Depth Z value accuracy of Gen2-head-posture-detection

Hi jakaskerl

I tried rotating the camera 90 degrees for installation and using the code you modified to detect head position and posture information. After entering 'r' on the keyboard, rotate the image by 90 degrees before checking the input model. I used ImageManip to set the angle value to rotate, and another ImageManip to set the size of the image back to (300, 300). However, testing has found that once ImageManip is used, although the image has not been rotated by 90 degrees, the measured ROI area is no longer on the face, but in a distant location. How to modify the ROI area to fit on the face, or is there any other way to rotate the image? Here is my code

from MultiMsgSync import TwoStageHostSeqSync
import blobconverter
import cv2
import depthai as dai
from tools import *
rgbRr = dai.RotatedRect()
def create_pipeline(stereo):
pipeline = dai.Pipeline()
print("Creating Color Camera...")
cam = pipeline.create(dai.node.ColorCamera)
cam.setPreviewSize(640, 480)
cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
cam.setInterleaved(False)
cam.setPreviewKeepAspectRatio(False)
cam.setBoardSocket(dai.CameraBoardSocket.RGB)

cam_xout = pipeline.create(dai.node.XLinkOut)
*# cam_xout.setStreamName("color")
# cam.video.link(cam_xout.input)

*if stereo:
    monoLeft = pipeline.create(dai.node.MonoCamera)
    monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
    monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)

    monoRight = pipeline.create(dai.node.MonoCamera)
    monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
    monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)

    stereo = pipeline.create(dai.node.StereoDepth)
    stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
    stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
    monoLeft.out.link(stereo.left)
    monoRight.out.link(stereo.right)

    *# Spatial Detection network if OAK-D*
    print("OAK-D detected, app will display spatial coordiantes")
    face_det_nn = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)
    stereo.depth.link(face_det_nn.inputDepth)
else: *# Detection network if OAK-1*
    print("OAK-1 detected, app won't display spatial coordiantes")
    face_det_nn = pipeline.create(dai.node.MobileNetDetectionNetwork)

face_det_nn.setConfidenceThreshold(0.5)
face_det_nn.setBoundingBoxScaleFactor(0.5)
face_det_nn.setBlobPath(blobconverter.from_zoo(name="face-detection-retail-0004", shaves=6))
*# cam.preview.link(face_det_nn.input)
# ------------------------------------------------------------------------------
*copy_manip = pipeline.create(dai.node.ImageManip)
rgbRr.center.x, rgbRr.center.y = cam.getPreviewWidth() // 2, cam.getPreviewHeight() // 2
rgbRr.size.width, rgbRr.size.height = cam.getPreviewHeight(), cam.getPreviewWidth()
rgbRr.angle = 0
copy_manip.initialConfig.setCropRotatedRect(rgbRr, False)
copy_manip.setNumFramesPool(15)
copy_manip.setMaxOutputFrameSize(3499200)
cam.preview.link(copy_manip.inputImage)
manipRgbCfg = pipeline.create(dai.node.XLinkIn)
manipRgbCfg.setStreamName("manipCfg")
manipRgbCfg.out.link(copy_manip.inputConfig)
face_det_manip = pipeline.create(dai.node.ImageManip)
face_det_manip.initialConfig.setResize(300,300)
face_det_manip.initialConfig.setFrameType(dai.RawImgFrame.Type.RGB888p)
copy_manip.out.link(face_det_manip.inputImage)
face_det_manip.out.link(face_det_nn.input)

cam_xout.setStreamName("color")
face_det_manip.out.link(cam_xout.input)
*# cam.video.link(cam_xout.input)
# -----------------------------------------------------------------------------------------
# Send face detections to the host (for bounding boxes)
*face_det_xout = pipeline.create(dai.node.XLinkOut)
face_det_xout.setStreamName("detection")
face_det_nn.out.link(face_det_xout.input)

*# Script node will take the output from the face detection NN as an input and set ImageManipConfig
# to the 'recognition_manip' to crop the initial frame
*image_manip_script = pipeline.create(dai.node.Script)
face_det_nn.out.link(image_manip_script.inputs['face_det_in'])

