Hi, got a few questions on [rgb_depth_alignment 12MP](https://gist.github.com/Erol444/25f374fa18efa7939ec9bb848b39249a). 1. `stereo.setOutputSize(1248, 936)` : - can the operation by this line of code be performed on the host (ie it is just an image resize and we can use opencv to do this) , and - can the parameters be changed midway through a code on the host (PC)? 2 I've detailed my steps to obtain depth within a bbox from obj detection. Please let me know if I'm doing it right - oak-D pro capture and sends 12MP RGB image to host (PC) - host performs obj detection on the RGB image using yolov5 -> we now have bbox coordinates - since the width of stereodepth returned from oak-D must be multiple of 16, we'll need to rescale my bbox and image to match that of stereodepth - using the coordinates of the rescaled bbox , I can now obtain the depth within my ROI in stereodepth

Hi @"glitchyordis"#p6836 , 1. You could, but depth would then be upscalled to rgb, in this case 12MP, which would cause issues. So it's better to set the size beforehand. You can't change this config dynamically/at runtime. 2. Yep correct. For BBs, you can keep them normalized (0..1) instead of pixel coordiantes, which will help you map object detection results to depth map. [Useful demo here ](https://github.com/luxonis/depthai-experiments/tree/master/gen2-calc-spatials-on-host)if you want to do this on the host. Thoughts? Thanks, Erik

Thanks @"erik"#p6842. The following applies to Oak-D pro AF cameras. 1. Is there a way to turn off the stereo streaming on the fly without affecting RGB's stream? I'm only aware of the `setStopStreaming` approach but that stops every stream. I'm trying to improve fps since we require 12MP rgb to be sent to host. 2. can we change the filter configurations & the IR projector on the fly? 3. As a reference to intelRealsense's guide on [postprocessing depth](https://dev.intelrealsense.com/docs/post-processing-filters) which is very similar to that in Oak-D Pro, can the filter approach described be applied for OAK-D Pro? [URL=https://i.imgur.com/d82hiUT.png][IMG]https://i.imgur.com/d82hiUT.png[/IMG][/URL]

Hi @"glitchyordis"#p6853 , 1. You could use Script node as a "pipeline manager" to either forward or discard frames - [example here](https://docs.luxonis.com/projects/api/en/latest/samples/Script/script_change_pipeline_flow/#script-change-pipeline-flow). 2. Yep:) Filters can be changed in runtime with StereoDepthConfig message. [Example here](https://docs.luxonis.com/projects/api/en/latest/samples/StereoDepth/stereo_depth_from_host/). For IR dot projector you can see [example here](https://docs.luxonis.com/projects/hardware/en/latest/pages/DM9098pro.html#getting-started). 3. I believe it's very similar, but will confirm with the team and we will update docs.:) Thoughts? Thanks, Erik

@"erik"#p6861 Hi. 1. Just wanted to double-check, does [rgb_depth_alignment 12MP](https://gist.github.com/Erol444/25f374fa18efa7939ec9bb848b39249a) syncs the RGB and depth frames? 2. I've noticed the following lines in the [rgb-depth-alignment](https://docs.luxonis.com/projects/api/en/latest/samples/StereoDepth/rgb_depth_aligned/#rgb-depth-alignment). what is the effect on the depth calculation if Oak-D pro took a picture at a different `lensPosition` and we perform depth alignment? ``` # For now, RGB needs fixed focus to properly align with depth. # This value was used during calibration try: calibData = device.readCalibration2() lensPosition = calibData.getLensPosition(dai.CameraBoardSocket.RGB) if lensPosition: camRgb.initialControl.setManualFocus(lensPosition) except: raise ```

Hi @"glitchyordis"#p6969 , 1. No, it doesn't look like it does. Best to use [seq-num syncing method](https://docs.luxonis.com/projects/api/en/latest/tutorials/message_syncing/#sequece-number-syncing). 2. The effect can be [seen here](https://github.com/luxonis/depthai-core/issues/463#issuecomment-1168216329). Thoguhts? Thanks, Erik

