From f5f96fcb1e7eed2a39e0e912b7e4715d5418e8d7 Mon Sep 17 00:00:00 2001 From: Leon Okrusch Date: Fri, 12 Jan 2024 15:58:51 +0100 Subject: [PATCH 1/8] feat(145): distance of objects complete --- code/agent/config/dev_objects.json | 14 ++-- code/agent/src/agent/agent.py | 6 +- code/perception/launch/perception.launch | 6 +- code/perception/src/lidar_distance.py | 90 ++++++++++++++++-------- code/perception/src/vision_node.py | 65 ++++++++++++++++- 5 files changed, 136 insertions(+), 45 deletions(-) diff --git a/code/agent/config/dev_objects.json b/code/agent/config/dev_objects.json index 90b24361..e6fbdeba 100644 --- a/code/agent/config/dev_objects.json +++ b/code/agent/config/dev_objects.json @@ -104,23 +104,23 @@ "type": "sensor.lidar.ray_cast", "id": "LIDAR", "spawn_point": { - "x": 0.7, - "y": 0.4, - "z": 1.60, + "x": 0.0, + "y": 0.0, + "z": 1.70, "roll": 0.0, "pitch": 0.0, "yaw": 0.0 }, - "range": 85, - "rotation_frequency": 10, + "range": 50, + "rotation_frequency": 15, "channels": 64, "upper_fov": 10, "lower_fov": -30, - "points_per_second": 600000, + "points_per_second": 1000000, "atmosphere_attenuation_rate": 0.004, "dropoff_general_rate": 0.45, "dropoff_intensity_limit": 0.8, - "dropoff_zero_intensity": 0.4 + "dropoff_zero_intensity": 0.2 }, { "type": "sensor.other.radar", diff --git a/code/agent/src/agent/agent.py b/code/agent/src/agent/agent.py index ad8d076c..2076bef3 100755 --- a/code/agent/src/agent/agent.py +++ b/code/agent/src/agent/agent.py @@ -23,10 +23,10 @@ def get_ros_entrypoint(self): def sensors(self): sensors = [ {'type': 'sensor.camera.rgb', 'id': 'Center', - 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, - 'yaw': 0.0, 'width': 300, 'height': 200, 'fov': 100}, + 'x': 0.0, 'y': 0.0, 'z': 1.70, 'roll': 0.0, 'pitch': 0.0, + 'yaw': 0.0, 'width': 1280, 'height': 720, 'fov': 100}, {'type': 'sensor.lidar.ray_cast', 'id': 'LIDAR', - 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, + 'x': 0.0, 'y': 0.0, 'z': 1.70, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0}, {'type': 'sensor.other.radar', 'id': 'RADAR', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, diff --git a/code/perception/launch/perception.launch b/code/perception/launch/perception.launch index be3c7d27..53c1c74b 100644 --- a/code/perception/launch/perception.launch +++ b/code/perception/launch/perception.launch @@ -54,7 +54,7 @@ - deeplabv3_resnet101 - yolov8x-seg --> - + @@ -66,8 +66,8 @@ - - + + diff --git a/code/perception/src/lidar_distance.py b/code/perception/src/lidar_distance.py index 1228c607..a8c4b35b 100755 --- a/code/perception/src/lidar_distance.py +++ b/code/perception/src/lidar_distance.py @@ -4,17 +4,16 @@ import numpy as np import lidar_filter_utility from sensor_msgs.msg import PointCloud2 - +# from perception.msg import MinDistance from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt -from perception.msg import MinDistance # from mpl_toolkits.mplot3d import Axes3D # from itertools import combinations import cv2 from sensor_msgs.msg import Image as ImageMsg from cv_bridge import CvBridge -from matplotlib.colors import LinearSegmentedColormap +# from matplotlib.colors import LinearSegmentedColormap class LidarDistance(): @@ -41,10 +40,9 @@ def callback(self, data): coordinates, max_x=rospy.get_param('~max_x', np.inf), min_x=rospy.get_param('~min_x', -np.inf), - max_y=rospy.get_param('~max_y', np.inf), - min_y=rospy.get_param('~min_y', -np.inf), - max_z=rospy.get_param('~max_z', np.inf), min_z=rospy.get_param('~min_z', -np.inf), + min_y=rospy.get_param('~min_y', -np.inf), + max_y=rospy.get_param('~max_y', np.inf), ) reconstruct_bit_mask = lidar_filter_utility.bounding_box( @@ -81,15 +79,19 @@ def callback(self, data): ) # handle minimum distance - plot = self.plot_blob(min_dist_coordinates_xyz) - img_msg = self.bridge.cv2_to_imgmsg(plot, - encoding="passthrough") - img_msg.header = data.header - self.min_dist_img_publisher.publish(img_msg) + if min_dist_coordinates_xyz.shape[0] > 0: + plot = self.plot_blob(min_dist_coordinates_xyz) + img_msg = self.bridge.cv2_to_imgmsg(plot, + encoding="passthrough") + img_msg.header = data.header + self.min_dist_img_publisher.publish(img_msg) + # else: + # self.pub_min_dist.publish(np.inf) # handle reconstruction of lidar points rainbow_cloud = self.reconstruct_img_from_lidar( reconstruct_coordinates_xyz) + img_msg = self.bridge.cv2_to_imgmsg(rainbow_cloud, encoding="passthrough") img_msg.header = data.header @@ -107,25 +109,28 @@ def listener(self): '~point_cloud_topic', '/carla/hero/' + rospy.get_namespace() + '_filtered' ), - PointCloud2 + PointCloud2, + queue_size=10 ) # publisher for the closest blob in the lidar point cloud - self.pub_min_dist = rospy.Publisher( + """self.pub_min_dist = rospy.Publisher( rospy.get_param( '~range_topic', '/paf/hero/Center/min_distance' ), - MinDistance + MinDistance, + queue_size=10 ) - + """ # publisher for reconstructed lidar image self.rainbow_publisher = rospy.Publisher( rospy.get_param( '~image_distance_topic', '/paf/hero/Center/rainbow_image' ), - ImageMsg + ImageMsg, + queue_size=10 ) # publisher for 3d blob graph @@ -134,7 +139,8 @@ def listener(self): '~image_distance_topic', '/paf/hero/Center/min_dist_image' ), - ImageMsg + ImageMsg, + queue_size=10 ) rospy.Subscriber(rospy.get_param('~source_topic', "/carla/hero/LIDAR"), @@ -145,19 +151,43 @@ def listener(self): def plot_blob(self, xyz_coords): # creates a 3d graph thazt highlights blobs of points xyz_coords_standardized = StandardScaler().fit_transform(xyz_coords) - pairwise_distances_x = np.abs(np.subtract.outer(xyz_coords[:, 0], - xyz_coords[:, 0])) - min_x_distance = np.min(pairwise_distances_x[pairwise_distances_x > 0]) - self.pub_min_dist.publish(min_x_distance) + # pairwise_distances_x = np.abs(np.subtract.outer(xyz_coords[:, 0], + # xyz_coords[:, 0])) + + # publish minimum distance + # min_x_distance = np.min(p + # airwise_distances_x[pairwise_distances_x > 0]) + eps = 0.2 min_samples = 5 dbscan = DBSCAN(eps=eps, min_samples=min_samples) labels = dbscan.fit_predict(xyz_coords_standardized) + min_distances_within_clusters = [] + + # Iterate through each cluster + for label in set(labels): + if label != -1: # Ignore noise points + cluster_points = xyz_coords[labels == label] + pairwise_distances = np.linalg.norm( + cluster_points - + np.mean(cluster_points, axis=0), axis=1) + min_distance_within_cluster = np.min( + pairwise_distances) + min_distances_within_clusters.append( + min_distance_within_cluster) + + # Find the overall minimum distance within clusters + # min_distance_within_clusters = np.min(min_distances_within_clusters) + + # Publish the minimum distance within clusters + # self.pub_min_dist.publish(min_distance_within_clusters) + fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for label in set(labels): + # print(label) if label == -1: ax.scatter(xyz_coords[labels == label][:, 0], xyz_coords[labels == label][:, 1], @@ -186,9 +216,9 @@ def