ramkunchur yes, that's the correct code. I have created another demo code that links 300x300 rotated frames to mobilenet. You will need to place this script into depthai-python/examples, as it requires mobilenet blob. Unfortunately, I don't have time to update the script you mentioned, but I am sure you will be able to update it yourself with the help of the demo script I have just created - it should be straightforward.
Thanks, Erik

    Hi erik ...

    Thanks I'm able to get it right this time..

    However, my full screen mode doesn't work with this, probably as it needs output resolution to be in multiples of 16...

    Not sure how to resolve this as having full-screen output would have been nice

    Thanks so much for your time and help... 🙂

    Thanks & Best Regards,
    Ram

    • erik replied to this.

      ramkunchur You could just use cv2.resize() function to upscale the 300x300 frame to the desired size. You could also stream 1080P video output to the device and display detections on the video frames - not 300x300 preview frame. So something similar to this example.
      Thanks, Erik

      a year later

      Hello All,

      I am trying to rotate my camera but I am confused by the links and syntax of this api and I need this done very soon for production. Here is my code:

      def get_pipeline():
          pipeline = dai.Pipeline()
      
          # # Define a source - color camera
          cam = pipeline.createColorCamera()
          cam.setBoardSocket(dai.CameraBoardSocket.RGB)
          # cam.setInterleaved(False)
          cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_48_MP)
          cam.setVideoSize(1920, 1080)
          cam.initialControl.setSceneMode(dai.CameraControl.SceneMode.FACE_PRIORITY)
      
          # Create MobileNet detection network
          mobilenet = pipeline.create(dai.node.MobileNetDetectionNetwork)
          mobilenet.setBlobPath(
              blobconverter.from_zoo(name="face-detection-retail-0004", shaves=3)
          )
          mobilenet.setConfidenceThreshold(0.7)
      
          crop_manip = pipeline.create(dai.node.ImageManip)
          crop_manip.initialConfig.setResize(300, 300)
          crop_manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
          cam.isp.link(crop_manip.inputImage)
          crop_manip.out.link(mobilenet.input)
      
          # Create an UVC (USB Video Class) output node. It needs 1920x1080, NV12 input
          uvc = pipeline.createUVC()
          cam.video.link(uvc.input)

      This is what I tried but I am just guessing.

      def get_pipeline():
      pipeline = dai.Pipeline()

          # # Define a source - color camera
          cam = pipeline.createColorCamera()
          cam.setBoardSocket(dai.CameraBoardSocket.RGB)
          # cam.setInterleaved(False)
          cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_48_MP)
          cam.setVideoSize(1920, 1080)
          cam.initialControl.setSceneMode(dai.CameraControl.SceneMode.FACE_PRIORITY)
      
          # Create MobileNet detection network
          mobilenet = pipeline.create(dai.node.MobileNetDetectionNetwork)
          mobilenet.setBlobPath(
              blobconverter.from_zoo(name="face-detection-retail-0004", shaves=3)
          )
          mobilenet.setConfidenceThreshold(0.7)
      
          #
      
          manipRgb = pipeline.createImageManip()
          rgbRr = dai.RotatedRect()
          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
          manipRgb.initialConfig.setCropRotatedRect(rgbRr, False)
          cam.preview.link(manipRgb.inputImage)
      
      
          #
      
          crop_manip = pipeline.create(dai.node.ImageManip)
          crop_manip.initialConfig.setResize(300, 300)
          crop_manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
          manipRgb.out.link(crop_manip.inputImage) #added
          cam.isp.link(crop_manip.inputImage)
          crop_manip.out.link(mobilenet.input)
      • erik replied to this.

        We're those guidelines for posting to the forum or submitting for review?
        We are using a UVC and I was trying to flip the image before output but I think it needs to be 1920,1080 so it is faulting.. Is it possible to rotate the image from a script?

        • erik replied to this.

