From cf41a43b1d8b180f86ad0f7dacc6d1bd2d1e03be Mon Sep 17 00:00:00 2001 From: jakaskerl <53253318+jakaskerl@users.noreply.github.com> Date: Thu, 14 Mar 2024 10:36:15 +0100 Subject: [PATCH] Docs/release 25 (#997) * Initial docs update for v2.25 * Added pcl example docs * Adding pointcloud control example * Updating pointcloud example --- .../images/components/clock-syncing.png | Bin 28505 -> 40911 bytes .../components/timestamp-difference.png | Bin 0 -> 106397 bytes .../images/examples/pointcloud_control.gif | Bin 0 -> 844235 bytes .../images/examples/visualize_pointcloud.png | Bin 0 -> 1287288 bytes docs/source/components/device.rst | 8 +- .../components/messages/pointcloud_config.rst | 98 +++++++++++ .../components/messages/pointcloud_data.rst | 31 ++++ docs/source/components/nodes/pointcloud.rst | 154 ++++++++++++++++++ .../samples/PointCloud/pointcloud_control.rst | 60 +++++++ .../PointCloud/visualize_pointcloud.rst | 58 +++++++ docs/source/tutorials/code_samples.rst | 7 + examples/PointCloud/pointcloud_control.py | 122 ++++++++++++++ examples/PointCloud/visualize_pointcloud.py | 1 - 13 files changed, 536 insertions(+), 3 deletions(-) create mode 100644 docs/source/_static/images/components/timestamp-difference.png create mode 100644 docs/source/_static/images/examples/pointcloud_control.gif create mode 100644 docs/source/_static/images/examples/visualize_pointcloud.png create mode 100644 docs/source/components/messages/pointcloud_config.rst create mode 100644 docs/source/components/messages/pointcloud_data.rst create mode 100644 docs/source/components/nodes/pointcloud.rst create mode 100644 docs/source/samples/PointCloud/pointcloud_control.rst create mode 100644 docs/source/samples/PointCloud/visualize_pointcloud.rst create mode 100644 examples/PointCloud/pointcloud_control.py diff --git a/docs/source/_static/images/components/clock-syncing.png b/docs/source/_static/images/components/clock-syncing.png index 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zo2IT5;M+Z9cW!B^L3YxVu8Ig7*6Y>RugkQOTZ9g)t8J+GfBVyWd8P+_YLTny;69iC zGl-;C4tmq<5X#hYW@~MMHZx;aRbKvSk-E!3{~h+vd;lXO zqmjs)rWUg|J3e|@q82P$dbD}C!S?+_X}_X(edrrG|BpW9D^OK%xWFhKY!_aMgsmM(dNJakM)=rxH&9 zL6tnNE+M2h?m#_VsC}VAItp3dlP1AJv9wG1Da0va4c`oPAZV@(d xSgpa574zc38~OOhtA*q$B&*KD8Jo2ovc~ikyJvJ6eSme{)-t$RqVX{N{{VDq8;k$| literal 0 HcmV?d00001 diff --git a/docs/source/components/device.rst b/docs/source/components/device.rst index 0f2e74fc8..83ccab3e0 100644 --- a/docs/source/components/device.rst +++ b/docs/source/components/device.rst @@ -67,9 +67,13 @@ Device clocks are synced at below 500µs accuracy for PoE cameras, and below 200 .. image:: /_static/images/components/clock-syncing.png -A graph representing the accuracy of the device clock with respect to the host clock. We had 3 devices connected (OAK PoE cameras), all were hardware synchronized using `FSYNC Y-adapter `__. +Above is a graph representing the accuracy of the device clock with respect to the host clock. We had 3 devices connected (OAK PoE cameras), all were hardware synchronized using `FSYNC Y-adapter `__. Raspberry Pi (the host) had an interrupt pin connected to the FSYNC line, so at the start of each frame the interrupt happened and the host clock was recorded. Then we compared frame (synced) timestamps with -host timestamps and computed the standard deviation. For the histogram above we ran this test for approximately 3 hours. +host timestamps and computed the standard deviation. For the histogram above we ran this test for approximately 3 hours. + +Below is a graph representing the difference between the device and host clock. The graph shows the difference between the device and host clock over time. The graph is a result of the same test as the previous one. + +.. image:: /_static/images/components/timestamp-difference.png .. code-block:: python diff --git a/docs/source/components/messages/pointcloud_config.rst b/docs/source/components/messages/pointcloud_config.rst new file mode 100644 index 000000000..e337ebf9c --- /dev/null +++ b/docs/source/components/messages/pointcloud_config.rst @@ -0,0 +1,98 @@ +PointCloudConfig +================ + +`PointCloudConfig` is a configuration class used to adjust settings for point cloud generation within the DepthAI ecosystem. It allows users to configure properties such as sparsity