CoIR-Retrieval/CodeSearchNet-ccr-python-queries-corpus

_id stringlengths 2 7	title stringlengths 1 88	partition stringclasses 3 values	text stringlengths 31 13.1k	language stringclasses 1 value	meta_information dict
q12300	EnsensoSensor._depth_im_from_pointcloud	train	def _depth_im_from_pointcloud(self, msg): """ Convert a pointcloud2 message to a depth image. """ # set format if self._format is None: self._set_format(msg) # rescale camera intr in case binning is turned on if msg.height != self._camera_intr.height: rescale_factor = float(msg.height) / self._camera_intr.height self._camera_intr = self._camera_intr.resize(rescale_factor) # read num points num_points = msg.height * msg.width # read buffer	python	{ "resource": "" }
q12301	EnsensoSensor.frames	train	def frames(self): """Retrieve a new frame from the Ensenso and convert it to a ColorImage, a DepthImage, and an IrImage. Returns ------- :obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray` The ColorImage, DepthImage, and IrImage of the current frame. Raises ------ RuntimeError If the Ensenso stream is not running. """ # wait for a new image while self._cur_depth_im is None:	python	{ "resource": "" }
q12302	IterativeRegistrationSolver.register	train	def register(self, source_point_cloud, target_point_cloud, source_normal_cloud, target_normal_cloud, matcher, num_iterations=1, compute_total_cost=True, match_centroids=False, vis=False): """ Iteratively register objects to one another. Parameters ---------- source_point_cloud : :obj:`autolab_core.PointCloud` source object points target_point_cloud : :obj`autolab_core.PointCloud` target object points source_normal_cloud : :obj:`autolab_core.NormalCloud` source object outward-pointing normals target_normal_cloud : :obj:`autolab_core.NormalCloud` target object outward-pointing normals matcher : :obj:`PointToPlaneFeatureMatcher`	python	{ "resource": "" }
q12303	TensorDatasetVirtualSensor.frames	train	def frames(self): """Retrieve the next frame from the tensor dataset and convert it to a ColorImage, a DepthImage, and an IrImage. Parameters ---------- skip_registration : bool If True, the registration step is skipped. Returns ------- :obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray` The ColorImage, DepthImage, and IrImage of the current frame. Raises ------ RuntimeError If the stream is not running or if all images in the directory have been used. """ if not self._running:	python	{ "resource": "" }
q12304	PrimesenseSensor._read_depth_image	train	def _read_depth_image(self): """ Reads a depth image from the device """ # read raw uint16 buffer im_arr = self._depth_stream.read_frame() raw_buf = im_arr.get_buffer_as_uint16() buf_array = np.array([raw_buf[i] for i in range(PrimesenseSensor.DEPTH_IM_WIDTH * PrimesenseSensor.DEPTH_IM_HEIGHT)]) # convert to image in meters depth_image = buf_array.reshape(PrimesenseSensor.DEPTH_IM_HEIGHT, PrimesenseSensor.DEPTH_IM_WIDTH)	python	{ "resource": "" }
q12305	PrimesenseSensor._read_color_image	train	def _read_color_image(self): """ Reads a color image from the device """ # read raw buffer im_arr = self._color_stream.read_frame() raw_buf = im_arr.get_buffer_as_triplet() r_array = np.array([raw_buf[i][0] for i in range(PrimesenseSensor.COLOR_IM_WIDTH * PrimesenseSensor.COLOR_IM_HEIGHT)]) g_array = np.array([raw_buf[i][1] for i in range(PrimesenseSensor.COLOR_IM_WIDTH * PrimesenseSensor.COLOR_IM_HEIGHT)]) b_array = np.array([raw_buf[i][2] for i in range(PrimesenseSensor.COLOR_IM_WIDTH * PrimesenseSensor.COLOR_IM_HEIGHT)])	python	{ "resource": "" }
q12306	PrimesenseSensor_ROS._read_depth_images	train	def _read_depth_images(self, num_images): """ Reads depth images from the device """ depth_images = self._ros_read_images(self._depth_image_buffer, num_images, self.staleness_limit) for i in range(0, num_images): depth_images[i] = depth_images[i] * MM_TO_METERS # convert to meters if self._flip_images:	python	{ "resource": "" }
q12307	PrimesenseSensor_ROS._read_color_images	train	def _read_color_images(self, num_images): """ Reads color images from the device """ color_images = self._ros_read_images(self._color_image_buffer, num_images, self.staleness_limit) for i in range(0, num_images): if self._flip_images: color_images[i] = np.flipud(color_images[i].astype(np.uint8))	python	{ "resource": "" }
q12308	ObjectRender.T_obj_camera	train	def T_obj_camera(self): """Returns the transformation from camera to object when the object is in the given stable pose. Returns ------- :obj:`autolab_core.RigidTransform` The desired transform. """ if self.stable_pose is None: T_obj_world =	python	{ "resource": "" }
q12309	QueryImageBundle.image	train	def image(self, render_mode): """Return an image generated with a particular render mode. Parameters ---------- render_mode : :obj:`RenderMode` The type of image we want. Returns ------- :obj:`Image`	python	{ "resource": "" }
q12310	BagOfFeatures.add	train	def add(self, feature): """ Add a new feature to the bag. Parameters ---------- feature : :obj:`Feature` feature to add """	python	{ "resource": "" }
q12311	BagOfFeatures.extend	train	def extend(self, features): """ Add a list of features to the bag. Parameters ---------- feature : :obj:`list` of :obj:`Feature` features to add """	python	{ "resource": "" }
q12312	BagOfFeatures.feature	train	def feature(self, index): """ Returns a feature. Parameters ---------- index : int index of feature in list Returns ------- :obj:`Feature`	python	{ "resource": "" }
q12313	WeightSensor.total_weight	train	def total_weight(self): """Read a weight from the sensor in grams. Returns ------- weight : float The sensor weight in grams. """ weights = self._raw_weights() if weights.shape[1] == 0: return	python	{ "resource": "" }
q12314	WeightSensor.individual_weights	train	def individual_weights(self): """Read individual weights from the load cells in grams. Returns ------- weight : float The sensor weight in grams. """ weights = self._raw_weights() if weights.shape[1] == 0:	python	{ "resource": "" }
q12315	WeightSensor._raw_weights	train	def _raw_weights(self): """Create a numpy array containing the raw sensor weights. """ if self._debug: return np.array([[],[],[],[]]) if not self._running: raise ValueError('Weight sensor is not running!') if len(self._weight_buffers) == 0: time.sleep(0.3)	python	{ "resource": "" }
q12316	WeightSensor._weights_callback	train	def _weights_callback(self, msg): """Callback for recording weights from sensor. """ # Read weights weights = np.array(msg.data) # If needed, initialize indiv_weight_buffers if len(self._weight_buffers) == 0: self._weight_buffers = [[] for i in range(len(weights))] # Record individual weights	python	{ "resource": "" }
q12317	CameraIntrinsics.crop	train	def crop(self, height, width, crop_ci, crop_cj): """ Convert to new camera intrinsics for crop of image from original camera. Parameters ---------- height : int height of crop window width : int width of crop window crop_ci : int row of crop window center crop_cj : int col of crop window center Returns -------	python	{ "resource": "" }
q12318	CameraIntrinsics.project	train	def project(self, point_cloud, round_px=True): """Projects a point cloud onto the camera image plane. Parameters ---------- point_cloud : :obj:`autolab_core.PointCloud` or :obj:`autolab_core.Point` A PointCloud or Point to project onto the camera image plane. round_px : bool If True, projections are rounded to the nearest pixel. Returns ------- :obj:`autolab_core.ImageCoords` or :obj:`autolab_core.Point` A corresponding set of image coordinates representing the given PointCloud's projections onto the camera image plane. If the input was a single Point, returns a 2D Point in the camera plane. Raises ------ ValueError If the input is not a PointCloud or Point in the same reference frame as the camera. """ if not isinstance(point_cloud, PointCloud) and not (isinstance(point_cloud, Point) and point_cloud.dim == 3): raise ValueError('Must provide PointCloud or 3D Point object for projection') if point_cloud.frame != self._frame: raise ValueError('Cannot project points in	python	{ "resource": "" }
q12319	CameraIntrinsics.deproject_to_image	train	def deproject_to_image(self, depth_image): """Deprojects a DepthImage into a PointCloudImage. Parameters ---------- depth_image : :obj:`DepthImage` The 2D depth image to projet into a point cloud. Returns ------- :obj:`PointCloudImage` A point cloud image created from the depth image. Raises ------ ValueError If depth_image is not a valid DepthImage in the same reference frame	python	{ "resource": "" }
q12320	load_images	train	def load_images(cfg): """Helper function for loading a set of color images, depth images, and IR camera intrinsics. The config dictionary must have these keys: - prestored_data -- If 1, use the virtual sensor, else use a real sensor. - prestored_data_dir -- A path to the prestored data dir for a virtual sensor. - sensor/frame -- The frame of reference for the sensor. - sensor/device_num -- The device number for the real Kinect. - sensor/pipeline_mode -- The mode for the real Kinect's packet pipeline. - num_images -- The number of images to generate. Parameters ---------- cfg : :obj:`dict` A config dictionary. Returns ------- :obj:`tuple` of :obj:`list` of :obj:`ColorImage`, :obj:`list` of :obj:`DepthImage`, :obj:`CameraIntrinsics` A set of ColorImages and DepthImages, and the Kinect's CameraIntrinsics for its IR sensor. """	python	{ "resource": "" }
q12321	Kinect2Sensor.start	train	def start(self): """Starts the Kinect v2 sensor stream. Raises ------ IOError If the Kinect v2 is not detected. """ # open packet pipeline if self._packet_pipeline_mode == Kinect2PacketPipelineMode.OPENGL: self._pipeline = lf2.OpenGLPacketPipeline() elif self._packet_pipeline_mode == Kinect2PacketPipelineMode.CPU: self._pipeline = lf2.CpuPacketPipeline() # setup logger self._logger = lf2.createConsoleLogger(lf2.LoggerLevel.Warning) lf2.setGlobalLogger(self._logger) # check devices self._fn_handle = lf2.Freenect2() self._num_devices = self._fn_handle.enumerateDevices() if self._num_devices == 0: raise IOError('Failed to start stream. No Kinect2 devices available!') if self._num_devices <= self._device_num: raise IOError('Failed to start stream. Device num %d unavailable!' %(self._device_num)) # open device self._serial = self._fn_handle.getDeviceSerialNumber(self._device_num)	python	{ "resource": "" }
