diff --git a/apps/README.md b/apps/README.md index e82b5a6a8..6493f3663 100644 --- a/apps/README.md +++ b/apps/README.md @@ -12,6 +12,7 @@ This section contains ready-to-use applications that demonstrate the capabilitie | [data-collection](data-collection/) | ❌ | ❌ | ✅ | | Demo showcasing how to use YOLOE for automatic data capture with an interactive UI for configuration. | | [dino-tracking](dino-tracking/) | ❌ | ❌ | ✅ | | Demo showcasing interactive, similarity-based object tracking using FastSAM segmentation and DINO embeddings, enabling click-to-select tracking without predefined classes. | | [people-demographics-and-sentiment-analysis](people-demographics-and-sentiment-analysis/) | ❌ | ❌ | ✅ | | Detects people and faces, tracks individuals over time, estimates age, gender, emotion and performs re-identification | +| [object-volume-measurement-3d](object-volume-measurement-3d) | ❌ | ❌ | ✅ | | Demonstrates a practical approach for measuring objects in 3D using DepthAI | | [p2p-measurement](p2p-measurement) | ❌ | ❌ | ✅ | | Real-time 3D distance measurement between two points using DepthAI | | [qr-tiling](qr-tiling/) | ❌ | ❌ | ✅ | | High-resolution QR code detection using dynamic image tiling with adaptive FPS control and an interactive UI for configuring the tiling grid. | | [ros-driver-basic](ros/ros-driver-basic/) | ❌ | ❌ | ✅ | | Demo showcasing how ROS driver can be run as an APP on RVC4 device. | diff --git a/apps/object-volume-measurement-3d/.oakappignore b/apps/object-volume-measurement-3d/.oakappignore new file mode 100644 index 000000000..63f808ef8 --- /dev/null +++ b/apps/object-volume-measurement-3d/.oakappignore @@ -0,0 +1,37 @@ +# Python virtual environments +venv/ +.venv/ + +# Node.js +# ignore node_modules, it will be reinstalled in the container +node_modules/ + +# Multimedia files +media/ + +# Local models +*.onnx + +# Documentation +README.md + +# VCS +.gitignore +.git/ +.github/ +.gitlab/ + +# The following files are ignored by default +# uncomment a line if you explicitly need it + +# !*.oakapp + +# Python +# !**/.mypy_cache/ +# !**/.ruff_cache/ + +# IDE files +# !**/.idea +# !**/.vscode +# !**/.zed + diff --git a/apps/object-volume-measurement-3d/README.md b/apps/object-volume-measurement-3d/README.md new file mode 100644 index 000000000..3afe638c4 --- /dev/null +++ b/apps/object-volume-measurement-3d/README.md @@ -0,0 +1,80 @@ +# Object Volume Measurement 3D + +This example demonstrates a practical approach for measuring objects in 3D using DepthAI.\ +On the DepthAI backend, it runs **YOLOE** model on-device, with configurable class labels and confidence threshold - both controllable via the frontend. +The custom frontend lets you click a detected object in the Video stream, the backend then segments that instance, builds a segmented point cloud, and computes dimensions and volume in real time. Users can switch between two measurement methods: Object-Oriented Bounding Box and Ground-plane Height Grid.\ +The frontend is built with `@luxonis/depthai-viewer-common` package, and combined with the [default oakapp docker image](https://hub.docker.com/r/luxonis/oakapp-base), enabling remote access via WebRTC. + +> **Note:** This example works only on RVC4 in standalone mode. + +## Demo + +![extended-3d-measurement](media/demo.gif) + +## Usage + +Running this example requires a **Luxonis device** connected to your computer. Refer to the [documentation](https://docs.luxonis.com/software-v3/) to setup your device if you haven't done it already. + +### Model Options + +This example currently uses **YOLOE** - a fast and efficient object detection model, that outputs bounding boxes and segmentation masks. + +### Measurement methods + +The app provides two ways to measure objects from the segmented point clouds: + +#### 1. Object-Oriented Bounding Box (OBB) + +This method uses Open3D's `get_minimal_oriented_bounding_box()`, which computes the minimal 3D box that encloses the segmented point cloud.\ +The resulting box provides the object's dimensions (L, W, H) and the volume is computed as: V = L x W x H\ +Temporal smoothing is applied to keep the box stable and prevents sudden flips. It combines a low pass filter (EMA) for center and size, and spherical linear interpolation (SLERP) for rotations.\ +This method is fast but may overestimate volume for objects with irregular shapes. + +#### 2. Ground-plane Height Grid (HG) + +For this method the objects are required to rest on a flat surface (e.g desk or floor). It uses the flat surface as a reference support plane, then estimates the footprint and the height by +grid-based slicing of the objects top surface. + +**How it works:** + +1. Plane capture: we run RANSAC on the scene point cloud and validate with the IMU that the plane is ground-like (plane normal parallel to gravity). + The app shows Calculating / OK / Failed status in the overlay of the Video Stream and re-requests capture if the camera has been moved or plane becomes invalid. +2. Transform the object point cloud into the ground/table frame. +3. Compute a minimum-area rectangle for the footprint of the object. From here we get the L, W and yaw (rotation along the z axis). +4. Volume calculation: the footprint polygon is divided into a 2D grid of square cells (default 5 mm each). For every cell inside the footprint, the algorithm estimates a height value by looking at the object points that fall into that cell. The base area of each cell = (cell size)² and height = cell height above the ground plane.\ + The total object volume is obtained by summing the volumes of each cell across the grid. The object's height H is computed from this height grid also. +5. Temporal smoothing is applied to the footprint, yaw, height, and dimensions (EMA-based), with rejection of sudden jumps. + +This grid-integration method makes the volume estimation more robust to irregular and uneven object surfaces compared to just taking the bounding box. However, it is sensitive to plane fitting errors. + +> **Note:** the object dimensions are still represented as a box, even for irregular objects. + +### Outputs + +The backend publishes: + +- Video Stream +- Detections Overlay with segmentation masks and bounding boxes +- Pointclouds Stream (whole scene and segmented when measuring an object) +- Measurements Overlay (OBB / HG wireframe from the object dimensions on the Video Stream) +- Plane status (HG only) +- Dimensions and volume measurements with the Detections Overlay + +## Standalone Mode (RVC4 only) + +Running the example in the standalone mode, app runs entirely on the device. +To run the example in this mode, first install the `oakctl` tool using the installation instructions [here](https://docs.luxonis.com/software-v3/oak-apps/oakctl). + +The app can then be run with: + +```bash +oakctl connect +oakctl app run . +``` + +Once the app is built and running you can access the DepthAI Viewer locally by opening `https://:9000/` in your browser (the exact URL will be shown in the terminal output). + +### Remote access + +1. You can upload oakapp to Luxonis Hub via oakctl +2. And then you can just remotely open App UI via App detail page diff --git a/apps/object-volume-measurement-3d/backend-run.sh b/apps/object-volume-measurement-3d/backend-run.sh new file mode 100644 index 000000000..71611722a --- /dev/null +++ b/apps/object-volume-measurement-3d/backend-run.sh @@ -0,0 +1,3 @@ +#!/bin/sh +echo "Starting Backend" +exec python3.11 /app/backend/src/main.py \ No newline at end of file diff --git a/apps/object-volume-measurement-3d/backend/src/depthai_models/yoloe_v8_l.RVC4.yaml b/apps/object-volume-measurement-3d/backend/src/depthai_models/yoloe_v8_l.RVC4.yaml new file mode 100644 index 000000000..1d059449d --- /dev/null +++ b/apps/object-volume-measurement-3d/backend/src/depthai_models/yoloe_v8_l.RVC4.yaml @@ -0,0 +1,2 @@ +model: yoloe-v8-l:640x640 +platform: RVC4 \ No newline at end of file diff --git a/apps/object-volume-measurement-3d/backend/src/main.py b/apps/object-volume-measurement-3d/backend/src/main.py new file mode 100644 index 000000000..40ad19beb --- /dev/null +++ b/apps/object-volume-measurement-3d/backend/src/main.py @@ -0,0 +1,235 @@ +import depthai as dai + +from depthai_nodes.node import ParsingNeuralNetwork, ImgDetectionsFilter + +from utils.helper_functions import extract_text_embeddings, read_intrinsics + +from utils.arguments import initialize_argparser +from utils.annotation_node import AnnotationNode +from utils.measurement_node import MeasurementNode + +_, args = initialize_argparser() + +IP = args.ip or "localhost" +PORT = args.port or 8080 + +CLASS_NAMES = ["person", "chair", "TV"] +MAX_NUM_CLASSES = 80 +CONFIDENCE_THRESHOLD = 0.15 + +visualizer = dai.RemoteConnection(serveFrontend=False) +device = dai.Device(dai.DeviceInfo(args.device)) if args.device else dai.Device() + +platform = device.getPlatformAsString() + +if platform != "RVC4": + raise ValueError("This example is supported only on RVC4 platform") + +device.setIrLaserDotProjectorIntensity(1.0) +device.setIrFloodLightIntensity(1) + +frame_type = dai.ImgFrame.Type.BGR888i +text_features = extract_text_embeddings( + class_names=CLASS_NAMES, max_num_classes=MAX_NUM_CLASSES +) + +if args.fps_limit is None: + args.fps_limit = 8 + print( + f"\nFPS limit set to {args.fps_limit} for {platform} platform. If you want to set a custom FPS limit, use the --fps_limit flag.\n" + ) + +with dai.Pipeline(device) as pipeline: + print("Creating pipeline...") + + model_description = dai.NNModelDescription.fromYamlFile( + f"yoloe_v8_l.{platform}.yaml" + ) + model_description.platform = platform + model_nn_archive = dai.NNArchive(dai.getModelFromZoo(model_description)) + model_w, model_h = model_nn_archive.getInputSize() + + cam = pipeline.create(dai.node.Camera).build( + boardSocket=dai.CameraBoardSocket.CAM_A + ) + cam_out = cam.requestOutput( + size=(640, 400), type=dai.ImgFrame.Type.RGB888i, fps=args.fps_limit + ) + + left = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_B) + right = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_C) + left_out = left.requestOutput( + (640, 400), type=dai.ImgFrame.Type.NV12, fps=args.fps_limit + ) + right_out = right.requestOutput( + (640, 400), type=dai.ImgFrame.Type.NV12, fps=args.fps_limit + ) + + stereo = pipeline.create(dai.node.StereoDepth).build( + left=left_out, + right=right_out, + presetMode=dai.node.StereoDepth.PresetMode.DEFAULT, + ) + + imu = pipeline.create(dai.node.IMU) + imu.enableIMUSensor(dai.IMUSensor.ACCELEROMETER_RAW, 100) + imu.setBatchReportThreshold(10) + imu.setMaxBatchReports(10) + + manip = pipeline.create(dai.node.ImageManip) + manip.initialConfig.setOutputSize( + model_w, model_h, dai.ImageManipConfig.ResizeMode.LETTERBOX + ) + manip.initialConfig.setFrameType(frame_type) + manip.setMaxOutputFrameSize(model_w * model_h * 3) + + align = pipeline.create(dai.node.ImageAlign) + + stereo.depth.link(align.input) + cam_out.link(align.inputAlignTo) + cam_out.link(manip.inputImage) + + input_node = manip.out + + nn_with_parser = pipeline.create(ParsingNeuralNetwork) + nn_with_parser.setNNArchive(model_nn_archive) + nn_with_parser.setBackend("snpe") + nn_with_parser.setBackendProperties( + {"runtime": "dsp", "performance_profile": "default"} + ) + nn_with_parser.setNumInferenceThreads(1) + nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) + + input_node.link(nn_with_parser.inputs["images"]) + + textInputQueue = nn_with_parser.inputs["texts"].createInputQueue() + nn_with_parser.inputs["texts"].setReusePreviousMessage(True) + + det_process_filter = pipeline.create(ImgDetectionsFilter).build(nn_with_parser.out) + det_process_filter.setLabels(labels=[i for i in range(len(CLASS_NAMES))], keep=True) + + # Annotation node + annotation_node = pipeline.create(AnnotationNode).build( + det_process_filter.out, + cam_out, + align.outputAligned, + label_encoding={k: v for k, v in enumerate(CLASS_NAMES)}, + ) + + # RGBD node for the segmented PCL + rgbd_seg = pipeline.create(dai.node.RGBD).build() + annotation_node.out_segm.link(rgbd_seg.inColor) + annotation_node.out_segm_depth.link(rgbd_seg.inDepth) + + # Measurement node + measurement_node = pipeline.create(MeasurementNode).build( + rgbd_seg.pcl, annotation_node.out_selection, imu.out + ) + measurement_node.out_result.link(annotation_node.in_meas_result) + + fx, fy, cx, cy = read_intrinsics(device, 640, 400) + measurement_node.setIntrinsics(fx, fy, cx, cy, imgW=640, imgH=400) + measurement_node.an_node = annotation_node + + # Service functions for all functionalities of the frontend + def class_update_service(new_classes: list[str]): + """Changes classes to detect based on the user input""" + if len(new_classes) == 0: + print("List of new classes empty, skipping.") + return + if len(new_classes) > MAX_NUM_CLASSES: + print( + f"Number of new classes ({len(new_classes)}) exceeds maximum number of classes ({MAX_NUM_CLASSES}), skipping." + ) + return + CLASS_NAMES = new_classes + + text_features = extract_text_embeddings( + class_names=CLASS_NAMES, + max_num_classes=MAX_NUM_CLASSES, + ) + inputNNData = dai.NNData() + inputNNData.addTensor( + "texts", text_features, dataType=dai.TensorInfo.DataType.FP16 + ) + textInputQueue.send(inputNNData) + + det_process_filter.setLabels( + labels=[i for i in range(len(CLASS_NAMES))], keep=True + ) + annotation_node.setLabelEncoding({k: v for k, v in enumerate(CLASS_NAMES)}) + print(f"Classes set to: {CLASS_NAMES}") + + def conf_threshold_update_service(new_conf_threshold: float): + """Changes confidence threshold based on the user input""" + CONFIDENCE_THRESHOLD = max(0, min(1, new_conf_threshold)) + nn_with_parser.getParser(0).setConfidenceThreshold(CONFIDENCE_THRESHOLD) + print(f"Confidence threshold set to: {CONFIDENCE_THRESHOLD}:") + + def selection_service(clicks: dict): + """Changes selected object based on the user click""" + if clicks.get("clear"): + annotation_node.clearSelection() + return {"ok": True, "cleared": True} + try: + x = float(clicks["x"]) + y = float(clicks["y"]) + except Exception as e: + return {"ok": False, "error": f"bad payload: {e}"} + + annotation_node.setSelectionPoint(x, y) + annotation_node.setKeepTopOnly(True) + + measurement_node.reset_measurements() + annotation_node.clearCachedMeasurements() + print(f"Selection point set to ({x:.3f}, {y:.3f})") + return {"ok": True} + + def measurement_method_service(payload: dict): + """ + Changes measurement method based on the user input + Expects: {"method": "obb"|"heightgrid"} + """ + method = str(payload.get("method", "")).lower() + if method not in ("obb", "heightgrid"): + return {"ok": False, "error": f"unknown method '{method}'"} + measurement_node.measurement_mode = method + if method == "heightgrid": + annotation_node.requestPlaneCapture(True) + else: + annotation_node.requestPlaneCapture(False) + measurement_node.reset_measurements() + print("Selected method: ", method) + return {"ok": True, "method": method, "have_plane": measurement_node.have_plane} + + # Connect the services in the frontend to functions in the backend + visualizer.registerService("Selection Service", selection_service) + visualizer.registerService("Class Update Service", class_update_service) + visualizer.registerService( + "Threshold Update Service", conf_threshold_update_service + ) + visualizer.registerService("Measurement Method Service", measurement_method_service) + + visualizer.addTopic("Video", cam_out, "images") + visualizer.addTopic("Detections", annotation_node.out_ann, "images") + visualizer.addTopic("Pointclouds", rgbd_seg.pcl, "point_clouds") + visualizer.addTopic("Measurement Overlay", measurement_node.out_ann, "images") + visualizer.addTopic("Plane Status", measurement_node.out_plane_status, "images") + + print("Pipeline created.") + + pipeline.start() + visualizer.registerPipeline(pipeline) + + inputNNData = dai.NNData() + inputNNData.addTensor("texts", text_features, dataType=dai.TensorInfo.DataType.FP16) + textInputQueue.send(inputNNData) + + print("Press 'q' to stop") + + while pipeline.isRunning(): + pipeline.processTasks() + key = visualizer.waitKey(1) + if key == ord("q"): + print("Got q key. Exiting...") + break diff --git a/apps/object-volume-measurement-3d/backend/src/requirements.txt b/apps/object-volume-measurement-3d/backend/src/requirements.txt new file mode 100644 index 000000000..48a916986 --- /dev/null +++ b/apps/object-volume-measurement-3d/backend/src/requirements.txt @@ -0,0 +1,11 @@ +depthai==3.2.1 +depthai-nodes==0.3.7 +opencv-python-headless~=4.10.0 +numpy>=1.22 +tokenizers~=0.21.0 +onnxruntime +open3d~=0.18 +scipy==1.11.4 +# onnxruntime-gpu # if you want to use CUDAExecutionProvider +requests +tqdm \ No newline at end of file diff --git a/apps/object-volume-measurement-3d/backend/src/utils/PointCloudMeasurement.py b/apps/object-volume-measurement-3d/backend/src/utils/PointCloudMeasurement.py new file mode 100644 index 000000000..d5b0e7dee --- /dev/null +++ b/apps/object-volume-measurement-3d/backend/src/utils/PointCloudMeasurement.py @@ -0,0 +1,852 @@ +import open3d as o3d +import numpy as np +from typing import List, Tuple, Optional +from scipy.spatial.transform import Rotation as R + + +class PointCloudMeasurement: + """ + A class to measure volume and dimensions of object pointclouds + """ + + def __init__(self): + # Note: parameters in millimeters + self.distance_thr = 10 + self.sample_points = 3 + self.max_iterations = 500 + self.ransac_inlier_ratio_thr = 0.17 + + self.voxel_size = 5 + self.points_buffer: np.ndarray = np.empty((0, 3), dtype=np.float64) + self.points_buffer_plane: np.ndarray = np.empty((0, 3), dtype=np.float64) + self.point_cloud: o3d.geometry.PointCloud = o3d.geometry.PointCloud() + + # For ground + HG + self.point_cloud_tf: o3d.geometry.PointCloud = o3d.geometry.PointCloud() + self.point_cloud_plane: o3d.geometry.PointCloud = o3d.geometry.PointCloud() + self.cell = 5.0 + self.fill_iters = 1 + self.z_base = 5.0 + self.plane_eq = None + self.center = None + self.corners3d = None + self.corners3d_table = None + self.R_w2t = None + self.t_w2t = None + + self.volume = None + self.dimensions = None + self.obb = None + + self.obb_pts = None + self.obb_edges = None + + # Smoothing/filtering of bbox + # OBB case + self.prev_c = None + self.prev_R = None + self.prev_e = None + + # Smoothing parameters + self.alpha_pos = 0.25 + self.alpha_rot = 0.20 + self.alpha_ext = 0.20 + + # Robustness + self.extent_delta_clamp = ( + 20.0 # mm; max per-frame size change we allow in the filter + ) + self.max_center_jump = ( + 100.0 # mm; if raw center jumps more than this, reject update + ) + + # HG case + self.hg_prev_center2 = None # center of footprint in table XY frame + self.hg_prev_yaw = None + self.hg_prev_LW = None + self.hg_prev_H = None + + self.extent_delta_clamp_hg = 20.0 + self.max_hg_center_jump = 100.0 + + self.from_reset = False + + self.reinit_inlier_ratio = 0.70 + self.inlier_margin_mm = 10.0 + + def _is_finite_like(self, x) -> bool: + if x is None: + return False + try: + arr = np.asarray(x, dtype=float) + except Exception: + return False + return arr.size > 0 and np.all(np.isfinite(arr)) + + def _need_init_state(self, *values) -> bool: + """Return True if any of the provided values is missing/non-finite.""" + return not all(self._is_finite_like(v) for v in values) + + def reset_hg_filter(self): + self.hg_prev_center2 = self.hg_prev_yaw = self.hg_prev_LW = self.hg_prev_H = ( + None + ) + + def reset_obb_filter(self): + self.prev_c = self.prev_R = self.prev_e = None + + # ---- Small math helpers ----------------------------------------------------- + @staticmethod + def _wrap_pi(a: float) -> float: + """Wrap angle to (-pi, pi].""" + a = (a + np.pi) % (2 * np.pi) - np.pi + return float(a) + + @staticmethod + def _yaw_vec(a: float) -> np.ndarray: + """Unit vector [cos(a), sin(a)].""" + return np.array([np.cos(a), np.sin(a)], float) + + @staticmethod + def _yaw_from_vec(v: np.ndarray) -> float: + return float(np.arctan2(v[1], v[0])) + + @staticmethod + def _best_rect_candidate( + yaw_raw: float, L: float, W: float, yaw_prev: float | None + ): + """ + Min-area rectangle has two equivalent orientations: + (yaw, L,W) or (yaw+pi/2, W,L) + Choose the one closest to yaw_prev (ignoring 180° flips). + """ + if yaw_prev is None: + return yaw_raw, np.array([L, W], float) + + u_prev = PointCloudMeasurement._yaw_vec(yaw_prev) + + # Candidate A + a1 = yaw_raw + u1 = PointCloudMeasurement._yaw_vec(a1) + if np.dot(u1, u_prev) < 0: + a1 += np.pi + u1 = -u1 + s1 = np.dot(u1, u_prev) + + # Candidate B (swap L/W and rotate by 90°) + a2 = yaw_raw + np.pi / 2.0 + u2 = PointCloudMeasurement._yaw_vec(a2) + if np.dot(u2, u_prev) < 0: + a2 += np.pi + u2 = -u2 + s2 = np.dot(u2, u_prev) + + # Choose the candidate with the better alignment score + if s2 > s1: + return PointCloudMeasurement._wrap_pi(a2), np.array([W, L], float) + else: + return PointCloudMeasurement._wrap_pi(a1), np.array([L, W], float) + + @staticmethod + def _rect_xy_from_params( + center2: np.ndarray, yaw: float, L: float, W: float + ) -> np.ndarray: + """Return 4×2 CCW rectangle corners in table XY given center, yaw, L, W.""" + c = center2.astype(float) + hx, hy = L * 0.5, W * 0.5 + R2 = np.array([[np.cos(yaw), -np.sin(yaw)], [np.sin(yaw), np.cos(yaw)]], float) + local = np.array([[-hx, -hy], [hx, -hy], [hx, hy], [-hx, hy]], float) + return c + local @ R2.T + + @staticmethod + def _ensure_right_handed(R: np.ndarray) -> np.ndarray: + """Make sure det(R)=+1 (proper rotation). If negative, flip Z axis.""" + if np.linalg.det(R) < 0: + R = R.copy() + R[:, 2] = -R[:, 2] + return R + + @staticmethod + def project_to_so3(M: np.ndarray) -> np.ndarray: + """Return the closest proper rotation matrix to M (polar decomposition).""" + U, _, Vt = np.linalg.svd(M) + Rproj = U @ Vt + if np.linalg.det(Rproj) < 0: # enforce right-handedness + U[:, -1] *= -1 + Rproj = U @ Vt + return Rproj + + @staticmethod + def _rot_to_quat(Rm: np.ndarray) -> np.ndarray: + """Rotation matrix -> unit quaternion in [w,x,y,z].""" + Rm = PointCloudMeasurement.project_to_so3(Rm) + q_xyzw = R.from_matrix(Rm).as_quat() + x, y, z, w = q_xyzw + q_wxyz = np.array([w, x, y, z], float) + return q_wxyz / (np.linalg.norm(q_wxyz) + 1e-12) + + @staticmethod + def _quat_to_rot(q_wxyz: np.ndarray) -> np.ndarray: + """Unit quaternion [w,x,y,z] -> rotation matrix""" + w, x, y, z = q_wxyz + q_xyzw = np.array([x, y, z, w], float) + Rm = R.from_quat(q_xyzw).as_matrix() + return Rm + + @staticmethod + def _slerp(q0: np.ndarray, q1: np.ndarray, alpha: float) -> np.ndarray: + """Spherical linear interpolation on unit quaternions""" + if np.dot(q0, q1) < 0.0: + q1 = -q1 + c = float(np.clip(np.dot(q0, q1), -1.0, 1.0)) + if c > 0.9995: + q = (1.0 - alpha) * q0 + alpha * q1 + return q / (np.linalg.norm(q) + 1e-12) + th = np.arccos(c) + s0 = np.sin((1.0 - alpha) * th) / np.sin(th) + s1 = np.sin(alpha * th) / np.sin(th) + return s0 * q0 + s1 * q1 + + @staticmethod + def _align_axes_to_prev( + R_new: np.ndarray, e_new: np.ndarray, R_ref: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + """ + Find permutation P and signs S so that R_new @ P @ S best aligns to R_ref. + - R_new: 3x3 rotation (columns are OBB axes) + - e_new: (3,) extents (mm) + - R_ref: 3x3 previous rotation + + Returns: + R_aligned (3x3): new rotation expressed with the same axis labeling as R_ref + e_perm (3,) : extents permuted to match those axes (always positive) + """ + best = None + for perm in ((0, 1, 2), (0, 2, 1), (1, 0, 2), (1, 2, 0), (2, 0, 1), (2, 1, 0)): + P = np.eye(3)[:, perm] + Rp = R_new @ P + M = R_ref.T @ Rp + s = np.sign(np.diag(M)) + s[s == 0] = 1.0 + S = np.diag(s) + Rps = Rp @ S + score = float(np.trace(R_ref.T @ Rps)) + if (best is None) or (score > best[0]): + best = (score, Rps, e_new[list(perm)]) + R_aligned = PointCloudMeasurement._ensure_right_handed(best[1]) + e_perm = best[2].astype(float) + return R_aligned, e_perm + + @staticmethod + def _build_corners(center: np.ndarray, R: np.ndarray, e: np.ndarray) -> np.ndarray: + """ + Corner order (stable): + 0..3: bottom ring CCW, then 4..7: top ring CCW + """ + hx, hy, hz = e / 2.0 + local = np.array( + [ + [-hx, -hy, -hz], + [hx, -hy, -hz], + [hx, hy, -hz], + [-hx, hy, -hz], + [-hx, -hy, hz], + [hx, -hy, hz], + [hx, hy, hz], + [-hx, hy, hz], + ], + dtype=float, + ) + return center + local @ R.T + + @staticmethod + def ratio_inside_box( + points: np.ndarray, C: np.ndarray, R: np.ndarray, E: np.ndarray, margin=10.0 + ): + """ + Returns ratio of points inside a box defined with: + C: (3,) box center + R: (3,3) box rotation + E: (3,) box extents (mm) + """ + H = E * 0.5 + margin + Q = (points - C) @ R + inside = (np.abs(Q) <= H + 1e-6).all(axis=1) + return float(np.mean(inside)) + + @staticmethod + def box_from_corners(corners: np.ndarray): + """ + Recovers box pose (C, R, E) from 8 corners (sorted bottom CCW then top CCW) + """ + C = corners.mean(axis=0) + ex = corners[1] - corners[0] + ey = corners[3] - corners[0] + ez = corners[4] - corners[0] + Ex, Ey, Ez = np.linalg.norm(ex), np.linalg.norm(ey), np.linalg.norm(ez) + X = ex / (Ex + 1e-12) + Y = ey - np.dot(ey, X) * X + Y /= np.linalg.norm(Y) + 1e-12 + Z = np.cross(X, Y) + Z /= np.linalg.norm(Z) + 1e-12 + R = np.column_stack([X, Y, Z]) + E = np.array([Ex, Ey, Ez], float) + return C, R, E + + def update_point_cloud(self, points: np.ndarray): + """ + Updates the Open3D point cloud data using an internal buffer, which makes execution faster. + + Args: + points (np.ndarray): A NumPy array of 3D points. + """ + if points.shape[0] > self.points_buffer.shape[0]: + self.points_buffer = np.empty((points.shape[0], 3), dtype=np.float64) + + np.copyto(self.points_buffer[: points.shape[0]], points) + self.point_cloud.points = o3d.utility.Vector3dVector( + self.points_buffer[: points.shape[0]] + ) + + def update_point_cloud_plane(self, points: np.ndarray): + """ + Updates the Open3D point cloud data using an internal buffer, which makes execution faster. + + Args: + points (np.ndarray): A NumPy array of 3D points. + """ + if points.shape[0] > self.points_buffer_plane.shape[0]: + self.points_buffer_plane = np.empty((points.shape[0], 3), dtype=np.float64) + + np.copyto(self.points_buffer_plane[: points.shape[0]], points) + self.point_cloud_plane.points = o3d.utility.Vector3dVector( + self.points_buffer_plane[: points.shape[0]] + ) + + def set_point_cloud( + self, pcl_points: np.ndarray, colors: Optional[np.ndarray] = None + ): + """ + Sets the point cloud for the fitter, applying filtering and downsampling. + + Args: + pcl_points (np.ndarray): The 3D points of the point cloud. + colors (np.ndarray, optional): The colors corresponding to the points. Defaults to None. + """ + self.update_point_cloud(pcl_points) + + if colors is not None and colors.shape == pcl_points.shape: + self.point_cloud.colors = o3d.utility.Vector3dVector(colors) + + self.filter_point_cloud() + idx = self.MAD_filtering(self.point_cloud, k=3) + if idx.size == 0: + return + + self.point_cloud = self.point_cloud.select_by_index(idx) + + def set_point_cloud_plane(self, pcl_points: np.ndarray): + self.update_point_cloud_plane(pcl_points) + self.filter_point_cloud_plane() + + def filter_point_cloud(self): + self.point_cloud = self.point_cloud.voxel_down_sample( + voxel_size=self.voxel_size + ) + self.point_cloud, _ = self.point_cloud.remove_statistical_outlier(20, 0.1) + + points = np.asarray(self.point_cloud.points) + + # removes points with near z = 0 values + eps = 1e-6 + mask = points[:, 2] > eps + self.point_cloud = self.point_cloud.select_by_index(np.where(mask)[0]) + + def filter_point_cloud_plane(self): + self.point_cloud_plane = self.point_cloud_plane.voxel_down_sample(voxel_size=10) + self.point_cloud_plane, _ = self.point_cloud_plane.remove_statistical_outlier( + 20, 0.1 + ) + + points = np.asarray(self.point_cloud_plane.points) + + # removes points with near z = 0 values + eps = 1e-6 + mask = points[:, 2] > eps + self.point_cloud_plane = self.point_cloud_plane.select_by_index( + np.where(mask)[0] + ) + + def MAD_filtering(self, pcl: o3d.geometry.PointCloud, k: int = 3): + """ + Filters the point cloud using the Median Absolute Deviation (MAD) method. + + This method is robust to outliers and filters points that are far from the median. + + Args: + pcl (o3d.geometry.PointCloud): The input point cloud. + k (int, optional): The number of MADs to use as a threshold. Defaults to 3. + + Returns: + Returns indices of inlier points after the filtering. + """ + points = np.asarray(pcl.points) + + if points.shape[0] == 0: + return np.empty(0, dtype=int) + + median_point = np.median(points, axis=0) + distances = np.linalg.norm(points - median_point, axis=1) + median_distance = np.median(distances) + mad = np.median(np.abs(distances - median_distance)) + + if mad == 0: + mask = np.ones_like(distances, dtype=bool) + else: + mask = np.abs(distances - median_distance) < k * mad + + return np.flatnonzero(mask) + + def fit_plane(self, g) -> Tuple[Optional[np.ndarray], Optional[List[int]], bool]: + """ + Fits a plane to the point cloud using RANSAC. + + Returns: + tuple: A tuple containing the plane equation, the inlier indices, and a boolean indicating success. + """ + if len(self.point_cloud_plane.points) < self.sample_points: + return None, None, False + + try: + plane_eq, plane_inliers = self.point_cloud_plane.segment_plane( + self.distance_thr, self.sample_points, self.max_iterations + ) + except RuntimeError: + return None, None, False + + inlier_ratio = len(plane_inliers) / len(self.point_cloud_plane.points) + # print('inlier ratio: ', inlier_ratio) + if inlier_ratio >= self.ransac_inlier_ratio_thr: + if self.is_ground_plane(g, plane_eq): + return np.array(plane_eq), plane_inliers, True + return None, None, False + + def is_ground_plane(self, g, plane_eq): + """ + Checks if the plane represents the ground using the gravity vector from the IMU. + If the plane is the ground, the normal of the plane and the gravity vector should be parallel (theta = 0). + """ + n = plane_eq[:3] + theta_max_deg = 10.0 + thr = -np.cos(np.deg2rad(theta_max_deg)) + ok = np.dot(n / np.linalg.norm(n), g / np.linalg.norm(g)) <= thr + return ok + + def clear_plane(self): + self.plane_eq = None + self.R_w2t = None + self.t_w2t = None + + def to_table_frame(self): + """ + Transforms point cloud from world to table/plane frame + """ + peq = np.asarray(self.plane_eq, dtype=np.float64).ravel() + if peq.size != 4 or not np.all(np.isfinite(peq)): + raise ValueError("Bad plane_eq (must be 4 finite coeffs)") + + a, b, c, d = peq + n = np.array([a, b, c], dtype=np.float64) + n_norm = np.linalg.norm(n) + if not np.isfinite(n_norm) or n_norm == 0.0: + raise ValueError("Bad plane normal") + + n /= n_norm + d /= n_norm + + # Find point on the plane + p0 = -d * n + + # Ensure the normal points up, away from the plane + if self.center is not None: + dotc = float(np.dot(self.center - p0, n)) + if np.isfinite(dotc) and dotc < 0.0: + n = -n + d = -d + p0 = -d * n + + # Build an orthonormal basis (X, Y, Z) for the table frame + ref = np.array([0.0, 0.0, 1.0], dtype=np.float64) + if abs(float(np.dot(n, ref))) > 0.99: + ref = np.array([1.0, 0.0, 0.0], dtype=np.float64) + z_axis = n + x_axis = np.cross(ref, n) + nx = np.linalg.norm(x_axis) + if nx == 0.0 or not np.isfinite(nx): + ref = np.array([0.0, 1.0, 0.0], dtype=np.float64) + x_axis = np.cross(ref, z_axis) + nx = np.linalg.norm(x_axis) + if nx == 0.0: + raise ValueError("Could not build orthonormal basis") + x_axis /= nx + y_axis = np.cross(z_axis, x_axis) + + # R transforms vectors from the world frame to the table frame + R = np.column_stack([x_axis, y_axis, z_axis]).T + t = -R @ p0 + + P = np.asarray(self.point_cloud.points, dtype=np.float64) + if P.ndim != 2 or P.shape[1] != 3 or P.size == 0: + raise ValueError("Empty or invalid object point cloud") + + P_tf = P @ R.T + t + P_tf = np.ascontiguousarray(P_tf, dtype=np.float64) + self.point_cloud_tf.points = o3d.utility.Vector3dVector(P_tf) + + if self.point_cloud.has_colors(): + self.point_cloud_tf.colors = self.point_cloud.colors + + self.R_w2t = R + self.t_w2t = t + + def size_from_table_frame(self): + pts = np.asarray(self.point_cloud_tf.points) + z_rel = np.clip(pts[:, 2] - self.z_base, 0, None) + H = float(np.percentile(z_rel, 98)) + L, W, rect_xy = self.min_area_rect_from_points(pts[:, :2]) + return L, W, H, rect_xy + + def convex_hull_2d(self, xy: np.ndarray): + """ + Convex hull of 2D points + Returns: + Hull vertices as an (M, 2) array in CCW order + """ + pts = np.unique(xy, axis=0) + pts = pts[np.lexsort((pts[:, 1], pts[:, 0]))] + + def cross(o, a, b): + return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0]) + + lower = [] + for p in pts: + while len(lower) >= 2 and cross(lower[-2], lower[-1], p) <= 0: + lower.pop() + lower.append(p) + upper = [] + for p in pts[::-1]: + while len(upper) >= 2 and cross(upper[-2], upper[-1], p) <= 0: + upper.pop() + upper.append(p) + return np.array(lower[:-1] + upper[:-1]) + + def min_area_rect_from_points(self, xy: np.ndarray): + hull = self.convex_hull_2d(xy) + # With less than 3 unique XY points, we cannot form a polygon hull - fallback to AABB + if hull.shape[0] < 3: + mn, mx = xy.min(0), xy.max(0) + L, W = mx - mn + rect_xy = np.array( + [[mn[0], mn[1]], [mx[0], mn[1]], [mx[0], mx[1]], [mn[0], mx[1]]], + dtype=float, + ) + if L < W: + L, W = W, L + rect_xy = rect_xy[[3, 0, 1, 2]] + return float(L), float(W), rect_xy + edges = np.diff(np.vstack([hull, hull[0]]), axis=0) + angs = np.unique(np.mod(np.arctan2(edges[:, 1], edges[:, 0]), np.pi / 2)) + best = (np.inf, None) + for th in angs: + c, s = np.cos(th), np.sin(th) + R = np.array([[c, -s], [s, c]]) + q = hull @ R.T + mn, mx = q.min(0), q.max(0) + area = (mx - mn).prod() + if area < best[0]: + best = (area, (mn, mx, R)) + mn, mx, R = best[1] + rect = np.array( + [[mn[0], mn[1]], [mx[0], mn[1]], [mx[0], mx[1]], [mn[0], mx[1]]] + ) + rect_xy = rect @ R + L, W = mx - mn + if L < W: + L, W = W, L + rect_xy = rect_xy[[3, 0, 1, 2]] + return float(L), float(W), rect_xy + + def points_in_poly(self, pts_xy: np.ndarray, poly_xy: np.ndarray): + x, y = pts_xy[:, 0], pts_xy[:, 1] + px, py = poly_xy[:, 0], poly_xy[:, 1] + n = len(poly_xy) + inside = np.zeros(len(pts_xy), dtype=bool) + j = n - 1 + for i in range(n): + xi, yi = px[i], py[i] + xj, yj = px[j], py[j] + cond = ((yi > y) != (yj > y)) & ( + x < (xj - xi) * (y - yi) / (yj - yi + 1e-12) + xi + ) + inside ^= cond + j = i + return inside + + def _grid_and_masks(self, polygon_xy, cell): + xmin, ymin = polygon_xy.min(axis=0) + xmax, ymax = polygon_xy.max(axis=0) + nx = int(np.ceil((xmax - xmin) / cell)) + ny = int(np.ceil((ymax - ymin) / cell)) + cx = xmin + (np.arange(nx) + 0.5) * cell + cy = ymin + (np.arange(ny) + 0.5) * cell + CX, CY = np.meshgrid(cx, cy) + inside_cells = self.points_in_poly( + np.c_[CX.ravel(), CY.ravel()], polygon_xy + ).reshape(ny, nx) + return xmin, ymin, nx, ny, inside_cells + + def _assign_vertex_heights( + self, pts, xmin, ymin, nx, ny, cell, inside_cells, z_base, fill_iters + ): + x, y, z = pts[:, 0], pts[:, 1], np.clip(pts[:, 2] - z_base, 0, None) + Hv = np.full((ny + 1, nx + 1), -np.inf, float) + i0 = np.floor((x - xmin) / cell).astype(int) + j0 = np.floor((y - ymin) / cell).astype(int) + for ii, jj, h in zip(i0, j0, z): + if ii < 0 or jj < 0 or ii >= nx or jj >= ny: + continue + if not inside_cells[jj, ii]: + continue + Hv[jj, ii] = max(Hv[jj, ii], h) + Hv[jj, ii + 1] = max(Hv[jj, ii + 1], h) + Hv[jj + 1, ii] = max(Hv[jj + 1, ii], h) + Hv[jj + 1, ii + 1] = max(Hv[jj + 1, ii + 1], h) + Hv[~np.isfinite(Hv)] = 0.0 + mask_v = np.zeros_like(Hv, dtype=bool) + mask_v[:-1, :-1] |= inside_cells + mask_v[1:, :-1] |= inside_cells + mask_v[:-1, 1:] |= inside_cells + mask_v[1:, 1:] |= inside_cells + for _ in range(fill_iters): + Hpad = np.pad(Hv, 1, mode="edge") + max8 = np.maximum.reduce( + [ + Hpad[0:-2, 0:-2], + Hpad[0:-2, 1:-1], + Hpad[0:-2, 2:], + Hpad[1:-1, 0:-2], + Hpad[1:-1, 2:], + Hpad[2:, 0:-2], + Hpad[2:, 1:-1], + Hpad[2:, 2:], + ] + ) + Hv = np.where((Hv == 0) & mask_v, max8, Hv) + return Hv, mask_v + + def make_height_surface_grid(self, polygon_xy): + pts = np.asarray(self.point_cloud_tf.points) + xmin, ymin, nx, ny, inside = self._grid_and_masks(polygon_xy, self.cell) + Hv, mask_v = self._assign_vertex_heights( + pts, xmin, ymin, nx, ny, self.cell, inside, self.z_base, self.fill_iters + ) + gx = xmin + np.arange(nx + 1) * self.cell + gy = ymin + np.arange(ny + 1) * self.cell + return dict( + xmin=xmin, + ymin=ymin, + nx=nx, + ny=ny, + cell=self.cell, + inside=inside, + Hv=Hv, + mask_v=mask_v, + z_base=self.z_base, + gx=gx, + gy=gy, + ) + + def volume_from_height_grid(self, G): + """Integrate the same top: two triangles per inside cell, vertex heights in Hv.""" + nx, ny, cell, inside, Hv = G["nx"], G["ny"], G["cell"], G["inside"], G["Hv"] + Atri = 0.5 * cell * cell + V = 0.0 + for j in range(ny): + for i in range(nx): + if not inside[j, i]: + continue + h00 = Hv[j, i] + h10 = Hv[j, i + 1] + h11 = Hv[j + 1, i + 1] + h01 = Hv[j + 1, i] + V += Atri * (h00 + h10 + h11) / 3.0 + V += Atri * (h00 + h11 + h01) / 3.0 + return float(V) # mm^3 + + # --------------- Filtering bbox positions------------------- + + def get_measurement_OBB(self): + """ + Object-oriented bbox with temporal stabilization/smoothing + Pipeline: + raw OBB -> axis alignment -> smoothing (center, extents, rotation) -> outlier rejection -> build corners + """ + + obb = self.point_cloud.get_minimal_oriented_bounding_box() + c_new = np.asarray(obb.center, float) + R_new = self._ensure_right_handed(np.asarray(obb.R, float)) + e_new = np.asarray(obb.extent, float) + + prev_c = self.prev_c + prev_R = self.prev_R + prev_e = self.prev_e + from_reset = bool(self.from_reset) + + # Init for filtering + need_init = from_reset or self._need_init_state(prev_c, prev_R, prev_e) + if need_init: + prev_c, prev_R, prev_e = c_new, R_new, e_new + self.from_reset = False + + # Axis alignment to previous frame's axes + R_aligned, e_perm = self._align_axes_to_prev(R_new, e_new, prev_R) + + # Filtering/smoothing + # center EMA + c_s = (1.0 - self.alpha_pos) * prev_c + self.alpha_pos * c_new + + # Rotation (SLERP on quaternions) + q_prev = self._rot_to_quat(prev_R) + q_new = self._rot_to_quat(R_aligned) + q_s = self._slerp(q_prev, q_new, self.alpha_rot) + R_s = self._quat_to_rot(q_s) + + # Extents - EMA + delta_e = np.clip( + e_perm - prev_e, -self.extent_delta_clamp, +self.extent_delta_clamp + ) + e_s = np.maximum(prev_e + self.alpha_ext * delta_e, 1e-3) + + # Outlier rejection + if np.linalg.norm(c_new - prev_c) > self.max_center_jump: + c_s, R_s, e_s = prev_c, prev_R, prev_e + + self.obb_pts = self._build_corners(c_s, R_s, e_s) + + self.dimensions = (e_s / 10.0).astype(float) + self.volume = float((e_s[0] * e_s[1] * e_s[2]) / 1000.0) + self.prev_c, self.prev_R, self.prev_e = c_s, R_s, e_s + + # Sanity check if points inside the box + P = np.asarray(self.point_cloud.points) + inside_ratio = self.ratio_inside_box( + P, c_s, R_s, e_s, margin=self.inlier_margin_mm + ) + if inside_ratio < self.reinit_inlier_ratio: + self.prev_c, self.prev_R, self.prev_e = c_new, R_new, e_new + self.obb_pts = self._build_corners(self.prev_c, self.prev_R, self.prev_e) + self.dimensions = (self.prev_e / 10.0).astype(float) + self.volume = float( + (self.prev_e[0] * self.prev_e[1] * self.prev_e[2]) / 1000.0 + ) + self.from_reset = True + + def get_measurement_groundHG(self): + """ + Height-grid measurement with temporal stabilization/filtering. + """ + + if self.plane_eq is None: + print("Ground plane not set!") + return + + # Transform current object points to the table frame + self.center = np.asarray(self.point_cloud.points).mean(axis=0) + self.to_table_frame() + + L, W, H, rect_xy = self.size_from_table_frame() + center2d = rect_xy.mean(axis=0) + # Yaw (rot angle along z) from min-area rectangle on (X,Y) + v = rect_xy[1] - rect_xy[0] + yaw = float(np.arctan2(v[1], v[0])) + + yaw_can, LW_can = self._best_rect_candidate(yaw, L, W, self.hg_prev_yaw) + L_can, W_can = float(LW_can[0]), float(LW_can[1]) + + prev_center2 = self.hg_prev_center2 + prev_H = self.hg_prev_H + prev_LW = self.hg_prev_LW + prev_yaw = self.hg_prev_yaw + from_reset = bool(self.from_reset) + + # Init filter + need_init = from_reset or self._need_init_state( + prev_center2, prev_H, prev_LW, prev_yaw + ) + if need_init: + prev_center2 = center2d + prev_yaw = yaw_can + prev_LW = np.array([L_can, W_can], float) + prev_H = H + self.from_reset = False + + # Filtering/smoothig + c2_s = (1.0 - self.alpha_pos) * prev_center2 + self.alpha_pos * center2d + + u_prev = self._yaw_vec(prev_yaw) + u_new = self._yaw_vec(yaw_can) + u_s = (1.0 - self.alpha_rot) * u_prev + self.alpha_rot * u_new + if np.allclose(u_s, 0): + u_s = u_prev + u_s /= np.linalg.norm(u_s) + 1e-12 + yaw_s = self._yaw_from_vec(u_s) + + dLW = np.clip( + np.array([L_can, W_can]) - prev_LW, + -self.extent_delta_clamp_hg, + +self.extent_delta_clamp_hg, + ) + LW_s = np.maximum(prev_LW + self.alpha_ext * dLW, 1e-3) + + # Outlier rejection (big center jump in plane) + if np.linalg.norm(center2d - prev_center2) > self.max_hg_center_jump: + c2_s, yaw_s, LW_s, H_s = prev_center2, prev_yaw, prev_LW, prev_H + + rect_xy_s = self._rect_xy_from_params(c2_s, yaw_s, LW_s[0], LW_s[1]) + + G = self.make_height_surface_grid(rect_xy_s) + Hv = G["Hv"] + mask_v = G["mask_v"] + if np.any(mask_v): + H = float(np.percentile(Hv[mask_v], 98)) + + # Smooth H + dH = np.clip( + float(H) - prev_H, -self.extent_delta_clamp_hg, +self.extent_delta_clamp_hg + ) + H_s = max(prev_H + self.alpha_ext * dH, 1e-3) + + # Build filtered corners + bottom_t = np.c_[rect_xy_s, np.full(4, self.z_base)] + top_t = np.c_[rect_xy_s, np.full(4, H_s + self.z_base)] + corners_t = np.vstack([bottom_t, top_t]) # 0..3 bottom CCW, 4..7 top + + # convert back to world/camera coords + corners_w = (corners_t - self.t_w2t) @ self.R_w2t + + self.corners3d_table = corners_t + self.corners3d = corners_w + self.dimensions = np.array([LW_s[0], LW_s[1], H_s], float) / 10.0 # cm + self.volume = self.volume_from_height_grid(G) / 1000.0 # cm^3 + + self.hg_prev_center2 = c2_s + self.hg_prev_yaw = yaw_s + self.hg_prev_LW = LW_s + self.hg_prev_H = H_s + + # Sanity check if points inside the box + C, R, E = self.box_from_corners(self.corners3d) + P = np.asarray(self.point_cloud.points) + inside_ratio = self.ratio_inside_box(P, C, R, E, margin=self.inlier_margin_mm) + if inside_ratio < self.reinit_inlier_ratio: + self.from_reset = True diff --git a/apps/object-volume-measurement-3d/backend/src/utils/__init__.py b/apps/object-volume-measurement-3d/backend/src/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/apps/object-volume-measurement-3d/backend/src/utils/annotation_node.py b/apps/object-volume-measurement-3d/backend/src/utils/annotation_node.py new file mode 100644 index 000000000..69030f768 --- /dev/null +++ b/apps/object-volume-measurement-3d/backend/src/utils/annotation_node.py @@ -0,0 +1,460 @@ +import depthai as dai +from depthai_nodes import ImgDetectionsExtended +from depthai_nodes.utils import AnnotationHelper +from typing import Dict, Tuple, Optional +import numpy as np +import cv2 +import json + +Point = Tuple[float, float] + + +class AnnotationNode(dai.node.ThreadedHostNode): + """Interactive annotation / selection node + + Inputs: + - in_detections: ImgDetectionsExtended produced by NN parser + - in_rgb_stream: RGB ImgFrame + - in_depth_stream: depth ImgFrame (will be masked to create segmented PCL) + + Outputs: + - out_ann: filtered detections (optionally only the clicked instance) + - out_segm: rgb frame for RGBD node + - out_segm_depth: masked depth (background set to 0) for RGBD node + - out_selection: Holds measuring mode value; 1 - object selected; 0 - no selection + """ + + MODE_NOSELECTION = 0 + MODE_MEASURE = 1 + MODE_PLANE_CAPTURE = 2 + + def __init__(self, label_encoding: Dict[int, str] = {}) -> None: + dai.node.ThreadedHostNode.