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Merge pull request #809 from luxonis/update_depth_visualziation
Updated depth colorization logic
2 parents f5117cc + 42d9f27 commit e6ac495

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+47
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‎docs/source/samples/SpatialDetection/spatial_calculator_multi_roi.rst

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@@ -11,7 +11,7 @@ scanning camera for mobile robots.
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Demo
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####
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.. image:: https://user-images.githubusercontent.com/18037362/190861621-b57fd1e3-5a3d-4d79-b1a7-d17a0b78c63e.gif
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.. image:: https://user-images.githubusercontent.com/18037362/231822498-6e3699a0-039e-424b-acb2-b246575e91ee.png
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Setup
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#####

‎docs/source/tutorials/code_samples.rst

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@@ -126,6 +126,7 @@ are presented with code.
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.. rubric:: SpatialDetection
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- :ref:`Spatial Calculator Multi-ROI` - Selects multiple ROIs and calculates spatial coordinates for each of them
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- :ref:`Spatial location calculator` - Demonstrates how to use the spatial location calculator
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- :ref:`RGB & MobilenetSSD with spatial data` - Displays RGB frames with MobileNet detections and spatial coordinates on them
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- :ref:`Mono & MobilenetSSD with spatial data` - Displays mono frames with MobileNet detections and spatial coordinates on them

‎examples/SpatialDetection/spatial_calculator_multi_roi.py

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@@ -3,6 +3,7 @@
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import cv2
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import depthai as dai
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import math
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import numpy as np
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# Create pipeline
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pipeline = dai.Pipeline()
@@ -29,7 +30,7 @@
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stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
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stereo.setLeftRightCheck(True)
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stereo.setExtendedDisparity(True)
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stereo.setSubpixel(True)
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spatialLocationCalculator.inputConfig.setWaitForMessage(False)
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# Create 10 ROIs
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depthFrame = inDepth.getFrame() # depthFrame values are in millimeters
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depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
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depthFrameColor = cv2.equalizeHist(depthFrameColor)
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depth_downscaled = depthFrame[::4]
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min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
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max_depth = np.percentile(depth_downscaled, 99)
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depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
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depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
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spatialData = spatialCalcQueue.get().getSpatialLocations()

‎examples/SpatialDetection/spatial_location_calculator.py

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@@ -2,7 +2,7 @@
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import cv2
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import depthai as dai
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import numpy as np
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stepSize = 0.05
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newConfig = False
@@ -30,12 +30,9 @@
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monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
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monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
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lrcheck = False
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subpixel = False
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stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
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stereo.setLeftRightCheck(lrcheck)
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stereo.setSubpixel(subpixel)
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stereo.setLeftRightCheck(True)
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stereo.setSubpixel(True)
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# Config
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topLeft = dai.Point2f(0.4, 0.4)
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depthFrame = inDepth.getFrame() # depthFrame values are in millimeters
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depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
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depthFrameColor = cv2.equalizeHist(depthFrameColor)
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depth_downscaled = depthFrame[::4]
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min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
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max_depth = np.percentile(depth_downscaled, 99)
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depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
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depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
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spatialData = spatialCalcQueue.get().getSpatialLocations()
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depthMax = depthData.depthMax
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fontType = cv2.FONT_HERSHEY_TRIPLEX
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cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX)
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cv2.putText(depthFrameColor, f"X: {int(depthData.spatialCoordinates.x)} mm", (xmin + 10, ymin + 20), fontType, 0.5, 255)
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cv2.putText(depthFrameColor, f"Y: {int(depthData.spatialCoordinates.y)} mm", (xmin + 10, ymin + 35), fontType, 0.5, 255)
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cv2.putText(depthFrameColor, f"Z: {int(depthData.spatialCoordinates.z)} mm", (xmin + 10, ymin + 50), fontType, 0.5, 255)
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cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, 1)
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cv2.putText(depthFrameColor, f"X: {int(depthData.spatialCoordinates.x)} mm", (xmin + 10, ymin + 20), fontType, 0.5, color)
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cv2.putText(depthFrameColor, f"Y: {int(depthData.spatialCoordinates.y)} mm", (xmin + 10, ymin + 35), fontType, 0.5, color)
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cv2.putText(depthFrameColor, f"Z: {int(depthData.spatialCoordinates.z)} mm", (xmin + 10, ymin + 50), fontType, 0.5, color)
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# Show the frame
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cv2.imshow("depth", depthFrameColor)
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‎examples/SpatialDetection/spatial_mobilenet.py