q12322	Kinect2Sensor.stop	train	def stop(self): """Stops the Kinect2 sensor stream. Returns ------- bool True if the stream was stopped, False if the device was already stopped or was not otherwise available. """ # check that everything is running if not self._running or self._device is None:	python	{ "resource": "" }
q12323	Kinect2Sensor._frames_and_index_map	train	def _frames_and_index_map(self, skip_registration=False): """Retrieve a new frame from the Kinect and return a ColorImage, DepthImage, IrImage, and a map from depth pixels to color pixel indices. Parameters ---------- skip_registration : bool If True, the registration step is skipped. Returns ------- :obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray` The ColorImage, DepthImage, and IrImage of the current frame, and an ndarray that maps pixels of the depth image to the index of the corresponding pixel in the color image. Raises ------ RuntimeError If the Kinect stream is not running. """ if not self._running: raise RuntimeError('Kinect2 device %s not runnning. Cannot read frames' %(self._device_num)) # read frames frames = self._listener.waitForNewFrame() unregistered_color = frames['color'] distorted_depth = frames['depth'] ir = frames['ir'] # apply color to depth registration color_frame = self._color_frame color = unregistered_color depth = distorted_depth color_depth_map = np.zeros([depth.height, depth.width]).astype(np.int32).ravel() if not skip_registration and self._registration_mode == Kinect2RegistrationMode.COLOR_TO_DEPTH: color_frame = self._ir_frame depth = lf2.Frame(depth.width, depth.height, 4, lf2.FrameType.Depth) color = lf2.Frame(depth.width, depth.height, 4, lf2.FrameType.Color)	python	{ "resource": "" }
q12324	KinectSensorBridged._color_image_callback	train	def _color_image_callback(self, image_msg): """ subscribe to image topic and keep it up to date """	python	{ "resource": "" }
q12325	KinectSensorBridged._depth_image_callback	train	def _depth_image_callback(self, image_msg): """ subscribe to depth image topic and keep it up to date """ encoding = image_msg.encoding try: depth_arr = self._bridge.imgmsg_to_cv2(image_msg, encoding) import pdb; pdb.set_trace() except CvBridgeError as e:	python	{ "resource": "" }
q12326	KinectSensorBridged.frames	train	def frames(self): """Retrieve a new frame from the Ensenso and convert it to a ColorImage, a DepthImage, IrImage is always none for this type Returns ------- :obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray` The ColorImage, DepthImage, and IrImage of the current frame. Raises ------ RuntimeError If the Kinect stream is not running. """ # wait for a new image	python	{ "resource": "" }
q12327	Kinect2SensorFactory.sensor	train	def sensor(sensor_type, cfg): """ Creates a Kinect2 sensor of the specified type. Parameters ---------- sensor_type : :obj:`str` the type of the sensor (real or virtual) cfg : :obj:`YamlConfig` dictionary of parameters for sensor initialization """ sensor_type = sensor_type.lower() if sensor_type == 'real': s = Kinect2Sensor(packet_pipeline_mode=cfg['pipeline_mode'], device_num=cfg['device_num'],	python	{ "resource": "" }
q12328	WeightPublisher._connect	train	def _connect(self, id_mask): """Connects to all of the load cells serially. """ # Get all devices attached as USB serial all_devices = glob.glob('/dev/ttyUSB*') # Identify which of the devices are LoadStar Serial Sensors sensors = [] for device in all_devices: try: ser = serial.Serial(port=device, timeout=0.5, exclusive=True) ser.write('ID\r') ser.flush() time.sleep(0.05) resp = ser.read(13) ser.close() if len(resp) >= 10 and resp[:len(id_mask)] == id_mask: sensors.append((device, resp.rstrip('\r\n')))	python	{ "resource": "" }
q12329	WeightPublisher._flush	train	def _flush(self): """Flushes all of the serial ports. """ for ser in self._serials: ser.flush()	python	{ "resource": "" }
q12330	WeightPublisher._read_weights	train	def _read_weights(self): """Reads weights from each of the load cells. """ weights = [] grams_per_pound = 453.592 # Read from each of the sensors for ser in self._serials: ser.write('W\r') ser.flush() time.sleep(0.02) for ser in self._serials: try: output_str = ser.readline() weight = float(output_str) * grams_per_pound	python	{ "resource": "" }
q12331	_Camera.run	train	def run(self): """ Continually write images to the filename specified by a command queue. """ if not self.camera.is_running: self.camera.start() while True: if not self.cmd_q.empty(): cmd = self.cmd_q.get() if cmd[0] == 'stop': self.out.close() self.recording = False elif cmd[0] == 'start': filename = cmd[1] self.out = si.FFmpegWriter(filename) self.recording = True self.count = 0 if self.recording: if self.count == 0: image, _, _ = self.camera.frames()	python	{ "resource": "" }
q12332	VideoRecorder.start	train	def start(self): """ Starts the camera recording process. """ self._started = True self._camera	python	{ "resource": "" }
q12333	VideoRecorder.start_recording	train	def start_recording(self, output_file): """ Starts recording to a given output video file. Parameters ---------- output_file : :obj:`str` filename to write video to """ if not self._started: raise Exception("Must start the video recorder first	python	{ "resource": "" }
q12334	VideoRecorder.stop_recording	train	def stop_recording(self): """ Stops writing video to file. """ if not self._recording: raise Exception("Cannot stop a video recording	python	{ "resource": "" }
q12335	VideoRecorder.stop	train	def stop(self): """ Stop the camera process. """ if not self._started: raise Exception("Cannot stop a video recorder before starting it!") self._started = False if self._actual_camera.is_running: self._actual_camera.stop()	python	{ "resource": "" }
q12336	Image._preprocess_data	train	def _preprocess_data(self, data): """Converts a data array to the preferred 3D structure. Parameters ---------- data : :obj:`numpy.ndarray` The data to process. Returns ------- :obj:`numpy.ndarray` The data re-formatted (if needed) as a 3D matrix Raises ------ ValueError If the data is not 1, 2, or 3D to begin with. """ original_type = data.dtype if len(data.shape) == 1:	python	{ "resource": "" }
q12337	Image.can_convert	train	def can_convert(x): """ Returns True if x can be converted to an image, False otherwise. """ if len(x.shape) < 2 or len(x.shape) > 3: return False dtype = x.dtype height = x.shape[0] width = x.shape[1]	python	{ "resource": "" }
q12338	Image.from_array	train	def from_array(x, frame='unspecified'): """ Converts an array of data to an Image based on the values in the array and the data format. """ if not Image.can_convert(x): raise ValueError('Cannot convert array to an Image!') dtype = x.dtype height = x.shape[0] width = x.shape[1] channels = 1 if len(x.shape) == 3: channels = x.shape[2] if dtype == np.uint8: if channels == 1: if np.any((x % BINARY_IM_MAX_VAL) > 0): return GrayscaleImage(x, frame) return BinaryImage(x, frame) elif channels == 3: return ColorImage(x, frame) else: raise ValueError( 'No available image conversion for uint8 array with 2 channels') elif dtype == np.uint16: if channels != 1: raise ValueError(	python	{ "resource": "" }
q12339	Image.align	train	def align(self, scale, center, angle, height, width): """ Create a thumbnail from the original image that is scaled by the given factor, centered on the center pixel, oriented along the grasp angle, and cropped to the desired height and width. Parameters ---------- scale : float scale factor to apply center : 2D array array containing the row and column index of the pixel to center on	python	{ "resource": "" }
q12340	Image.gradients	train	def gradients(self): """Return the gradient as a pair of numpy arrays. Returns ------- :obj:`tuple` of :obj:`numpy.ndarray` of float The gradients	python	{ "resource": "" }
q12341	Image.linear_to_ij	train	def linear_to_ij(self, linear_inds): """Converts linear indices to row and column coordinates. Parameters ---------- linear_inds : :obj:`numpy.ndarray` of int A list of linear coordinates. Returns ------- :obj:`numpy.ndarray` of int	python	{ "resource": "" }
q12342	Image.median_images	train	def median_images(images): """Create a median Image from a list of Images. Parameters ---------- :obj:`list` of :obj:`Image` A list of Image objects. Returns ------- :obj:`Image` A new Image of the same type whose data is the median of all of the images' data.	python	{ "resource": "" }
q12343	Image.min_images	train	def min_images(images): """Create a min Image from a list of Images. Parameters ---------- :obj:`list` of :obj:`Image` A list of Image objects. Returns ------- :obj:`Image` A new Image of the same type whose data is the min of all	python	{ "resource": "" }
q12344	Image.apply	train	def apply(self, method, args, *kwargs): """Create a new image by applying a function to this image's data. Parameters ---------- method : :obj:`function` A function to call on the data. This takes in a ndarray as its first argument and optionally takes other arguments. It should return a modified data ndarray. args : arguments Additional args for method. kwargs : keyword arguments Additional keyword arguments for method.	python	{ "resource": "" }
q12345	Image.focus	train	def focus(self, height, width, center_i=None, center_j=None): """Zero out all of the image outside of a crop box. Parameters ---------- height : int The height of the desired crop box. width : int The width of the desired crop box. center_i : int The center height point of the crop box. If not specified, the center of the image is used. center_j : int The center width point of the crop box. If not specified, the center of the image is used. Returns ------- :obj:`Image` A new Image of the same type and size that is zeroed out except within the crop box. """ if center_i is None: center_i = self.height / 2 if center_j is None: center_j = self.width / 2	python	{ "resource": "" }
