def test_destagger_type_good(meta, dtype) -> None: """Check that destaggering preserves dtype.""" h = meta.format.pixels_per_column w = meta.format.columns_per_frame assert client.destagger(meta, np.zeros((h, w), dtype)).dtype == dtype assert client.destagger(meta, np.zeros((h, w, 2), dtype)).dtype == dtype
def plot_all_channels(hostname: str, lidar_port: int = 7502, n_scans: int = 5) -> None: """Display all channels of n consecutive lidar scans taken live from sensor Args: hostname: hostname of the sensor lidar_port: UDP port to listen on for lidar data n_scans: number of scans to show """ import matplotlib.pyplot as plt # type: ignore # [doc-stag-display-all-2d] # take sample of n scans from sensor metadata, sample = client.Scans.sample(hostname, n_scans, lidar_port) # initialize and configure subplots fig, axarr = plt.subplots(n_scans, 4, sharex=True, sharey=True, figsize=(12.0, n_scans * .75), tight_layout=True) fig.suptitle("{} consecutive scans from {}".format(n_scans, hostname)) fig.canvas.set_window_title("example: display_all_2D") # set row and column titles of subplots column_titles = ["range", "reflectivity", "near_ir", "signal"] row_titles = ["Scan {}".format(i) for i in list(range(n_scans))] for ax, column_title in zip(axarr[0], column_titles): ax.set_title(column_title) for ax, row_title in zip(axarr[:, 0], row_titles): ax.set_ylabel(row_title) # plot 2D scans for count, scan in enumerate(next(sample)): axarr[count, 0].imshow( client.destagger(metadata, scan.field(client.ChanField.RANGE))) axarr[count, 1].imshow( client.destagger(metadata, scan.field(client.ChanField.REFLECTIVITY))) axarr[count, 2].imshow( client.destagger(metadata, scan.field(client.ChanField.NEAR_IR))) axarr[count, 3].imshow( client.destagger(metadata, scan.field(client.ChanField.SIGNAL))) # [doc-etag-display-all-2d] # configure and show plot [ax.get_xaxis().set_visible(False) for ax in axarr.ravel()] [ax.set_yticks([]) for ax in axarr.ravel()] [ax.set_yticklabels([]) for ax in axarr.ravel()] plt.show()
def test_destagger_inverse(meta) -> None: """Check that stagger/destagger are inverse operations.""" h = meta.format.pixels_per_column w = meta.format.columns_per_frame a = np.arange(h * w).reshape((h, w)) b = client.destagger(meta, a, inverse=True) c = client.destagger(meta, b) assert np.array_equal(a, c) d = client.destagger(meta, a) e = client.destagger(meta, d, inverse=True) assert np.array_equal(a, e)
def pcap_show_one_scan(source: client.PacketSource, metadata: client.SensorInfo, num: int = 0, destagger: bool = True) -> None: """Plot all channels of one scan in 2D using matplotlib.""" import matplotlib.pyplot as plt # type: ignore scan = nth(client.Scans(source), num) if not scan: print(f"ERROR: Scan # {num} in not present in pcap file") exit(1) # [doc-stag-pcap-show-one] fig = plt.figure(constrained_layout=True) axs = fig.subplots(len(client.ChanField), 1, sharey=True) for ax, field in zip(axs, client.ChanField): img = normalize(scan.field(field)) if destagger: img = client.destagger(metadata, img) ax.set_title(str(field), fontdict={'fontsize': 10}) ax.imshow(img, cmap='gray', resample=False) ax.set_yticklabels([]) ax.set_yticks([]) ax.set_xticks([0, scan.w]) plt.show()
def live_plot_signal(hostname: str, lidar_port: int = 7502) -> None: """Display signal from live sensor Args: hostname: hostname of the sensor lidar_port: UDP port to listen on for lidar data """ import cv2 # type: ignore print("press ESC from visualization to exit") # [doc-stag-live-plot-signal] # establish sensor connection with closing(client.Scans.stream(hostname, lidar_port, complete=False)) as stream: show = True while show: for scan in stream: # uncomment if you'd like to see frame id printed # print("frame id: {} ".format(scan.frame_id)) signal = client.destagger(stream.metadata, scan.field(client.ChanField.SIGNAL)) signal = (signal / np.max(signal) * 255).astype(np.uint8) cv2.imshow("scaled signal", signal) key = cv2.waitKey(1) & 0xFF # [doc-etag-live-plot-signal] # 27 is esc if key == 27: show = False break cv2.destroyAllWindows()
