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")