q12346	Image.center_nonzero	train	def center_nonzero(self): """Recenters the image on the mean of the coordinates of nonzero pixels. Returns ------- :obj:`Image` A new Image of the same type and size that is re-centered at the mean location of the non-zero pixels. """ # get the center of the nonzero pixels nonzero_px = np.where(self._data != 0.0) nonzero_px = np.c_[nonzero_px[0], nonzero_px[1]] mean_px = np.mean(nonzero_px, axis=0) center_px = (np.array(self.shape) / 2.0)[:2] diff_px = center_px - mean_px # transform image nonzero_px_tf = nonzero_px + diff_px nonzero_px_tf[:, 0] = np.max( np.c_[np.zeros(nonzero_px_tf[:, 0].shape), nonzero_px_tf[:, 0]], axis=1) nonzero_px_tf[:, 0] = np.min(np.c_[( self.height - 1) * np.ones(nonzero_px_tf[:, 0].shape), nonzero_px_tf[:, 0]], axis=1) nonzero_px_tf[:,	python	{ "resource": "" }
q12347	Image.nonzero_pixels	train	def nonzero_pixels(self): """ Return an array of the nonzero pixels. Returns ------- :obj:`numpy.ndarray` Nx2 array of the nonzero pixels """	python	{ "resource": "" }
q12348	Image.zero_pixels	train	def zero_pixels(self): """ Return an array of the zero pixels. Returns ------- :obj:`numpy.ndarray` Nx2 array of the zero pixels """	python	{ "resource": "" }
q12349	Image.nan_pixels	train	def nan_pixels(self): """ Return an array of the NaN pixels. Returns ------- :obj:`numpy.ndarray` Nx2 array of the NaN pixels """	python	{ "resource": "" }
q12350	Image.finite_pixels	train	def finite_pixels(self): """ Return an array of the finite pixels. Returns ------- :obj:`numpy.ndarray` Nx2 array of the finite pixels """	python	{ "resource": "" }
q12351	Image.replace_zeros	train	def replace_zeros(self, val, zero_thresh=0.0): """ Replaces all zeros in the image with a specified value Returns ------- image dtype value to replace zeros with """	python	{ "resource": "" }
q12352	Image.save	train	def save(self, filename): """Writes the image to a file. Parameters ---------- filename : :obj:`str` The file to save the image to. Must be one of .png, .jpg, .npy, or .npz. Raises ------ ValueError If an unsupported file type is specified. """ filename = str(filename) file_root, file_ext = os.path.splitext(filename) if file_ext in COLOR_IMAGE_EXTS: im_data = self._image_data() if im_data.dtype.type == np.uint8: pil_image = PImage.fromarray(im_data.squeeze()) pil_image.save(filename) else: try: import png except:	python	{ "resource": "" }
q12353	Image.savefig	train	def savefig(self, output_path, title, dpi=400, format='png', cmap=None): """Write the image to a file using pyplot. Parameters ---------- output_path : :obj:`str` The directory in which to place the file. title : :obj:`str` The title of the file in which to save the image. dpi : int The resolution in dots per inch. format : :obj:`str` The file format to save. Available options include .png, .pdf, .ps, .eps, and .svg. cmap : :obj:`Colormap`, optional A Colormap object fo the	python	{ "resource": "" }
q12354	Image.load_data	train	def load_data(filename): """Loads a data matrix from a given file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. Returns ------- :obj:`numpy.ndarray` The data array read from the file.	python	{ "resource": "" }
q12355	ColorImage._check_valid_data	train	def _check_valid_data(self, data): """Checks that the given data is a uint8 array with one or three channels. Parameters ---------- data : :obj:`numpy.ndarray` The data to check. Raises ------ ValueError If the data is invalid. """ if data.dtype.type is not np.uint8: raise ValueError(	python	{ "resource": "" }
q12356	ColorImage.swap_channels	train	def swap_channels(self, channel_swap): """ Swaps the two channels specified in the tuple. Parameters ---------- channel_swap : :obj:`tuple` of int the two channels to swap Returns ------- :obj:`ColorImage` color image with cols swapped """ if len(channel_swap) != 2: raise ValueError('Illegal value for channel swap') ci = channel_swap[0] cj = channel_swap[1] if ci < 0	python	{ "resource": "" }
q12357	ColorImage.find_chessboard	train	def find_chessboard(self, sx=6, sy=9): """Finds the corners of an sx X sy chessboard in the image. Parameters ---------- sx : int Number of chessboard corners in x-direction. sy : int Number of chessboard corners in y-direction. Returns ------- :obj:`list` of :obj:`numpy.ndarray` A list containing the 2D points of the corners of the detected chessboard, or None if no chessboard found. """ # termination criteria criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # prepare object points, like (0,0,0), (1,0,0), (2,0,0)	python	{ "resource": "" }
q12358	ColorImage.foreground_mask	train	def foreground_mask( self, tolerance, ignore_black=True, use_hsv=False, scale=8, bgmodel=None): """Creates a binary image mask for the foreground of an image against a uniformly colored background. The background is assumed to be the mode value of the histogram for each of the color channels. Parameters ---------- tolerance : int A +/- level from the detected mean backgroud color. Pixels withing this range will be classified as background pixels and masked out. ignore_black : bool If True, the zero pixels will be ignored when computing the background model. use_hsv : bool If True, image will be converted to HSV for background model generation. scale : int Size of background histogram bins -- there will be BINARY_IM_MAX_VAL/size bins	python	{ "resource": "" }
q12359	ColorImage.background_model	train	def background_model(self, ignore_black=True, use_hsv=False, scale=8): """Creates a background model for the given image. The background color is given by the modes of each channel's histogram. Parameters ---------- ignore_black : bool If True, the zero pixels will be ignored when computing the background model. use_hsv : bool If True, image will be converted to HSV for background model generation. scale : int Size of background histogram bins -- there will be BINARY_IM_MAX_VAL/size bins in the color histogram for each channel. Returns ------- A list containing the red, green, and blue channel modes of the background. """ # hsv color data = self.data if use_hsv: pil_im = PImage.fromarray(self._data) pil_im = pil_im.convert('HSV') data = np.asarray(pil_im) # find the black pixels nonblack_pixels = np.where(np.sum(self.data, axis=2) > 0) r_data = self.r_data g_data = self.g_data b_data = self.b_data	python	{ "resource": "" }
q12360	ColorImage.draw_box	train	def draw_box(self, box): """Draw a white box on the image. Parameters ---------- :obj:`autolab_core.Box` A 2D box to draw in the image. Returns ------- :obj:`ColorImage` A new image that is the same as the current one, but with the white box drawn in. """ box_data = self._data.copy() min_i = box.min_pt[1] min_j = box.min_pt[0] max_i = box.max_pt[1] max_j = box.max_pt[0] # draw the vertical lines for j in range(min_j, max_j): box_data[min_i,	python	{ "resource": "" }
q12361	ColorImage.nonzero_hsv_data	train	def nonzero_hsv_data(self): """ Computes non zero hsv values. Returns ------- :obj:`numpy.ndarray` array of the hsv values for the image """	python	{ "resource": "" }
q12362	ColorImage.segment_kmeans	train	def segment_kmeans(self, rgb_weight, num_clusters, hue_weight=0.0): """ Segment a color image using KMeans based on spatial and color distances. Black pixels will automatically be assigned to their own 'background' cluster. Parameters ---------- rgb_weight : float weighting of RGB distance relative to spatial and hue distance num_clusters : int number of clusters to use hue_weight : float weighting of hue from hsv relative to spatial and RGB distance Returns ------- :obj:`SegmentationImage` image containing the segment labels """ # form features array label_offset = 1 nonzero_px = np.where(self.data != 0.0) nonzero_px = np.c_[nonzero_px[0], nonzero_px[1]] # get hsv data if specified color_vals = rgb_weight * \ self._data[nonzero_px[:, 0], nonzero_px[:, 1], :] if hue_weight > 0.0: hsv_data = cv2.cvtColor(self.data, cv2.COLOR_BGR2HSV) color_vals = np.c_[color_vals, hue_weight	python	{ "resource": "" }
q12363	ColorImage.to_grayscale	train	def to_grayscale(self): """Converts the color image to grayscale using OpenCV. Returns ------- :obj:`GrayscaleImage` Grayscale image corresponding to original color image. """	python	{ "resource": "" }
q12364	ColorImage.open	train	def open(filename, frame='unspecified'): """Creates a ColorImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns	python	{ "resource": "" }
q12365	DepthImage._check_valid_data	train	def _check_valid_data(self, data): """Checks that the given data is a float array with one channel. Parameters ---------- data : :obj:`numpy.ndarray` The data to check. Raises ------ ValueError If the data is invalid. """ if data.dtype.type is not np.float32 and \ data.dtype.type is not np.float64: raise ValueError(	python	{ "resource": "" }
q12366	DepthImage.threshold	train	def threshold(self, front_thresh=0.0, rear_thresh=100.0): """Creates a new DepthImage by setting all depths less than front_thresh and greater than rear_thresh to 0. Parameters ---------- front_thresh : float The lower-bound threshold. rear_thresh : float The upper bound threshold. Returns ------- :obj:`DepthImage` A new DepthImage	python	{ "resource": "" }
q12367	DepthImage.threshold_gradients	train	def threshold_gradients(self, grad_thresh): """Creates a new DepthImage by zeroing out all depths where the magnitude of the gradient at that point is greater than grad_thresh. Parameters ---------- grad_thresh : float A threshold for the gradient magnitude. Returns ------- :obj:`DepthImage` A new DepthImage created from the thresholding operation. """ data = np.copy(self._data) gx, gy = self.gradients()	python	{ "resource": "" }
q12368	DepthImage.threshold_gradients_pctile	train	def threshold_gradients_pctile(self, thresh_pctile, min_mag=0.0): """Creates a new DepthImage by zeroing out all depths where the magnitude of the gradient at that point is greater than some percentile of all gradients. Parameters ---------- thresh_pctile : float percentile to threshold all gradients above min_mag : float minimum magnitude of the gradient Returns ------- :obj:`DepthImage` A new DepthImage created from the thresholding operation. """ data = np.copy(self._data)	python	{ "resource": "" }
q12369	DepthImage.invalid_pixel_mask	train	def invalid_pixel_mask(self): """ Returns a binary mask for the NaN- and zero-valued pixels. Serves as a mask for invalid pixels. Returns ------- :obj:`BinaryImage` Binary image where a pixel value greater than zero indicates an invalid pixel.	python	{ "resource": "" }