__init__(self) + + self.in_detections = self.createInput() + self.in_rgb_stream = self.createInput() + self.in_depth_stream = self.createInput() + self.in_meas_result = self.createInput() + + self.out_segm = self.createOutput() + self.out_segm_depth = self.createOutput() + self.out_selection = self.createOutput() + self.out_ann = self.createOutput() + + self._label_encoding = label_encoding + + self._selection_point: Optional[Tuple[float, float]] = None # normalized coords + self._keep_top_only: bool = True + + self._src_trans = None + self._dst_trans = None + + self._plane_capture: bool = False + + self._last_dims = None + self._last_vol = None + + def setLabelEncoding(self, label_encoding: Dict[int, str]) -> None: + """Sets the label encoding. + + @param label_encoding: The label encoding with labels as keys and label names as + values. + @type label_encoding: Dict[int, str] + """ + if not isinstance(label_encoding, Dict): + raise ValueError("label_encoding must be a dictionary.") + self._label_encoding = label_encoding + + def setSelectionPoint(self, nx: float, ny: float) -> None: + nx = float(max(0.0, min(1.0, nx))) + ny = float(max(0.0, min(1.0, ny))) + self._selection_point = (nx, ny) + + def clearSelection(self) -> None: + self._selection_point = None + print("Selection cleared.") + + def setKeepTopOnly(self, keep_top: bool) -> None: + """If True, keep only the highest-confidence detection under the click.""" + self._keep_top_only = bool(keep_top) + + def requestPlaneCapture(self, enable: bool = True) -> None: + """Ask the node to emit exactly one cycle with FULL depth (mode=2).""" + self._plane_capture = bool(enable) + + def clearCachedMeasurements(self): + self._last_dims = None + self._last_vol = None + + while True: + if not self.in_meas_result.tryGet(): + break + + def build( + self, + detections: dai.Node.Output, + rgb_stream: Optional[dai.Node.Output] = None, + depth_stream: Optional[dai.Node.Output] = None, + label_encoding: Optional[Dict[int, str]] = None, + ) -> "AnnotationNode": + if label_encoding is not None: + self.setLabelEncoding(label_encoding) + + detections.link(self.in_detections) + + if rgb_stream is not None: + rgb_stream.link(self.in_rgb_stream) + + if depth_stream is not None: + depth_stream.link(self.in_depth_stream) + + return self + + def _send_mode(self, mode: int, src_msg: dai.ImgFrame) -> None: + b = dai.Buffer() + b.setData(bytes([mode & 0xFF])) + b.setTimestamp(src_msg.getTimestamp()) + b.setSequenceNum(src_msg.getSequenceNum()) + self.out_selection.send(b) + + def point_in_convex_quad( + self, px: float, py: float, corners, eps: float = 1e-9 + ) -> bool: + """ + corners: list[(x,y)] + Returns True if (px, py) lies inside/on a convex quad defined by 'corners'. + """ + + def cross(o, a, b): + return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0]) + + s = [] + for i in range(4): + o = corners[i] + a = corners[(i + 1) % 4] + s.append(cross(o, a, (px, py))) + + return all(v >= -eps for v in s) or all(v <= eps for v in s) + + def _contains_point(self, det, sx_rgb, sy_rgb) -> bool: + # corners are NN-normalized → warp to RGB-normalized + rr = det.rotated_rect + pts_nn_norm = [(p.x, p.y) for p in rr.getPoints()] + pts_rgb_norm = self._nn_to_rgb_norm_pts(pts_nn_norm) + return self.point_in_convex_quad(sx_rgb, sy_rgb, pts_rgb_norm) + + def _prepare_mask_transform( + self, src_T: dai.ImgTransformation, dst_T: dai.ImgTransformation + ): + """Precompute homography to warp NN-space masks into RGB-space. + + Args: + src_T: ImgTransformation for the NN input space (sensor→NN) + dst_T: ImgTransformation for the RGB image space (sensor→RGB) + """ + A = np.array(src_T.getMatrix(), dtype=np.float32) + B = np.array(dst_T.getMatrix(), dtype=np.float32) + + # NN -> RGB + try: + self._H_nn_to_rgb = B @ np.linalg.inv(A) + except np.linalg.LinAlgError: + self._H_nn_to_rgb = B @ np.linalg.pinv(A) + + dw, dh = dst_T.getSize() + self._dst_size = (int(dw), int(dh)) + + def _mask_nn_to_rgb(self, mask_src_2d: np.ndarray) -> np.ndarray: + """ + Warp a NN-space mask to RGB-space using the precomputed homography. + """ + dst_w, dst_h = self._dst_size + + mask = mask_src_2d + if mask.dtype not in (np.int16, np.float32, np.uint8, np.uint16): + mask = mask.astype(np.int16, copy=False) + + return cv2.warpPerspective( + mask, + self._H_nn_to_rgb, + (dst_w, dst_h), + flags=cv2.INTER_NEAREST, + borderValue=-1, + ) + + # ---- drawing (NN-normalized coords for AnnotationHelper) ---- + def _draw_mask(self, helper: AnnotationHelper, mask: np.ndarray, idx: int): + h, w = mask.shape + binary = (mask == idx).astype(np.uint8) + if not np.any(binary): + return + contours, _ = cv2.findContours( + binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE + ) + for cnt in contours: + epsilon = 0.01 * cv2.arcLength(cnt, True) + approx = cv2.approxPolyDP(cnt, epsilon, True) + pts = approx.reshape(-1, 2) + # NN-normalized points + pts_norm = [(float(x) / w, float(y) / h) for x, y in pts] + self._warp_polygon_and_draw( + helper, + pts_norm, + outline_color=(1.0, 0.5, 1.0, 0.0), + fill_color=(1.0, 0.5, 1.0, 0.4), + thickness=1, + ) + + def _draw_rotrect_and_label(self, helper: AnnotationHelper, det, text: str): + # rr corners are NN-normalized already (0..1 in NN space) + rr = det.rotated_rect + corners = rr.getPoints() + pts_nn_norm = [(p.x, p.y) for p in corners] + # draw polygon in RGB space + self._warp_polygon_and_draw( + helper, + pts_nn_norm, + outline_color=(1.0, 0.5, 1.0, 1.0), + fill_color=None, + thickness=2, + ) + # label at first corner (warp a single point) + lbl_xy = self._nn_to_rgb_norm_pts([(pts_nn_norm[0][0], pts_nn_norm[0][1])])[0] + helper.draw_text( + text, + lbl_xy, + color=(0.5, 0.15, 1.0, 1.0), + background_color=(1.0, 1.0, 0.0, 0.8), + size=19, + ) + + def _nn_to_rgb_norm_pts(self, pts_norm): + """ + pts_norm: list of (u,v) normalized to NN input (0..1) + returns: list of (U,V) normalized to RGB frame (0..1) + """ + # sizes + sw, sh = self._src_trans.getSize() + dw, dh = self._dst_size + + # to NN pixels + P = np.array([[u * sw, v * sh, 1.0] for (u, v) in pts_norm], dtype=np.float32).T + # homography NN->RGB + Q = self._H_nn_to_rgb @ P + Q /= Q[2:3, :] + 1e-12 + # to RGB-normalized + U = (Q[0, :] / float(dw)).clip(0, 1) + V = (Q[1, :] / float(dh)).clip(0, 1) + return list(zip(U.tolist(), V.tolist())) + + def _warp_polygon_and_draw(self, helper, pts_nn_norm, **poly_kwargs): + pts_rgb_norm = self._nn_to_rgb_norm_pts(pts_nn_norm) + helper.draw_polyline(pts_rgb_norm, closed=True, **poly_kwargs) + + def _clear_dets_and_mask(self, det) -> None: + """ + Clears detections and masks + """ + det.detections = [] + m = getattr(det, "masks", None) + if m is not None and getattr(m, "size", 0): + # -1 - background + det.masks = np.full_like(m, -1) + + def _poll_measurement_result(self): + buf = self.in_meas_result.tryGet() + if not buf: + return + try: + data = bytes(buf.getData()).decode("utf-8") + j = json.loads(data) + self._last_dims = j.get("dims", None) + self._last_vol = j.get("vol", None) + except Exception as e: + print("AnnotationNode: bad meas payload:", e) + + def run(self) -> None: + while self.isRunning(): + det_msg = self.in_detections.get() + img_msg = self.in_rgb_stream.get() + depth_msg = self.in_depth_stream.get() + + depth_map = depth_msg.getFrame() + rgb = img_msg.getCvFrame() + + helper = AnnotationHelper() + + H, W = rgb.shape[:2] + + # Note: for temp sync of pcl and MODE messages in measurement node (to do) + rgbF = dai.ImgFrame() + rgbF.setType(img_msg.getType()) + rgbF.setWidth(W) + rgbF.setHeight(H) + rgbF.setData(img_msg.getData()) + rgbF.setTimestamp(depth_msg.getTimestamp()) + rgbF.setSequenceNum(depth_msg.getSequenceNum()) + rgbF.setTransformation(img_msg.getTransformation()) + + if self._src_trans is None or self._dst_trans is None: + self._src_trans = det_msg.getTransformation() + self._dst_trans = img_msg.getTransformation() + self._prepare_mask_transform( + self._src_trans, self._dst_trans + ) # To transform mask from NN space to rgb space + + assert isinstance(det_msg, ImgDetectionsExtended) + for detection in det_msg.detections: + detection.label_name = self._label_encoding.get( + detection.label, "unknown" + ) + + # Plane capture when switch to heightgrid + if self._plane_capture: + # self._send_mode(self.MODE_MEASURE, depth_msg) + self._send_mode(self.MODE_PLANE_CAPTURE, depth_msg) + self.out_ann.send( + AnnotationHelper().build( + img_msg.getTimestamp(), img_msg.getSequenceNum() + ) + ) + self.out_segm.send(rgbF) + self.out_segm_depth.send(depth_msg) # full-scene depth + # self._send_mode(self.MODE_MEASURE, img_msg) + # self._plane_capture = False + continue + + m = det_msg.masks + dets = det_msg.detections + mask_rgb = None + + # no click show everything + if self._selection_point is None: + # draw masks + boxes + labels for ALL detections + if m is not None and m.size: + # assume instance id == detection index (as produced by parser) + present_ids = set(int(v) for v in np.unique(m) if v >= 0) + for idx in present_ids: + self._draw_mask(helper, m, idx) + for d in dets: + label_txt = f"{d.label_name} {getattr(d, 'confidence', 0.0):.2f}" + self._draw_rotrect_and_label(helper, d, label_txt) + + # emit: annotations + full depth + all dets + ann_msg = helper.build(img_msg.getTimestamp(), img_msg.getSequenceNum()) + self._send_mode(self.MODE_NOSELECTION, depth_msg) + self.out_ann.send(ann_msg) + self.out_segm.send(rgbF) + self.out_segm_depth.send(depth_msg) + continue + + # with a click keep only the instance under the click + sx, sy = self._selection_point + kept_idx = [ + i for i, d in enumerate(dets) if self._contains_point(d, sx, sy) + ] + if self._keep_top_only and kept_idx: + kept_idx = [ + max(kept_idx, key=lambda i: getattr(dets[i], "confidence", 0.0)) + ] + + if not kept_idx: + # click hit nothing behave like no selection (signal NOSELECTION) + if m is not None and m.size: + present_ids = set(int(v) for v in np.unique(m) if v >= 0) + for idx in present_ids: + self._draw_mask(helper, m, idx) + for d in dets: + label_txt = f"{d.label_name} {getattr(d, 'confidence', 0.0):.2f}" + self._draw_rotrect_and_label(helper, d, label_txt) + + ann_msg = helper.build(img_msg.getTimestamp(), img_msg.getSequenceNum()) + self._send_mode(self.MODE_NOSELECTION, depth_msg) + self.out_ann.send(ann_msg) + self.out_segm.send(rgbF) + self.out_segm_depth.send(depth_msg) + + self.clearSelection() + self.clearCachedMeasurements() + + continue + + # transform mask NN space -> RGB space + if m is not None: + mask_rgb = self._mask_nn_to_rgb(m) + + # keep only the chosen detection + sel_i = kept_idx[0] + selected_mid = -1 + + # pick the mask instance id under the click + if mask_rgb is not None and mask_rgb.size: + h_mask, w_mask = mask_rgb.shape[:2] + px = int(np.clip(sx * w_mask, 0, w_mask - 1)) + py = int(np.clip(sy * h_mask, 0, h_mask - 1)) + selected_mid = ( + int(mask_rgb[py, px]) + if mask_rgb is not None and mask_rgb.size + else -1 + ) + + if m is not None and selected_mid != -1: + # draw the masks of every detection, measurements only for the selected one + present_ids = set(int(v) for v in np.unique(m) if v >= 0) + for idx in present_ids: + self._draw_mask(helper, m, idx) + + self._poll_measurement_result() + + for i, d in enumerate(dets): + if i == sel_i: + base = f"{d.label_name} {getattr(d, 'confidence', 0.0):.2f}" + extra = "" + if ( + isinstance(self._last_dims, list) + and len(self._last_dims) == 3 + ): + extra += f"\n{self._last_dims[0]:.1f} × {self._last_dims[1]:.1f} × {self._last_dims[2]:.1f} cm" + if isinstance(self._last_vol, (int, float)): + extra += f"\n{int(np.rint(float(self._last_vol)))} cm³" + label_txt = base + extra + self._draw_rotrect_and_label(helper, d, label_txt) + continue + base = f"{d.label_name} {getattr(d, 'confidence', 0.0):.2f}" + self._draw_rotrect_and_label(helper, d, base) + + # Mask depth to get segmented pointcloud + keep = mask_rgb == selected_mid + depth_u16 = np.ascontiguousarray(depth_map, dtype=np.uint16) + depth_u16[~keep] = 0 + + depthF = dai.ImgFrame() + depthF.setType(dai.ImgFrame.Type.RAW16) + depthF.setWidth(W) + depthF.setHeight(H) + depthF.setData(depth_u16.tobytes()) + depthF.setTimestamp(depth_msg.getTimestamp()) + depthF.setSequenceNum(depth_msg.getSequenceNum()) + depthF.setTransformation(depth_msg.getTransformation()) + + ann_msg = helper.build(img_msg.getTimestamp(), img_msg.getSequenceNum()) + self._send_mode(self.MODE_MEASURE, depthF) + self.out_ann.send(ann_msg) + self.out_segm.send(rgbF) + self.out_segm_depth.send(depthF) + continue + + # No valid instance under click - show all (signal NOSELECTION) + if m is not None and m.size: + present_ids = set(int(v) for v in np.unique(m) if v >= 0) + for idx in present_ids: + self._draw_mask(helper, m, idx) + for d in dets: + label_txt = f"{d.label_name} {getattr(d, 'confidence', 0.0):.2f}" + self._draw_rotrect_and_label(helper, d, label_txt) + + ann_msg = helper.build(img_msg.getTimestamp(), img_msg.getSequenceNum()) + self._send_mode(self.MODE_NOSELECTION, depth_msg) + self.out_ann.send(ann_msg) + self.out_segm.send(rgbF) + self.out_segm_depth.send(depth_msg) diff --git a/apps/object-volume-measurement-3d/backend/src/utils/arguments.py b/apps/object-volume-measurement-3d/backend/src/utils/arguments.py new file mode 100644 index 000000000..d9037db0d --- /dev/null +++ b/apps/object-volume-measurement-3d/backend/src/utils/arguments.py @@ -0,0 +1,45 @@ +import argparse + + +def initialize_argparser(): + """Initialize the argument parser for the script.""" + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "-d", + "--device", + help="Optional name, DeviceID or IP of the camera to connect to.", + required=False, + default=None, + type=str, + ) + + parser.add_argument( + "-fps", + "--fps_limit", + help="FPS limit for the model runtime.", + required=False, + default=None, + type=int, + ) + + parser.add_argument( + "-ip", + "--ip", + help="IP address to serve the frontend on.", + required=False, + type=str, + ) + parser.add_argument( + "-p", + "--port", + help="Port to serve the frontend on.", + required=False, + type=int, + ) + + args = parser.parse_args() + + return parser, args diff --git a/apps/object-volume-measurement-3d/backend/src/utils/helper_functions.py b/apps/object-volume-measurement-3d/backend/src/utils/helper_functions.py new file mode 100644 index 000000000..17ec8231a --- /dev/null +++ b/apps/object-volume-measurement-3d/backend/src/utils/helper_functions.py @@ -0,0 +1,147 @@ +from tokenizers import Tokenizer +from tqdm import tqdm +import os +import requests +import onnxruntime +import numpy as np +import depthai as dai + + +QUANT_VALUES = { + "yoloe": { + "quant_zero_point": 174.0, + "quant_scale": 0.003328413470, + }, +} + + +def pad_and_quantize_features( + features, max_num_classes=80, model_name="yoloe", precision="int8" +): + """ + Pad features to (1, 512, max_num_classes) and quantize if precision is int8. + For FP16, return padded float16 features (no quantization). + """ + num_padding = max_num_classes - features.shape[0] + padded_features = np.pad( + features, ((0, num_padding), (0, 0)), mode="constant" + ).T.reshape(1, 512, max_num_classes) + + if precision == "fp16": + return padded_features.astype(np.float16) + + quant_scale = QUANT_VALUES[model_name]["quant_scale"] + quant_zero_point = QUANT_VALUES[model_name]["quant_zero_point"] + quantized_features = (padded_features / quant_scale) + quant_zero_point + return quantized_features.astype("uint8") + + +def extract_text_embeddings(class_names, max_num_classes=80, precision="fp16"): + tokenizer_json_path = download_tokenizer( + url="https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json", + save_path="tokenizer.json", + ) + tokenizer = Tokenizer.from_file(tokenizer_json_path) + tokenizer.enable_padding( + pad_id=tokenizer.token_to_id("<|endoftext|>"), pad_token="<|endoftext|>" + ) + encodings = tokenizer.encode_batch(class_names) + + text_onnx = np.array([e.ids for e in encodings], dtype=np.int64) + + if text_onnx.shape[1] < 77: + text_onnx = np.pad( + text_onnx, ((0, 0), (0, 77 - text_onnx.shape[1])), mode="constant" + ) + + textual_onnx_model_path = "mobileclip_textual_hf.onnx" + download_base_model( + model_slug="luxonis/yoloe-v8-l:mobileclip-textual-hf", + local_filename=textual_onnx_model_path, + ) + + session_textual = onnxruntime.InferenceSession( + textual_onnx_model_path, + providers=[ + "TensorrtExecutionProvider", + "CUDAExecutionProvider", + "CPUExecutionProvider", + ], + ) + textual_output = session_textual.run( + None, + {session_textual.get_inputs()[0].name: text_onnx}, + )[0] + del session_textual + + textual_output /= np.linalg.norm(textual_output, ord=2, axis=-1, keepdims=True) + + return pad_and_quantize_features( + textual_output, max_num_classes, precision=precision + ) + + +def download_tokenizer(url, save_path): + if not os.path.exists(save_path): + print(f"Downloading tokenizer config from {url}...") + with open(save_path, "wb") as f: + f.write(requests.get(url).content) + return save_path + + +def download_base_model(model_slug: str, local_filename: str): + if os.path.exists(local_filename): + print(f"Model already exists at {local_filename}.") + return + + model_name_slug = model_slug.split("/")[-1].split(":")[0] + model_variant_slug = model_slug.split("/")[-1].split(":")[1] + + model_res = requests.get( + "https://easyml.cloud.luxonis.com/models/api/v1/models", + params={"slug": model_name_slug, "is_public": True}, + ) + model_id = model_res.json()[0]["id"] + variant_res = requests.get( + "https://easyml.cloud.luxonis.com/models/api/v1/modelVersions", + params={ + "model_id": model_id, + "variant_slug": model_variant_slug, + "is_public": True, + }, + ) + model_variant_id = variant_res.json()[0]["id"] + download_res = requests.get( + f"https://easyml.cloud.luxonis.com/models/api/v1/modelVersions/{model_variant_id}/download", + ) + download_link = download_res.json()[0]["download_link"] + download_file(download_link, local_filename) + + +def download_file(download_link: str, local_filename: str): + with requests.get(download_link, stream=True) as r: + r.raise_for_status() + + total_size = int(r.headers.get("content-length", 0)) + progress_bar = tqdm( + total=total_size, unit="iB", unit_scale=True, desc=local_filename + ) + + with open(local_filename, "wb") as f: + for chunk in r.iter_content(chunk_size=8192): + if chunk: + f.write(chunk) + progress_bar.update(len(chunk)) + progress_bar.close() + + +def read_intrinsics(device: dai.Device, width: int, height: int) -> tuple: + calibData = device.readCalibration2() + M2 = np.array( + calibData.getCameraIntrinsics(dai.CameraBoardSocket.CAM_A, width, height) + ) + fx = M2[0, 0] + fy = M2[1, 1] + cx = M2[0, 2] + cy = M2[1, 2] + return fx, fy, cx, cy diff --git a/apps/object-volume-measurement-3d/backend/src/utils/measurement_node.py b/apps/object-volume-measurement-3d/backend/src/utils/measurement_node.py new file mode 100644 index 000000000..a1417a7b4 --- /dev/null +++ b/apps/object-volume-measurement-3d/backend/src/utils/measurement_node.py @@ -0,0 +1,388 @@ +import depthai as dai +import numpy as np +import json +import time +from collections import deque + +from depthai_nodes.utils import AnnotationHelper +from .PointCloudMeasurement import PointCloudMeasurement + +BOX_EDGES = ( + (0, 1), + (1, 2), + (2, 3), + (3, 0), + (4, 5), + (5, 6), + (6, 7), + (7, 4), + (0, 4), + (1, 5), + (2, 6), + (3, 7), +) + + +class MeasurementNode(dai.node.ThreadedHostNode): + """ + Node for point cloud volume and dimensions measurement + + Inputs: + - in_pcl : PointCloudData (RGBD PCL) + - in_enable_measure : Buffer, holds measuring mode value (0 - disabled, 1 - enabled, 2 - plane capture) + - in_imu : IMU packets + + Outputs: + - out_result : Buffer(JSON) -> {"dims":[L,W,H], "vol": V} + - out_ann : Box/overlay annotation + - out_plane_status : Plane status overlay (Calculating / OK / Failed) + """ + + MODE_NOMEASURE = 0 + MODE_MEASURE = 1 + MODE_PLANE = 2 + + def __init__(self): + dai.node.ThreadedHostNode.__init__(self) + + self.in_pcl = self.createInput() + self.in_enable_measure = self.createInput() + self.in_imu = self.createInput() + + self.out_result = self.createOutput() + self.out_ann = self.createOutput() + self.out_plane_status = self.createOutput() + + self.pcl_measure = PointCloudMeasurement() + self.have_plane = False + self.measurement_mode = "obb" # "obb" | "heightgrid" + + # intrinsics for box outline projection + self.intrinsics = None + self.imgW = None + self.imgH = None + + # smoothing of measurement values + self._last_dims = deque(maxlen=30) + self._last_vol = deque(maxlen=30) + + # reference to Annotation node + self.an_node = None + + # plane / IMU state + self.fails = 0 + self.g_imu_plane = None + self.latest_mode = self.MODE_NOMEASURE + self.latest_imu = None + + def build( + self, pcl: dai.Node.Output, measure: dai.Node.Output, imu: dai.Node.Output + ): + pcl.link(self.in_pcl) + measure.link(self.in_enable_measure) + imu.link(self.in_imu) + return self + + def setIntrinsics(self, fx, fy, cx, cy, imgW, imgH): + self.intrinsics = (float(fx), float(fy), float(cx), float(cy)) + self.imgW = int(imgW) + self.imgH = int(imgH) + + def reset_plane(self): + self.pcl_measure.clear_plane() + self.have_plane = False + self.g_imu_plane = None + + def reset_measurements(self): + self.pcl_measure.from_reset = True + self._last_dims.clear() + self._last_vol.clear() + self.pcl_measure.reset_obb_filter() + self.pcl_measure.reset_hg_filter() + + # -------------- Emit measurements -------------------- + def push_measurements(self, dims_cm, vol_cm3): + if dims_cm is not None: + if dims_cm.size == 3 and np.all(np.isfinite(dims_cm)): + self._last_dims.append(tuple(float(x) for x in dims_cm)) + if vol_cm3 is not None: + if isinstance(vol_cm3, (int, float)) and np.isfinite(vol_cm3): + self._last_vol.append(float(vol_cm3)) + + def _emit_median(self, pcl_msg, dims_cm, vol_cm3): + self.push_measurements(dims_cm, vol_cm3) + + dims_out = None + vol_out = None + + if self._last_dims: + arr = np.asarray(self._last_dims, dtype=np.float64) + dims_out = np.median(arr, axis=0).round(2).tolist() + + if self._last_vol: + v = np.asarray(self._last_vol, dtype=np.float64) + vol_out = float(np.median(v).round(2)) + + self._emit_result_min(pcl_msg, dims_out, vol_out) + + def _emit_result_min(self, pcl_msg: dai.PointCloudData, dims_cm, volume_cm3): + payload = { + "dims": [round(float(x), 2) for x in dims_cm] + if dims_cm is not None + else None, + "vol": round(float(volume_cm3), 2) if volume_cm3 is not None else None, + } + b = dai.Buffer() + b.setData(json.dumps(payload).encode("utf-8")) + b.setTimestamp(pcl_msg.getTimestamp()) + b.setSequenceNum(pcl_msg.getSequenceNum()) + self.out_result.send(b) + + # ---------------- Projection / overlay helpers --------------- + def _proj_cam_to_rgb_norm(self, pts_cam): + if not self.intrinsics: + return [None] * len(pts_cam) + fx, fy, cx, cy = self.intrinsics + out = [] + for x, y, z in np.asarray(pts_cam, float): + if z <= 0: + out.append(None) + continue + u = fx * (x / z) + cx + v = fy * (y / z) + cy + out.append((u / self.imgW, v / self.imgH)) + return out + + def _emit_overlay( + self, + pcl_msg: dai.PointCloudData, + pts3d: np.ndarray, + edges: np.ndarray | None = None, + color=(0.0, 1.0, 0.0, 1.0), + thickness: int = 3, + ) -> None: + """ + Draw a wireframe overlay. + + - If 'edges' is provided: draws segments defined by (i,j) pairs over pts3d (OBB case). + - If 'edges' is None and len(pts3d) == 8: assumes a box and uses BOX_EDGES (height-grid case). + """ + helper = AnnotationHelper() + + if pts3d is None or len(pts3d) == 0: + self.out_ann.send( + helper.build(pcl_msg.getTimestamp(), pcl_msg.getSequenceNum()) + ) + return + + segs = edges + if segs is None: + if len(pts3d) == 8: + segs = BOX_EDGES + else: + self.out_ann.send( + helper.build(pcl_msg.getTimestamp(), pcl_msg.getSequenceNum()) + ) + return + + pts2d = self._proj_cam_to_rgb_norm(pts3d) + + for