Hi @"erik"#p6975, just double checking on Question 2. So there is an effect on the rgb image taken from the same position when changing focus and this affects the depth-rgb alignment. In my application using an Oak-D Pro AF, - device sends ISP 12MP image (lets call this Image A) to host, this may be at an arbitrary focus - host performs obj det -> we hv bbox coordinate Does this mean to get a more accurate spatial calculation (x,y,z), after taking Image A, I should - **manually set camera focus (see quote below)** - **then only get the depth data?** - and calculate the spatials > @"glitchyordis"#p6969 # For now, RGB needs fixed focus to properly align with depth. > # This value was used during calibration > try: > calibData = device.readCalibration2() > lensPosition = calibData.getLensPosition(dai.CameraBoardSocket.RGB) > if lensPosition: > camRgb.initialControl.setManualFocus(lensPosition) > except: > raise

RGB Depth alignment

glitchyordis

Hi erik, just double checking on Question 2.

So there is an effect on the rgb image taken from the same position when changing focus and this affects the depth-rgb alignment. In my application using an Oak-D Pro AF,

device sends ISP 12MP image (lets call this Image A) to host, this may be at an arbitrary focus
host performs obj det -> we hv bbox coordinate

Does this mean to get a more accurate spatial calculation (x,y,z), after taking Image A, I should

manually set camera focus (see quote below)
then only get the depth data?
and calculate the spatials

glitchyordis # For now, RGB needs fixed focus to properly align with depth.

This value was used during calibration

try:
calibData = device.readCalibration2()
lensPosition = calibData.getLensPosition(dai.CameraBoardSocket.RGB)
if lensPosition:
camRgb.initialControl.setManualFocus(lensPosition)
except:
raise

erik

Hi glitchyordis ,
To get perfectly aligned depth and RGB, you'd want to manually set lens position, as you do above. If you have an object close (eg. below 50cm), and especially very close (eg below 20cm), using AF will cause rgb-depth not being aligned. Not having perfectly aligned depth and RGB could lead to incorrect spatial detections, but usually it wouldn't, as there's bounding box scalling applied anyways.
Thanks, Erik

glitchyordis

Hi erik,

just to double check, do you mean:
- get obj det bbox
- scale bbox width height by 'bboxscalefactor' -> scaled bbox
- get depth within scaled bbox instead of obj det bbox?
Pls help me take a look at the following code for algining RGB to depth and synching; I was only able to get around 12 FPS (printed by depthai_sdk.fps FPSHandler). Any suggestions on how to improve on FPS? It gets even lower when I calc spatials based on depth frame received and bbox from host.

python
from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
import time 
import matplotlib.pyplot as plt
import math
import PySimpleGUI as gui
from depthai_sdk.fps import FPSHandler

 class HostSync:
# from https://github.com/luxonis/depthai-experiments/blob/master/gen2-depth-driven-focus/main.py
    def __init__(self):
        self.arrays = {}

    def add_msg(self, name, msg):
        if not name in self.arrays:
            self.arrays[name] = []
        # Add msg to array
        self.arrays[name].append({"msg": msg, "seq": msg.getSequenceNum()})

        synced = {}
        for name, arr in self.arrays.items():
            for i, obj in enumerate(arr):
                if msg.getSequenceNum() == obj["seq"]:
                    synced[name] = obj["msg"]
                    break
        # If there are 5 (all) synced msgs, remove all old msgs
        # and return synced msgs
        if len(synced) == (2):  # Color, Depth
            # Remove old msgs
            for name, arr in self.arrays.items():
                for i, obj in enumerate(arr):
                    if obj["seq"] < msg.getSequenceNum():
                        arr.remove(obj)
                    else:
                        break
            return synced
        return False

 def printSystemInformation(info):
# from https://docs.luxonis.com/projects/api/en/latest/samples/SystemLogger/system_information/#system-information
    m = 1024 * 1024 # MiB
    print(f"Ddr used / total - {info.ddrMemoryUsage.used / m:.2f} / {info.ddrMemoryUsage.total / m:.2f} MiB")
    print(f"Cmx used / total - {info.cmxMemoryUsage.used / m:.2f} / {info.cmxMemoryUsage.total / m:.2f} MiB")
    print(f"LeonCss heap used / total - {info.leonCssMemoryUsage.used / m:.2f} / {info.leonCssMemoryUsage.total / m:.2f} MiB")
    print(f"LeonMss heap used / total - {info.leonMssMemoryUsage.used / m:.2f} / {info.leonMssMemoryUsage.total / m:.2f} MiB")
    t = info.chipTemperature
    print(f"Chip temperature - average: {t.average:.2f}, css: {t.css:.2f}, mss: {t.mss:.2f}, upa: {t.upa:.2f}, dss: {t.dss:.2f}")
    print(f"Cpu usage - Leon CSS: {info.leonCssCpuUsage.average * 100:.2f}%, Leon MSS: {info.leonMssCpuUsage.average * 100:.2f} %")
    print("----------------------------------------")
 