          Some general feedback here would be great. I do not know enough to ask the right questions yet. We have a camera using UVC and face detection but it was longer than it was tall (1920, 1080) so we wanted to rotate the and camera and stream (1080,1920). When we rotate the camera, the face detection is not looking for the sideways faces so I need to flip the stream before it goes in to that I believe but not before the UVC input? :
          What is the max camRgb video size? We are using the OAK SOM.

          import os
          import sys
          import time
          
          import blobconverter
          import click
          import depthai as dai
          
          if sys.version_info[0] < 3:
              raise Exception["Doesn't work with Py2"]
          
          MJPEG = False
          
          os.environ["DEPTHAI_LEVEL"] = "debug"
          
          progressCalled = False
          # TODO move this under flash(), will need to handle `progressCalled` differently
          def progress(p):
              global progressCalled
              progressCalled = True
              print(f"Flashing progress: {p*100:.1f}%")
          
          
          # Will flash the bootloader if no pipeline is provided as argument
          def flash(pipeline=None):
              (f, bl) = dai.DeviceBootloader.getFirstAvailableDevice()
              bootloader = dai.DeviceBootloader(bl, True)
          
              startTime = time.monotonic()
              if pipeline is None:
                  print("Flashing bootloader...")
                  bootloader.flashBootloader(progress)
              else:
                  print("Flashing application pipeline...")
                  bootloader.flash(progress, pipeline)
          
              if not progressCalled:
                  raise RuntimeError("Flashing failed, please try again")
              elapsedTime = round(time.monotonic() - startTime, 2)
              print("Done in", elapsedTime, "seconds")
          
          
          @click.command()
          @click.option(
              "-fb",
              "--flash-bootloader",
              is_flag=True,
              help="Updates device bootloader prior to running",
          )
          @click.option(
              "-fp",
              "--flash-pipeline",
              is_flag=True,
              help="Flashes pipeline. If bootloader flash is also requested, this will be flashed after",
          )
          @click.option(
              "-gbs",
              "--get-boot-state",
              is_flag=True,
              help="Prints out the boot state of the connected MX"
          )
          def main(flash_bootloader, flash_pipeline, get_boot_state):
              
              def get_pipeline():
                  pipeline = dai.Pipeline()
          
                  # # Define a source - color camera
                  cam = pipeline.createColorCamera()
                  cam.setBoardSocket(dai.CameraBoardSocket.RGB)
                  cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_48_MP)
                  cam.setVideoSize(1920, 1080)
                  cam.initialControl.setSceneMode(dai.CameraControl.SceneMode.FACE_PRIORITY)
          
                  # Create MobileNet detection network
                  mobilenet = pipeline.create(dai.node.MobileNetDetectionNetwork)
                  mobilenet.setBlobPath(
                      blobconverter.from_zoo(name="face-detection-retail-0004", shaves=3)
                  )
                  mobilenet.setConfidenceThreshold(0.7)
          
                  crop_manip = pipeline.create(dai.node.ImageManip)
                  crop_manip.initialConfig.setResize(300, 300)
                  crop_manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
                  cam.isp.link(crop_manip.inputImage)
                  crop_manip.out.link(mobilenet.input)
          
                  # Create an UVC (USB Video Class) output node. It needs 1920x1080, NV12 input
                  uvc = pipeline.createUVC()
                  cam.video.link(uvc.input)
          
                  # Script node
                  script = pipeline.create(dai.node.Script)
                  mobilenet.out.link(script.inputs["dets"])
                  script.outputs["cam_cfg"].link(cam.inputConfig)
                  script.outputs["cam_ctrl"].link(cam.inputControl)
                  script.setScript(
                  """
                  ORIGINAL_SIZE = (5312, 6000) # 48MP with size constraints described on IMX582 luxonis page
                  SCENE_SIZE = (1920, 1080) # 1080P
                  x_arr = []
                  y_arr = []
                  AVG_MAX_NUM=7
                  limits = [0, 0] # xmin and ymin limits
                  limits.append((ORIGINAL_SIZE[0] - SCENE_SIZE[0]) / ORIGINAL_SIZE[0]) # xmax limit
                  limits.append((ORIGINAL_SIZE[1] - SCENE_SIZE[1]) / ORIGINAL_SIZE[1]) # ymax limit
                  cfg = ImageManipConfig()
                  ctrl = CameraControl()
                  def average_filter(x, y):
                      x_arr.append(x)
                      y_arr.append(y)
                      if AVG_MAX_NUM < len(x_arr): x_arr.pop(0)
                      if AVG_MAX_NUM < len(y_arr): y_arr.pop(0)
                      x_avg = 0
                      y_avg = 0
                      for i in range(len(x_arr)):
                          x_avg += x_arr[i]
                          y_avg += y_arr[i]
                      x_avg = x_avg / len(x_arr)
                      y_avg = y_avg / len(y_arr)
                      if x_avg < limits[0]: x_avg = limits[0]
                      if y_avg < limits[1]: y_avg = limits[1]
                      if limits[2] < x_avg: x_avg = limits[2]
                      if limits[3] < y_avg: y_avg = limits[3]
                      return x_avg, y_avg
                  while True:
                      dets = node.io['dets'].get().detections
                      if len(dets) == 0: continue
                       coords = dets[0] # take first
                      # Get detection center
                      x = (coords.xmin + coords.xmax) / 2
                      y = (coords.ymin + coords.ymax) / 2
                      x -= SCENE_SIZE[0] / ORIGINAL_SIZE[0] / 2
                      y -= SCENE_SIZE[1] / ORIGINAL_SIZE[1] / 2
                      # node.warn(f"{x=} {y=}")
                      x_avg, y_avg = average_filter(x,y)
                      