def plot_range_image(hostname: str, lidar_port: int = 7502) -> None: """Display range data taken live from sensor as an image Args: hostname: hostname of the sensor lidar_port: UDP port to listen on for lidar data """ import matplotlib.pyplot as plt # type: ignore # get single scan [doc-stag-single-scan] metadata, sample = client.Scans.sample(hostname, 1, lidar_port) scan = next(sample)[0] # [doc-etag-single-scan] # initialize plot fig, ax = plt.subplots() fig.canvas.set_window_title("example: plot_range_image") # plot using imshow range = scan.field(client.ChanField.RANGE) plt.imshow(client.destagger(metadata, range), resample=False) # configure and show plot plt.title("Range Data from {}".format(hostname)) plt.axis('off') plt.show()
def filter_3d_by_range_and_azimuth(hostname: str, lidar_port: int = 7502, range_min: int = 2) -> None: """Easily filter 3D Point Cloud by Range and Azimuth Using the 2D Representation Args: hostname: hostname of sensor lidar_port: UDP port to listen on for lidar data range_min: range minimum in meters """ import matplotlib.pyplot as plt # type: ignore import math # set up figure plt.figure() ax = plt.axes(projection='3d') r = 3 ax.set_xlim3d([-r, r]) ax.set_ylim3d([-r, r]) ax.set_zlim3d([-r, r]) plt.title("Filtered 3D Points from {}".format(hostname)) metadata, sample = client.Scans.sample(hostname, 2, lidar_port) scan = next(sample)[1] # [doc-stag-filter-3d] # obtain destaggered range range_destaggered = client.destagger(metadata, scan.field(client.ChanField.RANGE)) # obtain destaggered xyz representation xyzlut = client.XYZLut(metadata) xyz_destaggered = client.destagger(metadata, xyzlut(scan)) # select only points with more than min range using the range data xyz_filtered = xyz_destaggered * (range_destaggered[:, :, np.newaxis] > (range_min * 1000)) # get first 3/4 of scan to_col = math.floor(metadata.mode.cols * 3 / 4) xyz_filtered = xyz_filtered[:, 0:to_col, :] # [doc-etag-filter-3d] [x, y, z] = [c.flatten() for c in np.dsplit(xyz_filtered, 3)] ax.scatter(x, y, z, c=z / max(z), s=0.2) plt.show()
def test_destagger_xyz(meta, scan) -> None: """Check that we can destagger the output of xyz projection.""" h = meta.format.pixels_per_column w = meta.format.columns_per_frame xyz = client.XYZLut(meta)(scan) destaggered = client.destagger(meta, xyz) assert destaggered.shape == (h, w, 3)
def test_destagger_correct(meta, scan) -> None: """Compare client destagger function to reference implementation.""" # get destaggered range field using reference implementation destagger_ref = reference.destagger(meta.format.pixel_shift_by_row, scan.field(client.ChanField.RANGE)) # obtain destaggered range field using client implemenation destagger_client = client.destagger(meta, scan.field(client.ChanField.RANGE)) assert np.array_equal(destagger_ref, destagger_client)
def test_destagger_correct_multi(meta, scan) -> None: """Compare client destagger function to reference on stacked fields.""" near_ir = scan.field(client.ChanField.NEAR_IR) near_ir_stacked = np.repeat(near_ir[..., None], 5, axis=2) ref = reference.destagger(meta.format.pixel_shift_by_row, near_ir) ref_stacked = np.repeat(ref[..., None], 5, axis=2) destaggered_stacked = client.destagger(meta, near_ir_stacked) assert near_ir_stacked.dtype == np.uint32 assert destaggered_stacked.dtype == np.uint32 assert np.array_equal(ref_stacked, destaggered_stacked)