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@@ -60,6 +60,7 @@
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stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
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# Align depth map to the perspective of RGB camera, on which inference is done
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stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
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stereo.setSubpixel(True)
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stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())
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spatialDetectionNetwork.setBlobPath(nnBlobPath)
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depthFrame = depth.getFrame() # depthFrame values are in millimeters
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depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
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depthFrameColor = cv2.equalizeHist(depthFrameColor)
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depth_downscaled = depthFrame[::4]
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min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
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max_depth = np.percentile(depth_downscaled, 99)
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depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
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depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
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detections = inDet.detections
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ymin = int(topLeft.y)
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xmax = int(bottomRight.x)
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ymax = int(bottomRight.y)
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cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX)
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cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, 1)
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# Denormalize bounding box
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x1 = int(detection.xmin * width)

‎examples/SpatialDetection/spatial_mobilenet_mono.py

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@@ -59,6 +59,7 @@
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# StereoDepth
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stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
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stereo.setSubpixel(True)
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# Define a neural network that will make predictions based on the source frames
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spatialDetectionNetwork.setConfidenceThreshold(0.5)
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depthFrame = inDepth.getFrame() # depthFrame values are in millimeters
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depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
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depthFrameColor = cv2.equalizeHist(depthFrameColor)
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depth_downscaled = depthFrame[::4]
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min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
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max_depth = np.percentile(depth_downscaled, 99)
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depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
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depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
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detections = inDet.detections

‎examples/SpatialDetection/spatial_tiny_yolo.py

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# Align depth map to the perspective of RGB camera, on which inference is done
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stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
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stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())
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stereo.setSubpixel(True)
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spatialDetectionNetwork.setBlobPath(nnBlobPath)
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spatialDetectionNetwork.setConfidenceThreshold(0.5)
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frame = inPreview.getCvFrame()
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depthFrame = depth.getFrame() # depthFrame values are in millimeters
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depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
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depthFrameColor = cv2.equalizeHist(depthFrameColor)
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depth_downscaled = depthFrame[::4]
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min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
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max_depth = np.percentile(depth_downscaled, 99)
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depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
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depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
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counter+=1
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ymin = int(topLeft.y)
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xmax = int(bottomRight.x)
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ymax = int(bottomRight.y)
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cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX)
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cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), color, 1)
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# Denormalize bounding box
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x1 = int(detection.xmin * width)

‎examples/StereoDepth/depth_crop_control.py

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manip.initialConfig.setCropRect(topLeft.x, topLeft.y, bottomRight.x, bottomRight.y)
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manip.setMaxOutputFrameSize(monoRight.getResolutionHeight()*monoRight.getResolutionWidth()*3)
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stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
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stereo.setSubpixel(True)
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# Linking
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configIn.out.link(manip.inputConfig)
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depthFrame = inDepth.getFrame() # depthFrame values are in millimeters
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# Frame is transformed, the color map will be applied to highlight the depth info
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depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
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depthFrameColor = cv2.equalizeHist(depthFrameColor)
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depth_downscaled = depthFrame[::4]
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min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
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max_depth = np.percentile(depth_downscaled, 99)
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depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
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depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
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# Frame is ready to be shown

‎examples/mixed/rotated_spatial_detections.py

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import sys
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import cv2
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import depthai as dai
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import numpy as np
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'''
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Spatial object detections demo for 180° rotated OAK camera.
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'''
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stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
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# Align depth map to the perspective of RGB camera, on which inference is done
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stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
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stereo.setSubpixel(True)
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stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())
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rotate_stereo_manip = pipeline.createImageManip()
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frame = inPreview.getCvFrame()
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depthFrame = depth.getFrame() # depthFrame values are in millimeters
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depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
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depthFrameColor = cv2.equalizeHist(depthFrameColor)
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depth_downscaled = depthFrame[::4]
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min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
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max_depth = np.percentile(depth_downscaled, 99)
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depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
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depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
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detections = inDet.detections

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