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Hello fredjh,
unfortunately, these 2 cameras you mentioned have incompatible connectors (with the IMX378 on OAK-1).
You can, however, swap mono cameras on OAK-D (since they have the same connector) with these.
Thanks, Erik

    I see. The mono camera on OAK-D is fine, if I have to go for that, it has about 1Mpiexls. Openmv comes only with 0.3mpixels. Therefore now I got a HW issue with getting enough res on Openmv platform.
    I hear that there are no precanned examples for blob tracking on OAK cameras? Therefore it is a lot harder?

    • erik replied to this.

      Hello fredjh,
      you could always downscale/crop frames from the color camera before manipulating it further.
      We don't support that out-of-the-box, but you could train a simple NN that would detect/track this "blob". Here are several ML training demos, which also have instructions on how to deploy it to a depthai device.
      Thanks, Erik

        8 days later

        Hi again,

        So I did buy and test the Openmv. Its very intuitive and easy. But, I see that anything above 320x240 resolution is challenging for performance and memory, let alone 1024x768. Thus my interest to follow this topic further.

        I learned that in order for the blob detection to benefit from the intel VPU is that the code is implemented in cuda (or Movidus has some other name for tis cores). Is there such an implementation for OpenCV HoughCirclesDetector Class or SimpleBlobDetector or anything similar? What will happen if there is no cuda implementation, performance or it will not run at all?

        cuda implementation of HoughCirclesDetector class
        https://docs.opencv.org/3.4/da/d80/classcv_1_1cuda_1_1HoughCirclesDetector.html

        Thanks,

        • erik replied to this.

          Hello fredjh,
          is the blob you are trying to detect so small that you need such high resolution to detect it? So another user from our community has similar use-case (link here) - but the detection it's not on a large frame. For our VPU the model has to be in .blob which is compiled from IR (xml/bin - openvino format). You could create your own NN model that would essentially do the same as the HoughCirclesDetector/SimpleBlobDetector, here's a tutorial on how to create custom model, and a harris filter here. So that would allow you to achieve high fps/accuracy on large frames. I will also ask our ML engineer if he could look into implementing such logic (HoughCirclesDetector/SimpleBlobDetector) with pytorch.
          Thanks, Erik

            Just searching a bit on google, found hough transform implemented in TF here. I didn't go into details, but seems like it could be a useful starting point to creating your own hough circle detector .blob.

            Hi again Erik, and thanks for your detailed reply.

            The ball shows as a 10x10 circle blob in the grayscale 1024x768 image. It is in bright color while background is darker. Something like about 80 to 100 less for color value. The guide how to implement is interesting. I am afraid that it looks beyond my expertise to implement a hough transform though. I would have to find someone else to help me out on that.

            However the other option of training a custom NN to look for the blob can be interesting. The only reason I have avoided that has been that I was worried that the NN based blob detection may not provide a precise boundingbox. My aim is to know if the ball is moving or not, so it is essential to get as good of a bounding box as possible that repeats frame after frame. What is your expectation on the results of an own NN plus Harris corner detection (wonder if it works on circles?).

            Fred

              Hello fredjh,
              I also looked into hough transform a bit, and from what I understand one would really need to dig into it to implement, so training your own NN might be less time-consuming. From the link above (the user from our community) the results are quite promising, so if the ball was still there would be less than a few pixel change (on the bounding box).
              Since you will need a host for the OAK-1 connection, it might be easiest to use HoughCirclesDetector from cv2 on the host.
              Thanks, Erik

                Hi, that helped a lot. I was hoping that the OAK would be able to do it all. I will have to look into this more. I would perhaps go back the Raspberry Pi route or maybe Jetson Nano. There seems to be more ready made implementations.
                I am however, quite excited about the Open CVs and Luxonis initiative to introduce a robust smart camera and hope to use it in some future project.

                Cheers

                  fredjh Thanks.

                  Yes, we will add blob detection as well onboard. And in which case I think we will have what you are looking for.

                  By the time that our next KickStarter (here) is delivered, we will likely have this. So before the end of this year.

                  Thanks,
                  Brandon

                      a year later

                      Brandon Any news on this? I'm trying to track an IR LED with depth. Does it even make sense do these non-ML things on the oak-d or am I better off just streaming the stereo images from the oak-d and doing the blob tracking on the host?

                      • erik replied to this.

                          Hi Arthur ,
                          Due to the upcoming release of Series 3 which has on-board quadcore ARM, we haven't pursued any kind of blob detection on the RVC2, as you can just use opencv for this on RVC3. So for the RVC2 devices it would be easiest to do blob tracking on the host, or perhaps try the custom NN implementation of hugh transform blob detection. Thoughts?
                          Thanks, Erik