def pcap_2d_viewer(source: client.PacketSource, metadata: client.SensorInfo, num: int = 0) -> None: """Visualize channel fields in 2D using opencv.""" import cv2 # type: ignore # [doc-stag-pcap-display-live] print("press ESC from visualization to exit") quit = False paused = False destagger = True num = 0 for scan in client.Scans(source): print("frame id: {}, num = {}".format(scan.frame_id, num)) fields = [scan.field(ch) for ch in client.ChanField] if destagger: fields = [client.destagger(metadata, f) for f in fields] combined_images = np.vstack( [np.pad(normalize(f), 2, constant_values=1.0) for f in fields]) cv2.imshow("4 channels: ", combined_images) # handle keys presses while True: key = cv2.waitKey(1) & 0xFF # 100 is d if key == 100: destagger = not destagger # 32 is SPACE if key == 32: paused = not paused # 27 is ESC elif key == 27: quit = True if not paused: break time.sleep(0.1) if quit: break num += 1 cv2.destroyAllWindows()
def update_data(vis: o3d.visualization.Visualizer): xyz = xyzlut(scan.field(range_for_field(fields[field_ind]))) key = scan.field(fields[field_ind]).astype(float) # apply colormap to field values aes[field_ind](key) color_img = colorize(key) # prepare point cloud for Open3d Visualiser cloud.points = o3d.utility.Vector3dVector(xyz.reshape((-1, 3))) cloud.colors = o3d.utility.Vector3dVector(color_img.reshape((-1, 3))) # prepare canvas for 2d image gray_img = np.dstack([key] * 3) canvas_set_image_data(image, client.destagger(metadata, gray_img)) # signal that point cloud and needs to be re-rendered vis.update_geometry(cloud)
def pcap_2d_viewer( pcap_path: str, metadata_path: str, num: int = 0, # not used in this example rate: float = 0.0) -> None: """Simple sensor field visualization pipeline as 2D images from pcap file (*pcap_path*) Args: pcap_path: path to the pcap file metadata_path: path to the .json with metadata (aka :class:`.SensorInfo`) rate: read speed of packets from the pcap file (**1.0** - corresponds to real-time by packets timestamp, **0.0** - as fast as it reads from file without any delay) """ import cv2 # type: ignore # [doc-stag-pcap-display-live] metadata = read_metadata(metadata_path) source = pcap.Pcap(pcap_path, metadata, rate=rate) with closing(source) as source: scans = iter(client.Scans(source)) print("press ESC from visualization to exit") channels = [ client.ChanField.RANGE, client.ChanField.SIGNAL, client.ChanField.NEAR_IR, client.ChanField.REFLECTIVITY ] paused = False destagger = True num = 0 scan = next(scans, None) while scan: print("frame id: {}, num = {}".format(scan.frame_id, num)) fields_values = [scan.field(ch) for ch in channels] if destagger: fields_values = [ client.destagger(metadata, field_val) for field_val in fields_values ] fields_images = [ae(field_val) for field_val in fields_values] combined_images = np.vstack( [np.pad(img, 2, constant_values=1.0) for img in fields_images]) cv2.imshow("4 channels: ", combined_images) key = cv2.waitKey(1) & 0xFF # 100 is d if key == 100: destagger = not destagger # 32 is SPACE if key == 32: paused = not paused # 27 is ESC elif key == 27: break if not paused: scan = next(scans, None) num += 1 cv2.destroyAllWindows()
def pcap_to_csv(pcap_path: str, metadata_path: str, num: int = 0, csv_dir: str = ".", csv_prefix: str = "pcap_out", csv_ext: str = "csv") -> None: """Write scans from pcap file (*pcap_path*) to plain csv files (one per lidar scan). If the *csv_ext* ends in ``.gz``, the file is automatically saved in compressed gzip format. :func:`.numpy.loadtxt` can be used to read gzipped files transparently back to :class:`.numpy.ndarray`. Number of saved lines per csv file is always [H x W], which corresponds to a full 2D image representation of a lidar scan. Each line in a csv file is: RANGE (mm), SIGNAL, NEAR_IR, REFLECTIVITY, X (m), Y (m), Z (m) Args: