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detector.py
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detector.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jun 11 06:14:09 2019
@author: SONKARyasshu
"""
# import necessary modules
import cv2
import sys
# importing the cascade classifier for face and eye
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
# check for input video
# if no input is given, default camera is choosen as input source
if len(sys.argv) == 1:
cap = 0
else:
cap = sys.argv[1]
# initialize input head, with source
video = cv2.VideoCapture(cap)
# Run an infinite loop, until user quit(press 'q')
while True:
# reading frame from the video source
_, frame =video.read()
# cinverting frame to Gray scale to pass on classifier
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces and return coordinates of rectangle
# This is the section, where you need to work
# To get more accurate result, you need to play with this parameters
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=4)
# make a rectangle around face detected
for (x,y,w,h) in faces:
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
# extract the rectangle, containing face
roi_gray = gray[y:y+h, x:x+w]
roi_color = frame[y:y+h, x:x+w]
# As eye without a face doen't make any sense
# so we search for eye, within the face only
# this reduces the computational load an also increases accuracy
# detect eyes and return coordinates of rectangle
eyes = eye_cascade.detectMultiScale(roi_gray)
# make a rectangle around face detected
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
# show the processed frame
cv2.imshow('Output',frame)
# If 'q' pressed => Quit
key = cv2.waitKey(1)
if key == ord('q'):
# Release the video object
video.release()
# close all open windows
cv2.destroyAllWindows()
exit(0)