# pipeline ---------------------------------------------------------------------------------------
device = dai.Device("184430102145421300", maxUsbSpeed=dai.UsbSpeed.SUPER_PLUS)

# Create pipeline
pipeline = dai.Pipeline()
pipeline.setXLinkChunkSize(0)
# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)

xoutRgb = pipeline.create(dai.node.XLinkOut)
xoutDepth = pipeline.create(dai.node.XLinkOut) 

xoutRgb.setStreamName("rgb") 
xoutDepth.setStreamName("depth")

# Properties
set_fps = 30
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_12_MP)
camRgb.setFps(set_fps)
# camRgb.setInterleaved(False) 
try:
    calibData = device.readCalibration2()
    lensPosition = calibData.getLensPosition(dai.CameraBoardSocket.RGB)
    if lensPosition:
        camRgb.initialControl.setManualFocus(lensPosition)
    else:
        gui.popup("No Calib data for lensposition")
except:
    raise

monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoLeft.setFps(set_fps)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
monoRight.setFps(set_fps)

# Setting node configs
stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
# this is similar doing threshold (int) -> stereo_depth.initialConfig.setConfidenceThreshold(threshold)
# Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default)

stereo.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7)

# stereoConfig
stereoConfig = stereo.initialConfig.get()
stereoConfig.postProcessing.speckleFilter.enable = True
stereoConfig.postProcessing.speckleFilter.speckleRange = 50
# stereoConfig.postProcessing.temporalFilter.enable = True
stereoConfig.postProcessing.spatialFilter.enable = True
stereoConfig.postProcessing.spatialFilter.holeFillingRadius = 2
stereoConfig.postProcessing.spatialFilter.numIterations = 1
stereoConfig.postProcessing.thresholdFilter.minRange = 150
stereoConfig.postProcessing.thresholdFilter.maxRange = 1000
stereoConfig.postProcessing.decimationFilter.decimationFactor = 2
stereo.initialConfig.set(stereoConfig)

# Align depth map to the perspective of RGB camera, on which inference is done
stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
stereo.setLeftRightCheck(True)
stereo.setExtendedDisparity(True)
stereo.setOutputSize(800, 599)
 
# Linking
monoLeft.out.link(stereo.left)
monoRight.out.link(stereo.right)
 
camRgb.isp.link(xoutRgb.input)
stereo.depth.link(xoutDepth.input) 

sysLog = pipeline.create(dai.node.SystemLogger)
linkOut = pipeline.create(dai.node.XLinkOut)
linkOut.setStreamName("sysinfo")
sysLog.setRate(0.2)   
sysLog.out.link(linkOut.input)

# tells depth when hover mouse over window 
point = (0, 0)
def show_distance(event, x,y, args, params):
    global point
    point = (x,y)
    print(f"point: {point}")

cv2.namedWindow("12mp")
cv2.setMouseCallback("12mp", show_distance)
 
# main ----------------------------------------------------------------------------------------
device.startPipeline(pipeline)
device.setLogLevel(dai.LogLevel.INFO)
device.setLogOutputLevel(dai.LogLevel.INFO) 

calibData = device.readCalibration() 

outputs = ['rgb', 'depth'] 

queues  = [device.getOutputQueue(name, 4, False) for name in outputs]
qSysInfo = device.getOutputQueue(name="sysinfo", maxSize=4, blocking=False)
sync = HostSync()

color = (220, 220, 220)
fps = FPSHandler()

while True: 
    sysInfo = qSysInfo.tryGet()
    if sysInfo is not None:
        printSystemInformation(sysInfo)

    for q in queues:
        if q.has():
            synced_msgs = sync.add_msg(q.getName(), q.get())
            if synced_msgs:
                fps.nextIter()
                print('FPS', fps.fps())
                frame = synced_msgs["rgb"].getCvFrame()

                depthFrame = None # If debug
                if 'depth' in synced_msgs:
                    depthFrame: dai.ImgFrame = synced_msgs["depth"].getFrame()
                    fov = synced_msgs['depth'].getInstanceNum()


                frame_resized = cv2.resize(frame,(800,599), interpolation=cv2.INTER_NEAREST)