pcap_path: path to the pcap file metadata_path: path to the .json with metadata (aka :class:`.SensorInfo`) num: number of scans to save from pcap to csv files csv_dir: path to the directory where csv files will be saved csv_prefix: the filename prefix that will be appended with frame number and *csv_ext* csv_ext: file extension to use. If it ends with ``.gz`` the output is gzip compressed """ from itertools import islice # ensure that base csv_dir exists if not os.path.exists(csv_dir): os.makedirs(csv_dir) metadata = read_metadata(metadata_path) source = pcap.Pcap(pcap_path, metadata) # [doc-stag-pcap-to-csv] field_names = 'RANGE (mm), SIGNAL, NEAR_IR, REFLECTIVITY, X (m), Y (m), Z (m)' field_fmts = ['%d', '%d', '%d', '%d', '%.8f', '%.8f', '%.8f'] channels = [ client.ChanField.RANGE, client.ChanField.SIGNAL, client.ChanField.NEAR_IR, client.ChanField.REFLECTIVITY ] with closing(pcap.Pcap(pcap_path, metadata)) as source: # precompute xyzlut to save computation in a loop xyzlut = client.XYZLut(metadata) # create an iterator of LidarScans from pcap and bound it if num is specified scans = iter(client.Scans(source)) if num: scans = islice(scans, num) for idx, scan in enumerate(scans): fields_values = [scan.field(ch) for ch in channels] xyz = xyzlut(scan) # get lidar data as one frame of [H x W x 7], "fat" 2D image frame = np.dstack((*fields_values, xyz)) frame = client.destagger(metadata, frame) csv_path = os.path.join(csv_dir, f'{csv_prefix}_{idx:06d}.{csv_ext}') header = '\n'.join([ f'pcap file: {pcap_path}', f'frame num: {idx}', f'metadata file: {metadata_path}', field_names ]) print(f'write frame #{idx}, to file: {csv_path}') np.savetxt(csv_path, np.reshape(frame, (-1, frame.shape[2])), fmt=field_fmts, delimiter=',', header=header)
def main(): """PointViz visualizer examples.""" parser = argparse.ArgumentParser( description=main.__doc__, formatter_class=argparse.RawTextHelpFormatter) parser.add_argument('pcap_path', nargs='?', metavar='PCAP', help='path to pcap file') parser.add_argument('meta_path', nargs='?', metavar='METADATA', help='path to metadata json') args = parser.parse_args() pcap_path = os.getenv("SAMPLE_DATA_PCAP_PATH", args.pcap_path) meta_path = os.getenv("SAMPLE_DATA_JSON_PATH", args.meta_path) if not pcap_path or not meta_path: print("ERROR: Please add SAMPLE_DATA_PCAP_PATH and SAMPLE_DATA_JSON_PATH to" + " environment variables or pass <pcap_path> and <meta_path>") sys.exit() print(f"Using:\n\tjson: {meta_path}\n\tpcap: {pcap_path}") # Getting data sources meta = client.SensorInfo(open(meta_path).read()) packets = pcap.Pcap(pcap_path, meta) scans = iter(client.Scans(packets)) # ============================== print("Ex 0: Empty Point Viz") # [doc-stag-empty-pointviz] # Creating a point viz instance point_viz = viz.PointViz("Example Viz") viz.add_default_controls(point_viz) # ... add objects here # update internal objects buffers and run visualizer point_viz.update() point_viz.run() # [doc-etag-empty-pointviz] # ========================================================================= print("Ex 1.0:\tImages and Labels: the Image object and 2D Image set_position() - height-normalized screen coordinates") label_top = viz.Label("[0, 1]", 0.5, 0.0, align_top=True) label_top.set_scale(2) point_viz.add(label_top) label_bot = viz.Label("[0, -1]", 0.5, 1, align_top=False) label_bot.set_scale(2) point_viz.add(label_bot) # [doc-stag-image-pos-center] img = viz.Image() img.set_image(np.full((10, 10), 0.5)) img.set_position(-0.5, 0.5, -0.5, 0.5) point_viz.add(img) # [doc-etag-image-pos-center] # visualize point_viz.update() point_viz.run() # ========================================================================= print("Ex 1.1:\tImages and Labels: Window-aligned images with 2D Image