The code contains two files. Do you want me to copy & paste the codes here?

Best

Henry

main.py

`from MultiMsgSync import TwoStageHostSeqSync
import blobconverter
import cv2
import depthai as dai
import numpy as np
import datetime
import time

print("DepthAI version", dai.version)
def frame_norm(frame, bbox):
normVals = np.full(len(bbox), frame.shape[0])
normVals[::2] = frame.shape[1]
return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)

def create_pipeline(stereo):
pipeline = dai.Pipeline()

print("Creating Color Camera...")
cam = pipeline.create(dai.node.ColorCamera)
cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_12_MP)
cam.setIspScale(1,5)
cam.setPreviewSize(676, 506)
cam.setInterleaved(False)
cam.setBoardSocket(dai.CameraBoardSocket.RGB)
cam.setFps(10)

# Workaround: remove in 2.18, use `cam.setPreviewNumFramesPool(10)`
# This manip uses 15*3.5 MB => 52 MB of RAM.
copy_manip = pipeline.create(dai.node.ImageManip)
copy_manip.setNumFramesPool(15)
# copy_manip.setMaxOutputFrameSize(3499200)
copy_manip.setMaxOutputFrameSize(676*506*3)

# Crop range
topLeft = dai.Point2f(0.3, 0.3)
bottomRight = dai.Point2f(0.7, 0.7)
# copy_manip.initialConfig.setCropRect(topLeft.x, topLeft.y, bottomRight.x, bottomRight.y)

cam.preview.link(copy_manip.inputImage)

cam_xout = pipeline.create(dai.node.XLinkOut)
cam_xout.setStreamName("color")
copy_manip.out.link(cam_xout.input)

# ImageManip will resize the frame before sending it to the Face detection NN node
face_det_manip = pipeline.create(dai.node.ImageManip)
# face_det_manip.initialConfig.setResize(300, 300)
face_det_manip.initialConfig.setResize(672, 384)
face_det_manip.initialConfig.setKeepAspectRatio(False)
face_det_manip.initialConfig.setFrameType(dai.RawImgFrame.Type.RGB888p)
copy_manip.out.link(face_det_manip.inputImage)

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)
    
    stereo.setOutputSize(monoLeft.getResolutionWidth(), monoLeft.getResolutionHeight())
    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)
    face_det_nn.setBoundingBoxScaleFactor(0.8)
    face_det_nn.setDepthLowerThreshold(100)
    face_det_nn.setDepthUpperThreshold(5000)
    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.setBlobPath(blobconverter.from_zoo(name="face-detection-retail-0004", shaves=6))
face_det_nn.setBlobPath(blobconverter.from_zoo(name="face-detection-adas-0001", shaves=6))
face_det_nn.input.setQueueSize(1)
face_det_manip.out.link(face_det_nn.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'])

# Remove in 2.18 and use `imgFrame.getSequenceNum()` in Script node
face_det_nn.passthrough.link(image_manip_script.inputs['passthrough'])

copy_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(bb):
    if bb.xmin < 0: bb.xmin = 0.001
    if bb.ymin < 0: bb.ymin = 0.001
    if bb.xmax > 1: bb.xmax = 0.999
    if bb.ymax > 1: bb.ymax = 0.999
    return bb

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()
            correct_bb(det)
            cfg.setCropRect(det.xmin, det.ymin, det.xmax, det.ymax)
            # node.warn(f"Sending {i + 1}. det. Seq {seq}. Det {det.xmin}, {det.ymin}, {det.xmax}, {det.ymax}")
            cfg.setResize(62, 62)
            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(62, 62)
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="age-gender-recognition-retail-0013", 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

prefix = './images/' + datetime.datetime.now().strftime("%Y-%m-%d %H%M%S_")
startTime = time.time()
count = 0
wait_time = 200
max_images = 10
with dai.Device() as device:
stereo = 1 < len(device.getConnectedCameras())
device.startPipeline(create_pipeline(stereo))

sync = TwoStageHostSeqSync()
queues = {}
# 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()

        if time.time() - startTime > wait_time:
            count += 1
            if count < max_images:
                cv2.imwrite(prefix + str(count) + '.jpg', frame)
            else:
                break