*# Only send metadata, we are only interested in timestamp, so we can sync
# depth frames with NN output
*face_det_nn.passthrough.link(image_manip_script.inputs['passthrough'])
*# cam.preview.link(image_manip_script.inputs['preview'])*
face_det_manip.out.link(image_manip_script.inputs['preview'])

image_manip_script.setScript("""
import time
msgs = dict()

def add_msg(msg, name, seq = None):
    global msgs
    if seq is None:
        seq = msg.getSequenceNum()
    seq = str(seq)
    # node.warn(f"New msg {name}, seq {seq}")

    # Each seq number has it's own dict of msgs
    if seq not in msgs:
        msgs[seq] = dict()
    msgs[seq][name] = msg

    # To avoid freezing (not necessary for this ObjDet model)
    if 15 < len(msgs):
        node.warn(f"Removing first element! len {len(msgs)}")
        msgs.popitem() # Remove first element

def get_msgs():
    global msgs
    seq_remove = [] # Arr of sequence numbers to get deleted
    for seq, syncMsgs in msgs.items():
        seq_remove.append(seq) # Will get removed from dict if we find synced msgs pair
        # node.warn(f"Checking sync {seq}")

        # Check if we have both detections and color frame with this sequence number
        if len(syncMsgs) == 2: # 1 frame, 1 detection
            for rm in seq_remove:
                del msgs[rm]
            # node.warn(f"synced {seq}. Removed older sync values. len {len(msgs)}")
            return syncMsgs # Returned synced msgs
    return None

def correct_bb(xmin,ymin,xmax,ymax):
    if xmin < 0: xmin = 0.001
    if ymin < 0: ymin = 0.001
    if xmax > 1: xmax = 0.999
    if ymax > 1: ymax = 0.999
    return [xmin,ymin,xmax,ymax]

while True:
    time.sleep(0.001) # Avoid lazy looping

    preview = node.io['preview'].tryGet()
    if preview is not None:
        add_msg(preview, 'preview')

    face_dets = node.io['face_det_in'].tryGet()
    if face_dets is not None:
        # TODO: in 2.18.0.0 use face_dets.getSequenceNum()
        passthrough = node.io['passthrough'].get()
        seq = passthrough.getSequenceNum()
        add_msg(face_dets, 'dets', seq)

    sync_msgs = get_msgs()
    if sync_msgs is not None:
        img = sync_msgs['preview']
        dets = sync_msgs['dets']
        for i, det in enumerate(dets.detections):
            cfg = ImageManipConfig()
            bb = correct_bb(det.xmin-0.03, det.ymin-0.03, det.xmax+0.03, det.ymax+0.03)
            cfg.setCropRect(\*bb)
            # node.warn(f"Sending {i + 1}. det. Seq {seq}. Det {det.xmin}, {det.ymin}, {det.xmax}, {det.ymax}")
            cfg.setResize(60, 60)
            cfg.setKeepAspectRatio(False)
            node.io['manip_cfg'].send(cfg)
            node.io['manip_img'].send(img)
""")
recognition_manip = pipeline.create(dai.node.ImageManip)
recognition_manip.initialConfig.setResize(60, 60)
recognition_manip.setWaitForConfigInput(True)
image_manip_script.outputs['manip_cfg'].link(recognition_manip.inputConfig)
image_manip_script.outputs['manip_img'].link(recognition_manip.inputImage)

*# Second stange recognition NN*
print("Creating recognition Neural Network...")
recognition_nn = pipeline.create(dai.node.NeuralNetwork)
recognition_nn.setBlobPath(blobconverter.from_zoo(name="head-pose-estimation-adas-0001", shaves=6))
recognition_manip.out.link(recognition_nn.input)

recognition_xout = pipeline.create(dai.node.XLinkOut)
recognition_xout.setStreamName("recognition")
recognition_nn.out.link(recognition_xout.input)

return pipeline

with dai.Device() as device:
stereo = 1 < len(device.getConnectedCameras())
device.startPipeline(create_pipeline(stereo))
sync = TwoStageHostSeqSync()
queues = {}
qManipCfg = device.getInputQueue(name="manipCfg")
# Create output queues
for name in ["color", "detection", "recognition"]:
queues[name] = device.getOutputQueue(name)