set_hshift() - width-normalized [-1, 1] horizontal shift") # [doc-stag-image-pos-left] # move img to the left img.set_position(0, 1, -0.5, 0.5) img.set_hshift(-1) # [doc-etag-image-pos-left] # visualize point_viz.update() point_viz.run() # [doc-stag-image-pos-right] # move img to the right img.set_position(-1, 0, -0.5, 0.5) img.set_hshift(1) # [doc-etag-image-pos-right] # visualize point_viz.update() point_viz.run() # [doc-stag-image-pos-right-bottom] # move img to the right bottom img.set_position(-1, 0, -1, 0) img.set_hshift(1) # [doc-etag-image-pos-right-bottom] # visualize point_viz.update() point_viz.run() # remove_objs(point_viz, [label_top, label_mid, label_bot, img]) remove_objs(point_viz, [label_top, label_bot, img]) # ======================================= print("Ex 1.2:\tImages and Labels: Lidar Scan Fields as Images") # [doc-stag-scan-fields-images] scan = next(scans) img_aspect = (meta.beam_altitude_angles[0] - meta.beam_altitude_angles[-1]) / 360.0 img_screen_height = 0.4 # [0..2] img_screen_len = img_screen_height / img_aspect # prepare field data ranges = scan.field(client.ChanField.RANGE) ranges = client.destagger(meta, ranges) ranges = np.divide(ranges, np.amax(ranges), dtype=np.float32) signal = scan.field(client.ChanField.SIGNAL) signal = client.destagger(meta, signal) signal = np.divide(signal, np.amax(signal), dtype=np.float32) # creating Image viz elements range_img = viz.Image() range_img.set_image(ranges) # top center position range_img.set_position(-img_screen_len / 2, img_screen_len / 2, 1 - img_screen_height, 1) point_viz.add(range_img) signal_img = viz.Image() signal_img.set_image(signal) img_aspect = (meta.beam_altitude_angles[0] - meta.beam_altitude_angles[-1]) / 360.0 img_screen_height = 0.4 # [0..2] img_screen_len = img_screen_height / img_aspect # bottom center position signal_img.set_position(-img_screen_len / 2, img_screen_len / 2, -1, -1 + img_screen_height) point_viz.add(signal_img) # [doc-etag-scan-fields-images] # visualize point_viz.update() point_viz.run() print("Ex 1.3:\tImages and Labels: Adding labels") # [doc-stag-scan-fields-images-labels] range_label = viz.Label(str(client.ChanField.RANGE), 0.5, 0, align_top=True) range_label.set_scale(1) point_viz.add(range_label) signal_label = viz.Label(str(client.ChanField.SIGNAL), 0.5, 1 - img_screen_height / 2, align_top=True) signal_label.set_scale(1) point_viz.add(signal_label) # [doc-etag-scan-fields-images-labels] # visualize point_viz.update() point_viz.run() # =============================================================== print("Ex 2.0:\tPoint Clouds: As Structured Points") # [doc-stag-scan-structured] cloud_scan = viz.Cloud(meta) cloud_scan.set_range(scan.field(client.ChanField.RANGE)) cloud_scan.set_key(signal) point_viz.add(cloud_scan) # [doc-etag-scan-structured] # visualize point_viz.update() point_viz.run() remove_objs(point_viz, [cloud_scan]) # =============================================================== print("Ex 2.1:\tPoint Clouds: As Unstructured Points") # [doc-stag-scan-unstructured] # transform scan data to 3d points xyzlut = client.XYZLut(meta) xyz = xyzlut(scan.field(client.ChanField.RANGE)) cloud_xyz = viz.Cloud(xyz.shape[0] * xyz.shape[1]) cloud_xyz.set_xyz(np.reshape(xyz, (-1, 3))) cloud_xyz.set_key(signal.ravel()) point_viz.add(cloud_xyz) # [doc-etag-scan-unstructured] point_viz.camera.dolly(150) # visualize point_viz.update() point_viz.run() # ======================================================= print("Ex 2.2:\tPoint Clouds: Custom Axes Helper as Unstructured Points") # [doc-stag-axes-helper] # basis vectors x_ = np.array([1, 0, 0]).reshape((-1, 1)) y_ = np.array([0, 1, 0]).reshape((-1, 1)) z_ = np.array([0, 0, 1]).reshape((-1, 1)) axis_n = 100 line = np.linspace(0, 1, axis_n).reshape((1, -1)) # basis