                # get frame sizes
                imgH, imgW, _ = frame.shape
                resizedh, resizedw, _ = frame_resized.shape 

                #### without:
                 host obj det on frame, get bbox, cal spatial using depthFrame 
                ####

                cv2.circle(frame_resized, point, 10, (0,0,255))
                
                distance = depthFrame[point[1]][point[0]] 
                cv2.putText(frame_resized, "{}cm".format(distance), (point[0],point[1]), cv2.FONT_HERSHEY_PLAIN, 2 , (255,255,255), 2 )
                cv2.imshow("12mp", frame_resized)
    
    if cv2.waitKey(1) == ord('q'):
        break

device.close()

example FPS printed (No object detection on Host/device) by depthai_sdk.fps FPS Handler

FPS 14.071720381301297
FPS 13.953488372094823
Ddr used / total - 108.02 / 340.42 MiB
Cmx used / total - 2.09 / 2.50 MiB
LeonCss heap used / total - 47.33 / 77.32 MiB
LeonMss heap used / total - 4.59 / 41.23 MiB
Chip temperature - average: 46.39, css: 47.35, mss: 46.45, upa: 46.68, dss: 45.09
Cpu usage - Leon CSS: 48.04%, Leon MSS: 19.31 %

example FPS printed (object detection on Host, calc spatials on host based on depth from device) by depthai_sdk.fps FPS Handler

FPS 6.550218340610157
FPS 6.56959024720649
Ddr used / total - 108.02 / 340.42 MiB
Cmx used / total - 2.09 / 2.50 MiB
LeonCss heap used / total - 47.33 / 77.32 MiB
LeonMss heap used / total - 4.59 / 41.23 MiB
Chip temperature - average: 46.45, css: 47.35, mss: 46.45, upa: 46.00, dss: 46.00
Cpu usage - Leon CSS: 47.33%, Leon MSS: 20.71 %

erik

Hi glitchyordis , Please provide full MRE https://docs.luxonis.com/en/latest/pages/support/

glitchyordis

Hi erik , here you go.

device: Oak-D PRO AF
latest version of depthai and depthai-sdk

Note device = dai.Device("111111111111111111", maxUsbSpeed=dai.UsbSpeed.SUPER_PLUS), please change it to your own device mxID.

# %%
from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
import time 
import matplotlib.pyplot as plt
import math
import PySimpleGUI as gui
from depthai_sdk.fps import FPSHandler
import torch

# %%
class HostSync:
    # adapted from https://github.com/luxonis/depthai-experiments/blob/master/gen2-depth-driven-focus/main.py
    
    def __init__(self):
        self.arrays = {}

    def add_msg(self, name, msg):
        if not name in self.arrays:
            self.arrays[name] = []
        # Add msg to array
        self.arrays[name].append({"msg": msg, "seq": msg.getSequenceNum()})

        synced = {}
        for name, arr in self.arrays.items():
            for i, obj in enumerate(arr):
                if msg.getSequenceNum() == obj["seq"]:
                    synced[name] = obj["msg"] 

        if len(synced) == (2):  # Color, Depth
            # Remove old msgs
            for name, arr in self.arrays.items():
                for i, obj in enumerate(arr):
                    if obj["seq"] < msg.getSequenceNum():
                        arr.remove(obj)
                    else:
                        break
            return synced
        return False
    
class TextHelper:
    # from: https://github.com/luxonis/depthai-experiments/blob/master/gen2-calc-spatials-on-host/utility.py
    def __init__(self) -> None:
        self.bg_color = (0, 0, 0)
        self.color = (255, 255, 255)
        self.text_type = cv2.FONT_HERSHEY_SIMPLEX
        self.line_type = cv2.LINE_AA
    def putText(self, frame, text, coords):
        cv2.putText(frame, text, coords, self.text_type, 0.5, self.bg_color, 3, self.line_type)
        cv2.putText(frame, text, coords, self.text_type, 0.5, self.color, 1, self.line_type)
    def rectangle(self, frame, p1, p2):
        cv2.rectangle(frame, p1, p2, self.bg_color, 3)
        cv2.rectangle(frame, p1, p2, self.color, 1)