while True:
    for name, q in queues.items():
        *# Add all msgs (color frames, object detections and recognitions) to the Sync class.*
        if q.has():
            sync.add_msg(q.get(), name)

    msgs = sync.get_msgs()
    if msgs is not None:
        frame = msgs["color"].getCvFrame()
        detections = msgs["detection"].detections
        recognitions = msgs["recognition"]

        for i, detection in enumerate(detections):
            bbox = frame_norm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
            roiData = detection.boundingBoxMapping
            roi = roiData.roi
            roi = roi.denormalize(frame.shape[1], frame.shape[0])   *# Normalize bounding box*
            topLeft = roi.topLeft()
            bottomRight = roi.bottomRight()
            xmin = int(topLeft.x)
            ymin = int(topLeft.y)
            xmax = int(bottomRight.x)
            ymax = int(bottomRight.y)
            *# Decoding of recognition results*
            rec = recognitions[i]
            yaw = rec.getLayerFp16('angle_y_fc')[0]
            pitch = rec.getLayerFp16('angle_p_fc')[0]
            roll = rec.getLayerFp16('angle_r_fc')[0]
            decode_pose(yaw, pitch, roll, bbox, frame)

            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (10, 245, 10), 2)
            cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 0, 0), 2)


            y = (bbox[1] + bbox[3]) // 2
            if stereo:
                *# You could also get detection.spatialCoordinates.x and detection.spatialCoordinates.y coordinates*
                coords = "Z: {:.2f} m".format(detection.spatialCoordinates.z/1000)
                cv2.putText(frame, coords, (bbox[0], y + 60), cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0, 0), 8)
                cv2.putText(frame, coords, (bbox[0], y + 60), cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 255), 2)

        cv2.imshow("Camera", frame)
    key = cv2.waitKey(1)
    if key == ord('q'):
        break
    elif key ==ord('r'):
        new_angle = 90
        cfg = dai.ImageManipConfig()
        rgbRr.angle = new_angle
        cfg.setCropRotatedRect(rgbRr, False)
        qManipCfg.send(cfg)
        print(f"Image rotated to {rgbRr.angle} degrees")

