# Depth Post-Processing This example shows how you can run depth post-processing filters on the device itself to reduce noise, smooth the depth map and overall improve the depth map quality. Post-processing can be added to [StereoDepth](https://docs.luxonis.com/software/depthai-components/nodes/stereo_depth.md) node. ## Demo ## Depth filters ### Median This is a non-edge preserving [Median filter](https://en.wikipedia.org/wiki/Median_filter), which can be used to reduce noise and smoothen the depth map. Median filter is implemented in hardware, so it's the fastest filter. ### depthai.StereoDepthConfig.MedianFilter Kind: Class Median filter config Members: MEDIAN_OFF KERNEL_3x3 KERNEL_5x5 KERNEL_7x7 #### KERNEL_3x3: typing.ClassVar[MedianFilter] Kind: Class Variable #### KERNEL_5x5: typing.ClassVar[MedianFilter] Kind: Class Variable #### KERNEL_7x7: typing.ClassVar[MedianFilter] Kind: Class Variable #### MEDIAN_OFF: typing.ClassVar[MedianFilter] Kind: Class Variable #### __members__: typing.ClassVar[dict[str, MedianFilter]] Kind: Class Variable #### __eq__(self, other: typing.Any) -> bool: bool Kind: Method #### __getstate__(self) -> int: int Kind: Method #### __hash__(self) -> int: int Kind: Method #### __index__(self) -> int: int Kind: Method #### __init__(self, value: typing.SupportsInt) Kind: Method #### __int__(self) -> int: int Kind: Method #### __ne__(self, other: typing.Any) -> bool: bool Kind: Method #### __repr__(self) -> str: str Kind: Method #### __setstate__(self, state: typing.SupportsInt) Kind: Method #### __str__(self) -> str: str Kind: Method #### name Kind: Property #### value Kind: Property ### Speckle Speckle Filter is used to reduce the speckle noise. Speckle noise is a region with huge variance between neighboring disparity/depth pixels, and speckle filter tries to filter this region. ### depthai.StereoDepthConfig.PostProcessing.SpeckleFilter Kind: Class Speckle filtering. Removes speckle noise. #### __init__(self: RawStereoDepthConfig.PostProcessing.SpeckleFilter) Kind: Method #### differenceThreshold Kind: Property Maximum difference between neighbor disparity pixels to put them into the same blob. Units in disparity integer levels. #### differenceThreshold.setter(self, arg0: typing.SupportsInt) Kind: Method #### enable Kind: Property Whether to enable or disable the filter. #### enable.setter(self, arg0: bool) Kind: Method #### speckleRange Kind: Property Speckle search range. #### speckleRange.setter(self, arg0: typing.SupportsInt) Kind: Method ### Temporal Temporal Filter is intended to improve the depth data persistency by manipulating per-pixel values based on previous frames. The filter performs a single pass on the data, adjusting the depth values while also updating the tracking history. In cases where the pixel data is missing or invalid, the filter uses a user-defined persistency mode to decide whether the missing value should be rectified with stored data. Note that due to its reliance on historic data, the filter may introduce visible blurring/smearing artifacts, and therefore is best-suited for static scenes. ### depthai.StereoDepthConfig.PostProcessing.TemporalFilter Kind: Class Temporal filtering with optional persistence. #### depthai.StereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode Kind: Class Persistency algorithm type. Members: PERSISTENCY_OFF : VALID_8_OUT_OF_8 : VALID_2_IN_LAST_3 : VALID_2_IN_LAST_4 : VALID_2_OUT_OF_8 : VALID_1_IN_LAST_2 : VALID_1_IN_LAST_5 : VALID_1_IN_LAST_8 : PERSISTENCY_INDEFINITELY : ##### PERSISTENCY_INDEFINITELY: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### PERSISTENCY_OFF: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### VALID_1_IN_LAST_2: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### VALID_1_IN_LAST_5: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### VALID_1_IN_LAST_8: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### VALID_2_IN_LAST_3: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### VALID_2_IN_LAST_4: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### VALID_2_OUT_OF_8: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### VALID_8_OUT_OF_8: typing.ClassVar[RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode] Kind: Class Variable ##### __members__: typing.ClassVar[dict[str, RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode]] Kind: Class Variable ##### __eq__(self, other: typing.Any) -> bool: bool Kind: Method ##### __getstate__(self) -> int: int Kind: Method ##### __hash__(self) -> int: int Kind: Method ##### __index__(self: RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode) -> int: int Kind: Method ##### __init__(self: RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode, value: typing.SupportsInt) Kind: Method ##### __int__(self: RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode) -> int: int Kind: Method ##### __ne__(self, other: typing.Any) -> bool: bool Kind: Method ##### __repr__(self) -> str: str Kind: Method ##### __setstate__(self: RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode, state: typing.SupportsInt) Kind: Method ##### __str__(self) -> str: str Kind: Method ##### name Kind: Property ##### value Kind: Property #### __init__(self: RawStereoDepthConfig.PostProcessing.TemporalFilter) Kind: Method #### alpha Kind: Property The Alpha factor in an exponential moving average with Alpha=1 - no filter. Alpha = 0 - infinite filter. Determines the extent of the temporal history that should be averaged. #### alpha.setter(self, arg0: typing.SupportsFloat) Kind: Method #### delta Kind: Property Step-size boundary. Establishes the threshold used to preserve surfaces (edges). If the disparity value between neighboring pixels exceed the disparity threshold set by this delta parameter, then filtering will be temporarily disabled. Default value 0 means auto: 3 disparity integer levels. In case of subpixel mode it's 3*number of subpixel levels. #### delta.setter(self, arg0: typing.SupportsInt) Kind: Method #### enable Kind: Property Whether to enable or disable the filter. #### enable.setter(self, arg0: bool) Kind: Method #### persistencyMode Kind: Property Persistency mode. If the current disparity/depth value is invalid, it will be replaced by an older value, based on persistency mode. #### persistencyMode.setter(self, arg0: RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode) Kind: Method ### Spatial Spatial Edge-Preserving Filter will fill invalid depth pixels with valid neighboring depth pixels. It performs a series of 1D horizontal and vertical passes or iterations, to enhance the smoothness of the reconstructed data. It is based on [this research paper](https://www.inf.ufrgs.br/~eslgastal/DomainTransform/). ### depthai.StereoDepthConfig.PostProcessing.SpatialFilter Kind: Class 1D edge-preserving spatial filter using high-order domain transform. #### __init__(self: RawStereoDepthConfig.PostProcessing.SpatialFilter) Kind: Method #### alpha Kind: Property The Alpha factor in an exponential moving average with Alpha=1 - no filter. Alpha = 0 - infinite filter. Determines the amount of smoothing. #### alpha.setter(self, arg0: typing.SupportsFloat) Kind: Method #### delta Kind: Property Step-size boundary. Establishes the threshold used to preserve "edges". If the disparity value between neighboring pixels exceed the disparity threshold set by this delta parameter, then filtering will be temporarily disabled. Default value 0 means auto: 3 disparity integer levels. In case of subpixel mode it's 3*number of subpixel levels. #### delta.setter(self, arg0: typing.SupportsInt) Kind: Method #### enable Kind: Property Whether to enable or disable the filter. #### enable.setter(self, arg0: bool) Kind: Method #### holeFillingRadius Kind: Property An in-place heuristic symmetric hole-filling mode applied horizontally during the filter passes. Intended to rectify minor artefacts with minimal performance impact. Search radius for hole filling. #### holeFillingRadius.setter(self, arg0: typing.SupportsInt) Kind: Method #### numIterations Kind: Property Number of iterations over the image in both horizontal and vertical direction. #### numIterations.setter(self, arg0: typing.SupportsInt) Kind: Method ### Threshold Threshold Filter filters out all disparity/depth pixels outside the configured min/max threshold values. ```python class ThresholdFilter: ''' Spatial Edge-Preserving Filter will fill invalid depth pixels with valid neighboring depth pixels. It performs a series of 1D horizontal and vertical passes or iterations, to enhance the smoothness of the reconstructed data. It is based on [this research paper](https://www.inf.ufrgs.br/~eslgastal/DomainTransform/). ''' ``` ### Similar samples: * [Depth Preview](https://docs.luxonis.com/software/depthai/examples/depth_preview.md) * [Stereo Depth from host](https://docs.luxonis.com/software/depthai/examples/stereo_depth_from_host.md) ## Setup Please run the [install script](https://github.com/luxonis/depthai-python/blob/main/examples/install_requirements.py) to download all required dependencies. Please note that this script must be ran from git context, so you have to download the [depthai-python](https://github.com/luxonis/depthai-python) repository first and then run the script ```bash git clone https://github.com/luxonis/depthai-python.git cd depthai-python/examples python3 install_requirements.py ``` For additional information, please follow the [installation guide](https://docs.luxonis.com/software/depthai/manual-install.md). ## Source code #### Python ```python #!