i, j in segs: + pi, pj = pts2d[i], pts2d[j] + if pi and pj: + helper.draw_line(pi, pj, color=color, thickness=thickness) + + self.out_ann.send( + helper.build(pcl_msg.getTimestamp(), pcl_msg.getSequenceNum()) + ) + + # ---------------- Height-grid method helpers --------------- + def _moved_since_plane( + self, + g_now: np.ndarray, + angle_deg_thresh: float = 3.0, + acc_std_thresh: float = 0.20, + ) -> bool: + """ + Returns True if the camera orientation/accel changed enough to invalidate the plane. + - angle between g vectors > angle_deg_thresh (default ~3°) + - OR accel noise (std of recent window) is high (optional jitter guard) + """ + if not hasattr(self, "g_imu_plane") or self.g_imu_plane is None: + return False + + # Angle change test (orientation) + g_now_n = g_now / (np.linalg.norm(g_now) + 1e-9) + cosang = float(np.clip(np.dot(self.g_imu_plane, g_now_n), -1.0, 1.0)) + ang_deg = float(np.degrees(np.arccos(cosang))) + if ang_deg > angle_deg_thresh: + return True + return False + + def _status_style(self, s): + if s == "ok": + return "OK — plane captured", (0.10, 0.85, 0.10, 1.0) + if s == "calculating": + return "Calculating plane… hold still", (1.00, 0.75, 0.00, 1.0) + if s == "failed": + return "Failed — adjust camera view to include ground plane", ( + 0.95, + 0.20, + 0.20, + 1.0, + ) + + def _emit_plane_status(self, pcl_msg, status: str): + helper = AnnotationHelper() + + label, dot = self._status_style(status) + + pad_x, pad_y = 0.02, 0.02 + font_px = 18 + s = font_px / self.imgH + + BASELINE_OFFSET = 0.90 + ASCENT_CENTER = 0.38 + DOT_RADIUS = 0.60 + GAP_AFTER_DOT = 0.055 + + baseline_y = pad_y + BASELINE_OFFSET * s + cy = baseline_y - ASCENT_CENTER * s + dot_r = DOT_RADIUS * s + dot_cx = pad_x + 0.025 + + helper.draw_circle( + (dot_cx, cy), dot_r, outline_color=dot, fill_color=dot, thickness=1 + ) + helper.draw_text( + label, + (pad_x + GAP_AFTER_DOT, baseline_y), + color=dot, + size=18, + ) + + self.out_plane_status.send( + helper.build(pcl_msg.getTimestamp(), pcl_msg.getSequenceNum()) + ) + + def _extract_g_imu(self, imu_msg): + if not imu_msg: + return None + for pkt in imu_msg.packets: + if hasattr(pkt, "acceleroMeter") and pkt.acceleroMeter is not None: + a = pkt.acceleroMeter + return np.array([a.x, a.y, a.z], dtype=np.float64) + return None + + def run(self): + while self.isRunning(): + read_any = False + + while True: + mb = self.in_enable_measure.tryGet() + if not mb: + break + try: + self.latest_mode = int(bytes(mb.getData())[0]) + except Exception: + self.latest_mode = self.MODE_NOMEASURE + read_any = True + + while True: + imu_msg = self.in_imu.tryGet() + if not imu_msg: + break + self.latest_imu = imu_msg + read_any = True + + processed = False + while True: + pcl_msg = self.in_pcl.tryGet() + if not pcl_msg: + break + processed = True + + mode_val = self.latest_mode + try: + points, colors = pcl_msg.getPointsRGB() + except Exception as e: + print("MeasurementNode: getPointsRGB() failed:", e) + continue + if points is None or len(points) == 0: + continue + + if mode_val == self.MODE_PLANE: + g_imu = self._extract_g_imu(self.latest_imu) + if g_imu is None: + continue + + self.pcl_measure.set_point_cloud_plane(points) + plane, _, ok = self.pcl_measure.fit_plane(g_imu) + + if ok: + self.pcl_measure.plane_eq = plane + self.have_plane = True + self.an_node.requestPlaneCapture(False) + self.g_imu_plane = g_imu / (np.linalg.norm(g_imu) + 1e-9) + self._emit_plane_status(pcl_msg, "ok") + self.fails = 0 + + else: + self.fails += 1 + self.have_plane = False + self.an_node.requestPlaneCapture(True) + if self.fails > 5: + self._emit_plane_status(pcl_msg, "failed") + time.sleep(0.005) + continue + + if mode_val != self.MODE_MEASURE: + if self.measurement_mode == "heightgrid" and self.have_plane: + self._emit_plane_status(pcl_msg, "ok") + continue + + bgr = colors[:, :3] + rgb = bgr[:, ::-1].astype(np.float64) / 255.0 + + if self.measurement_mode == "obb": + self.an_node.requestPlaneCapture(False) + self.pcl_measure.set_point_cloud(points, rgb) + self.pcl_measure.get_measurement_OBB() + + self._emit_overlay(pcl_msg, self.pcl_measure.obb_pts) + self._emit_median( + pcl_msg, self.pcl_measure.dimensions, self.pcl_measure.volume + ) + + elif self.measurement_mode == "heightgrid": + if not self.have_plane or self.pcl_measure.plane_eq is None: + self.an_node.requestPlaneCapture(True) + self._emit_plane_status(pcl_msg, "calculating") + self._emit_result_min(pcl_msg, None, None) + continue + + g_imu = self._extract_g_imu(self.latest_imu) + if ( + g_imu is None + or not self.pcl_measure.is_ground_plane( + g_imu, self.pcl_measure.plane_eq + ) + or self._moved_since_plane(g_imu) + ): + self.have_plane = False + self.an_node.requestPlaneCapture(True) + self._emit_plane_status(pcl_msg, "calculating") + self._emit_result_min(pcl_msg, None, None) + continue + + self.pcl_measure.set_point_cloud(points, rgb) + self.pcl_measure.get_measurement_groundHG() + + self._emit_overlay(pcl_msg, self.pcl_measure.corners3d) + self._emit_median( + pcl_msg, self.pcl_measure.dimensions, self.pcl_measure.volume + ) + self._emit_plane_status(pcl_msg, "ok") + + if not (read_any or processed): + time.sleep(0.002) diff --git a/apps/object-volume-measurement-3d/frontend/.gitignore b/apps/object-volume-measurement-3d/frontend/.gitignore new file mode 100644 index 000000000..dafadc4b9 --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/.gitignore @@ -0,0 +1,28 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? + +## Panda +styled-system +styled-system-studio diff --git a/apps/object-volume-measurement-3d/frontend/biome.json b/apps/object-volume-measurement-3d/frontend/biome.json new file mode 100644 index 000000000..5d96cd100 --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/biome.json @@ -0,0 +1,30 @@ +{ + "$schema": "https://biomejs.dev/schemas/1.9.4/schema.json", + "vcs": { + "enabled": false, + "clientKind": "git", + "useIgnoreFile": false + }, + "files": { + "ignoreUnknown": false, + "ignore": ["node_modules/", "dist/"] + }, + "formatter": { + "enabled": true, + "indentStyle": "tab" + }, + "organizeImports": { + "enabled": true + }, + "linter": { + "enabled": true, + "rules": { + "recommended": true + } + }, + "javascript": { + "formatter": { + "quoteStyle": "single" + } + } +} diff --git a/apps/object-volume-measurement-3d/frontend/index.html b/apps/object-volume-measurement-3d/frontend/index.html new file mode 100644 index 000000000..3feabbcfa --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + Local Frontend Example + + +
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"@pandacss/dev": "0.53.0", + "@types/react": "^19.0.0", + "@types/react-dom": "^19.0.0", + "@vitejs/plugin-react": "^4.3.4", + "globals": "^16.0.0", + "typescript": "~5.7.2", + "vite": "^6.3.1" + } +} diff --git a/apps/object-volume-measurement-3d/frontend/panda.config.ts b/apps/object-volume-measurement-3d/frontend/panda.config.ts new file mode 100644 index 000000000..f1c3e8249 --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/panda.config.ts @@ -0,0 +1,12 @@ +import { defineConfig, defineGlobalStyles } from "@pandacss/dev"; +import { pandaPreset } from "@luxonis/common-fe-components"; + +export default defineConfig({ + presets: [pandaPreset], + preflight: true, + include: ["./src/**/*.{ts,tsx}"], + exclude: [], + jsxFramework: "react", + outdir: "styled-system", + forceConsistentTypeExtension: true, +}); diff --git a/apps/object-volume-measurement-3d/frontend/postcss.config.mjs b/apps/object-volume-measurement-3d/frontend/postcss.config.mjs new file mode 100644 index 000000000..cdaaad9ae --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/postcss.config.mjs @@ -0,0 +1,5 @@ +export default { + plugins: { + '@pandacss/dev/postcss': {}, + }, +} \ No newline at end of file diff --git a/apps/object-volume-measurement-3d/frontend/public/logo.svg b/apps/object-volume-measurement-3d/frontend/public/logo.svg new file mode 100644 index 000000000..58c3db897 --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/public/logo.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/apps/object-volume-measurement-3d/frontend/src/App.tsx b/apps/object-volume-measurement-3d/frontend/src/App.tsx new file mode 100644 index 000000000..3e649a41a --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/src/App.tsx @@ -0,0 +1,85 @@ +import { css } from "../styled-system/css/css.mjs"; +import { Streams, useDaiConnection } from "@luxonis/depthai-viewer-common"; +import { ConfidenceSlider } from "./ConfidenceSlider.tsx"; +import { ClickCatcher } from "./ClickOverlay.tsx"; +import { ClassSelector } from "./ClassSelector.tsx"; +import { useRef } from "react"; +import { MeasurementMethodSelector } from "./MeasurementMethodSelector"; +import { Button } from "@luxonis/common-fe-components"; +import { TopBar } from "./TopBar.tsx"; + +function App() { + const connection = useDaiConnection(); + const viewerRef = useRef(null); + + const clearSelection = () => { + (connection as any)?.daiConnection?.postToService("Selection Service", { clear: true }); + }; + + return ( +
+
+ + +
+ + + +
+
+ +
+ +
+

Object Volume Measurement 3D

+

+ This example combines a YOLOE segmentation model with DepthAI point clouds to measure real-world objects in 3D. + Click any detected object in the Video panel to segment it and get its dimensions and volume. +

+ + + + +
+
+ {connection.connected ? "Connected to device" : "Disconnected"} +
+
+
+ ); +} + +export default App; \ No newline at end of file diff --git a/apps/object-volume-measurement-3d/frontend/src/ClassSelector.tsx b/apps/object-volume-measurement-3d/frontend/src/ClassSelector.tsx new file mode 100644 index 000000000..57769210c --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/src/ClassSelector.tsx @@ -0,0 +1,53 @@ +import { Flex, Button, Input } from "@luxonis/common-fe-components"; +import { useRef, useState } from "react"; +import { css } from "../styled-system/css/css.mjs"; +import { useDaiConnection } from "@luxonis/depthai-viewer-common"; + +export function ClassSelector() { + const inputRef = useRef(null); + const connection = useDaiConnection(); + const [selectedClasses, setSelectedClasses] = useState(["person", "chair", "TV"]); + + const handleSendMessage = () => { + if (inputRef.current) { + const value = inputRef.current.value; + const updatedClasses = value + .split(',') + .map(c => c.trim()) + .filter(Boolean); + + console.log('Sending new class list to backend:', updatedClasses); + + connection.daiConnection?.postToService( + // @ts-ignore - Custom service + "Class Update Service", + updatedClasses, + () => { + console.log('Backend acknowledged class update'); + setSelectedClasses(updatedClasses); + } + ); + + inputRef.current.value = ''; + } + }; + + return ( +
+ {/* Class List Display */} +

Update Classes with Text Input:

+
    + {selectedClasses.map((cls, idx) => ( +
  • {cls}
    • + ))} +
+ + + {/* Input + Button */} + + + + +