vector to point cloud axis_points = np.hstack((x_ @ line, y_ @ line, z_ @ line)).transpose() # colors for basis vectors axis_color_mask = np.vstack(( np.full((axis_n, 4), [1, 0.1, 0.1, 1]), np.full((axis_n, 4), [0.1, 1, 0.1, 1]), np.full((axis_n, 4), [0.1, 0.1, 1, 1]))) cloud_axis = viz.Cloud(axis_points.shape[0]) cloud_axis.set_xyz(axis_points) cloud_axis.set_key(np.full(axis_points.shape[0], 0.5)) cloud_axis.set_mask(axis_color_mask) cloud_axis.set_point_size(3) point_viz.add(cloud_axis) # [doc-etag-axes-helper] point_viz.camera.dolly(50) # visualize point_viz.update() point_viz.run() remove_objs(point_viz, [ range_img, range_label, signal_img, signal_label, cloud_axis, cloud_xyz ]) # =============================================================== print("Ex 2.3:\tPoint Clouds: the LidarScanViz class") # [doc-stag-lidar-scan-viz] # Creating LidarScan visualizer (3D point cloud + field images on top) ls_viz = viz.LidarScanViz(meta, point_viz) # adding scan to the lidar scan viz ls_viz.scan = scan # refresh viz data ls_viz.draw() # visualize # update() is not needed for LidatScanViz because it's doing it internally point_viz.run() # [doc-etag-lidar-scan-viz] # =================================================== print("Ex 3.0:\tAugmenting point clouds with 3D Labels") # [doc-stag-lidar-scan-viz-labels] # Adding 3D Labels label1 = viz.Label("Label1: [1, 2, 4]", 1, 2, 4) point_viz.add(label1) label2 = viz.Label("Label2: [2, 1, 4]", 2, 1, 4) label2.set_scale(2) point_viz.add(label2) label3 = viz.Label("Label3: [4, 2, 1]", 4, 2, 1) label3.set_scale(3) point_viz.add(label3) # [doc-etag-lidar-scan-viz-labels] point_viz.camera.dolly(-100) # visualize point_viz.update() point_viz.run() # =============================================== print("Ex 4.0:\tOverlay 2D Images and 2D Labels") # [doc-stag-overlay-images-labels] # Adding image 1 with aspect ratio preserved img = viz.Image() img_data = make_checker_board(10, (2, 4)) mask_data = np.zeros((30, 30, 4)) mask_data[:15, :15] = np.array([1, 0, 0, 1]) img.set_mask(mask_data) img.set_image(img_data) ypos = (0, 0.5) xlen = (ypos[1] - ypos[0]) * img_data.shape[1] / img_data.shape[0] xpos = (0, xlen) img.set_position(*xpos, *ypos) img.set_hshift(-0.5) point_viz.add(img) # Adding Label for image 1: positioned at bottom left corner img_label = viz.Label("ARRrrr!", 0.25, 0.5) img_label.set_rgba((1.0, 1.0, 0.0, 1)) img_label.set_scale(2) point_viz.add(img_label) # Adding image 2: positioned to the right of the window img2 = viz.Image() img_data2 = make_checker_board(10, (4, 2)) mask_data2 = np.zeros((30, 30, 4)) mask_data2[15:25, 15:25] = np.array([0, 1, 0, 0.5]) img2.set_mask(mask_data2) img2.set_image(img_data2) ypos2 = (0, 0.5) xlen2 = (ypos2[1] - ypos2[0]) * img_data2.shape[1] / img_data2.shape[0] xpos2 = (-xlen2, 0) img2.set_position(*xpos2, *ypos2) img2.set_hshift(1.0) point_viz.add(img2) # Adding Label for image 2: positioned at top left corner img_label2 = viz.Label("Second", 1.0, 0.25, align_top=True, align_right=True) img_label2.set_rgba((0.0, 1.0, 1.0, 1)) img_label2.set_scale(1) point_viz.add(img_label2) # [doc-etag-overlay-images-labels] # visualize point_viz.update() point_viz.run() # =============================================================== print("Ex 5.0:\tAdding key handlers: 'R' for random camera dolly") # [doc-stag-key-handlers] def handle_dolly_random(ctx, key, mods) -> bool: if key == 82: # key R dolly_num = random.randrange(-15, 15) print(f"Random Dolly: {dolly_num}") point_viz.camera.dolly(dolly_num) point_viz.update() return True point_viz.push_key_handler(handle_dolly_random) # [doc-etag-key-handlers] # visualize point_viz.update() point_viz.run()