reconstruct_img_from_lidar(self, coordinates_xyz): # intrinsic matrix for camera: # width -> 300, height -> 200, fov -> 100 (agent.py) im = np.identity(3) - im[0, 2] = 300 / 2.0 - im[1, 2] = 200 / 2.0 - im[0, 0] = im[1, 1] = 300 / (2.0 * np.tan(100 * np.pi / 360.0)) + im[0, 2] = 1280 / 2.0 + im[1, 2] = 720 / 2.0 + im[0, 0] = im[1, 1] = 1280 / (2.0 * np.tan(100 * np.pi / 360.0)) # extrinsic matrix for camera ex = np.zeros(shape=(3, 4)) @@ -198,16 +228,16 @@ def reconstruct_img_from_lidar(self, coordinates_xyz): m = np.matmul(im, ex) # reconstruct camera image with LIDAR-Data - img = np.zeros(shape=(200, 300), dtype=np.uint8) + img = np.zeros(shape=(720, 1280), dtype=np.float32) for c in coordinates_xyz: point = np.array([c[1], c[2], c[0], 1]) pixel = np.matmul(m, point) x, y = int(pixel[0]/pixel[2]), int(pixel[1]/pixel[2]) - if x >= 0 and x <= 300 and y >= 0 and y <= 200: - img[199-y][299-x] = 255 - c[0] + if x >= 0 and x <= 1280 and y >= 0 and y <= 720: + img[719-y][1279-x] = c[0] # Rainbox color mapping to highlight distances - colors = [(0, 0, 0)] + [(1, 0, 0), (1, 1, 0), + """colors = [(0, 0, 0)] + [(1, 0, 0), (1, 1, 0), (0, 1, 0), (0, 1, 1), (0, 0, 1)] cmap_name = 'rainbow' @@ -216,9 +246,9 @@ def reconstruct_img_from_lidar(self, coordinates_xyz): N=256) img_colored = (rainbow_cmap(img / np.max(img)) * 255).astype(np.uint8) - img_bgr = cv2.cvtColor(img_colored, cv2.COLOR_RGBA2BGR) + img_bgr = cv2.cvtColor(img_colored, cv2.COLOR_RGBA2BGR)""" - return img_bgr + return img if __name__ == '__main__': diff --git a/code/perception/src/vision_node.py b/code/perception/src/vision_node.py index 06dab72a..e21d3656 100755 --- a/code/perception/src/vision_node.py +++ b/code/perception/src/vision_node.py @@ -14,11 +14,12 @@ import cv2 from rospy.numpy_msg import numpy_msg from sensor_msgs.msg import Image as ImageMsg -from std_msgs.msg import Header +from std_msgs.msg import Header, Float32MultiArray from cv_bridge import CvBridge from torchvision.utils import draw_bounding_boxes, draw_segmentation_masks import numpy as np from ultralytics import NAS, YOLO, RTDETR, SAM, FastSAM + """ VisionNode: @@ -81,10 +82,13 @@ def __init__(self, name, **kwargs): self.side = self.get_param("side", "Center") self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.depth_images = [] # publish / subscribe setup self.setup_camera_subscriptions() + self.setup_rainbow_subscription() self.setup_camera_publishers() + self.setup_object_distance_publishers() self.image_msg_header = Header() self.image_msg_header.frame_id = "segmented_image_frame" @@ -94,6 +98,7 @@ def __init__(self, name, **kwargs): self.weights = model_info[1] self.type = model_info[2] self.framework = model_info[3] + self.save = True print("Vision Node Configuration:") print("Device -> ", self.device) print(f"Model -> {self.get_param('model')},") @@ -120,6 +125,14 @@ def setup_camera_subscriptions(self): qos_profile=1 ) + def setup_rainbow_subscription(self): + self.new_subscription( + msg_type=numpy_msg(ImageMsg), + callback=self.handle_rainbow_image, + topic='/paf/hero/Center/rainbow_image', + qos_profile=1 + ) + def setup_camera_publishers(self): self.publisher = self.new_publisher( msg_type=numpy_msg(ImageMsg), @@ -127,6 +140,13 @@ def setup_camera_publishers(self): qos_profile=1 ) + def setup_object_distance_publishers(self): + self.distance_publisher = self.new_publisher( + msg_type=numpy_msg(Float32MultiArray), + topic=f"/paf/{self.role_name}/{self.side}/object_distance", + qos_profile=1 + ) + def handle_camera_image(self, image): # free up cuda memory if self.device == "cuda": @@ -146,6 +166,24 @@ def handle_camera_image(self, image): pass + def handle_rainbow_image(self, image): + cv_image = self.bridge.imgmsg_to_cv2(img_msg=image, + desired_encoding='passthrough') + # cv_image = cv2.cvtColor(cv_image, cv2.COLOR_RGB2BGR) + self.depth_images.append(cv_image) + if len(self.depth_images) > 1: + self.depth_images.pop(0) + """if self.save: + for i in range(len(self.depth_images)): + cv2.imshow(f"{i}", self.depth_images[i]) + + self.save = False + cv2.waitKey(0) # Wait for any key press + cv2.destroyAllWindows()""" + + else: + self.logerr("Depth-Fiel build up! No distances available yet!") + def predict_torch(self, image): self.model.eval() cv_image = self.bridge.imgmsg_to_cv2(img_msg=image, @@ -175,7 +213,30 @@ def predict_ultralytics(self, image): cv_image = cv2.cvtColor(cv_image, cv2.COLOR_RGB2BGR) output = self.model(cv_image) - + distance_output = [] + for r in output: + boxes = r.boxes + for box in boxes: + cls = box.cls.item() + pixels = box.xyxy[0] + if len(self.depth_images) > 0: + distances = np.asarray( + [self.depth_images[i][int(pixels[1]):int(pixels[3]):1, + int(pixels[0]):int(pixels[2]):1] + for i in range(len(self.depth_images))]) + non_zero_filter = distances[distances != 0] + + if len(non_zero_filter) > 0: + obj_dist = np.min(non_zero_filter) + else: + obj_dist = np.inf + + distance_output.append([cls, obj_dist]) + + # print(distance_output) + # self.logerr(distance_output) + self.distance_publisher.publish( + Float32MultiArray(data=distance_output)) return output[0].plot() def create_mask(self, input_image, model_output): From acc6896b83c305180d2d37ae08278c85e2287f86 Mon Sep 17 00:00:00 2001 From: Leon Okrusch Date: Fri, 12 Jan 2024 18:10:10 +0100 Subject: [PATCH 2/8] feat(145): visualization of distance --- code/perception/launch/perception.launch | 36 ++++++++--------- code/perception/src/lidar_distance.py | 45 +++++++++------------ code/perception/src/lidar_filter_utility.py | 4 ++ code/perception/src/vision_node.py | 31 ++++++++++++-- 4 files changed, 68 insertions(+), 48 deletions(-) diff --git a/code/perception/launch/perception.launch b/code/perception/launch/perception.launch index 53c1c74b..a85d012a 100644 --- a/code/perception/launch/perception.launch +++ b/code/perception/launch/perception.launch @@ -34,25 +34,25 @@ - + Image-Segmentation: + - deeplabv3_resnet101 + - yolov8x-seg --> diff --git a/code/perception/src/lidar_distance.py b/code/perception/src/lidar_distance.py index a8c4b35b..6a1caa4e 100755 --- a/code/perception/src/lidar_distance.py +++ b/code/perception/src/lidar_distance.py @@ -4,7 +4,7 @@ import numpy as np import lidar_filter_utility from sensor_msgs.msg import PointCloud2 -# from perception.msg import MinDistance +from std_msgs.msg import Float32 from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt @@ -38,11 +38,11 @@ def callback(self, data): # ndarray-made-up-of-numpy-void-numbers min_dist_bit_mask = lidar_filter_utility.bounding_box( coordinates, - max_x=rospy.get_param('~max_x', np.inf), - min_x=rospy.get_param('~min_x', -np.inf), + max_x=50., + min_x=2., min_z=rospy.get_param('~min_z', -np.inf), - min_y=rospy.get_param('~min_y', -np.inf), - max_y=rospy.get_param('~max_y', np.inf), + min_y=-2.5, + max_y=2.5, ) reconstruct_bit_mask = lidar_filter_utility.bounding_box( @@ -85,8 +85,8 @@ def callback(self, data): encoding="passthrough") img_msg.header = data.header self.min_dist_img_publisher.publish(img_msg) - # else: - # self.pub_min_dist.publish(np.inf) + else: + self.pub_min_dist.publish(np.inf) # handle reconstruction of lidar points rainbow_cloud = self.reconstruct_img_from_lidar( @@ -114,15 +114,15 @@ def listener(self): ) # publisher for the closest blob in the lidar point cloud - """self.pub_min_dist = rospy.Publisher( + self.pub_min_dist = rospy.Publisher( rospy.get_param( '~range_topic', '/paf/hero/Center/min_distance' ), - MinDistance, + Float32, queue_size=10 ) - """ + # publisher for reconstructed lidar image self.rainbow_publisher = rospy.Publisher( rospy.get_param( @@ -151,37 +151,28 @@ def listener(self): def plot_blob(self, xyz_coords): # creates a 3d graph thazt highlights blobs of points xyz_coords_standardized = StandardScaler().fit_transform(xyz_coords) - # pairwise_distances_x = np.abs(np.subtract.outer(xyz_coords[:, 0], - # xyz_coords[:, 0])) + """pairwise_distances_x = np.abs(np.subtract.outer(xyz_coords[:, 0], + xyz_coords[:, 0]))""" # publish minimum distance - # min_x_distance = np.min(p - # airwise_distances_x[pairwise_distances_x > 0]) - eps = 0.2 min_samples = 5 dbscan = DBSCAN(eps=eps, min_samples=min_samples) labels = dbscan.fit_predict(xyz_coords_standardized) - min_distances_within_clusters = [] + min_distance_cluster = [] # Iterate through each cluster for label in set(labels): if label != -1: # Ignore noise points cluster_points = xyz_coords[labels == label] - pairwise_distances = np.linalg.norm( - cluster_points - - np.mean(cluster_points, axis=0), axis=1) - min_distance_within_cluster = np.min( - pairwise_distances) - min_distances_within_clusters.append( - min_distance_within_cluster) - - # Find the overall minimum distance within clusters - # min_distance_within_clusters = np.min(min_distances_within_clusters) + min_distance_cluster.append(np.min(cluster_points[:, 0])) # Publish the minimum distance within clusters - # self.pub_min_dist.publish(min_distance_within_clusters) + if len(min_distance_cluster) > 0: + self.pub_min_dist.publish(min(min_distance_cluster)) + else: + self.pub_min_dist.publish(np.inf) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') diff --git a/code/perception/src/lidar_filter_utility.py b/code/perception/src/lidar_filter_utility.py index 4cd50260..5871ba35 100755 --- a/code/perception/src/lidar_filter_utility.py +++ b/code/perception/src/lidar_filter_utility.py @@ -31,6 +31,10 @@ def bounding_box(points, min_x=-np.inf, max_x=np.inf, min_y=-np.inf, """ + """print(min_x, max_x, "X") + print(min_y, max_y, "Y") + print(min_z, max_z, "Z")""" + bound_x = np.logical_and(points['x'] > min_x, points['x'] < max_x) bound_y = np.logical_and(points['y'] > min_y, points['y'] < max_y) bound_z = np.logical_and(points['z'] > min_z, points['z'] < max_z) diff --git a/code/perception/src/vision_node.py b/code/perception/src/vision_node.py index e21d3656..2d9ea6ae 100755 --- a/code/perception/src/vision_node.py +++ b/code/perception/src/vision_node.py @@ -214,6 +214,8 @@ def predict_ultralytics(self, image): output = self.model(cv_image) distance_output = [] + c_boxes = [] + c_labels = [] for r in output: boxes = r.boxes for box in boxes: @@ -231,13 +233,34 @@ def predict_ultralytics(self, image): else: obj_dist = np.inf + c_boxes.append(torch.tensor(pixels)) + c_labels.append(f"Class: {cls}, Meters: {obj_dist}") distance_output.append([cls, obj_dist]) # print(distance_output) # self.logerr(distance_output) self.distance_publisher.publish( Float32MultiArray(data=distance_output)) - return