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
In [1]:
from pynq import Overlay
Overlay("base.bit").download()
In [2]:
from pynq.drivers.video import HDMI
hdmi_out = HDMI('out', video_mode=HDMI.VMODE_1920x1080)
hdmi_in = HDMI('in', init_timeout=10, frame_list=hdmi_out.frame_list)
hdmi_in.start()
hdmi_out.start()
In [3]:
from IPython.display import Image
frame = hdmi_in.frame()
orig_img_path = '/home/xilinx/jupyter_notebooks/examples/data/face_detect.jpg'
frame.save_as_jpeg(orig_img_path)
Image(filename=orig_img_path)
Out[3]:
In [4]:
import cv2
import numpy as np
frame = hdmi_in.frame_raw()
np_frame= (np.frombuffer(frame, dtype=np.uint8)).reshape(1080,1920,3)
face_cascade = cv2.CascadeClassifier(
'./data/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(
'./data/haarcascade_eye.xml')
gray = cv2.cvtColor(np_frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
cv2.rectangle(np_frame,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = np_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)
In [5]:
hdmi_in.frame_index_next()
Out[5]:
In [6]:
hdmi_out.frame_raw(bytearray(np_frame))
In [7]:
orig_img_path = '/home/xilinx/jupyter_notebooks/examples/data/face_detect.jpg'
hdmi_out.frame().save_as_jpeg(orig_img_path)
Image(filename=orig_img_path)
Out[7]:
In [8]:
hdmi_out.stop()
hdmi_in.stop()
del hdmi_in, hdmi_out