DEPRECATED

Xailient FaceSDK portal will no longer be available from 17th July 2020 onwards. Please use Xailient AI Console instead to get the updated FaceDetector SDK and more detector models. This documentation is out-of-date and will no longer be maintained. Visit our new documentation.

Face Detection on Static Image

import dnn
import cv2 as cv

#By default Low resolution DNN for face detector will be loaded.
#To load the high resolution Face detector please comment the below lines.
#dnn.FaceDetector.set_param("resolution", dnn.RESOLUTION.HIGH)
detectum = dnn.FaceDetector()
THRESHOLD = 0.4 # Value between 0 and 1 for confidence score

im = cv.imread('../data/beatles.jpg')
_, bboxes = detectum.process_frame(im, THRESHOLD)

# Loop through list (if empty this will be skipped) and overlay green bboxes
for i in bboxes:
    cv.rectangle(im, (i[0], i[1]), (i[2], i[3]), (0, 255, 0), 3)

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

Face Detection on Pi Camera Streaming Video

# This script has been tested using a Raspberry Pi Camera Modeule v1.3

from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2 as cv
import math
import dnn
import numpy as np

detectum = dnn.FaceDetector()
THRESHOLD = 0.8

# initialize the camera and grab a reference to the raw camera capture
RES_W = 640 # 1280 # 640 # 256 # 320 # 480 # pixels
RES_H = 480 # 720 # 480 # 144 # 240 # 360 # pixels

camera = PiCamera()
camera.resolution = (RES_W, RES_H)
camera.framerate = 30 # FPS
rawCapture = PiRGBArray(camera, size=(RES_W, RES_H))

# allow the camera to warmup
time.sleep(0.1)

# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
    # grab the raw NumPy array representing the image, then initialize the timestamp
    # and occupied/unoccupied text
    image = frame.array
    image_cpy = np.copy(image) # Create copy since cant modify orig

    # Return's list of bounding box co-ordinates
    _, bboxes = detectum.process_frame(image, THRESHOLD)

    # Loop through list (if empty this will be skipped) and overlay green bboxes
    for i in bboxes:
        cv.rectangle(image_cpy, (i[0], i[1]), (i[2], i[3]), (0, 255, 0), 3)

    # show the frame
    cv.imshow("Frame", image_cpy)
    key = cv.waitKey(1) & 0xFF

    # clear the stream in preparation for the next frame
    rawCapture.truncate(0)

    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break