output[0].plot() + + transposed_image = np.transpose(cv_image, (2, 0, 1)) + image_np_with_detections = torch.tensor(transposed_image, + dtype=torch.uint8) + + c_boxes = torch.stack(c_boxes) + print(image_np_with_detections.shape, c_boxes.shape, c_labels) + box = draw_bounding_boxes(image_np_with_detections, + c_boxes, + c_labels, + colors='blue', + width=3, + font_size=12) + # print(box.shape) + np_box_img = np.transpose(box.detach().numpy(), + (1, 2, 0)) + box_img = cv2.cvtColor(np_box_img, cv2.COLOR_BGR2RGB) + return box_img + + # return output[0].plot() def create_mask(self, input_image, model_output): output_predictions = torch.argmax(model_output, dim=0) @@ -268,8 +291,10 @@ def apply_bounding_boxes(self, input_image, model_output): box = draw_bounding_boxes(image_np_with_detections, boxes, labels, - colors='red', - width=2) + colors='blue', + width=3, + font_size=24) + np_box_img = np.transpose(box.detach().numpy(), (1, 2, 0)) box_img = cv2.cvtColor(np_box_img, cv2.COLOR_BGR2RGB) From 6e57d225d5bb21eae0acf2eabe762307f98b8a1f Mon Sep 17 00:00:00 2001 From: okrusch <102369315+okrusch@users.noreply.github.com> Date: Sat, 13 Jan 2024 08:23:17 +0100 Subject: [PATCH 3/8] Add files via upload --- doc/00_assets/2_15_layover.png | Bin 0 -> 14721 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 doc/00_assets/2_15_layover.png diff --git a/doc/00_assets/2_15_layover.png b/doc/00_assets/2_15_layover.png new file mode 100644 index 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doc/06_perception/12_distance_to_objects.md | 65 +++++++++++++++------ 1 file changed, 48 insertions(+), 17 deletions(-) diff --git a/doc/06_perception/12_distance_to_objects.md b/doc/06_perception/12_distance_to_objects.md index 59863390..721e9eeb 100644 --- a/doc/06_perception/12_distance_to_objects.md +++ b/doc/06_perception/12_distance_to_objects.md @@ -39,14 +39,14 @@ The goal is to calculate the projection of point P and find its Pixl-Coordinates To do this you need a couple of thins: 1. Camera-Calibration - 1. Width - 2. Height - 3. Field-of-View + 1. Width + 2. Height + 3. Field-of-View 2. LIDAR-Sensor-Position - 1. Origin of 3D-World-Coordinates + 1. Origin of 3D-World-Coordinates 3. Camera-Sensor - 1. Position - 2. Orientation + 1. Position + 2. Orientation The formula for this projection proposed by the literature looks like this: @@ -55,11 +55,11 @@ The formula for this projection proposed by the literature looks like this: To get the camera-intrinsic matrix we need the width, height and fov of the image produced by the camera. Luckily we cn easly get these values from the sensor configuration in (agent.py) -In our case we use the following configuration: Width: 300, Height: 200, FOV: 100 +In our case we use the following configuration: Width: 1280, Height: 720, FOV: 100 -The Intrinsic Matrix is calculated within the code using these values and a piece of code from pyLot (insert link) +The Intrinsic Matrix is calculated within the code using these values and a piece of code from pyLot. -Next up we need the extrinsic Camera-Matrix. We can set this Matrix to the Identity-Matrix, if both LIDAR and Camera are in the exact same position (e.g (0,0, 0)) in the world. +Next up we need the extrinsic Camera-Matrix. We can set this Matrix to the Identity-Matrix, if both LIDAR and Camera are in the exact same position (e.g (0, 0, 0)) in the world. ### Purpose @@ -72,16 +72,47 @@ U can imageine putting both images on top of each other. ### Implementation -All that is left now is the calculation of the Pixel-Coordinates for every LIDAR-Point in front of the vehicle and some nice Visualization. +To reconstruct the depth image, we simply implement the above formulas using numpy. In order to get better results we had to adjust the LIDAR-Sensor Setup. Check the topic below for more details. -LIDAR-Points that are not within the field of view of the camera would be projected to (u, v)-Points that dont match the Camera-Image -(for example: (-100, 23)) +The resulting Image takes the distance in meters as values for its pixels. It therefore is a grayscale image. -To visualize the reconstruced Image we create a cv2 Image where the RGB-Values a mapped to a rainbox-color scheme according to the distance at each pixel. +![Grayscale Depth Image](../00_assets/3d_2d_formula.png) -insert image +In the next step we want to get the distance for every bounding box the object-detection found. -## Next Steps +We want to return a list of tuple containing a class and a distance, like this: -- Combine Object-Detection with LIDAR-Reconstruction to get a depth image -- Provide List of Objects with distance in a publisher +[ + [class_id, distance_meters], + [12.0, 3.456], + ..., + [2.0, np.inf], + [2.0, 7.890], +] + +Since we cant be certain we will find a Lidar-Point in the depth image for every Pixel in the bounding box of the original image, +we will check for the minimum value within the bounding box in the depth image. + +This makes sense, since the LIDAR doesn´t recognize points behind any object. + +If there is no distance found in the depth image, we will return infinity for this bounding box. + +## LIDAR-Configuration + +This topic came to our attention, as we realised that the LIDAR was flickering, as you can see in the following image series. + +![Grayscale Depth Image](../00_assets/3d_2d_formula.png) +![Grayscale Depth Image](../00_assets/3d_2d_formula.png) +![Grayscale Depth Image](../00_assets/3d_2d_formula.png) + +These are the Grayscale-Depth Images reconstructed within 600 milliseconds. + +We can see that we usually only see one half of the LIDAR-Points. + +The simple reason for this is a lower spin rate in the LIDAR-Configuration. + +By adjusting the spin rate we can achieve a fully reconstructed image. However, a higher spin rate results in a lower Resolution of LIDAR-Points. + +We now use a slightly higher spin rate, that consistently produces a full depth image, but also receives only sightly less LIDAR-Points. + +Doing this, we noticed, that the LIDAR-Sensor wasn´t positioned correctly. For some reason it was offset to the front left. 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z+@RI~Z|FF-6utx4dbjuoRsXVc_dgST|6ieMdUInDy=k1dgLfN1miSWvPd9hsgfU}< S*&i-~^XJakovu3Nlky+aNI7l* literal 0 HcmV?d00001 From 594caee671f2220cad96919dc4099208a1079141 Mon Sep 17 00:00:00 2001 From: okrusch <102369315+okrusch@users.noreply.github.com> Date: Sat, 13 Jan 2024 09:02:17 +0100 Subject: [PATCH 6/8] Update 12_distance_to_objects.md --- doc/06_perception/12_distance_to_objects.