Hi erik
After installing the camera by rotating it clockwise by 90 degrees, I rotated both the image and depth by 90 degrees, and marked the ROI area with boxes on both the depth map and RGB image. The ROI area does appear on the face in the RGB image, but it deviates a lot from the face in the depth map. I am not sure if it is a problem with the size of the depth map
The following is my code, which uses tools.ty and MultiMonsSync.by from the gen2 head post detection/pai folder.
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from MultiMsgSync import TwoStageHostSeqSync
import blobconverter
import cv2
import depthai as dai
from tools import *
rgbRr = dai.RotatedRect()
def create_pipeline(stereo):
    pipeline = dai.Pipeline()
    print("Creating Color Camera...")
    cam = pipeline.create(dai.node.ColorCamera)
    cam.setPreviewSize(640,400)
    cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
    cam.setInterleaved(False)
    cam.setPreviewKeepAspectRatio(False)
    cam.setBoardSocket(dai.CameraBoardSocket.RGB)
    if stereo:
        monoLeft = pipeline.create(dai.node.MonoCamera)
        monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
        monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
        monoRight = pipeline.create(dai.node.MonoCamera)
        monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
        monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
        stereo = pipeline.create(dai.node.StereoDepth)
        stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
        stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
        monoLeft.out.link(stereo.left)
        monoRight.out.link(stereo.right)
        print("OAK-D detected, app will display spatial coordiantes")
        face_det_nn = pipeline.create(dai.node.MobileNetSpatialDetectionNetwork)
        manip_depth = pipeline.create(dai.node.ImageManip)
        rotated_rect = dai.RotatedRect()
        rotated_rect.center.x, rotated_rect.center.y = monoRight.getResolutionWidth() // 2, monoRight.getResolutionHeight() // 2
        rotated_rect.size.width, rotated_rect.size.height = monoRight.getResolutionHeight(), monoRight.getResolutionWidth()
        rotated_rect.angle = 90
        manip_depth.initialConfig.setCropRotatedRect(rotated_rect, False)
        stereo.depth.link(manip_depth.inputImage)
        manip_depth.out.link(face_det_nn.inputDepth)
    else:
        print("OAK-1 detected, app won't display spatial coordiantes")
        face_det_nn = pipeline.create(dai.node.MobileNetDetectionNetwork)
    face_det_nn.setConfidenceThreshold(0.5)
    face_det_nn.setBoundingBoxScaleFactor(0.5)
    face_det_nn.setBlobPath(blobconverter.from_zoo(name="face-detection-retail-0004", shaves=6))
    copy_manip = pipeline.create(dai.node.ImageManip)
    rgbRr.center.x, rgbRr.center.y = cam.getPreviewWidth() // 2, cam.getPreviewHeight() // 2
    rgbRr.size.width, rgbRr.size.height = cam.getPreviewHeight(), cam.getPreviewWidth()
    rgbRr.angle = 90
    copy_manip.initialConfig.setCropRotatedRect(rgbRr, False)
    copy_manip.setNumFramesPool(15)
    copy_manip.setMaxOutputFrameSize(3499200)
    cam.preview.link(copy_manip.inputImage)
    face_det_manip = pipeline.create(dai.node.ImageManip)
    face_det_manip.initialConfig.setResize(300, 300)
    face_det_manip.initialConfig.setFrameType(dai.RawImgFrame.Type.RGB888p)
    copy_manip.out.link(face_det_manip.inputImage)
    face_det_manip.out.link(face_det_nn.input)
    cam_xout = pipeline.create(dai.node.XLinkOut)
    cam_xout.setStreamName("color")
    face_det_manip.out.link(cam_xout.input)
    face_det_xout = pipeline.create(dai.node.XLinkOut)
    face_det_xout.setStreamName("detection")
    face_det_nn.out.link(face_det_xout.input)
    image_manip_script = pipeline.create(dai.node.Script)
    face_det_nn.out.link(image_manip_script.inputs['face_det_in'])
    face_det_nn.passthrough.link(image_manip_script.inputs['passthrough'])
    cam.preview.link(image_manip_script.inputs['preview'])
    image_manip_script.setScript("""
    import time
    msgs = dict()
    def add_msg(msg, name, seq = None):
        global msgs
        if seq is None:
            seq = msg.getSequenceNum()
        seq = str(seq)
        if seq not in msgs:
            msgs[seq] = dict()
        msgs[seq][name] = msg
        if 15 < len(msgs):
            node.warn(f"Removing first element! len {len(msgs)}")
            msgs.popitem() # Remove first element
    def get_msgs():
        global msgs
        seq_remove = [] # Arr of sequence numbers to get deleted
        for seq, syncMsgs in msgs.items():
            seq_remove.append(seq) # Will get removed from dict if we find synced msgs pair
            if len(syncMsgs) == 2: # 1 frame, 1 detection
                for rm in seq_remove:
                    del msgs[rm]
                return syncMsgs # Returned synced msgs
        return None
    def correct_bb(xmin,ymin,xmax,ymax):
        if xmin < 0: xmin = 0.001
        if ymin < 0: ymin = 0.001
        if xmax > 1: xmax = 0.999
        if ymax > 1: ymax = 0.999
        return [xmin,ymin,xmax,ymax]
    while True:
        time.sleep(0.001) # Avoid lazy looping