+ ); +} diff --git a/apps/object-volume-measurement-3d/frontend/src/ClickOverlay.tsx b/apps/object-volume-measurement-3d/frontend/src/ClickOverlay.tsx new file mode 100644 index 000000000..335997b88 --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/src/ClickOverlay.tsx @@ -0,0 +1,129 @@ +// ClickCatcher.tsx +import { useEffect } from "react"; +import { useDaiConnection } from "@luxonis/depthai-viewer-common"; + +const clamp = (v:number)=>Math.max(0, Math.min(1, v)); + +export function ClickCatcher({ + containerRef, + frameWidth = 640, // <-- required: the stream’s image size + frameHeight = 640, + serviceName = "Selection Service", + debug = false, + allowedPanelTitle, +}: { + containerRef: React.RefObject; + frameWidth?: number; + frameHeight?: number; + serviceName?: string; + allowedPanelTitle?: string; + debug?: boolean; +}) { + const { daiConnection } = useDaiConnection(); + + useEffect(() => { + const host = containerRef.current; + if (!host) return; + + const onClick = (e: MouseEvent) => { + // ignore toolbar/buttons + const path = (e.composedPath?.() || []) as HTMLElement[]; + if (path.some(el => el?.closest?.('button,[role="button"]'))) return; + + // find the media element (canvas/video/img) + const media = path.find( + (el) => + el instanceof HTMLCanvasElement || + el instanceof HTMLVideoElement || + el instanceof HTMLImageElement + ) as HTMLCanvasElement | HTMLVideoElement | HTMLImageElement | undefined; + + if (!media) return; + + // The Streams panel name is embedded in the nearest
's text, e.g. "Video(640x640)" or "Pointclouds3D" + const panel = media.closest("section") as HTMLElement | null; + const panelText = panel?.textContent?.trim().toLowerCase() ?? ""; + + if (debug) { + console.log("[ClickCatcher] panelText:", panelText); + console.log("[ClickCatcher] allowedPanelTitle:", allowedPanelTitle); + } + + // Allow only if the panel text contains the expected title (e.g., "images" or "video") + if (allowedPanelTitle && !panelText.includes(allowedPanelTitle.toLowerCase())) { + if (debug) console.log("ignored: panel text mismatch"); + return; + } + + const looks3D = + media instanceof HTMLCanvasElement && + (media.hasAttribute("data-camera-controls-version") || + getComputedStyle(media).touchAction === "none"); + + if (looks3D) { + if (debug) console.log("ignored: 3D/Pointcloud canvas"); + return; + } + + const rect = media.getBoundingClientRect(); + const px = e.clientX - rect.left; + const py = e.clientY - rect.top; + + // ---- compute letterboxed content box from KNOWN frame aspect ---- + const ar = frameWidth / frameHeight; + const boxAr = rect.width / rect.height; + + let contentW: number, contentH: number, offX = 0, offY = 0; + if (boxAr > ar) { + // bars left/right + contentH = rect.height; + contentW = contentH * ar; + offX = (rect.width - contentW) / 2; + } else { + // bars top/bottom + contentW = rect.width; + contentH = contentW / ar; + offY = (rect.height - contentH) / 2; + } + + // ignore clicks in gray bars + if (px < offX || px > offX + contentW || py < offY || py > offY + contentH) { + if (debug) console.log("ignored letterbox click"); + return; + } + + const nx = clamp((px - offX) / contentW); + const ny = clamp((py - offY) / contentH); + + if (debug) console.log("norm(image only):", { nx, ny }); + + (daiConnection as any)?.postToService( + serviceName, + { x: nx, y: ny }, + (resp:any) => debug && console.log("ack:", resp) + ); + }; + + const onContextMenu = (e: MouseEvent) => { + const path = (e.composedPath?.() || []) as HTMLElement[]; + const onMedia = path.some( + (el) => + el instanceof HTMLCanvasElement || + el instanceof HTMLVideoElement || + el instanceof HTMLImageElement + ); + if (!onMedia) return; + e.preventDefault(); + (daiConnection as any)?.postToService(serviceName, { clear: true }); + }; + + host.addEventListener("click", onClick); + host.addEventListener("contextmenu", onContextMenu); + return () => { + host.removeEventListener("click", onClick); + host.removeEventListener("contextmenu", onContextMenu); + }; + }, [containerRef, frameWidth, frameHeight, serviceName, debug, daiConnection, allowedPanelTitle]); + + return null; +} diff --git a/apps/object-volume-measurement-3d/frontend/src/ConfidenceSlider.tsx b/apps/object-volume-measurement-3d/frontend/src/ConfidenceSlider.tsx new file mode 100644 index 000000000..0ba748c08 --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/src/ConfidenceSlider.tsx @@ -0,0 +1,70 @@ +import { css } from "../styled-system/css/css.mjs"; +import { useState } from "react"; +import { useDaiConnection } from "@luxonis/depthai-viewer-common"; + +interface ConfidenceSliderProps { + initialValue?: number; +} + +export function ConfidenceSlider({ initialValue = 0.5 }: ConfidenceSliderProps) { + const connection = useDaiConnection(); + const [value, setValue] = useState(initialValue); + + const handleCommit = () => { + if (typeof value === "number" && !isNaN(value)) { + console.log('Sending threshold to backend:', value); + + connection.daiConnection?.postToService( + // @ts-ignore - Custom service + "Threshold Update Service", + value, + (response: any) => { + console.log('Backend acknowledged threshold update:', response); + } + ); + } else { + console.warn("Invalid value, skipping update:", value); + } + }; + + return ( +
+ + setValue(parseFloat(e.target.value))} + onMouseUp={handleCommit} + onTouchEnd={handleCommit} + className={css({ + width: '100%', + appearance: 'none', + height: '4px', + borderRadius: 'full', + backgroundColor: 'gray.300', + '&::-webkit-slider-thumb': { + appearance: 'none', + width: '12px', + height: '12px', + borderRadius: 'full', + backgroundColor: 'blue.500', + cursor: 'pointer', + }, + '&::-moz-range-thumb': { + appearance: 'none', + width: '12px', + height: '12px', + borderRadius: 'full', + backgroundColor: 'blue.500', + cursor: 'pointer', + } + })} + /> +
+ ); +} diff --git a/apps/object-volume-measurement-3d/frontend/src/ImageUploader.tsx b/apps/object-volume-measurement-3d/frontend/src/ImageUploader.tsx new file mode 100644 index 000000000..01b61afa5 --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/src/ImageUploader.tsx @@ -0,0 +1,74 @@ +import { Button } from "@luxonis/common-fe-components"; +import { css } from "../styled-system/css/css.mjs"; +import { useState } from "react"; +import { useDaiConnection } from "@luxonis/depthai-viewer-common"; + +export function ImageUploader() { + const connection = useDaiConnection(); + const [selectedFile, setSelectedFile] = useState(null); + + const handleFileSelect = (event: React.ChangeEvent) => { + const file = event.target.files?.[0] || null; + setSelectedFile(file); + }; + + const handleUpload = () => { + if (!selectedFile) { + return; + } + + const reader = new FileReader(); + reader.onload = () => { + const fileData = reader.result; + + console.log("Uploading image to backend:", selectedFile.name); + + // @ts-ignore - Custom service + connection.daiConnection?.postToService( + "Image Upload Service", + { + filename: selectedFile.name, + type: selectedFile.type, + data: fileData + } + ); + }; + + reader.readAsDataURL(selectedFile); + }; + + return ( +
+

Update Classes with Image Input (YOLO-World only):

+ + {/* Clickable file selection area */} + + + {/* Hidden file input */} + + + {/* Upload button */} + +
+ ); +} diff --git a/apps/object-volume-measurement-3d/frontend/src/MeasurementMethodSelector.tsx b/apps/object-volume-measurement-3d/frontend/src/MeasurementMethodSelector.tsx new file mode 100644 index 000000000..87588cfbe --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/src/MeasurementMethodSelector.tsx @@ -0,0 +1,90 @@ +// src/MeasurementMethodSelector.tsx +import { useState } from "react"; +import { css } from "../styled-system/css/css.mjs"; +import { useDaiConnection } from "@luxonis/depthai-viewer-common"; + +export type MeasurementMethod = "obb" | "heightgrid"; + +// Keep all copy in one place +const DESCRIPTIONS: Record = { + obb: [ + "Minimal 3D box that encloses the segmented object.", + "Volume is computed as L × W × H of the box.", + "Provides a fast upper bound on the object's volume.", + ], + heightgrid: [ + "Requires the object to rest on a flat surface (e.g. desk or floor).", + "Builds a height grid over the object's footprint on the support plane. Total volume is the sum of the grid cell volumes.", + "Dimensions are still shown as a box (L, W, H), but the volume comes from the height grid integration.", + "More accurate for irregular shapes, but sensitive to errors in plane fitting.", + ], +}; + +export function MeasurementMethodSelector() { + const connection = useDaiConnection(); + const [method, setMethod] = useState("obb"); + + const sendToBackend = (next: MeasurementMethod) => { + (connection as any)?.daiConnection?.postToService( + "Measurement Method Service", + { method: next }, + () => { + // optional ack + } + ); + }; + + const descriptionId = "measurement-method-desc"; + + return ( +
+ + + + + {/* Bulleted description */} +
    + {DESCRIPTIONS[method].map((line, i) => ( +
  • {line}
    • + ))} +
+
+ ); +} diff --git a/apps/object-volume-measurement-3d/frontend/src/TopBar.tsx b/apps/object-volume-measurement-3d/frontend/src/TopBar.tsx new file mode 100644 index 000000000..93b1de6ed --- /dev/null +++ b/apps/object-volume-measurement-3d/frontend/src/TopBar.tsx @@ -0,0 +1,126 @@ +import React from 'react'; +import { + Button, + DropdownMenu, DropdownMenuCheckboxItem, DropdownMenuContent, + DropdownMenuItem, DropdownMenuSubTrigger, DropdownMenuTrigger, + Flex, Icon, Label, Tooltip, Separator, +} from '@luxonis/common-fe-components'; +import { + useDaiConnection, sortStreamsDefault, HIDDEN_STREAMS, +} from '@luxonis/depthai-viewer-common'; +import { useLocation } from 'react-router-dom'; +import { FaUsersViewfinder as VisualizerIcon } from 'react-icons/fa6'; +import { StreamTypes } from '@luxonis/depthai-viewer-common'; + +const TopicSwitcher = () => { + const { topics } = useDaiConnection(); + return topics.length !== 0 && ( + + + + + + + + + + + ); +}; + +const TopicSwitcherItems = () => { + const { topics, toggleTopic } = useDaiConnection(); + const location = useLocation(); + const urlParams = new URLSearchParams(location.search); + + const [topicsList, setTopicsList] = React.useState | null>( + topics.map((t) => t.name).includes(StreamTypes.NeuralNetwork) + ? { [StreamTypes.NeuralNetwork]: false } + : null, + ); + + React.useEffect(() => { + if (topics.length === 0) return; + setTopicsList( + topics.reduce>((acc, topic) => { + if (urlParams.get('streams')?.includes(topic.name) && !topic.name.startsWith('_')) { + acc[topic.name] = true; + } + return acc; + }, {}), + ); + if (topics.find((n) => + n.name === StreamTypes.PointCloud || + n.name === StreamTypes._PointCloudColor || + n.name === StreamTypes.PointCloudColor, + )?.enabled) { + setTopicsList((prev) => ({ ...prev, [StreamTypes.PointCloud]: true })); + } + }, [topics, urlParams.get('streams')]); + + const checkTopic = React.useCallback((name: string, checked: boolean) => { + setTopicsList((prev) => ({ ...prev, [name]: checked })); + }, []); + + const sortedTopics = React.useMemo( + () => sortStreamsDefault( + topics.filter( + (t) => !HIDDEN_STREAMS.includes(t.name as (typeof HIDDEN_STREAMS)[number]) && !t.name.startsWith('_'), + ), + ), + [topics], + ); + + return sortedTopics.length !== 0 && topicsList ? ( + sortedTopics.map((topic, i) => ( + toggleTopic(topic.name)} + checked={topicsList[topic.name]} + onCheckedChange={(checked) => checkTopic(topic.name, checked)} + > + {topic.name} + + )) + ) : ( + No streams available + ); +}; + +const TopicSwitcherButton = ({ type = 'button' }: { type?: 'trigger' | 'button' }) => + type === 'button' ? ( +