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/06_perception/12_distance_to_objects.md b/doc/06_perception/12_distance_to_objects.md index 721e9eeb..e909fede 100644 --- a/doc/06_perception/12_distance_to_objects.md +++ b/doc/06_perception/12_distance_to_objects.md @@ -76,7 +76,7 @@ To reconstruct the depth image, we simply implement the above formulas using num The resulting Image takes the distance in meters as values for its pixels. It therefore is a grayscale image. -![Grayscale Depth Image](../00_assets/3d_2d_formula.png) +![Grayscale Depth Image](../00_assets/2_15_layover.png) In the next step we want to get the distance for every bounding box the object-detection found. @@ -101,9 +101,9 @@ If there is no distance found in the depth image, we will return infinity for th This topic came to our attention, as we realised that the LIDAR was flickering, as you can see in the following image series. -![Grayscale Depth Image](../00_assets/3d_2d_formula.png) -![Grayscale Depth Image](../00_assets/3d_2d_formula.png) -![Grayscale Depth Image](../00_assets/3d_2d_formula.png) +![Grayscale Depth Image](../00_assets/2_layover.png) +![Grayscale Depth Image](../00_assets/3_layover.png) +![Grayscale Depth Image](../00_assets/4_layover.png) These are the Grayscale-Depth Images reconstructed within 600 milliseconds. From d6a66096793f11458187cd3632774dd45213d0ec Mon Sep 17 00:00:00 2001 From: okrusch <102369315+okrusch@users.noreply.github.com> Date: Mon, 15 Jan 2024 14:31:57 +0100 Subject: [PATCH 7/8] Update 12_distance_to_objects.md --- doc/06_perception/12_distance_to_objects.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/06_perception/12_distance_to_objects.md b/doc/06_perception/12_distance_to_objects.md index e909fede..0d6f1825 100644 --- a/doc/06_perception/12_distance_to_objects.md +++ b/doc/06_perception/12_distance_to_objects.md @@ -90,7 +90,7 @@ We want to return a list of tuple containing a class and a distance, like this: [2.0, 7.890], ] -Since we cant be certain we will find a Lidar-Point in the depth image for every Pixel in the bounding box of the original image, +Since we cant be certain we will find a Lidar-Point in the depth image for every Pixel in the bounding box of the original image, we will check for the minimum value within the bounding box in the depth image. This makes sense, since the LIDAR doesn´t recognize points behind any object. From 706d01870c6fba9310d9f7cab20f52599008ba6c Mon Sep 17 00:00:00 2001 From: Leon Okrusch Date: Wed, 24 Jan 2024 13:59:10 +0100 Subject: [PATCH 8/8] feat(145): resolve merge conflict --- code/perception/src/vision_node.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/code/perception/src/vision_node.py b/code/perception/src/vision_node.py index 820c4f69..c62212c1 100755 --- a/code/perception/src/vision_node.py +++ b/code/perception/src/vision_node.py @@ -145,6 +145,8 @@ def setup_object_distance_publishers(self): self.distance_publisher = self.new_publisher( msg_type=numpy_msg(Float32MultiArray), topic=f"/paf/{self.role_name}/{self.side}/object_distance", + qos_profile=1) + def setup_traffic_light_publishers(self): self.traffic_light_publisher = self.new_publisher( msg_type=numpy_msg(ImageMsg), @@ -215,7 +217,7 @@ def predict_ultralytics(self, image): cv_image = self.bridge.imgmsg_to_cv2(img_msg=image, desired_encoding='passthrough') cv_image = cv2.cvtColor(cv_image, cv2.COLOR_RGB2BGR) - + output = self.model(cv_image, half=True, verbose=False) distance_output = [] c_boxes = [] @@ -253,7 +255,6 @@ def predict_ultralytics(self, image): if 9 in output[0].boxes.cls: self.process_traffic_lights(output[0], cv_image, image.header) - c_boxes = torch.stack(c_boxes) print(image_np_with_detections.shape, c_boxes.shape, c_labels) box = draw_bounding_boxes(image_np_with_detections,