                      # node.warn(f"{x_avg=} {y_avg=}")
                      cfg.setCropRect(x_avg, y_avg, 0, 0)
                      node.io['cam_cfg'].send(cfg)
                      node.io['cam_ctrl'].send(ctrl)
                  """
                  )
                  return pipeline
          
              if flash_bootloader or flash_pipeline:
                  if flash_bootloader: flash()
                  if flash_pipeline: flash(get_pipeline())
                  print("Flashing successful. Please power-cycle the device")
                  quit()
          
              if get_boot_state:
                  (f, bl) = dai.DeviceBootloader.getFirstAvailableDevice()
                  print(f"Device state: {bl.state.name}")
          
          
              # with dai.Device(get_pipeline(), usb2Mode=True) as dev:
              with dai.Device(get_pipeline()) as dev:
                  print(f"Connection speed: {dev.getUsbSpeed()}")
          
                  # Doing nothing here, just keeping the host feeding the watchdog
                  while True:
                      try:
                          time.sleep(0.1)
                      except KeyboardInterrupt:
                          break
          
          
          if __name__ == "__main__":
              try:
                  main()
              except KeyboardInterrupt:
                  sys.exit(0)

          Hi chandrian ,
          For UVC, I believe the current limitation is that frames need to be 720P and in NV12 format, so you would likely need to rotate the image after retrieving it on the host, or use some other option (eg streaming via dephtai library, then creating virtual camera on the host). Would that work for your application?
          THanks, Erik

            erik

            Thanks erik you've been so helpful on this. I dont think we can flip it after the host has it... I think the idea was to rotate it so that it has more height to work with in the frame analyzing.

            What size is the image coming out?

            What does this crop do?:
            crop_manip = pipeline.create(dai.node.ImageManip)
            crop_manip.initialConfig.setResize(300, 300)
            crop_manip.initialConfig.setFrameType(dai.ImgFrame.Type.BGR888p)
            cam.isp.link(crop_manip.inputImage)
            crop_manip.out.link(mobilenet.input)

            I think the idea was to rotate it so that it has more height to work with in the frame analyzing.
            This was the wrong assumption above. I think I can just make the face-detection-crop more tall than long and I'll be ok. It is hard to follow the dimensions.

            Is it possible to crop into a different (smaller) size image for the face tracker? Where in the code does it need to be 1920x1080? before or after the script running?

            • erik replied to this.

              Hi chandrian ,

              1. The image should be full HD if you are using depthai with UVC pipeline (docs here).
              2. The code snippet resizes input frame to 300x300 and converts it to 8bit BGR format.
              3. Yep that should be possible🙂
              4. Can you please share what exactly you want to achieve?

              Thanks, Erik


              Thanks again for the response Erik. Basically we need to zoom in on the person like this and crop to this more vertical size.

              • erik replied to this.

                Hi chandrian ,
                with UVC mode this (currently) isn't possible, as UVC node needs full hd images. You could, however, stream exact same image but rotated by 90deg. Thoughts?
                Thanks, Erik

                Yes I attempted that but was not successful. Can you give me general instructions of where to implement that? The problems I faced were that the UVC needed 1920x1080 and when I rotated that, it was 1080x1920, and that the face recognition did not work when the camera was rotated 90 degrees.

                Thanks,
                Aaron

                • erik replied to this.

                  Hi chandrian ,
                  I assume you are using something similar to Lossless Zooming. So first you would want to rotate the frame 90deg (so people are upright), do the face detection, crop the original (rotated) 4k image into 1080x1920 (as in the lossless zooming example), then rotate that to 1080P, which you can feed into the UVC node. Thougths?
                  Thanks ,Erik

                  Ok so this wouldnt be in the script then. I realize script is mostly for changing the pipeline anyway. Yes that sounds like a plan for me. I will attempt and let you know. Thanks!!

                  I will probably need to remove this before the rotate then? : cam.setVideoSize(1920, 1080)

                  • erik replied to this.