        preview = node.io['preview'].tryGet()
        if preview is not None:
            add_msg(preview, 'preview')
        face_dets = node.io['face_det_in'].tryGet()
        if face_dets is not None:
            passthrough = node.io['passthrough'].get()
            seq = passthrough.getSequenceNum()
            add_msg(face_dets, 'dets', seq)
        sync_msgs = get_msgs()
        if sync_msgs is not None:
            img = sync_msgs['preview']
            dets = sync_msgs['dets']
            for i, det in enumerate(dets.detections):
                cfg = ImageManipConfig()
                bb = correct_bb(det.xmin-0.03, det.ymin-0.03, det.xmax+0.03, det.ymax+0.03)
                cfg.setCropRect(*bb)
                cfg.setResize(60, 60)
                cfg.setKeepAspectRatio(False)
                node.io['manip_cfg'].send(cfg)
                node.io['manip_img'].send(img)
    """)
    recognition_manip = pipeline.create(dai.node.ImageManip)
    recognition_manip.initialConfig.setResize(60, 60)
    recognition_manip.setWaitForConfigInput(True)
    image_manip_script.outputs['manip_cfg'].link(recognition_manip.inputConfig)
    image_manip_script.outputs['manip_img'].link(recognition_manip.inputImage)
    print("Creating recognition Neural Network...")
    recognition_nn = pipeline.create(dai.node.NeuralNetwork)
    recognition_nn.setBlobPath(blobconverter.from_zoo(name="head-pose-estimation-adas-0001", shaves=6))
    recognition_manip.out.link(recognition_nn.input)
    recognition_xout = pipeline.create(dai.node.XLinkOut)
    recognition_xout.setStreamName("recognition")
    xoutDepth = pipeline.create(dai.node.XLinkOut)
    xoutDepth.setStreamName("depth")
    face_det_nn.passthroughDepth.link(xoutDepth.input)
    recognition_nn.out.link(recognition_xout.input)
    return pipeline
with dai.Device() as device:
    stereo = 1 < len(device.getConnectedCameras())
    device.startPipeline(create_pipeline(stereo))
    sync = TwoStageHostSeqSync()
    queues = {}
    for name in ["color", "detection", "recognition", "depth"]:
        queues[name] = device.getOutputQueue(name)
    depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False)
    while True:
        depth = depthQueue.get()
        for name, q in queues.items():
            if q.has():
                sync.add_msg(q.get(), name)
        msgs = sync.get_msgs()
        depthFrame = depth.getFrame()  # depthFrame values are in millimeters
        depth_downscaled = depthFrame[::4]
        if np.all(depth_downscaled == 0):
            min_depth = 0  # Set a default minimum depth value when all elements are zero
        else:
            min_depth = np.percentile(depth_downscaled[depth_downscaled != 0], 1)
        max_depth = np.percentile(depth_downscaled, 99)
        depthFrameColor = np.interp(depthFrame, (min_depth, max_depth), (0, 255)).astype(np.uint8)
        depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_TURBO)
        if msgs is not None:
            frame = msgs["color"].getCvFrame()
            detections = msgs["detection"].detections
            recognitions = msgs["recognition"]
            for i, detection in enumerate(detections):
                bbox = frame_norm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
                roiData = detection.boundingBoxMapping
                roi = roiData.roi
                depthroi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0])
                depthtopLeft = depthroi.topLeft()
                depthbottomRight = depthroi.bottomRight()
                depthxmin = int(depthtopLeft.x)
                depthymin = int(depthtopLeft.y)
                depthxmax = int(depthbottomRight.x)
                depthymax = int(depthbottomRight.y)
                cv2.rectangle(depthFrameColor, (depthxmin, depthymin), (depthxmax, depthymax),  (255, 255, 255), 1)
                roi = roi.denormalize(frame.shape[1], frame.shape[0])   # Normalize bounding box
                topLeft = roi.topLeft()
                bottomRight = roi.bottomRight()
                xmin = int(topLeft.x)
                ymin = int(topLeft.y)
                xmax = int(bottomRight.x)
                ymax = int(bottomRight.y)
                rec = recognitions[i]
                yaw = rec.getLayerFp16('angle_y_fc')[0]
                pitch = rec.getLayerFp16('angle_p_fc')[0]
                roll = rec.getLayerFp16('angle_r_fc')[0]
                decode_pose(yaw, pitch, roll, bbox, frame)
                cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (10, 245, 10), 2)
                cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 0, 0), 2)
                y = (bbox[1] + bbox[3]) // 2
                if stereo:
                    coords = "Z: {:.2f} m".format(detection.spatialCoordinates.z/1000)
                    cv2.putText(frame, coords, (bbox[0], y + 60), cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0, 0), 8)
                    cv2.putText(frame, coords, (bbox[0], y + 60), cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 255), 2)
            cv2.imshow("Camera", frame)
            cv2.imshow("depth", depthFrameColor)
        if cv2.waitKey(1) == ord('q'):
            break`