# %%
def printSystemInformation(info):
    # from https://docs.luxonis.com/projects/api/en/latest/samples/SystemLogger/system_information/#system-information
    
    m = 1024 * 1024 # MiB
    print(f"Ddr used / total - {info.ddrMemoryUsage.used / m:.2f} / {info.ddrMemoryUsage.total / m:.2f} MiB")
    print(f"Cmx used / total - {info.cmxMemoryUsage.used / m:.2f} / {info.cmxMemoryUsage.total / m:.2f} MiB")
    print(f"LeonCss heap used / total - {info.leonCssMemoryUsage.used / m:.2f} / {info.leonCssMemoryUsage.total / m:.2f} MiB")
    print(f"LeonMss heap used / total - {info.leonMssMemoryUsage.used / m:.2f} / {info.leonMssMemoryUsage.total / m:.2f} MiB")
    t = info.chipTemperature
    print(f"Chip temperature - average: {t.average:.2f}, css: {t.css:.2f}, mss: {t.mss:.2f}, upa: {t.upa:.2f}, dss: {t.dss:.2f}")
    print(f"Cpu usage - Leon CSS: {info.leonCssCpuUsage.average * 100:.2f}%, Leon MSS: {info.leonMssCpuUsage.average * 100:.2f} %")
    print("----------------------------------------")

# %%
def calc_spatials(frame, frame_resized, result, depthFrame, calibData, depthData):
    # adapted from : https://github.com/luxonis/depthai-experiments/blob/master/gen2-calc-spatials-on-host/calc.py

    def calc_angle(frame, offset, HFOV):
        return math.atan(math.tan(HFOV / 2.0) * offset / (frame.shape[1] / 2.0))

    imgH, imgW, _ = frame.shape
    resizedh, resizedw, _ = frame_resized.shape

    # only process the first detection
    scaled_bb = {
        "xmin": int(result.xmin[0]/imgW*resizedw),
        "ymin": int(result.ymin[0]/imgH*resizedh),
        "xmax": int(result.xmax[0]/imgW*resizedw),
        "ymax": int(result.ymax[0]/imgH*resizedh),
        } 

    depthROI = depthFrame[scaled_bb["ymin"]:scaled_bb["ymax"], scaled_bb["xmin"]:scaled_bb["xmax"]]
    inRange = (150 <= depthROI) & (depthROI <= 1000) # treshold

    averageDepth = np.mean(depthROI[inRange])

    centroid = { # Get centroid of the ROI
        'x': int((scaled_bb["xmax"] + scaled_bb["xmin"]) / 2),
        'y': int((scaled_bb["ymax"] + scaled_bb["ymin"]) / 2)
        }
    
    midW = int(depthFrame.shape[1] / 2) # middle of the depth img width
    midH = int(depthFrame.shape[0] / 2) # middle of the depth img height
    bb_x_pos = centroid['x'] - midW
    bb_y_pos = centroid['y'] - midH

    HFOV = np.deg2rad(calibData.getFov(dai.CameraBoardSocket(depthData.getInstanceNum())))

    angle_x = calc_angle(depthFrame, bb_x_pos, HFOV)
    angle_y = calc_angle(depthFrame, bb_y_pos, HFOV)

    spatials = {
                'z': averageDepth,
                'x': averageDepth * math.tan(angle_x),
                'y': -averageDepth * math.tan(angle_y)
            } 

    return spatials, scaled_bb, centroid

# %%
device = dai.Device("184430102145421300", maxUsbSpeed=dai.UsbSpeed.SUPER_PLUS)

# %%
# Create pipeline
pipeline = dai.Pipeline()
pipeline.setXLinkChunkSize(0)

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)

xoutRgb = pipeline.create(dai.node.XLinkOut)
xoutDepth = pipeline.create(dai.node.XLinkOut) 

xoutRgb.setStreamName("rgb") 
xoutDepth.setStreamName("depth")

