OpenCV Face Detection HDMI

In this notebook, opencv face detection will be applied to HDMI input images.

To run all cells in this notebook a HDMI input source and HDMI output monitor are required.

References:

https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xml

Step 1: Load the overlay



In [1]:

    
from pynq.overlays.base import BaseOverlay
from pynq.lib.video import *

base = BaseOverlay("base.bit")
hdmi_in = base.video.hdmi_in
hdmi_out = base.video.hdmi_out

Step 2: Initialize HDMI I/O



In [2]:

    
hdmi_in.configure(PIXEL_RGB)
hdmi_out.configure(hdmi_in.mode, PIXEL_RGB)

hdmi_in.start()
hdmi_out.start()









    Out[2]:





<contextlib._GeneratorContextManager at 0x30185870>

Step 3: Show input frame using IPython Image

Source: http://gardnerproductions.ca/wp-content/uploads/2015/11/bigstock-People-with-different-emotions-930x701-thegem-blog-default.jpg



In [3]:

    
import PIL.Image
frame = hdmi_in.readframe()
img = PIL.Image.fromarray(frame)
img.save("/home/xilinx/jupyter_notebooks/base/video/data/face_detect.jpg")

img









    Out[3]:

Step 4: Apply the face detection to the input



In [4]:

    
import cv2
import numpy as np

frame = hdmi_in.readframe()

face_cascade = cv2.CascadeClassifier(
    '/home/xilinx/jupyter_notebooks/base/video/data/'
    'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(
    '/home/xilinx/jupyter_notebooks/base/video/data/'
    'haarcascade_eye.xml')

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

for (x,y,w,h) in faces:
    cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = frame[y:y+h, x:x+w]

    eyes = eye_cascade.detectMultiScale(roi_gray)
    for (ex,ey,ew,eh) in eyes:
        cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

Step 5: Show results on HDMI output

Output OpenCV results via HDMI.



In [5]:

    
hdmi_out.writeframe(frame)

Step 6: Show results within notebook

Output OpenCV results as JPEG.



In [6]:

    
img = PIL.Image.fromarray(frame)
img.save("/home/xilinx/jupyter_notebooks/base/video/data/face_detect.jpg")

img









    Out[6]:

Step 7: Release HDMI



In [7]:

    
hdmi_out.stop()
hdmi_in.stop()
del hdmi_in, hdmi_out