q12370	DepthImage.pixels_farther_than	train	def pixels_farther_than(self, depth_im, filter_equal_depth=False): """ Returns the pixels that are farther away than those in the corresponding depth image. Parameters ---------- depth_im : :obj:`DepthImage` depth image to query replacement with filter_equal_depth : bool whether or not to mark depth values that are equal Returns ------- :obj:`numpy.ndarray` the pixels """	python	{ "resource": "" }
q12371	DepthImage.combine_with	train	def combine_with(self, depth_im): """ Replaces all zeros in the source depth image with the value of a different depth image Parameters ---------- depth_im : :obj:`DepthImage` depth image to combine with Returns ------- :obj:`DepthImage` the combined depth image """ new_data = self.data.copy() # replace zero pixels new_data[new_data == 0] = depth_im.data[new_data == 0]	python	{ "resource": "" }
q12372	DepthImage.to_binary	train	def to_binary(self, threshold=0.0): """Creates a BinaryImage from the depth image. Points where the depth is greater than threshold are converted to ones, and all other points are zeros. Parameters ---------- threshold : float The depth threshold. Returns ------- :obj:`BinaryImage` A BinaryImage where all 1 points had	python	{ "resource": "" }
q12373	DepthImage.to_color	train	def to_color(self, normalize=False): """ Convert to a color image. Parameters ---------- normalize : bool whether or not to normalize by the maximum depth Returns ------- :obj:`ColorImage`	python	{ "resource": "" }
q12374	DepthImage.to_float	train	def to_float(self): """ Converts to 32-bit data. Returns ------- :obj:`DepthImage` depth image with 32 bit float	python	{ "resource": "" }
q12375	DepthImage.point_normal_cloud	train	def point_normal_cloud(self, camera_intr): """Computes a PointNormalCloud from the depth image. Parameters ---------- camera_intr : :obj:`CameraIntrinsics` The camera parameters on which this depth image was taken. Returns ------- :obj:`autolab_core.PointNormalCloud` A PointNormalCloud created from the depth image. """	python	{ "resource": "" }
q12376	DepthImage.open	train	def open(filename, frame='unspecified'): """Creates a DepthImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns ------- :obj:`DepthImage` The new depth image.	python	{ "resource": "" }
q12377	IrImage.open	train	def open(filename, frame='unspecified'): """Creates an IrImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns -------	python	{ "resource": "" }
q12378	GrayscaleImage.to_color	train	def to_color(self): """Convert the grayscale image to a ColorImage. Returns ------- :obj:`ColorImage` A color image equivalent to the grayscale one. """	python	{ "resource": "" }
q12379	GrayscaleImage.open	train	def open(filename, frame='unspecified'): """Creates a GrayscaleImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns	python	{ "resource": "" }
q12380	BinaryImage.pixelwise_or	train	def pixelwise_or(self, binary_im): """ Takes OR operation with other binary image. Parameters ---------- binary_im : :obj:`BinaryImage` binary image for and operation	python	{ "resource": "" }
q12381	BinaryImage.contour_mask	train	def contour_mask(self, contour): """ Generates a binary image with only the given contour filled in. """ # fill in new data new_data = np.zeros(self.data.shape) num_boundary = contour.boundary_pixels.shape[0] boundary_px_ij_swapped = np.zeros([num_boundary, 1, 2]) boundary_px_ij_swapped[:, 0, 0] = contour.boundary_pixels[:, 1] boundary_px_ij_swapped[:, 0, 1] = contour.boundary_pixels[:, 0] cv2.fillPoly( new_data, pts=[ boundary_px_ij_swapped.astype(	python	{ "resource": "" }
q12382	BinaryImage.boundary_map	train	def boundary_map(self): """ Computes the boundary pixels in the image and sets them to nonzero values. Returns ------- :obj:`BinaryImage` binary image with nonzeros on the boundary of the original image """ # compute contours contours = self.find_contours() # fill in nonzero pixels new_data	python	{ "resource": "" }
q12383	BinaryImage.most_free_pixel	train	def most_free_pixel(self): """ Find the black pixel with the largest distance from the white pixels. Returns ------- :obj:`numpy.ndarray` 2-vector containing the most free pixel """ dist_tf	python	{ "resource": "" }
q12384	BinaryImage.diff_with_target	train	def diff_with_target(self, binary_im): """ Creates a color image to visualize the overlap between two images. Nonzero pixels that match in both images are green. Nonzero pixels of this image that aren't in the other image are yellow Nonzero pixels of the other image that aren't in this image are red Parameters ---------- binary_im : :obj:`BinaryImage` binary image to take the difference with Returns ------- :obj:`ColorImage` color image to visualize the image difference """ red = np.array([BINARY_IM_MAX_VAL, 0, 0]) yellow = np.array([BINARY_IM_MAX_VAL, BINARY_IM_MAX_VAL, 0]) green = np.array([0, BINARY_IM_MAX_VAL, 0])	python	{ "resource": "" }
q12385	BinaryImage.num_adjacent	train	def num_adjacent(self, i, j): """ Counts the number of adjacent nonzero pixels to a given pixel. Parameters ---------- i : int row index of query pixel j : int col index of query pixel Returns ------- int number of adjacent nonzero pixels """ # check values if i < 1 or i > self.height - 2 or j < 1 and j > self.width - 2:	python	{ "resource": "" }
q12386	BinaryImage.to_sdf	train	def to_sdf(self): """ Converts the 2D image to a 2D signed distance field. Returns ------- :obj:`numpy.ndarray` 2D float array of the signed distance field """ # compute medial axis transform skel, sdf_in = morph.medial_axis(self.data, return_distance=True) useless_skel, sdf_out	python	{ "resource": "" }
q12387	BinaryImage.to_color	train	def to_color(self): """Creates a ColorImage from the binary image. Returns ------- :obj:`ColorImage` The newly-created color image. """ color_data = np.zeros([self.height, self.width, 3]) color_data[:, :, 0] = self.data	python	{ "resource": "" }
q12388	BinaryImage.open	train	def open(filename, frame='unspecified'): """Creates a BinaryImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns ------- :obj:`BinaryImage`	python	{ "resource": "" }
q12389	RgbdImage._check_valid_data	train	def _check_valid_data(self, data): """Checks that the given data is a float array with four channels. Parameters ---------- data : :obj:`numpy.ndarray` The data to check. Raises ------ ValueError If the data is invalid. """ if data.dtype.type is not np.float32 and \ data.dtype.type is not np.float64: raise ValueError( 'Illegal data type. RGB-D images only support float arrays') if len(data.shape) != 3 and data.shape[2] != 4:	python	{ "resource": "" }
q12390	RgbdImage.from_color_and_depth	train	def from_color_and_depth(color_im, depth_im): """ Creates an RGB-D image from a separate color and depth image. """ # check shape if color_im.height != depth_im.height or color_im.width != depth_im.width: raise ValueError('Color and depth images must have the same shape')	python	{ "resource": "" }
q12391	RgbdImage.color	train	def color(self): """ Returns the color image. """ return	python	{ "resource": "" }
q12392	RgbdImage.combine_with	train	def combine_with(self, rgbd_im): """ Replaces all zeros in the source rgbd image with the values of a different rgbd image Parameters ---------- rgbd_im : :obj:`RgbdImage` rgbd image to combine with Returns ------- :obj:`RgbdImage` the combined rgbd image """ new_data = self.data.copy() depth_data = self.depth.data other_depth_data = rgbd_im.depth.data depth_zero_px = self.depth.zero_pixels() depth_replace_px = np.where( (other_depth_data != 0) & ( other_depth_data < depth_data)) depth_replace_px = np.c_[depth_replace_px[0], depth_replace_px[1]]	python	{ "resource": "" }
q12393	GdImage.from_grayscale_and_depth	train	def from_grayscale_and_depth(gray_im, depth_im): """ Creates an G-D image from a separate grayscale and depth image. """ # check shape if gray_im.height != depth_im.height or gray_im.width != depth_im.width: raise ValueError( 'Grayscale and depth images must have the same shape') # check frame if gray_im.frame != depth_im.frame: raise ValueError( 'Grayscale and depth images must have the same frame')	python	{ "resource": "" }
q12394	GdImage.gray	train	def gray(self): """ Returns the grayscale image. """ return GrayscaleImage(	python	{ "resource": "" }
q12395	SegmentationImage.border_pixels	train	def border_pixels( self, grad_sigma=0.5, grad_lower_thresh=0.1, grad_upper_thresh=1.0): """ Returns the pixels on the boundary between all segments, excluding the zero segment. Parameters ---------- grad_sigma : float standard deviation used for gaussian gradient filter grad_lower_thresh : float lower threshold on gradient threshold used to determine the boundary pixels grad_upper_thresh : float upper threshold on gradient threshold used to determine the boundary pixels Returns ------- :obj:`numpy.ndarray` Nx2 array of pixels on the boundary """ # boundary pixels boundary_im = np.ones(self.shape) for i in range(1, self.num_segments): label_border_im = self.data.copy() label_border_im[self.data == 0] = i grad_mag = sf.gaussian_gradient_magnitude(	python	{ "resource": "" }
q12396	SegmentationImage.segment_mask	train	def segment_mask(self, segnum): """ Returns a binary image of just the segment corresponding to the given number. Parameters ---------- segnum : int the number of the segment to generate a	python	{ "resource": "" }
q12397	SegmentationImage.open	train	def open(filename, frame='unspecified'): """ Opens a segmentation image """	python	{ "resource": "" }
q12398	PointCloudImage.to_point_cloud	train	def to_point_cloud(self): """Convert the image to a PointCloud object. Returns ------- :obj:`autolab_core.PointCloud` The corresponding PointCloud. """ return PointCloud(	python	{ "resource": "" }
q12399	PointCloudImage.normal_cloud_im	train	def normal_cloud_im(self, ksize=3): """Generate a NormalCloudImage from the PointCloudImage using Sobel filtering. Parameters ---------- ksize : int Size of the kernel to use for derivative computation Returns ------- :obj:`NormalCloudImage` The corresponding NormalCloudImage. """ # compute direction via cross product of derivatives gy = cv2.Sobel(self.data, cv2.CV_64F, 1, 0, ksize=ksize) gx = cv2.Sobel(self.data, cv2.CV_64F, 0, 1, ksize=ksize) gx_data = gx.reshape(self.height * self.width, 3) gy_data = gy.reshape(self.height * self.width, 3) pc_grads = np.cross(gx_data, gy_data) # default to point toward camera # normalize pc_grad_norms = np.linalg.norm(pc_grads, axis=1)	python	{ "resource": "" }