def pcap_to_csv(source: client.PacketSource, metadata: client.SensorInfo, num: int = 0, csv_dir: str = ".", csv_base: str = "pcap_out", csv_ext: str = "csv") -> None: """Write scans from a pcap to csv files (one per lidar scan). The number of saved lines per csv file is always H x W, which corresponds to a full 2D image representation of a lidar scan. Each line in a csv file is (for LEGACY profile): TIMESTAMP, RANGE (mm), SIGNAL, NEAR_IR, REFLECTIVITY, X (mm), Y (mm), Z (mm) If ``csv_ext`` ends in ``.gz``, the file is automatically saved in compressed gzip format. :func:`.numpy.loadtxt` can be used to read gzipped files transparently back to :class:`.numpy.ndarray`. Args: source: PacketSource from pcap metadata: associated SensorInfo for PacketSource num: number of scans to save from pcap to csv files csv_dir: path to the directory where csv files will be saved csv_base: string to use as the base of the filename for pcap output csv_ext: file extension to use, "csv" by default """ # ensure that base csv_dir exists if not os.path.exists(csv_dir): os.makedirs(csv_dir) # construct csv header and data format def get_fields_info(scan: client.LidarScan) -> Tuple[str, List[str]]: field_names = 'TIMESTAMP (ns)' field_fmts = ['%d'] for chan_field in scan.fields: field_names += f', {chan_field}' if chan_field in [client.ChanField.RANGE, client.ChanField.RANGE2]: field_names += ' (mm)' field_fmts.append('%d') field_names += ', X (mm), Y (mm), Z (mm)' field_fmts.extend(3 * ['%d']) return field_names, field_fmts field_names: str = '' field_fmts: List[str] = [] # [doc-stag-pcap-to-csv] from itertools import islice # precompute xyzlut to save computation in a loop xyzlut = client.XYZLut(metadata) # create an iterator of LidarScans from pcap and bound it if num is specified scans = iter(client.Scans(source)) if num: scans = islice(scans, num) for idx, scan in enumerate(scans): # initialize the field names for csv header if not field_names or not field_fmts: field_names, field_fmts = get_fields_info(scan) # copy per-column timestamps for each channel timestamps = np.tile(scan.timestamp, (scan.h, 1)) # grab channel data fields_values = [scan.field(ch) for ch in scan.fields] # use integer mm to avoid loss of precision casting timestamps xyz = (xyzlut(scan) * 1000).astype(np.int64) # get all data as one H x W x 8 int64 array for savetxt() frame = np.dstack((timestamps, *fields_values, xyz)) # not necessary, but output points in "image" vs. staggered order frame = client.destagger(metadata, frame) # write csv out to file csv_path = os.path.join(csv_dir, f'{csv_base}_{idx:06d}.{csv_ext}') print(f'write frame #{idx}, to file: {csv_path}') header = '\n'.join([f'frame num: {idx}', field_names]) np.savetxt(csv_path, frame.reshape(-1, frame.shape[2]), fmt=field_fmts, delimiter=',', header=header)
def prepare_field_image(scan, key, metadata, destagger=True): f = ae(scan.field(key)) if destagger: return client.destagger(metadata, f) return f
def test_destagger_shape_bad(meta) -> None: """Check that arrays of the wrong shape are rejected.""" h = meta.format.pixels_per_column w = meta.format.columns_per_frame with pytest.raises(ValueError): client.destagger(meta, np.zeros((0, w))) with pytest.raises(ValueError): client.destagger(meta, np.zeros((h, 0, 2))) with pytest.raises(ValueError): client.destagger(meta, np.zeros((h, w + 1))) with pytest.raises(ValueError): client.destagger(meta, np.zeros((h - 1, w))) with pytest.raises(ValueError): client.destagger(meta, np.zeros((h, w - 1, 1))) with pytest.raises(ValueError): client.destagger(meta, np.zeros((h + 1, w, 2)))
def test_destagger_shape_good(meta, shape) -> None: """Check that (de)staggering preserves shape.""" assert client.destagger(meta, np.zeros(shape)).shape == shape assert client.destagger(meta, np.zeros(shape), inverse=True).shape == shape