# mono and rgb properties
set_fps = 30
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_12_MP)
camRgb.setFps(set_fps)
# camRgb.setInterleaved(False) 
try:
    calibData = device.readCalibration2()
    lensPosition = calibData.getLensPosition(dai.CameraBoardSocket.RGB)
    if lensPosition:
        camRgb.initialControl.setManualFocus(lensPosition)
    else:
        gui.popup("No Calib data for lensposition")
except:
    raise

monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoLeft.setFps(set_fps)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
monoRight.setFps(set_fps)

# stereoConfig
stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY) 

stereo.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7)
 
stereoConfig = stereo.initialConfig.get()
stereoConfig.postProcessing.speckleFilter.enable = True
stereoConfig.postProcessing.speckleFilter.speckleRange = 50 
stereoConfig.postProcessing.spatialFilter.enable = True
stereoConfig.postProcessing.spatialFilter.holeFillingRadius = 2
stereoConfig.postProcessing.spatialFilter.numIterations = 1
stereoConfig.postProcessing.thresholdFilter.minRange = 150
stereoConfig.postProcessing.thresholdFilter.maxRange = 1000
stereoConfig.postProcessing.decimationFilter.decimationFactor = 2
stereo.initialConfig.set(stereoConfig)

stereo.setLeftRightCheck(True)
stereo.setExtendedDisparity(True)

# Align depth map to rgb
stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
stereo.setOutputSize(800, 599)
 
# Linking
monoLeft.out.link(stereo.left)
monoRight.out.link(stereo.right)
 
camRgb.isp.link(xoutRgb.input)
stereo.depth.link(xoutDepth.input) 

# logger
sysLog = pipeline.create(dai.node.SystemLogger)
linkOut = pipeline.create(dai.node.XLinkOut)
linkOut.setStreamName("sysinfo")
sysLog.setRate(0.5)   
sysLog.out.link(linkOut.input)

# gets coordinate (pixels) of mouse on cv2 window when mouse hover overs cv2 window
point = (0, 0)
def show_distance(event, x,y, args, params):
    global point
    point = (x,y)
    # print(f"point: {point}")

cv2.namedWindow("12mp")
cv2.setMouseCallback("12mp", show_distance)
 
# %%
# obj detection
model = torch.hub.load('ultralytics/yolov5', "yolov5s") 
model.classes = [0] # detects `people` only

# %%
device.startPipeline(pipeline)
device.setLogLevel(dai.LogLevel.INFO)
device.setLogOutputLevel(dai.LogLevel.INFO) 

calibData = device.readCalibration() 

outputs = ['rgb', 'depth'] 
queues  = [device.getOutputQueue(name, 4, False) for name in outputs]
qSysInfo = device.getOutputQueue(name="sysinfo", maxSize=4, blocking=False)

sync = HostSync()
fps = FPSHandler()
text = TextHelper()

synced_msgs = None
while True: 
    sysInfo = qSysInfo.tryGet()
    if sysInfo is not None:
        printSystemInformation(sysInfo)

    for q in queues:
        if q.has():
            synced_msgs = sync.add_msg(q.getName(), q.get())

            if synced_msgs:
                fps.nextIter()
                print('FPS', fps.fps())

                frame = synced_msgs["rgb"].getCvFrame()

                if 'depth' in synced_msgs:
                    depthFrame: dai.ImgFrame = synced_msgs["depth"].getFrame()
                    fov = synced_msgs['depth'].getInstanceNum()

                frame_resized = cv2.resize(frame,(800,599), interpolation=cv2.INTER_NEAREST)
 
                # object det yolov5 on host
                result = model(frame[:,:,::-1],size=840)
                result = result.pandas().xyxy[0]
                
                if len(result)>0:
                    spatials, scaled_bb, centroid =  calc_spatials(frame, frame_resized, result, depthFrame, calibData, synced_msgs["depth"])
                    text.rectangle(frame_resized, (scaled_bb["xmin"], scaled_bb["ymin"]), (scaled_bb["xmax"], scaled_bb["ymax"]))
                    text.putText(frame_resized, "X: " + ("{:.1f}cm".format(spatials['x']/10) if not math.isnan(spatials['x']) else "--"), (centroid["x"] + 10, centroid["y"] + 20))
                    text.putText(frame_resized, "Y: " + ("{:.1f}cm".format(spatials['y']/10) if not math.isnan(spatials['y']) else "--"), (centroid["x"] + 10, centroid["y"] + 35))
                    text.putText(frame_resized, "Z: " + ("{:.1f}cm".format(spatials['z']/10) if not math.isnan(spatials['z']) else "--"), (centroid["x"] + 10, centroid["y"] + 50))