q12300

EnsensoSensor._depth_im_from_pointcloud

train

def _depth_im_from_pointcloud(self, msg): """ Convert a pointcloud2 message to a depth image. """ # set format if self._format is None: self._set_format(msg) # rescale camera intr in case binning is turned on if msg.height != self._camera_intr.height: rescale_factor = float(msg.height) / self._camera_intr.height self._camera_intr = self._camera_intr.resize(rescale_factor) # read num points num_points = msg.height * msg.width # read buffer

python

{ "resource": "" }

q12301

EnsensoSensor.frames

train

def frames(self): """Retrieve a new frame from the Ensenso and convert it to a ColorImage, a DepthImage, and an IrImage. Returns ------- :obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray` The ColorImage, DepthImage, and IrImage of the current frame. Raises ------ RuntimeError If the Ensenso stream is not running. """ # wait for a new image while self._cur_depth_im is None:

python

{ "resource": "" }

q12302

IterativeRegistrationSolver.register

train

def register(self, source_point_cloud, target_point_cloud, source_normal_cloud, target_normal_cloud, matcher, num_iterations=1, compute_total_cost=True, match_centroids=False, vis=False): """ Iteratively register objects to one another. Parameters ---------- source_point_cloud : :obj:`autolab_core.PointCloud` source object points target_point_cloud : :obj`autolab_core.PointCloud` target object points source_normal_cloud : :obj:`autolab_core.NormalCloud` source object outward-pointing normals target_normal_cloud : :obj:`autolab_core.NormalCloud` target object outward-pointing normals matcher : :obj:`PointToPlaneFeatureMatcher`

python

{ "resource": "" }

q12303

TensorDatasetVirtualSensor.frames

train

def frames(self): """Retrieve the next frame from the tensor dataset and convert it to a ColorImage, a DepthImage, and an IrImage. Parameters ---------- skip_registration : bool If True, the registration step is skipped. Returns ------- :obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray` The ColorImage, DepthImage, and IrImage of the current frame. Raises ------ RuntimeError If the stream is not running or if all images in the directory have been used. """ if not self._running:

python

{ "resource": "" }

q12304

PrimesenseSensor._read_depth_image

train

def _read_depth_image(self): """ Reads a depth image from the device """ # read raw uint16 buffer im_arr = self._depth_stream.read_frame() raw_buf = im_arr.get_buffer_as_uint16() buf_array = np.array([raw_buf[i] for i in range(PrimesenseSensor.DEPTH_IM_WIDTH * PrimesenseSensor.DEPTH_IM_HEIGHT)]) # convert to image in meters depth_image = buf_array.reshape(PrimesenseSensor.DEPTH_IM_HEIGHT, PrimesenseSensor.DEPTH_IM_WIDTH)

python

{ "resource": "" }

q12305

PrimesenseSensor._read_color_image

train

def _read_color_image(self): """ Reads a color image from the device """ # read raw buffer im_arr = self._color_stream.read_frame() raw_buf = im_arr.get_buffer_as_triplet() r_array = np.array([raw_buf[i][0] for i in range(PrimesenseSensor.COLOR_IM_WIDTH * PrimesenseSensor.COLOR_IM_HEIGHT)]) g_array = np.array([raw_buf[i][1] for i in range(PrimesenseSensor.COLOR_IM_WIDTH * PrimesenseSensor.COLOR_IM_HEIGHT)]) b_array = np.array([raw_buf[i][2] for i in range(PrimesenseSensor.COLOR_IM_WIDTH * PrimesenseSensor.COLOR_IM_HEIGHT)])

python

{ "resource": "" }

q12306

PrimesenseSensor_ROS._read_depth_images

train

def _read_depth_images(self, num_images): """ Reads depth images from the device """ depth_images = self._ros_read_images(self._depth_image_buffer, num_images, self.staleness_limit) for i in range(0, num_images): depth_images[i] = depth_images[i] * MM_TO_METERS # convert to meters if self._flip_images:

python

{ "resource": "" }

q12307

PrimesenseSensor_ROS._read_color_images

train

def _read_color_images(self, num_images): """ Reads color images from the device """ color_images = self._ros_read_images(self._color_image_buffer, num_images, self.staleness_limit) for i in range(0, num_images): if self._flip_images: color_images[i] = np.flipud(color_images[i].astype(np.uint8))

python

{ "resource": "" }

q12308

ObjectRender.T_obj_camera

train

def T_obj_camera(self): """Returns the transformation from camera to object when the object is in the given stable pose. Returns ------- :obj:`autolab_core.RigidTransform` The desired transform. """ if self.stable_pose is None: T_obj_world =

python

{ "resource": "" }

q12309

QueryImageBundle.image

train

def image(self, render_mode): """Return an image generated with a particular render mode. Parameters ---------- render_mode : :obj:`RenderMode` The type of image we want. Returns ------- :obj:`Image`

python

{ "resource": "" }

q12310

BagOfFeatures.add

train

def add(self, feature): """ Add a new feature to the bag. Parameters ---------- feature : :obj:`Feature` feature to add """

python

{ "resource": "" }

q12311

BagOfFeatures.extend

train

def extend(self, features): """ Add a list of features to the bag. Parameters ---------- feature : :obj:`list` of :obj:`Feature` features to add """

python

{ "resource": "" }

q12312

BagOfFeatures.feature

train

def feature(self, index): """ Returns a feature. Parameters ---------- index : int index of feature in list Returns ------- :obj:`Feature`

python

{ "resource": "" }

q12313

WeightSensor.total_weight

train

def total_weight(self): """Read a weight from the sensor in grams. Returns ------- weight : float The sensor weight in grams. """ weights = self._raw_weights() if weights.shape[1] == 0: return

python

{ "resource": "" }

q12314

WeightSensor.individual_weights

train

def individual_weights(self): """Read individual weights from the load cells in grams. Returns ------- weight : float The sensor weight in grams. """ weights = self._raw_weights() if weights.shape[1] == 0:

python

{ "resource": "" }

q12315

WeightSensor._raw_weights

train

def _raw_weights(self): """Create a numpy array containing the raw sensor weights. """ if self._debug: return np.array([[],[],[],[]]) if not self._running: raise ValueError('Weight sensor is not running!') if len(self._weight_buffers) == 0: time.sleep(0.3)

python

{ "resource": "" }

q12316

WeightSensor._weights_callback

train

def _weights_callback(self, msg): """Callback for recording weights from sensor. """ # Read weights weights = np.array(msg.data) # If needed, initialize indiv_weight_buffers if len(self._weight_buffers) == 0: self._weight_buffers = [[] for i in range(len(weights))] # Record individual weights

python

{ "resource": "" }

q12317

CameraIntrinsics.crop

train

def crop(self, height, width, crop_ci, crop_cj): """ Convert to new camera intrinsics for crop of image from original camera. Parameters ---------- height : int height of crop window width : int width of crop window crop_ci : int row of crop window center crop_cj : int col of crop window center Returns -------

python

{ "resource": "" }

q12318

CameraIntrinsics.project

train

def project(self, point_cloud, round_px=True): """Projects a point cloud onto the camera image plane. Parameters ---------- point_cloud : :obj:`autolab_core.PointCloud` or :obj:`autolab_core.Point` A PointCloud or Point to project onto the camera image plane. round_px : bool If True, projections are rounded to the nearest pixel. Returns ------- :obj:`autolab_core.ImageCoords` or :obj:`autolab_core.Point` A corresponding set of image coordinates representing the given PointCloud's projections onto the camera image plane. If the input was a single Point, returns a 2D Point in the camera plane. Raises ------ ValueError If the input is not a PointCloud or Point in the same reference frame as the camera. """ if not isinstance(point_cloud, PointCloud) and not (isinstance(point_cloud, Point) and point_cloud.dim == 3): raise ValueError('Must provide PointCloud or 3D Point object for projection') if point_cloud.frame != self._frame: raise ValueError('Cannot project points in

python

{ "resource": "" }

q12319

CameraIntrinsics.deproject_to_image

train

def deproject_to_image(self, depth_image): """Deprojects a DepthImage into a PointCloudImage. Parameters ---------- depth_image : :obj:`DepthImage` The 2D depth image to projet into a point cloud. Returns ------- :obj:`PointCloudImage` A point cloud image created from the depth image. Raises ------ ValueError If depth_image is not a valid DepthImage in the same reference frame

python

{ "resource": "" }

q12320

load_images

train

def load_images(cfg): """Helper function for loading a set of color images, depth images, and IR camera intrinsics. The config dictionary must have these keys: - prestored_data -- If 1, use the virtual sensor, else use a real sensor. - prestored_data_dir -- A path to the prestored data dir for a virtual sensor. - sensor/frame -- The frame of reference for the sensor. - sensor/device_num -- The device number for the real Kinect. - sensor/pipeline_mode -- The mode for the real Kinect's packet pipeline. - num_images -- The number of images to generate. Parameters ---------- cfg : :obj:`dict` A config dictionary. Returns ------- :obj:`tuple` of :obj:`list` of :obj:`ColorImage`, :obj:`list` of :obj:`DepthImage`, :obj:`CameraIntrinsics` A set of ColorImages and DepthImages, and the Kinect's CameraIntrinsics for its IR sensor. """

python

{ "resource": "" }

q12321

Kinect2Sensor.start

train

def start(self): """Starts the Kinect v2 sensor stream. Raises ------ IOError If the Kinect v2 is not detected. """ # open packet pipeline if self._packet_pipeline_mode == Kinect2PacketPipelineMode.OPENGL: self._pipeline = lf2.OpenGLPacketPipeline() elif self._packet_pipeline_mode == Kinect2PacketPipelineMode.CPU: self._pipeline = lf2.CpuPacketPipeline() # setup logger self._logger = lf2.createConsoleLogger(lf2.LoggerLevel.Warning) lf2.setGlobalLogger(self._logger) # check devices self._fn_handle = lf2.Freenect2() self._num_devices = self._fn_handle.enumerateDevices() if self._num_devices == 0: raise IOError('Failed to start stream. No Kinect2 devices available!') if self._num_devices <= self._device_num: raise IOError('Failed to start stream. Device num %d unavailable!' %(self._device_num)) # open device self._serial = self._fn_handle.getDeviceSerialNumber(self._device_num)

