H_RGBFromDepth[2,0] * xyzDepth[0] +
H_RGBFromDepth[2,1] * xyzDepth[1] +
H_RGBFromDepth[2,2] * xyzDepth[2] +
H_RGBFromDepth[2,3])
invRGB_Z = 1.0 / xyzRGB[2]
undistorted[0] = (rgbFx * xyzRGB[0]) * invRGB_Z + rgbCx
undistorted[1] = (rgbFy * xyzRGB[1]) * invRGB_Z + rgbCy
uRGB = int(undistorted[0] + 0.5)
vRGB = int(undistorted[1] + 0.5)
if (uRGB < 0 or uRGB >= registeredWidth) or (vRGB < 0 or vRGB >= registeredHeight):
continue
registeredDepth = xyzRGB[2]
if registeredDepth > registeredDepthMap[vRGB,uRGB]:
registeredDepthMap[vRGB,uRGB] = registeredDepth
return registeredDepthMap
def registeredDepthMapToPointCloud(depthMap, rgbImage, rgbK, organized=True):
rgbCx = rgbK[0,2]
rgbCy = rgbK[1,2]
invRGBFx = 1.0/rgbK[0,0]
invRGBFy = 1.0/rgbK[1,1]
height = depthMap.shape[0]
width = depthMap.shape[1]
if organized:
cloud = np.empty((height, width, 6), dtype=np.float)
else:
cloud = np.empty((1, height*width, 6), dtype=np.float)
goodPointsCount = 0
for v in range(height):
for u in range(width):
depth = depthMap[v,u]
if organized:
row = v
col = u
else:
row = 0
col = goodPointsCount
if depth <= 0:
if organized:
if depth <= 0:
cloud[row,col,0] = float('nan')
cloud[row,col,1] = float('nan')
cloud[row,col,2] = float('nan')
cloud[row,col,3] = 0
cloud[row,col,4] = 0
cloud[row,col,5] = 0
continue
cloud[row,col,0] = (u - rgbCx) * depth * invRGBFx
cloud[row,col,1] = (v - rgbCy) * depth * invRGBFy
cloud[row,col,2] = depth
cloud[row,col,3] = rgbImage[v,u,0]
cloud[row,col,4] = rgbImage[v,u,1]
cloud[row,col,5] = rgbImage[v,u,2]
if not organized:
goodPointsCount += 1
if not organized:
cloud = cloud[:,:goodPointsCount,:]
return cloud
def writePLY(filename, cloud, faces=[]):
if len(cloud.shape) != 3:
print "Expected pointCloud to have 3 dimensions. Got %d instead" % len(cloud.shape)
return
color = True if cloud.shape[2] == 6 else False
num_points = cloud.shape[0]*cloud.shape[1]
header_lines = [
'ply',
'format ascii 1.0',
'element vertex %d' % num_points,
'property float x',
'property float y',
'property float z',
]
if color:
header_lines.extend([
'property uchar diffuse_red',
'property uchar diffuse_green',
'property uchar diffuse_blue',
])
if faces != None:
header_lines.extend([
'element face %d' % len(faces),
'property list uchar int vertex_indices'
])
header_lines.extend([
'end_header',
])
f = open(filename, 'w+')
f.write('\n'.join(header_lines))
f.write('\n')
lines = []
for i in range(cloud.shape[0]):
for j in range(cloud.shape[1]):
if color:
lines.append('%s %s %s %d %d %d' % tuple(cloud[i, j, :].tolist()))
else:
lines.append('%s %s %s' % tuple(cloud[i, j, :].tolist()))
for face in faces:
lines.append(('%d' + ' %d'*len(face)) % tuple([len(face)] + list(face)))
f.write('\n'.join(lines) + '\n')
f.close()
def writePCD(pointCloud, filename, ascii=False):
if len(pointCloud.shape) != 3:
print "Expected pointCloud to have 3 dimensions. Got %d instead" % len(pointCloud.shape)
return
with open(filename, 'w') as f:
height = pointCloud.shape[0]
width = pointCloud.shape[1]
f.write("# .PCD v.7 - Point Cloud Data file format\n")
f.write("VERSION .7\n")
if pointCloud.shape[2] == 3:
f.write("FIELDS x y z\n")
f.write("SIZE 4 4 4\n")
f.write("TYPE F F F\n")
f.write("COUNT 1 1 1\n")
else:
f.write("FIELDS x y z rgb\n")
f.write("SIZE 4 4 4 4\n")
f.write("TYPE F F F F\n")
f.write("COUNT 1 1 1 1\n")
f.write("WIDTH %d\n" % width)
f.write("HEIGHT %d\n" % height)
f.write("VIEWPOINT 0 0 0 1 0 0 0\n")
f.write("POINTS %d\n" % (height * width))
if ascii:
f.write("DATA ascii\n")
for row in range(height):
for col in range(width):
if pointCloud.shape[2] == 3:
f.write("%f %f %f\n" % tuple(pointCloud[row, col, :]))
else:
f.write("%f %f %f" % tuple(pointCloud[row, col, :3]))
r = int(pointCloud[row, col, 3])
g = int(pointCloud[row, col, 4])
b = int(pointCloud[row, col, 5])
rgb_int = (r << 16) | (g << 8) | b
packed = pack('i', rgb_int)
rgb = unpack('f', packed)[0]
f.write(" %.12e\n" % rgb)
else:
f.write("DATA binary\n")
if pointCloud.shape[2] == 6:
# These are written as bgr because rgb is interpreted as a single
# little-endian float.
dt = np.dtype([('x', np.float32),
('y', np.float32),
('z', np.float32),
('b', np.uint8),
('g', np.uint8),
('r', np.uint8),
('I', np.uint8)])
pointCloud_tmp = np.zeros((height*width, 1), dtype=dt)
for i, k in enumerate(['x', 'y', 'z', 'r', 'g', 'b']):
pointCloud_tmp[k] = pointCloud[:, :, i].reshape((height*width, 1))
pointCloud_tmp.tofile(f)
else:
dt = np.dtype([('x', np.float32),
('y', np.float32),
('z', np.float32),
('I', np.uint8)])
pointCloud_tmp = np.zeros((height*width, 1), dtype=dt)
for i, k in enumerate(['x', 'y', 'z']):
pointCloud_tmp[k] = pointCloud[:, :, i].reshape((height*width, 1))
pointCloud_tmp.tofile(f)
def getRGBFromDepthTransform(calibration, camera, referenceCamera):
irKey = "H_{0}_ir_from_{1}".format(camera, referenceCamera)
rgbKey = "H_{0}_from_{1}".format(camera, referenceCamera)
rgbFromRef = calibration[rgbKey][:]
irFromRef = calibration[irKey][:]
return np.dot(rgbFromRef, np.linalg.inv(irFromRef))
if __name__ == "__main__":
referenceCamera = "NP5"
if not os.path.exists(ycb_data_folder+"/"+target_object+"/clouds"):
os.makedirs(ycb_data_folder+"/"+target_object+"/clouds")
basename = "{0}_{1}".format(viewpoint_camera, viewpoint_angle)
depthFilename = os.path.join(ycb_data_folder+ target_object, basename + ".h5")
rgbFilename = os.path.join(ycb_data_folder + target_object, basename + ".jpg")
calibrationFilename = os.path.join(ycb_data_folder+target_object, "calibration.h5")
calibration = h5.File(calibrationFilename)
if not os.path.isfile(rgbFilename):
print "The rgbd data is not available for the target object \"%s\". Please download the data first." % target_object
exit(1)
rgbImage = imread(rgbFilename)
depthK = calibration["{0}_ir_K".format(viewpoint_camera)][:]
rgbK = calibration["{0}_rgb_K".format(viewpoint_camera)][:]
depthScale = np.array(calibration["{0}_ir_depth_scale".format(viewpoint_camera)]) * .0001 # 100um to meters
H_RGBFromDepth = getRGBFromDepthTransform(calibration, viewpoint_camera, referenceCamera)
unregisteredDepthMap = h5.File(depthFilename)["depth"][:]
unregisteredDepthMap = filterDiscontinuities(unregisteredDepthMap) * depthScale
registeredDepthMap = registerDepthMap(unregisteredDepthMap,
rgbImage,
depthK,
rgbK,
H_RGBFromDepth)
pointCloud = registeredDepthMapToPointCloud(registeredDepthMap, rgbImage, rgbK)
writePLY(ycb_data_folder+target_object+"/clouds/pc_"+viewpoint_camera+"_"+referenceCamera+"_"+viewpoint_angle+".ply", pointCloud)