                distance = depthFrame[point[1]][point[0]] 
                cv2.circle(frame_resized, point, 10, (0,0,255))
                cv2.putText(frame_resized, "{}cm".format(distance), (point[0],point[1]), cv2.FONT_HERSHEY_PLAIN, 2 , (255,255,255), 2 )
                cv2.imshow("12mp", frame_resized)

    if cv2.waitKey(1) == ord('q'):
        break

device.close()

erik

Hi glitchyordis , This is far from MRE. Please make is as short as possible.

glitchyordis

Hi erik , thanks for the feedback. is the following alright?

import cv2
import depthai as dai
import numpy as np 
import matplotlib.pyplot as plt  
from depthai_sdk.fps import FPSHandler
import torch

# %%
class HostSync:
    # adapted from https://github.com/luxonis/depthai-experiments/blob/master/gen2-depth-driven-focus/main.py
    
    def __init__(self):
        self.arrays = {}

    def add_msg(self, name, msg):
        if not name in self.arrays:
            self.arrays[name] = []
        # Add msg to array
        self.arrays[name].append({"msg": msg, "seq": msg.getSequenceNum()})

        synced = {}
        for name, arr in self.arrays.items():
            for i, obj in enumerate(arr):
                if msg.getSequenceNum() == obj["seq"]:
                    synced[name] = obj["msg"] 

        if len(synced) == (2):  # Color, Depth
            # Remove old msgs
            for name, arr in self.arrays.items():
                for i, obj in enumerate(arr):
                    if obj["seq"] < msg.getSequenceNum():
                        arr.remove(obj)
                    else:
                        break
            return synced
        return False

# %%
device = dai.Device("184430102145421300", maxUsbSpeed=dai.UsbSpeed.SUPER_PLUS)

# %%
# Create pipeline
pipeline = dai.Pipeline()
pipeline.setXLinkChunkSize(0)

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)

xoutRgb = pipeline.create(dai.node.XLinkOut)
xoutDepth = pipeline.create(dai.node.XLinkOut) 

xoutRgb.setStreamName("rgb") 
xoutDepth.setStreamName("depth")

# mono and rgb properties
set_fps = 30
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_12_MP)
camRgb.setFps(set_fps)
# camRgb.setInterleaved(False) 
try:
    calibData = device.readCalibration2()
    lensPosition = calibData.getLensPosition(dai.CameraBoardSocket.RGB)
    if lensPosition:
        camRgb.initialControl.setManualFocus(lensPosition) 
except:
    raise

monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoLeft.setFps(set_fps)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
monoRight.setFps(set_fps)

# stereoConfig
stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY) 

stereo.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7)
 
stereoConfig = stereo.initialConfig.get()
stereoConfig.postProcessing.speckleFilter.enable = True
stereoConfig.postProcessing.speckleFilter.speckleRange = 50 
stereoConfig.postProcessing.spatialFilter.enable = True
stereoConfig.postProcessing.spatialFilter.holeFillingRadius = 2
stereoConfig.postProcessing.spatialFilter.numIterations = 1
stereoConfig.postProcessing.thresholdFilter.minRange = 150
stereoConfig.postProcessing.thresholdFilter.maxRange = 1000
stereoConfig.postProcessing.decimationFilter.decimationFactor = 2
stereo.initialConfig.set(stereoConfig)

stereo.setLeftRightCheck(True)
stereo.setExtendedDisparity(True)

# Align depth map to rgb
stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
stereo.setOutputSize(800, 599)
 
# Linking
monoLeft.out.link(stereo.left)
monoRight.out.link(stereo.right)
 
camRgb.isp.link(xoutRgb.input)
stereo.depth.link(xoutDepth.input) 

point = (0, 0)
def show_distance(event, x,y, args, params):
    global point
    point = (x,y)
 
cv2.namedWindow("12mp")
cv2.setMouseCallback("12mp", show_distance)

# %%
# obj detection
model = torch.hub.load('ultralytics/yolov5', "yolov5s") 
model.classes = [0] # detects `people` only