python

{ "resource": "" }

q12322

Kinect2Sensor.stop

train

def stop(self): """Stops the Kinect2 sensor stream. Returns ------- bool True if the stream was stopped, False if the device was already stopped or was not otherwise available. """ # check that everything is running if not self._running or self._device is None:

python

{ "resource": "" }

q12323

Kinect2Sensor._frames_and_index_map

train

def _frames_and_index_map(self, skip_registration=False): """Retrieve a new frame from the Kinect and return a ColorImage, DepthImage, IrImage, and a map from depth pixels to color pixel indices. Parameters ---------- skip_registration : bool If True, the registration step is skipped. Returns ------- :obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray` The ColorImage, DepthImage, and IrImage of the current frame, and an ndarray that maps pixels of the depth image to the index of the corresponding pixel in the color image. Raises ------ RuntimeError If the Kinect stream is not running. """ if not self._running: raise RuntimeError('Kinect2 device %s not runnning. Cannot read frames' %(self._device_num)) # read frames frames = self._listener.waitForNewFrame() unregistered_color = frames['color'] distorted_depth = frames['depth'] ir = frames['ir'] # apply color to depth registration color_frame = self._color_frame color = unregistered_color depth = distorted_depth color_depth_map = np.zeros([depth.height, depth.width]).astype(np.int32).ravel() if not skip_registration and self._registration_mode == Kinect2RegistrationMode.COLOR_TO_DEPTH: color_frame = self._ir_frame depth = lf2.Frame(depth.width, depth.height, 4, lf2.FrameType.Depth) color = lf2.Frame(depth.width, depth.height, 4, lf2.FrameType.Color)

python

{ "resource": "" }

q12324

KinectSensorBridged._color_image_callback

train

def _color_image_callback(self, image_msg): """ subscribe to image topic and keep it up to date """

python

{ "resource": "" }

q12325

KinectSensorBridged._depth_image_callback

train

def _depth_image_callback(self, image_msg): """ subscribe to depth image topic and keep it up to date """ encoding = image_msg.encoding try: depth_arr = self._bridge.imgmsg_to_cv2(image_msg, encoding) import pdb; pdb.set_trace() except CvBridgeError as e:

python

{ "resource": "" }

q12326

KinectSensorBridged.frames

train

def frames(self): """Retrieve a new frame from the Ensenso and convert it to a ColorImage, a DepthImage, IrImage is always none for this type Returns ------- :obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray` The ColorImage, DepthImage, and IrImage of the current frame. Raises ------ RuntimeError If the Kinect stream is not running. """ # wait for a new image

python

{ "resource": "" }

q12327

Kinect2SensorFactory.sensor

train

def sensor(sensor_type, cfg): """ Creates a Kinect2 sensor of the specified type. Parameters ---------- sensor_type : :obj:`str` the type of the sensor (real or virtual) cfg : :obj:`YamlConfig` dictionary of parameters for sensor initialization """ sensor_type = sensor_type.lower() if sensor_type == 'real': s = Kinect2Sensor(packet_pipeline_mode=cfg['pipeline_mode'], device_num=cfg['device_num'],

python

{ "resource": "" }

q12328

WeightPublisher._connect

train

def _connect(self, id_mask): """Connects to all of the load cells serially. """ # Get all devices attached as USB serial all_devices = glob.glob('/dev/ttyUSB*') # Identify which of the devices are LoadStar Serial Sensors sensors = [] for device in all_devices: try: ser = serial.Serial(port=device, timeout=0.5, exclusive=True) ser.write('ID\r') ser.flush() time.sleep(0.05) resp = ser.read(13) ser.close() if len(resp) >= 10 and resp[:len(id_mask)] == id_mask: sensors.append((device, resp.rstrip('\r\n')))

python

{ "resource": "" }

q12329

WeightPublisher._flush

train

def _flush(self): """Flushes all of the serial ports. """ for ser in self._serials: ser.flush()

python

{ "resource": "" }

q12330

WeightPublisher._read_weights

train

def _read_weights(self): """Reads weights from each of the load cells. """ weights = [] grams_per_pound = 453.592 # Read from each of the sensors for ser in self._serials: ser.write('W\r') ser.flush() time.sleep(0.02) for ser in self._serials: try: output_str = ser.readline() weight = float(output_str) * grams_per_pound

python

{ "resource": "" }

q12331

_Camera.run

train

def run(self): """ Continually write images to the filename specified by a command queue. """ if not self.camera.is_running: self.camera.start() while True: if not self.cmd_q.empty(): cmd = self.cmd_q.get() if cmd[0] == 'stop': self.out.close() self.recording = False elif cmd[0] == 'start': filename = cmd[1] self.out = si.FFmpegWriter(filename) self.recording = True self.count = 0 if self.recording: if self.count == 0: image, _, _ = self.camera.frames()

python

{ "resource": "" }

q12332

VideoRecorder.start

train

def start(self): """ Starts the camera recording process. """ self._started = True self._camera

python

{ "resource": "" }

q12333

VideoRecorder.start_recording

train

def start_recording(self, output_file): """ Starts recording to a given output video file. Parameters ---------- output_file : :obj:`str` filename to write video to """ if not self._started: raise Exception("Must start the video recorder first

python

{ "resource": "" }

q12334

VideoRecorder.stop_recording

train

def stop_recording(self): """ Stops writing video to file. """ if not self._recording: raise Exception("Cannot stop a video recording

python

{ "resource": "" }

q12335

VideoRecorder.stop

train

def stop(self): """ Stop the camera process. """ if not self._started: raise Exception("Cannot stop a video recorder before starting it!") self._started = False if self._actual_camera.is_running: self._actual_camera.stop()

python

{ "resource": "" }

q12336

Image._preprocess_data

train

def _preprocess_data(self, data): """Converts a data array to the preferred 3D structure. Parameters ---------- data : :obj:`numpy.ndarray` The data to process. Returns ------- :obj:`numpy.ndarray` The data re-formatted (if needed) as a 3D matrix Raises ------ ValueError If the data is not 1, 2, or 3D to begin with. """ original_type = data.dtype if len(data.shape) == 1:

python

{ "resource": "" }

q12337

Image.can_convert

train

def can_convert(x): """ Returns True if x can be converted to an image, False otherwise. """ if len(x.shape) < 2 or len(x.shape) > 3: return False dtype = x.dtype height = x.shape[0] width = x.shape[1]

python

{ "resource": "" }

q12338

Image.from_array

train

def from_array(x, frame='unspecified'): """ Converts an array of data to an Image based on the values in the array and the data format. """ if not Image.can_convert(x): raise ValueError('Cannot convert array to an Image!') dtype = x.dtype height = x.shape[0] width = x.shape[1] channels = 1 if len(x.shape) == 3: channels = x.shape[2] if dtype == np.uint8: if channels == 1: if np.any((x % BINARY_IM_MAX_VAL) > 0): return GrayscaleImage(x, frame) return BinaryImage(x, frame) elif channels == 3: return ColorImage(x, frame) else: raise ValueError( 'No available image conversion for uint8 array with 2 channels') elif dtype == np.uint16: if channels != 1: raise ValueError(

python

{ "resource": "" }

q12339

Image.align

train

def align(self, scale, center, angle, height, width): """ Create a thumbnail from the original image that is scaled by the given factor, centered on the center pixel, oriented along the grasp angle, and cropped to the desired height and width. Parameters ---------- scale : float scale factor to apply center : 2D array array containing the row and column index of the pixel to center on

python

{ "resource": "" }

q12340

Image.gradients

train

def gradients(self): """Return the gradient as a pair of numpy arrays. Returns ------- :obj:`tuple` of :obj:`numpy.ndarray` of float The gradients

python

{ "resource": "" }

q12341

Image.linear_to_ij

train

def linear_to_ij(self, linear_inds): """Converts linear indices to row and column coordinates. Parameters ---------- linear_inds : :obj:`numpy.ndarray` of int A list of linear coordinates. Returns ------- :obj:`numpy.ndarray` of int

python

{ "resource": "" }

q12342

Image.median_images

train

def median_images(images): """Create a median Image from a list of Images. Parameters ---------- :obj:`list` of :obj:`Image` A list of Image objects. Returns ------- :obj:`Image` A new Image of the same type whose data is the median of all of the images' data.

python

{ "resource": "" }

q12343

Image.min_images

train

def min_images(images): """Create a min Image from a list of Images. Parameters ---------- :obj:`list` of :obj:`Image` A list of Image objects. Returns ------- :obj:`Image` A new Image of the same type whose data is the min of all

python

{ "resource": "" }

q12344

Image.apply

train

def apply(self, method, *args, **kwargs): """Create a new image by applying a function to this image's data. Parameters ---------- method : :obj:`function` A function to call on the data. This takes in a ndarray as its first argument and optionally takes other arguments. It should return a modified data ndarray. args : arguments Additional args for method. kwargs : keyword arguments Additional keyword arguments for method.

