def save_calib(self):
print('Saving calibration to ', self.output_dir)
np.save(os.path.join(self.output_dir, 'dc.npy'),
np.asarray(self.calibrator.distortion, np.float32))
np.save(os.path.join(self.output_dir, 'cm.npy'),
np.asarray(self.calibrator.intrinsics, np.float32))
cv.Save(os.path.join(self.output_dir, 'camera_matrix.xml'),
self.calibrator.intrinsics)
cv.Save(os.path.join(self.output_dir, 'distortion_coefficients.xml'),
self.calibrator.distortion)
def do_save(self):
print('{0}.do_save'.format(self.__class__))
numpy.save('dc.npy', numpy.asarray(self.distortion, numpy.float32))
numpy.save('cm.npy', numpy.asarray(self.intrinsics, numpy.float32))
cv.Save('camera_matrix.xml', self.intrinsics)
cv.Save('distortion_coefficients.xml', self.distortion)
filename = 'calibrationdata.tar.gz'
tf = tarfile.open(filename, 'w:gz')
self.do_tarfile_save(tf) # Must be overridden in subclasses
tf.close()
print "Wrote calibration data to", filename
def invoke(self, arg, from_tty):
args = gdb.string_to_argv(arg)
v = gdb.parse_and_eval(args[0])
strType = gdb.execute("print " + args[0] + ".type()", False, True)
# strType contains gdb answers as a string of the form "$2 = 42"
img = cv.CreateMat(v['rows'], v['cols'], int(strType.split(" ")[2]))
# convert v['data'] to char*
char_type = gdb.lookup_type("char")
char_pointer_type = char_type.pointer()
buffer = v['data'].cast(char_pointer_type)
# read bytes from inferior's process memory
buf = v['step']['buf']
bytes = buf[0] * v['rows']
inferior = gdb.selected_inferior()
mem = inferior.read_memory(buffer, bytes)
# set the matrix raw data
cv.SetData(img, mem)
# save matrix as an xml file and open it with matrix viewer
cv.Save("/tmp/dump.xml", img, "matrix")
call(["matrix-viewer", "/tmp/dump.xml"])
def subspace_project(self, test_features):
projected_file = os.path.join(self.model_dir, 'proj.xml')
# creating copy of subspace file since we over write the same file
temp_subspace_file = os.path.join(
self.model_dir, 'temp_subspace_file_%s.xml' % datetime.utcnow())
shutil.copy(self.subspace_file, temp_subspace_file)
# create a feature file
feature_file = os.path.join(self.model_dir, 'test_features.xml')
la = np.asarray([[-1] * test_features.shape[0]])
cv_feats = np.transpose(np.concatenate(
(la, test_features.transpose())))
mat_to_save = cv_feats.transpose().copy()
cv.Save(feature_file, cv.fromarray(mat_to_save))
# call binary
bin_dir = config.bin_dir()
run_cmd([
os.path.join(bin_dir, 'aff_face_subspace_project'), feature_file,
temp_subspace_file, projected_file
])
return temp_subspace_file, projected_file
def train(self, features, labels):
'''
features = all the features for the faces, each row is a feature
labels = a list of labels representing the id
for each corresponding row in the features array
'''
if not self._is_trained:
assert len(labels) == features.shape[0]
self.model_dir = mkdtemp()
# create feature file with given features
# feature file needs to be such that the first dim in the label and
# then each row in a feature
self.feature_file = os.path.join(self.model_dir, 'features.xml')
la = np.asarray([labels])
cv_feats = np.transpose(np.concatenate((la, features.transpose())))
mat_to_save = cv_feats.transpose().copy()
# nFaces = mat_to_save->cols;
# nDesc = mat_to_save->rows-1;
cv.Save(self.feature_file, cv.fromarray(mat_to_save))
self.subspace_file = os.path.join(self.model_dir,
'learned_subspace.xml')
self.face_subspace()
self._is_trained = True
for i in range(0, image.height):
for j in range(0, image.width):
# keep closer object
if cv.GetReal2D(disparity, i, j) > threshold:
cv.Set2D(disparity, i, j, cv.Get2D(image, i, j))
# loading the stereo pair
left = cv.LoadImage('scene_l.bmp', cv.CV_LOAD_IMAGE_GRAYSCALE)
right = cv.LoadImage('scene_r.bmp', cv.CV_LOAD_IMAGE_GRAYSCALE)
disparity_left = cv.CreateMat(left.height, left.width, cv.CV_16S)
disparity_right = cv.CreateMat(left.height, left.width, cv.CV_16S)
# data structure initialization
state = cv.CreateStereoGCState(16, 2)
# running the graph-cut algorithm
cv.FindStereoCorrespondenceGC(left, right, disparity_left, disparity_right,
state)
disp_left_visual = cv.CreateMat(left.height, left.width, cv.CV_8U)
cv.ConvertScale(disparity_left, disp_left_visual, -16)
cv.Save("disparity.pgm", disp_left_visual)
# save the map
# cutting the object farthest of a threshold (120)
cut(disp_left_visual, left, 120)
cv.NamedWindow('Disparity map', cv.CV_WINDOW_AUTOSIZE)
cv.ShowImage('Disparity map', disp_left_visual)
cv.WaitKey()