# %%
device.startPipeline(pipeline) 

calibData = device.readCalibration() 

outputs = ['rgb', 'depth'] 
queues  = [device.getOutputQueue(name, 4, False) for name in outputs]

sync = HostSync()
fps = FPSHandler() 

synced_msgs = None
while True:  

    for q in queues:
        if q.has():
            synced_msgs = sync.add_msg(q.getName(), q.get())

            if synced_msgs:
                fps.nextIter()
                print('FPS', fps.fps())

                frame = synced_msgs["rgb"].getCvFrame()

                if 'depth' in synced_msgs:
                    depthFrame: dai.ImgFrame = synced_msgs["depth"].getFrame()
                    fov = synced_msgs['depth'].getInstanceNum()

                frame_resized = cv2.resize(frame,(800,599), interpolation=cv2.INTER_NEAREST)
 
                # object det yolov5 on host
                result = model(frame[:,:,::-1],size=840)
                result = result.pandas().xyxy[0]
 
                distance = depthFrame[point[1]][point[0]] 
                cv2.circle(frame_resized, point, 10, (0,0,255))
                cv2.putText(frame_resized, "{}cm".format(distance), (point[0],point[1]), cv2.FONT_HERSHEY_PLAIN, 2 , (255,255,255), 2 )
                cv2.imshow("12mp", frame_resized)

    if cv2.waitKey(1) == ord('q'):
        break

device.close()

erik

Hi glitchyordis ,
After removing a bunch of unneeded code, I get to about 17FPS for both depth and RGB. A single 12MP frame (isp, meaning YUV420, 1.5bytes/pixel) is 18MB (144Mbits), so at 17FPS that would be 2.5gpbs. Depth isn't nearly as large.
Looking at OAK bandwidth test where I got max 2273mbps for downlink, 2.5gpbs (+depth) is actually quite good.

So overall, the bandwidth is the bottleneck here. I don't have 10gbps support on my laptop so I can't select SUPER_PLUS speeds. Tested code:

Thanks, Erik

glitchyordis

Hi erik , thank you for the explanation. Got a few questions for Oak-D Pro:

After performing (Depth alignment with 12MP RGB) https://gist.github.com/Erol444/25f374fa18efa7939ec9bb848b39249a, if we calculate the spatial based on ROI, are the spatial (x,y,z) relative to RGB camera or mono cam?
Is it recommended to perform rgb-undistorted depth alignment ?

erik

Hi glitchyordis ,

They are relative to RGB camera, as depth was aligned to RGB camera.
Usually that's not needed - it was developed for wide FOV cameras (OAK-D W*), where images are very distorted.

Thanks, Erik

ManiRadhakrishnan

Hey eric I have the folllowing setup .

a) .I am running a object detect using Yolo and finding out depth
stereo.setDepthAlign(dai.CameraBoardSocket.RGB) .
spatialDetectionNetwork = pipeline.createYoloSpatialDetectionNetwork()

in this setup the depth coming out seems to be wrt to RGB

b)I am using performing the aruco detection without Depthalign and using
hostSpatials = HostSpatialsCalc(device)
In this the depth coming out be the right camera.

i want a consistent location wrt to rgb camera
I should be able to change the camera reference whenever i want

Can you tell how to achieve this?

jakaskerl

Hi ManiRadhakrishnan
Any specific reason why you are not using the depthalign in the second case as well?

jakaskerl

ManiRadhakrishnan I am using performing the aruco detection without Depthalign and using
hostSpatials = HostSpatialsCalc(device)
In this the depth coming out be the right camera.

If you align the depth to the RGB camera, then that depth map will be aligned to that camera. What do you mean the values are high?

ManiRadhakrishnan I should be able to change the camera reference whenever i want

You should be able to change the alignment by altering the stereo config during runtime (not completely sure).

Thanks,
Jaka

jakaskerl

Hi ManiRadhakrishnan
Are you talking about the depth map here? - it should stay the same. If the error is in the end result, it means something if wrong with the calculation you are running on host side.

ManiRadhakrishnan hostSpatials = HostSpatialsCalc(device)

Where is this HostSpatialsCalc from?

Thanks,
Jaka

ManiRadhakrishnan

jakaskerl I am using depth Align only but I don't know how to verify that …and the values of location is very high

ManiRadhakrishnan

The depth value coming out is in 1 metres ,2 metres .Before using the align ,it was perfect with 2 mm accuarcy.