Isolating data from images using Segnet

Autonomous Machines Jetson & Embedded Systems Jetson Nano
1

Hello,

I am new to Jetson and trying to use semantic segmentation to filter out unnecessary data (in my case, walls and floors) from a webcam video stream. The data processing part of my script is shown below, using OpenCV and SegNet:

     net = jetson.inference.segNet("fcn-resnet18-sun-512x400")
    _,image = camera.read()
    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGBA).astype(np.float32)
    img_input = jetson.utils.cudaFromNumpy(img)
    net.Process(img_input)
    net.Mask(img_input)
    jetson.utils.cudaDeviceSynchronize()
    img_cv2 = jetson.utils.cudaToNumpy(img_input)
    img_cv2 = cv2.cvtColor(img_cv2, cv2.COLOR_RGBA2BGR).astype(np.float32)
    out.write(np.uint8(img_cv2))
    cv2.imshow('OpenCV Output', img_cv2/255)
    if cv2.waitKey(1) & 0xFF == ord('q'):
            break

I want to retrieve the parts of the image that are not classed as walls or floors (classes 2 and 3?) so I can apply another algorithm to what’s left.

Clearly there must be somewhere where the changes to the image are stored, because the cv2.imshow of img_cv2 displays the mask with different class colors. I’m not sure how to access it.

Ideally I would have something similar to the .Detection method for DetectNet. I can’t seem to find anything similar for SegNet in the documentation.

Thanks!

3

Hi,

Sorry for the late update.

Have you fixed this issue already?
If not, please check if following discussion can meet you requirement:

Thanks.

4

Hello,

Thank you! Using the code from that thread, I can get a (13,16,1) numpy array of the class IDs. I think that will be all I need. The one change I had to make was changing the format in jetson.utils.cudaAllocMapped() to ‘gray8’. When I used the frame format, I got an array of the class colors. I think that will be all I need for this process.

Thank you!

6

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