Object Detection using Raspberry pi and OpenCv

Object Detection using Raspberry pi and OpenCv


import numpy as np
import cv2
import io
import picamera

stream = io.BytesIO()

with picamera.PiCamera() as camera:
    camera.resolution = (320, 240)
    camera.capture(stream, format='jpeg')

buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)


img = cv2.imdecode(buff, 1)

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

watch_cascade = cv2.CascadeClassifier('haarcascade_watch.xml')

while 1:
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
   
    watches = watch_cascade.detectMultiScale(gray, 50, 50)
    
   
    for (x,y,w,h) in watches:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)

    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

        
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = img[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)

    cv2.imshow('picture.jpg',img)

  

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Face and Eye Detection using Raspberry pi and OpenCv

Face and Eye  Detection using Raspberry pi and OpenCv:

import io

import picamera

import cv2

import numpy

stream = io.BytesIO()

with picamera.PiCamera() as camera:

    camera.resolution = (320, 240)

    camera.capture(stream, format='jpeg')

buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)

img = cv2.imdecode(buff, 1)

face_cascade = cv2.CascadeClassifier('/home/pi/opencvfolder/haarcascade_frontalface_alt.xml')

eye_cascade = cv2.CascadeClassifier('/home/pi/opencvfolder/haarcascade_eye.xml')

#Convert to grayscale

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.3, 5)

print "Found "+str(len(faces))+" face(s)"

#Draw a rectangle around every found face

for (x,y,w,h) in faces:

        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

        roi_gray = gray[y:y+h, x:x+w]

        roi_color = img[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)

cv2.imwrite('picture.jpg',img)

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Face Detection using Raspberry pi and OpenCv

Face Detection using Raspberry pi and OpenCv


import io
import picamera
import cv2
import numpy

stream = io.BytesIO()

with picamera.PiCamera() as camera:
    camera.resolution = (320, 240)
    camera.capture(stream, format='jpeg')

buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)

image = cv2.imdecode(buff, 1)

face_cascade = cv2.CascadeClassifier('/usr/share/opencvfolder/haarcascade_frontalface_alt.xml')

#Convert to grayscale
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.1, 5)

print "Found "+str(len(faces))+" face(s)"

for (x,y,w,h) in faces:
    cv2.rectangle(image,(x,y),(x+w,y+h),(255,255,0),2)

cv2.imwrite('picture.jpg',image)

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