/usr/bin/env python3 import cv2 import depthai as dai import numpy as np # Closer-in minimum depth, disparity range is doubled (from 95 to 190): extended_disparity = False # Better accuracy for longer distance, fractional disparity 32-levels: subpixel = False # Better handling for occlusions: lr_check = True # Create pipeline pipeline = dai.Pipeline() # Define sources and outputs monoLeft = pipeline.create(dai.node.MonoCamera) monoRight = pipeline.create(dai.node.MonoCamera) depth = pipeline.create(dai.node.StereoDepth) xout = pipeline.create(dai.node.XLinkOut) xout.setStreamName("disparity") # Properties monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) monoLeft.setCamera("left") monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P) monoRight.setCamera("right") # Create a node that will produce the depth map (using disparity output as it's easier to visualize depth this way) depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY) # Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default) depth.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7) depth.setLeftRightCheck(lr_check) depth.setExtendedDisparity(extended_disparity) depth.setSubpixel(subpixel) config = depth.initialConfig.get() config.postProcessing.speckleFilter.enable = False config.postProcessing.speckleFilter.speckleRange = 50 config.postProcessing.temporalFilter.enable = True config.postProcessing.spatialFilter.enable = True config.postProcessing.spatialFilter.holeFillingRadius = 2 config.postProcessing.spatialFilter.numIterations = 1 config.postProcessing.thresholdFilter.minRange = 400 config.postProcessing.thresholdFilter.maxRange = 15000 config.postProcessing.decimationFilter.decimationFactor = 1 depth.initialConfig.set(config) # Linking monoLeft.out.link(depth.left) monoRight.out.link(depth.right) depth.disparity.link(xout.input) # Connect to device and start pipeline with dai.Device(pipeline) as device: # Output queue will be used to get the disparity frames from the outputs defined above q = device.getOutputQueue(name="disparity", maxSize=4, blocking=False) while True: inDisparity = q.get() # blocking call, will wait until a new data has arrived frame = inDisparity.getFrame() # Normalization for better visualization frame = (frame * (255 / depth.initialConfig.getMaxDisparity())).astype(np.uint8) cv2.imshow("disparity", frame) # Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html frame = cv2.applyColorMap(frame, cv2.COLORMAP_JET) cv2.imshow("disparity_color", frame) if cv2.waitKey(1) == ord('q'): break ``` #### C++ ```cpp #include // Inludes common necessary includes for development using depthai library #include "depthai/depthai.hpp" // Closer-in minimum depth, disparity range is doubled (from 95 to 190): static std::atomic extended_disparity{false}; // Better accuracy for longer distance, fractional disparity 32-levels: static std::atomic subpixel{false}; // Better handling for occlusions: static std::atomic lr_check{true}; int main() { // Create pipeline dai::Pipeline pipeline; // Define sources and outputs auto monoLeft = pipeline.create(); auto monoRight = pipeline.create(); auto depth = pipeline.create(); auto xout = pipeline.create(); xout->setStreamName("disparity"); // Properties monoLeft->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P); monoLeft->setCamera("left"); monoRight->setResolution(dai::MonoCameraProperties::SensorResolution::THE_400_P); monoRight->setCamera("right"); // Create a node that will produce the depth map (using disparity output as it's easier to visualize depth this way) depth->setDefaultProfilePreset(dai::node::StereoDepth::PresetMode::HIGH_DENSITY); // Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default) depth->initialConfig.setMedianFilter(dai::MedianFilter::KERNEL_7x7); depth->setLeftRightCheck(lr_check); depth->setExtendedDisparity(extended_disparity); depth->setSubpixel(subpixel); auto config = depth->initialConfig.get(); config.postProcessing.speckleFilter.enable = false; config.postProcessing.speckleFilter.speckleRange = 50; config.postProcessing.temporalFilter.enable = true; config.postProcessing.spatialFilter.enable = true; config.postProcessing.spatialFilter.holeFillingRadius = 2; config.postProcessing.spatialFilter.numIterations = 1; config.postProcessing.thresholdFilter.minRange = 400; config.postProcessing.thresholdFilter.maxRange = 15000; config.postProcessing.decimationFilter.decimationFactor = 1; depth->initialConfig.set(config); // Linking monoLeft->out.link(depth->left); monoRight->out.link(depth->right); depth->disparity.link(xout->input); // Connect to device and start pipeline dai::Device device(pipeline); // Output queue will be used to get the disparity frames from the outputs defined above auto q = device.getOutputQueue("disparity", 4, false); while(true) { auto inDepth = q->get(); auto frame = inDepth->getFrame(); // Normalization for better visualization frame.convertTo(frame, CV_8UC1, 255 / depth->initialConfig.getMaxDisparity()); cv::imshow("disparity", frame); // Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html cv::applyColorMap(frame, frame, cv::COLORMAP_JET); cv::imshow("disparity_color", frame); int key = cv::waitKey(1); if(key == 'q' || key == 'Q') { return 0; } } return 0; } ``` ## Pipeline ### examples/depth_post_processing.pipeline.json ```json 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``` ### Need assistance? 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