python

{ "resource": "" }

q12345

Image.focus

train

def focus(self, height, width, center_i=None, center_j=None): """Zero out all of the image outside of a crop box. Parameters ---------- height : int The height of the desired crop box. width : int The width of the desired crop box. center_i : int The center height point of the crop box. If not specified, the center of the image is used. center_j : int The center width point of the crop box. If not specified, the center of the image is used. Returns ------- :obj:`Image` A new Image of the same type and size that is zeroed out except within the crop box. """ if center_i is None: center_i = self.height / 2 if center_j is None: center_j = self.width / 2

python

{ "resource": "" }

q12346

Image.center_nonzero

train

def center_nonzero(self): """Recenters the image on the mean of the coordinates of nonzero pixels. Returns ------- :obj:`Image` A new Image of the same type and size that is re-centered at the mean location of the non-zero pixels. """ # get the center of the nonzero pixels nonzero_px = np.where(self._data != 0.0) nonzero_px = np.c_[nonzero_px[0], nonzero_px[1]] mean_px = np.mean(nonzero_px, axis=0) center_px = (np.array(self.shape) / 2.0)[:2] diff_px = center_px - mean_px # transform image nonzero_px_tf = nonzero_px + diff_px nonzero_px_tf[:, 0] = np.max( np.c_[np.zeros(nonzero_px_tf[:, 0].shape), nonzero_px_tf[:, 0]], axis=1) nonzero_px_tf[:, 0] = np.min(np.c_[( self.height - 1) * np.ones(nonzero_px_tf[:, 0].shape), nonzero_px_tf[:, 0]], axis=1) nonzero_px_tf[:,

python

{ "resource": "" }

q12347

Image.nonzero_pixels

train

def nonzero_pixels(self): """ Return an array of the nonzero pixels. Returns ------- :obj:`numpy.ndarray` Nx2 array of the nonzero pixels """

python

{ "resource": "" }

q12348

Image.zero_pixels

train

def zero_pixels(self): """ Return an array of the zero pixels. Returns ------- :obj:`numpy.ndarray` Nx2 array of the zero pixels """

python

{ "resource": "" }

q12349

Image.nan_pixels

train

def nan_pixels(self): """ Return an array of the NaN pixels. Returns ------- :obj:`numpy.ndarray` Nx2 array of the NaN pixels """

python

{ "resource": "" }

q12350

Image.finite_pixels

train

def finite_pixels(self): """ Return an array of the finite pixels. Returns ------- :obj:`numpy.ndarray` Nx2 array of the finite pixels """

python

{ "resource": "" }

q12351

Image.replace_zeros

train

def replace_zeros(self, val, zero_thresh=0.0): """ Replaces all zeros in the image with a specified value Returns ------- image dtype value to replace zeros with """

python

{ "resource": "" }

q12352

Image.save

train

def save(self, filename): """Writes the image to a file. Parameters ---------- filename : :obj:`str` The file to save the image to. Must be one of .png, .jpg, .npy, or .npz. Raises ------ ValueError If an unsupported file type is specified. """ filename = str(filename) file_root, file_ext = os.path.splitext(filename) if file_ext in COLOR_IMAGE_EXTS: im_data = self._image_data() if im_data.dtype.type == np.uint8: pil_image = PImage.fromarray(im_data.squeeze()) pil_image.save(filename) else: try: import png except:

python

{ "resource": "" }

q12353

Image.savefig

train

def savefig(self, output_path, title, dpi=400, format='png', cmap=None): """Write the image to a file using pyplot. Parameters ---------- output_path : :obj:`str` The directory in which to place the file. title : :obj:`str` The title of the file in which to save the image. dpi : int The resolution in dots per inch. format : :obj:`str` The file format to save. Available options include .png, .pdf, .ps, .eps, and .svg. cmap : :obj:`Colormap`, optional A Colormap object fo the

python

{ "resource": "" }

q12354

Image.load_data

train

def load_data(filename): """Loads a data matrix from a given file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. Returns ------- :obj:`numpy.ndarray` The data array read from the file.

python

{ "resource": "" }

q12355

ColorImage._check_valid_data

train

def _check_valid_data(self, data): """Checks that the given data is a uint8 array with one or three channels. Parameters ---------- data : :obj:`numpy.ndarray` The data to check. Raises ------ ValueError If the data is invalid. """ if data.dtype.type is not np.uint8: raise ValueError(

python

{ "resource": "" }

q12356

ColorImage.swap_channels

train

def swap_channels(self, channel_swap): """ Swaps the two channels specified in the tuple. Parameters ---------- channel_swap : :obj:`tuple` of int the two channels to swap Returns ------- :obj:`ColorImage` color image with cols swapped """ if len(channel_swap) != 2: raise ValueError('Illegal value for channel swap') ci = channel_swap[0] cj = channel_swap[1] if ci < 0

python

{ "resource": "" }

q12357

ColorImage.find_chessboard

train

def find_chessboard(self, sx=6, sy=9): """Finds the corners of an sx X sy chessboard in the image. Parameters ---------- sx : int Number of chessboard corners in x-direction. sy : int Number of chessboard corners in y-direction. Returns ------- :obj:`list` of :obj:`numpy.ndarray` A list containing the 2D points of the corners of the detected chessboard, or None if no chessboard found. """ # termination criteria criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # prepare object points, like (0,0,0), (1,0,0), (2,0,0)

python

{ "resource": "" }

q12358

ColorImage.foreground_mask

train

def foreground_mask( self, tolerance, ignore_black=True, use_hsv=False, scale=8, bgmodel=None): """Creates a binary image mask for the foreground of an image against a uniformly colored background. The background is assumed to be the mode value of the histogram for each of the color channels. Parameters ---------- tolerance : int A +/- level from the detected mean backgroud color. Pixels withing this range will be classified as background pixels and masked out. ignore_black : bool If True, the zero pixels will be ignored when computing the background model. use_hsv : bool If True, image will be converted to HSV for background model generation. scale : int Size of background histogram bins -- there will be BINARY_IM_MAX_VAL/size bins

python

{ "resource": "" }

q12359

ColorImage.background_model

train

def background_model(self, ignore_black=True, use_hsv=False, scale=8): """Creates a background model for the given image. The background color is given by the modes of each channel's histogram. Parameters ---------- ignore_black : bool If True, the zero pixels will be ignored when computing the background model. use_hsv : bool If True, image will be converted to HSV for background model generation. scale : int Size of background histogram bins -- there will be BINARY_IM_MAX_VAL/size bins in the color histogram for each channel. Returns ------- A list containing the red, green, and blue channel modes of the background. """ # hsv color data = self.data if use_hsv: pil_im = PImage.fromarray(self._data) pil_im = pil_im.convert('HSV') data = np.asarray(pil_im) # find the black pixels nonblack_pixels = np.where(np.sum(self.data, axis=2) > 0) r_data = self.r_data g_data = self.g_data b_data = self.b_data

python

{ "resource": "" }

q12360

ColorImage.draw_box

train

def draw_box(self, box): """Draw a white box on the image. Parameters ---------- :obj:`autolab_core.Box` A 2D box to draw in the image. Returns ------- :obj:`ColorImage` A new image that is the same as the current one, but with the white box drawn in. """ box_data = self._data.copy() min_i = box.min_pt[1] min_j = box.min_pt[0] max_i = box.max_pt[1] max_j = box.max_pt[0] # draw the vertical lines for j in range(min_j, max_j): box_data[min_i,

python

{ "resource": "" }

q12361

ColorImage.nonzero_hsv_data

train

def nonzero_hsv_data(self): """ Computes non zero hsv values. Returns ------- :obj:`numpy.ndarray` array of the hsv values for the image """

python

{ "resource": "" }

q12362

ColorImage.segment_kmeans

train

def segment_kmeans(self, rgb_weight, num_clusters, hue_weight=0.0): """ Segment a color image using KMeans based on spatial and color distances. Black pixels will automatically be assigned to their own 'background' cluster. Parameters ---------- rgb_weight : float weighting of RGB distance relative to spatial and hue distance num_clusters : int number of clusters to use hue_weight : float weighting of hue from hsv relative to spatial and RGB distance Returns ------- :obj:`SegmentationImage` image containing the segment labels """ # form features array label_offset = 1 nonzero_px = np.where(self.data != 0.0) nonzero_px = np.c_[nonzero_px[0], nonzero_px[1]] # get hsv data if specified color_vals = rgb_weight * \ self._data[nonzero_px[:, 0], nonzero_px[:, 1], :] if hue_weight > 0.0: hsv_data = cv2.cvtColor(self.data, cv2.COLOR_BGR2HSV) color_vals = np.c_[color_vals, hue_weight

python

{ "resource": "" }

q12363

ColorImage.to_grayscale

train

def to_grayscale(self): """Converts the color image to grayscale using OpenCV. Returns ------- :obj:`GrayscaleImage` Grayscale image corresponding to original color image. """

python

{ "resource": "" }

q12364

ColorImage.open

train

def open(filename, frame='unspecified'): """Creates a ColorImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns

python

{ "resource": "" }

q12365

DepthImage._check_valid_data

train

def _check_valid_data(self, data): """Checks that the given data is a float array with one channel. Parameters ---------- data : :obj:`numpy.ndarray` The data to check. Raises ------ ValueError If the data is invalid. """ if data.dtype.type is not np.float32 and \ data.dtype.type is not np.float64: raise ValueError(

python

{ "resource": "" }

q12366

DepthImage.threshold

train

def threshold(self, front_thresh=0.0, rear_thresh=100.0): """Creates a new DepthImage by setting all depths less than front_thresh and greater than rear_thresh to 0. Parameters ---------- front_thresh : float The lower-bound threshold. rear_thresh : float The upper bound threshold. Returns ------- :obj:`DepthImage` A new DepthImage

python

{ "resource": "" }

q12367

DepthImage.threshold_gradients

train

def threshold_gradients(self, grad_thresh): """Creates a new DepthImage by zeroing out all depths where the magnitude of the gradient at that point is greater than grad_thresh. Parameters ---------- grad_thresh : float A threshold for the gradient magnitude. Returns ------- :obj:`DepthImage` A new DepthImage created from the thresholding operation. """ data = np.copy(self._data) gx, gy = self.gradients()

python

{ "resource": "" }

q12368

DepthImage.threshold_gradients_pctile

train

def threshold_gradients_pctile(self, thresh_pctile, min_mag=0.0): """Creates a new DepthImage by zeroing out all depths where the magnitude of the gradient at that point is greater than some percentile of all gradients. Parameters ---------- thresh_pctile : float percentile to threshold all gradients above min_mag : float minimum magnitude of the gradient Returns ------- :obj:`DepthImage` A new DepthImage created from the thresholding operation. """ data = np.copy(self._data)

python

{ "resource": "" }

q12369

DepthImage.invalid_pixel_mask

train

def invalid_pixel_mask(self): """ Returns a binary mask for the NaN- and zero-valued pixels. Serves as a mask for invalid pixels. Returns ------- :obj:`BinaryImage` Binary image where a pixel value greater than zero indicates an invalid pixel.