and transformation matrices, which are crucial for tailoring the point cloud data to specific application requirements. + +Configuration Options +###################### + +- **Sparsity**: Determines whether the generated point cloud should be sparse. Sparse point clouds may omit points based on certain criteria, such as depth value thresholds, to reduce data volume and processing requirements. +- **Transformation Matrix**: Applies a transformation matrix to the point cloud data, enabling rotations, translations, and scaling to align the point cloud with a world or application-specific coordinate system. + +Usage +##### + +Configuring `PointCloudConfig` allows for precise control over the generation of point cloud data. Here's an example of how to configure and apply `PointCloudConfig` in a DepthAI application: + +.. tabs:: + + .. code-tab:: py + + import depthai as dai + + # Create pipeline + pipeline = dai.Pipeline() + + # Create PointCloud node + pointCloud = pipeline.create(dai.node.PointCloud) + + pointCloud.initialConfig.setSparse(True) # Enable sparse point cloud generation + + # Define a transformation matrix + transformationMatrix = [ + [1.0, 0.0, 0.0, 0.0], + [0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0] + ] + pointCloud.initialConfig.setTransformationMatrix(transformationMatrix) # Apply transformation matrix + + # Further pipeline setup and execution... + + .. code-tab:: c++ + + #include "depthai/depthai.hpp" + + int main() { + // Create pipeline + dai::Pipeline pipeline; + + // Create PointCloud node + auto pointCloud = pipeline.create(); + + pointCloud->initialConfig.setSparse(true); // Enable sparse point cloud generation + + // Define a transformation matrix + std::vector> transformationMatrix = { + {1.0, 0.0, 0.0, 0.0}, + {0.0, 1.0, 0.0, 0.0}, + {0.0, 0.0, 1.0, 0.0}, + {0.0, 0.0, 0.0, 1.0} + }; + pointCloud->initialConfig.setTransformationMatrix(transformationMatrix); // Apply transformation matrix + + // Further pipeline setup and execution... + + return 0; + } + +This example demonstrates initializing `PointCloudConfig`, setting it to generate sparse point clouds, and applying a transformation matrix. This configuration is then applied to a `PointCloud` node within the DepthAI pipeline. + +Examples of Functionality +######################### + +- **3D Object Localization**: Adjusting the transformation matrix to align point clouds with a known coordinate system for precise object placement. +- **Scene Optimization**: Utilizing sparse point clouds for efficient processing in large-scale or complex scenes. +- **Data Alignment**: Applying transformation matrices for seamless integration of point cloud data with other sensor data or pre-existing 3D models. + +Reference +######### + +.. tabs:: + + .. tab:: Python + + .. autoclass:: depthai.PointCloudConfig + :members: + :inherited-members: + :noindex: + + .. tab:: C++ + + .. doxygenclass:: dai::PointCloudConfig + :project: depthai-core + :members: + :private-members: + :undoc-members: + +.. include:: ../../includes/footer-short.rst \ No newline at end of file diff --git a/docs/source/components/messages/pointcloud_data.rst b/docs/source/components/messages/pointcloud_data.rst new file mode 100644 index 000000000..93ae65796 --- /dev/null +++ b/docs/source/components/messages/pointcloud_data.rst @@ -0,0 +1,31 @@ +PointCloudData +============== + +:ref:`PointCloudData` encapsulates 3D spatial information, representing a collection of points in a 3D space. Each point within the point cloud has its own position (X, Y, Z coordinates). +PointCloudData is used to represent the output of :ref:`PointCloud` nodes, and can be used to perform a variety of spatial analysis and reconstruction tasks. + +Setter methods are only used to provide metadata to the :ref:`PointCloudData` message (will be used for recording and replaying pointclouds). + +Reference +######### + +The detailed API for `PointCloudData` offers control over the generation, manipulation, and retrieval of 3D point cloud data. + +.. tabs:: + + .. tab:: Python + + .. autoclass:: depthai.PointCloudData + :members: + :inherited-members: + :noindex: + + .. tab:: C++ + + .. doxygenclass:: dai::PointCloudData + :project: depthai-core + :members: + :private-members: + :undoc-members: + +.. include:: ../../includes/footer-short.rst \ No newline at end of file diff --git a/docs/source/components/nodes/pointcloud.rst b/docs/source/components/nodes/pointcloud.rst new file mode 100644 index 000000000..812accc23 --- /dev/null +++ b/docs/source/components/nodes/pointcloud.rst @@ -0,0 +1,154 @@ +PointCloud +========== + +The PointCloud node enables on-device point cloud generation from depth map. + +How to place it +############### + +.. tabs:: + + .. code-tab:: py + + pipeline = dai.Pipeline() + pointCloud = pipeline.create(dai.node.PointCloud) + + .. code-tab:: c++ + + dai::Pipeline pipeline; + auto pointCloud = pipeline.create(); + + +Inputs and Outputs +################## + +.. code-block:: + + ┌─────────────────────┐ + │ │ + inputConfig │ PointCloud │ + ──────────────►│ │ outputPointCloud + │ ├────────────────► + inputDepth │ │ passthroughDepth + ──────────────►│---------------------├────────────────► + └─────────────────────┘ + +**Message types** + +- :code:`inputDepth` - :ref:`ImgFrame` +- :code:`inputConfig` - :ref:`PointCloudConfig` +- :code:`outputPointCloud` - :ref:`PointCloudData` +- :code:`passthroughDepth` - :ref:`ImgFrame` (passthrough input depth map) + + +Example visualization with Open3D +################################# + +.. tabs:: + + .. code-tab:: py + + import open3d as o3d + import numpy as np + import depthai as dai + + pcd = o3d.geometry.PointCloud() + vis = o3d.visualization.VisualizerWithKeyCallback() + vis.create_window() + + with dai.Device(pipeline) as device: + coordinateFrame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=1000, origin=[0,0,0]) + vis.add_geometry(coordinateFrame) + + while device.isPipelineRunning(): + inMessage = q.get() + inColor = inMessage["rgb"] + inPointCloud = inMessage["pcl"] + cvColorFrame = inColor.getCvFrame() + + if inPointCloud: + points = inPointCloud.getPoints().astype(np.float64) + pcd.points = o3d.utility.Vector3dVector(points) + colors = (cvRGBFrame.reshape(-1, 3) / 255.0).astype(np.float64) + pcd.colors = o3d.utility.Vector3dVector(colors) + vis.update_geometry(pcd) + + vis.poll_events() + vis.update_renderer() + + vis.destroy_window() + + + .. code-tab:: c++ + + #include + #include + #include + + int main() { + auto viewer = std::make_unique("Cloud Viewer"); + viewer->addPointCloud(cloud, "cloud"); + + dai::Device device(pipeline); + + auto q = device.getOutputQueue("out", 8, false); + auto qDepth = device.getOutputQueue("depth", 8, false); + + while(true) { + std::cout << "Waiting for data" << std::endl; + auto depthImg = qDepth->get(); + auto pclMsg = q->get(); + + if(!pclMsg) { + std::cout << "No data" << std::endl; + continue; + } + + auto frame = depthImg->getCvFrame(); + frame.convertTo(frame, CV_8UC1, 255 / depth->initialConfig.getMaxDisparity()); + + if(pclMsg->getPoints().empty()) { + std::cout << "Empty point cloud" << std::endl; + continue; + } + + pcl::PointCloud::Ptr cloud = pclMsg->getPclData(); + viewer->updatePointCloud(cloud, "cloud"); + + viewer->spinOnce(10); + + if(viewer->wasStopped()) { + break; + } + } + + +Examples using PointCloud +######################### + +- :ref:`PointCloud Visualization` +- :ref:`PointCloud Control` + + +Reference +######### + +.. tabs:: + + .. tab:: Python + + .. autoclass:: depthai.node.PointCloud + :members: + :inherited-members: + :noindex: + + .. tab:: C++ + + .. doxygenclass:: dai::node::PointCloud + :project: depthai-core + :members: + :private-members: + :undoc-members: + + +.. include:: ../../includes/footer-short.rst diff --git a/docs/source/samples/PointCloud/pointcloud_control.rst b/docs/source/samples/PointCloud/pointcloud_control.rst new file mode 100644 index 000000000..44db747d5 --- /dev/null +++ b/docs/source/samples/PointCloud/pointcloud_control.rst @@ -0,0 +1,60 @@ +PointCloud Control +================== + +This example demonstrates how to use :ref:`PointCloudConfig` message to dynamically update the transformation matrix of a point cloud and visualize the transformed point cloud using Open3D. + +The transformation matrix is updated to make it look like the point cloud is rotating about the Z-axis. This is achieved by first moving the pointcloud along Y axis: + +.. code-block:: python + + # Move the point cloud along Y axis + translate_y_matrix = np.array([[1, 0, 0, 0], + [0, 1, 0, 500], + [0, 0, 1, 0], + [0, 0, 0, 