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Xailient SDK API Documentation

Xailient SDK Version: 1.0.7

dnn.Detector Class

detector = dnn.Detector(int)


Arguments Input Type Default Value Description
threads (optional) int 1 Number of threads to start for running the Detector. This gives the ability to run multiple threads for inferencing. This value should be tuned and select based on the use case to maximise overall performance.


  1. Using 2 threads

    For example, your device has 4 cores and each core runs 1 process thread. If you want 1 thread to be running dedicatedly for camera inputs, one thread for post processing, then you can reserve two threads for the Detector so that it will perform efficiently.

    This is how you would initialize the Detector so that it uses two cores:

    python detector = dnn.Detector(2)

  2. Using 1 thread

    python detector = dnn.Detector()


    python detector = dnn.Detector(1)

  3. Using 4 threads

    python detector = dnn.Detector(4)

process_frame Method

_, bboxes = detector.process_frame(numpy.ndarray, float)


Arguments Input Type Default Value Description
image numpy.ndarray - Image as numpy array
threshold (optional) float The default threshold is built into the SDK and is an optimal value determined when the model was trained. Value between 0 and 1 for confidence score


Returns Type Description
_ - -
bboxes list Each item (bbox) in the list is a list of bounding box coordinates, where bbox[0] = xmin, bbox[1] = ymin, bbox[2] = xmax, bbox[3] = ymax


from xailient import dnn
import cv2 as cv

detector = dnn.Detector()

im = cv.imread('../data/beatles.jpg')

_, bboxes = detector.process_frame(im, THRESHOLD)

# Loop through list (if empty this will be skipped) and overlay green bboxes
# Format of bboxes is: xmin, ymin (top left), xmax, ymax (bottom right)
for bbox in bboxes:
    cv.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 3)

cv.imwrite('../data/beatles_output.jpg', im)