python

{ "resource": "" }

q12370

DepthImage.pixels_farther_than

train

def pixels_farther_than(self, depth_im, filter_equal_depth=False): """ Returns the pixels that are farther away than those in the corresponding depth image. Parameters ---------- depth_im : :obj:`DepthImage` depth image to query replacement with filter_equal_depth : bool whether or not to mark depth values that are equal Returns ------- :obj:`numpy.ndarray` the pixels """

python

{ "resource": "" }

q12371

DepthImage.combine_with

train

def combine_with(self, depth_im): """ Replaces all zeros in the source depth image with the value of a different depth image Parameters ---------- depth_im : :obj:`DepthImage` depth image to combine with Returns ------- :obj:`DepthImage` the combined depth image """ new_data = self.data.copy() # replace zero pixels new_data[new_data == 0] = depth_im.data[new_data == 0]

python

{ "resource": "" }

q12372

DepthImage.to_binary

train

def to_binary(self, threshold=0.0): """Creates a BinaryImage from the depth image. Points where the depth is greater than threshold are converted to ones, and all other points are zeros. Parameters ---------- threshold : float The depth threshold. Returns ------- :obj:`BinaryImage` A BinaryImage where all 1 points had

python

{ "resource": "" }

q12373

DepthImage.to_color

train

def to_color(self, normalize=False): """ Convert to a color image. Parameters ---------- normalize : bool whether or not to normalize by the maximum depth Returns ------- :obj:`ColorImage`

python

{ "resource": "" }

q12374

DepthImage.to_float

train

def to_float(self): """ Converts to 32-bit data. Returns ------- :obj:`DepthImage` depth image with 32 bit float

python

{ "resource": "" }

q12375

DepthImage.point_normal_cloud

train

def point_normal_cloud(self, camera_intr): """Computes a PointNormalCloud from the depth image. Parameters ---------- camera_intr : :obj:`CameraIntrinsics` The camera parameters on which this depth image was taken. Returns ------- :obj:`autolab_core.PointNormalCloud` A PointNormalCloud created from the depth image. """

python

{ "resource": "" }

q12376

DepthImage.open

train

def open(filename, frame='unspecified'): """Creates a DepthImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns ------- :obj:`DepthImage` The new depth image.

python

{ "resource": "" }

q12377

IrImage.open

train

def open(filename, frame='unspecified'): """Creates an IrImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns -------

python

{ "resource": "" }

q12378

GrayscaleImage.to_color

train

def to_color(self): """Convert the grayscale image to a ColorImage. Returns ------- :obj:`ColorImage` A color image equivalent to the grayscale one. """

python

{ "resource": "" }

q12379

GrayscaleImage.open

train

def open(filename, frame='unspecified'): """Creates a GrayscaleImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns

python

{ "resource": "" }

q12380

BinaryImage.pixelwise_or

train

def pixelwise_or(self, binary_im): """ Takes OR operation with other binary image. Parameters ---------- binary_im : :obj:`BinaryImage` binary image for and operation

python

{ "resource": "" }

q12381

BinaryImage.contour_mask

train

def contour_mask(self, contour): """ Generates a binary image with only the given contour filled in. """ # fill in new data new_data = np.zeros(self.data.shape) num_boundary = contour.boundary_pixels.shape[0] boundary_px_ij_swapped = np.zeros([num_boundary, 1, 2]) boundary_px_ij_swapped[:, 0, 0] = contour.boundary_pixels[:, 1] boundary_px_ij_swapped[:, 0, 1] = contour.boundary_pixels[:, 0] cv2.fillPoly( new_data, pts=[ boundary_px_ij_swapped.astype(

python

{ "resource": "" }

q12382

BinaryImage.boundary_map

train

def boundary_map(self): """ Computes the boundary pixels in the image and sets them to nonzero values. Returns ------- :obj:`BinaryImage` binary image with nonzeros on the boundary of the original image """ # compute contours contours = self.find_contours() # fill in nonzero pixels new_data

python

{ "resource": "" }

q12383

BinaryImage.most_free_pixel

train

def most_free_pixel(self): """ Find the black pixel with the largest distance from the white pixels. Returns ------- :obj:`numpy.ndarray` 2-vector containing the most free pixel """ dist_tf

python

{ "resource": "" }

q12384

BinaryImage.diff_with_target

train

def diff_with_target(self, binary_im): """ Creates a color image to visualize the overlap between two images. Nonzero pixels that match in both images are green. Nonzero pixels of this image that aren't in the other image are yellow Nonzero pixels of the other image that aren't in this image are red Parameters ---------- binary_im : :obj:`BinaryImage` binary image to take the difference with Returns ------- :obj:`ColorImage` color image to visualize the image difference """ red = np.array([BINARY_IM_MAX_VAL, 0, 0]) yellow = np.array([BINARY_IM_MAX_VAL, BINARY_IM_MAX_VAL, 0]) green = np.array([0, BINARY_IM_MAX_VAL, 0])

python

{ "resource": "" }

q12385

BinaryImage.num_adjacent

train

def num_adjacent(self, i, j): """ Counts the number of adjacent nonzero pixels to a given pixel. Parameters ---------- i : int row index of query pixel j : int col index of query pixel Returns ------- int number of adjacent nonzero pixels """ # check values if i < 1 or i > self.height - 2 or j < 1 and j > self.width - 2:

python

{ "resource": "" }

q12386

BinaryImage.to_sdf

train

def to_sdf(self): """ Converts the 2D image to a 2D signed distance field. Returns ------- :obj:`numpy.ndarray` 2D float array of the signed distance field """ # compute medial axis transform skel, sdf_in = morph.medial_axis(self.data, return_distance=True) useless_skel, sdf_out

python

{ "resource": "" }

q12387

BinaryImage.to_color

train

def to_color(self): """Creates a ColorImage from the binary image. Returns ------- :obj:`ColorImage` The newly-created color image. """ color_data = np.zeros([self.height, self.width, 3]) color_data[:, :, 0] = self.data

python

{ "resource": "" }

q12388

BinaryImage.open

train

def open(filename, frame='unspecified'): """Creates a BinaryImage from a file. Parameters ---------- filename : :obj:`str` The file to load the data from. Must be one of .png, .jpg, .npy, or .npz. frame : :obj:`str` A string representing the frame of reference in which the new image lies. Returns ------- :obj:`BinaryImage`

python

{ "resource": "" }

q12389

RgbdImage._check_valid_data

train

def _check_valid_data(self, data): """Checks that the given data is a float array with four channels. Parameters ---------- data : :obj:`numpy.ndarray` The data to check. Raises ------ ValueError If the data is invalid. """ if data.dtype.type is not np.float32 and \ data.dtype.type is not np.float64: raise ValueError( 'Illegal data type. RGB-D images only support float arrays') if len(data.shape) != 3 and data.shape[2] != 4:

python

{ "resource": "" }

q12390

RgbdImage.from_color_and_depth

train

def from_color_and_depth(color_im, depth_im): """ Creates an RGB-D image from a separate color and depth image. """ # check shape if color_im.height != depth_im.height or color_im.width != depth_im.width: raise ValueError('Color and depth images must have the same shape')

python

{ "resource": "" }

q12391

RgbdImage.color

train

def color(self): """ Returns the color image. """ return

python

{ "resource": "" }

q12392

RgbdImage.combine_with

train

def combine_with(self, rgbd_im): """ Replaces all zeros in the source rgbd image with the values of a different rgbd image Parameters ---------- rgbd_im : :obj:`RgbdImage` rgbd image to combine with Returns ------- :obj:`RgbdImage` the combined rgbd image """ new_data = self.data.copy() depth_data = self.depth.data other_depth_data = rgbd_im.depth.data depth_zero_px = self.depth.zero_pixels() depth_replace_px = np.where( (other_depth_data != 0) & ( other_depth_data < depth_data)) depth_replace_px = np.c_[depth_replace_px[0], depth_replace_px[1]]

python

{ "resource": "" }

q12393

GdImage.from_grayscale_and_depth

train

def from_grayscale_and_depth(gray_im, depth_im): """ Creates an G-D image from a separate grayscale and depth image. """ # check shape if gray_im.height != depth_im.height or gray_im.width != depth_im.width: raise ValueError( 'Grayscale and depth images must have the same shape') # check frame if gray_im.frame != depth_im.frame: raise ValueError( 'Grayscale and depth images must have the same frame')

python

{ "resource": "" }

q12394

GdImage.gray

train

def gray(self): """ Returns the grayscale image. """ return GrayscaleImage(

python

{ "resource": "" }

q12395

SegmentationImage.border_pixels

train

def border_pixels( self, grad_sigma=0.5, grad_lower_thresh=0.1, grad_upper_thresh=1.0): """ Returns the pixels on the boundary between all segments, excluding the zero segment. Parameters ---------- grad_sigma : float standard deviation used for gaussian gradient filter grad_lower_thresh : float lower threshold on gradient threshold used to determine the boundary pixels grad_upper_thresh : float upper threshold on gradient threshold used to determine the boundary pixels Returns ------- :obj:`numpy.ndarray` Nx2 array of pixels on the boundary """ # boundary pixels boundary_im = np.ones(self.shape) for i in range(1, self.num_segments): label_border_im = self.data.copy() label_border_im[self.data == 0] = i grad_mag = sf.gaussian_gradient_magnitude(

python

{ "resource": "" }

q12396

SegmentationImage.segment_mask

train

def segment_mask(self, segnum): """ Returns a binary image of just the segment corresponding to the given number. Parameters ---------- segnum : int the number of the segment to generate a

python

{ "resource": "" }

q12397

SegmentationImage.open

train

def open(filename, frame='unspecified'): """ Opens a segmentation image """

python

{ "resource": "" }

q12398

PointCloudImage.to_point_cloud

train

def to_point_cloud(self): """Convert the image to a PointCloud object. Returns ------- :obj:`autolab_core.PointCloud` The corresponding PointCloud. """ return PointCloud(

python

{ "resource": "" }

q12399

PointCloudImage.normal_cloud_im

train

def normal_cloud_im(self, ksize=3): """Generate a NormalCloudImage from the PointCloudImage using Sobel filtering. Parameters ---------- ksize : int Size of the kernel to use for derivative computation Returns ------- :obj:`NormalCloudImage` The corresponding NormalCloudImage. """ # compute direction via cross product of derivatives gy = cv2.Sobel(self.data, cv2.CV_64F, 1, 0, ksize=ksize) gx = cv2.Sobel(self.data, cv2.CV_64F, 0, 1, ksize=ksize) gx_data = gx.reshape(self.height * self.width, 3) gy_data = gy.reshape(self.height * self.width, 3) pc_grads = np.cross(gx_data, gy_data) # default to point toward camera # normalize pc_grad_norms = np.linalg.norm(pc_grads, axis=1)

python

{ "resource": "" }