1]]) + + +Then, the point cloud is rotated about Z axis: + +.. code-block:: python + + # Rotate the point cloud about Z axis + rotate_z_matrix = np.array([[np.cos(angle), -np.sin(angle), 0, 0], + [np.sin(angle), np.cos(angle), 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1]]) + + +Demo +#### + +.. image:: /_static/images/examples/pointcloud_control.gif + :alt: PointCloud control + +Setup +##### + +Ensure DepthAI and Open3D are installed in your Python environment: + +.. code-block:: bash + + python3 -m pip install depthai open3d + + +Source code +########### + +The example initializes the DepthAI pipeline with color and mono cameras and a stereo depth node to generate depth information. It then creates a point cloud node, dynamically updates its transformation matrix based on a rotation value, and visualizes this transformed point cloud using Open3D. Each point's color corresponds to the color image captured by the RGB camera. + +.. tabs:: + + .. tab:: Python + + Also `available on GitHub `__ + + .. literalinclude:: ../../../../examples/PointCloud/pointcloud_control.py + :language: python + :linenos: + + +.. include:: /includes/footer-short.rst \ No newline at end of file diff --git a/docs/source/samples/PointCloud/visualize_pointcloud.rst b/docs/source/samples/PointCloud/visualize_pointcloud.rst new file mode 100644 index 000000000..0d30f05d0 --- /dev/null +++ b/docs/source/samples/PointCloud/visualize_pointcloud.rst @@ -0,0 +1,58 @@ +PointCloud Visualization +======================== + +This example demonstrates how to visualize an on-device created point cloud using DepthAI and Open3D. It captures color and depth data from an OAK device, combines them to create a colored point cloud, and displays it in real-time. The FPS counter is included to monitor performance. + +Key Concepts +############ + +- Capturing synchronized color and depth frames using the DepthAI API. +- Creating a point cloud with color data for visualization. +- Real-time visualization of the point cloud using Open3D. +- Implementing an FPS counter to monitor the application's performance. + + +Demo +#### + +.. image:: /_static/images/examples/visualize_pointcloud.png + :alt: Visualizing pointcloud + +Setup +##### + +If you are using Python, ensure you have DepthAI and Open3D installed in your Python environment: + +.. code-block:: bash + + python3 -m pip install depthai open3d + + +If Open3D is not already installed, the script will prompt you to install it. + +Source code +########### + +The example initializes the DepthAI pipeline with color and mono cameras and a stereo depth node to generate depth information. It then creates a point cloud node and configures it for sparse point cloud generation. The script visualizes this point cloud using Open3D, with each point's color corresponding to the color image captured by the RGB camera. + + +.. tabs:: + + .. tab:: Python + + Also `available on GitHub `__ + + .. literalinclude:: ../../../../examples/PointCloud/visualize_pointcloud.py + :language: python + :linenos: + + .. tab:: C++ + + Also `available on GitHub `__ + + .. literalinclude:: ../../../../depthai-core/examples/PointCloud/visualize_pointcloud.cpp + :language: cpp + :linenos: + + +.. include:: /includes/footer-short.rst \ No newline at end of file diff --git a/docs/source/tutorials/code_samples.rst b/docs/source/tutorials/code_samples.rst index cbb25f600..9cf7c35aa 100644 --- a/docs/source/tutorials/code_samples.rst +++ b/docs/source/tutorials/code_samples.rst @@ -23,6 +23,7 @@ Code Samples ../samples/Script/* ../samples/SpatialDetection/* ../samples/StereoDepth/* + ../samples/PointCloud/* ../samples/Sync/* ../samples/SystemLogger/* ../samples/ToF/* @@ -161,6 +162,12 @@ are presented with code. - :ref:`RGB Depth alignment` - Displays RGB depth aligned frames +.. rubric:: PointCloud + +- :ref:`PointCloud Visualization` - Creates point cloud from depth frames and displays it using Open3D +- :ref:`PointCloud Control` - Demonstrates how to control point cloud from the host + + .. rubric:: Sync Node - :ref:`Depth and Video Sync` - Synchronizes depth and video streams for real-time processing and display. diff --git a/examples/PointCloud/pointcloud_control.py b/examples/PointCloud/pointcloud_control.py new file mode 100644 index 000000000..6ebe5b9a5 --- /dev/null +++ b/examples/PointCloud/pointcloud_control.py @@ -0,0 +1,122 @@ +import depthai as dai +import numpy as np +import cv2 +import sys + +try: + import open3d as o3d +except ImportError: + sys.exit(f"Critical dependency missing: Open3D. Please install it using the command: '{sys.executable} -m pip install open3d' and then rerun the script.") + + +# Continue with the DepthAI and Open3D setup as before +pipeline = dai.Pipeline() +camRgb = pipeline.create(dai.node.ColorCamera) +monoLeft = pipeline.create(dai.node.MonoCamera) +monoRight = pipeline.create(dai.node.MonoCamera) +depth = pipeline.create(dai.node.StereoDepth) +pointcloud: dai.node.PointCloud = pipeline.create(dai.node.PointCloud) +sync = pipeline.create(dai.node.Sync) +xOut = pipeline.create(dai.node.XLinkOut) +xOut.input.setBlocking(False) + + +camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P) +camRgb.setBoardSocket(dai.CameraBoardSocket.CAM_A) +camRgb.setIspScale(1,3) + +monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) +monoLeft.setCamera("left") + +monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) +monoRight.setCamera("right") + + +depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY) +depth.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7) +depth.setLeftRightCheck(True) +depth.setExtendedDisparity(False) +depth.setSubpixel(True) +depth.setDepthAlign(dai.CameraBoardSocket.CAM_A) +config = depth.initialConfig.get() +config.postProcessing.thresholdFilter.minRange = 200 +config.postProcessing.thresholdFilter.maxRange = 1000 +depth.initialConfig.set(config) + +monoLeft.out.link(depth.left) +monoRight.out.link(depth.right) +depth.depth.link(pointcloud.inputDepth) +camRgb.isp.link(sync.inputs["rgb"]) +pointcloud.outputPointCloud.link(sync.inputs["pcl"]) +pointcloud.initialConfig.setSparse(False) +sync.out.link(xOut.input) +xOut.setStreamName("out") + +inConfig = pipeline.create(dai.node.XLinkIn) +inConfig.setStreamName("config") +inConfig.out.link(pointcloud.inputConfig) + +with dai.Device(pipeline) as device: + + isRunning = True + def key_callback(vis, action, mods): + global isRunning + if action == 0: + isRunning = False + + q = device.getOutputQueue(name="out", maxSize=4, blocking=False) + pclConfIn = device.getInputQueue(name="config", maxSize=4, blocking=False) + + vis = o3d.visualization.Visualizer() + vis.create_window() + pcd = o3d.geometry.PointCloud() + coordinateFrame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=1000, origin=[0,0,0]) + vis.add_geometry(coordinateFrame) + vis.add_geometry(pcd) + + first = True + rot = 0 + while device.isPipelineRunning(): + + inMessage = q.get() + inColor = inMessage["rgb"] + inPointCloud = inMessage["pcl"] + cvColorFrame = inColor.getCvFrame() + + cvRGBFrame = cv2.cvtColor(cvColorFrame, cv2.COLOR_BGR2RGB) + + if inPointCloud: + points = inPointCloud.getPoints().astype(np.float64) + pcd.points = o3d.utility.Vector3dVector(points) + colors = (cvRGBFrame.reshape(-1, 3) / 255.0).astype(np.float64) + pcd.colors = o3d.utility.Vector3dVector(colors) + + vis.update_geometry(pcd) + vis.poll_events() + vis.update_renderer() + + # Create a transformation matrix that rotates around all 3 axes at the same time + rotate_z_matrix = np.array([[np.cos(rot), -np.sin(rot), 0, 0], + [np.sin(rot), np.cos(rot), 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1]]) + + translate_y_matrix = np.array([[1, 0, 0, 0], + [0, 1, 0, 500], + [0, 0, 1, 0], + [0, 0, 0, 1]]) + + + # Combine the 3 transformation matrices into one + transformation_matrix = rotate_z_matrix @ translate_y_matrix + + rot += 0.05 + + pclConfig = dai.PointCloudConfig() + pclConfig.setTransformationMatrix(transformation_matrix) + pclConfig.setSparse(False) + pclConfIn.send(pclConfig) + + + vis.destroy_window() + cv2.destroyAllWindows() diff --git a/examples/PointCloud/visualize_pointcloud.py b/examples/PointCloud/visualize_pointcloud.py index 2ff669491..564329e4f 100644 --- a/examples/PointCloud/visualize_pointcloud.py +++ b/examples/PointCloud/visualize_pointcloud.py @@ -74,7 +74,6 @@ def key_callback(vis, action, mods): isRunning = False q = device.getOutputQueue(name="out", maxSize=4, blocking=False) - pc = o3d.geometry.PointCloud() vis = o3d.visualization.VisualizerWithKeyCallback() vis.create_window() vis.register_key_action_callback(81, key_callback)