        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))

            # Decoding of recognition results
            rec = recognitions[i]
            age = int(float(np.squeeze(np.array(rec.getLayerFp16('age_conv3')))) * 100)
            gender = np.squeeze(np.array(rec.getLayerFp16('prob')))
            gender_str = "female" if gender[0] > gender[1] else "male"

            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (10, 245, 10), 2)
            y = (bbox[1] + bbox[3]) // 2
            cv2.putText(frame, str(age), (bbox[0], y), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (0, 0, 0), 8)
            cv2.putText(frame, str(age), (bbox[0], y), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (255, 255, 255), 2)
            cv2.putText(frame, gender_str, (bbox[0], y + 30), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (0, 0, 0), 8)
            cv2.putText(frame, gender_str, (bbox[0], y + 30), cv2.FONT_HERSHEY_TRIPLEX, 1.5, (255, 255, 255), 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)

        if time.time() - startTime < wait_time:
            cv2.putText(frame, "Wait for " + str(wait_time - int(time.time() - startTime)) + " seconds.", (frame.shape[1]-400, 50), cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 255), 2)

        cv2.imshow("Camera", frame)
    if cv2.waitKey(1) == ord('q'):
        break

`

MultiMsgSync.py

`# Color frames (ImgFrame), object detection (ImgDetections) and recognition (NNData)

messages arrive to the host all with some additional delay.

For each ImgFrame there's one ImgDetections msg, which has multiple detections, and for each

detection there's a NNData msg which contains recognition results.

How it works:

Every ImgFrame, ImgDetections and NNData message has it's own sequence number, by which we can sync messages.

class TwoStageHostSeqSync:
def init(self):
self.msgs = {}

name: color, detection, or recognition

def add_msg(self, msg, name):
seq = str(msg.getSequenceNum())
if seq not in self.msgs:
self.msgs[seq] = {} # Create directory for msgs
if "recognition" not in self.msgs[seq]:
self.msgs[seq]["recognition"] = [] # Create recognition array

    if name == "recognition":
        # Append recognition msgs to an array
        self.msgs[seq]["recognition"].append(msg)
        # print(f'Added recognition seq {seq}, total len {len(self.msgs[seq]["recognition"])}')

    elif name == "detection":
        # Save detection msg in the directory
        self.msgs[seq][name] = msg
        self.msgs[seq]["len"] = len(msg.detections)
        # print(f'Added detection seq {seq}')

    elif name == "color": # color
        # Save color frame in the directory
        self.msgs[seq][name] = msg
        # print(f'Added frame seq {seq}')


def get_msgs(self):
    seq_remove = [] # Arr of sequence numbers to get deleted

    for seq, msgs in self.msgs.items():
        seq_remove.append(seq) # Will get removed from dict if we find synced msgs pair

        # Check if we have both detections and color frame with this sequence number
        if "color" in msgs and "len" in msgs:

            # Check if all detected objects (faces) have finished recognition inference
            if msgs["len"] == len(msgs["recognition"]):
                # print(f"Synced msgs with sequence number {seq}", msgs)

                # We have synced msgs, remove previous msgs (memory cleaning)
                for rm in seq_remove:
                    del self.msgs[rm]

                return msgs # Returned synced msgs

    return None # No synced msgs`

These are the two Python files needed to run the code.

The majority of the code is from depthai_gen2_age_gender repository. The only thing I modified is to do the crop
# Crop range
topLeft = dai.Point2f(0.3, 0.3)
bottomRight = dai.Point2f(0.7, 0.7)
copy_manip.initialConfig.setCropRect(topLeft.x, topLeft.y, bottomRight.x, bottomRight.y)