Haar module detection

  1. Harr cascade detection이란?

 :  이번 챕터에서 face recognition에 대해 다룰 것이다.

  -> face detection을 위해 Object detection module 또는 Deep learning module(dnn module)이 사용 가능하다.

   :서로 융합하면 장단점이 있다.

 

-> Object Detection: Haar cascade object detection 사용. 

-> 장점: 굉장히 빠르게 object detection을 이용한다.  단점: 아무래도 CNN 보다 정확도가 떨어진다. (예외 상황에 인식 힘듦 : 얼굴 돌리거나, 선글라스 낀다던가...)  

각각 부분을 나눠서 인식한다.

좌표를 통해 인식한다.

 

눈 , 코 등 특정 부위를 인식할때 상당히 효과적.

 

 

2.  Haar 방식을 이용한 Face detection

  -> face_cascade/ eyes_cascade 함수가 모두 이미 opencv안에 구현되어 있었다.

 

얼굴인식을 빙자한 덕질

   아 ㅁㅊ 콧구멍이 눈인줄 알았나봄ㅋㅋㅋㅋㅋ=͟͟͞͞개=͟͟͞͞웃=͟͟͞͞겨=͟͟͞͞개=͟͟͞͞웃=͟͟͞͞겨=͟͟͞͞개=͟͟͞͞웃=͟͟͞͞겨=͟͟͞͞개=͟͟͞͞웃=͟͟͞͞겨

 

 

opencv 분발해ㅡㅡ

 

# -*- coding: utf-8 -*-
import cv2
import numpy as np

def detectAndDisplay(frame):
    frame_gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    frame_gray= cv2.equalizeHist(frame_gray)
    #-- Detect faces
    faces= face_cascade.detectMultiScale(frame_gray)
    for(x,y,w,h) in faces:
        center=(x+w//2,y+h//2)
        #center location of face
        frame=cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),4)
        faceROI=frame_gray[y:y+h,x:x+w]
        #region of interest
        eyes=eyes_cascade.detectMultiScale(faceROI)
        #drawing a picture using location
        for(x2,y2,w2,h2) in eyes:
            eye_center=(x+x2+w2//2, y+y2+h2//2)
            radius=int(round((w2+h2)*0.25))
            frame=cv2.circle(frame,eye_center,radius,(255,0,0),4)
        cv2.imshow('Capture-face detection',frame)
         
         
print("OpenCV version:")
print(cv2.__version__)

img=cv2.imread("ryujin.jpg")
print("width:{} pixels".format(img.shape[1]))
print("height:{} pixels".format(img.shape[0]))
print("channels: {}".format(img.shape[2]))



cv2.imshow("ryujin", img)

face_cascade_name = 'haarcascade_frontalface_alt.xml'
eyes_cascade_name = 'haarcascade_eye_tree_eyeglasses.xml'

face_cascade = cv2.CascadeClassifier()
eyes_cascade = cv2.CascadeClassifier()
#recognizing face & eyes

#-- 1. Load the cascades
if not face_cascade.load(cv2.samples.findFile(face_cascade_name)):
    print('--(!)Error loading face cascade')
    exit(0)
if not eyes_cascade.load(cv2.samples.findFile(eyes_cascade_name)):
    print('--(!)Error loading eyes cascade')
    exit(0)

detectAndDisplay(img)




cv2.waitKey(0)
cv2.destroyAllWindows()

=> haar module에는 이미 eye, face등의 특정한 부위를 인식하는 mask가 구현되어 있는 것 같다. 위치만 설정해줘서 얼굴 사진에 맞게끔만 씌워주면 된다. 옆얼굴이나, 모자를 썼거나, 얼굴이 제대로 안보이면 눈인식도 잘 못하는듯.. 쩝 

 

<GUI로도 만들어줄 수 있다>

import cv2
import numpy as np
from tkinter import *
from PIL import Image
from PIL import ImageTk
from tkinter import filedialog

face_cascade_name = '../opencv/data/haarcascades/haarcascade_frontalface_alt.xml'
eyes_cascade_name = '../opencv/data/haarcascades/haarcascade_eye_tree_eyeglasses.xml'
file_name = 'image/marathon_01.jpg'
title_name = 'Haar cascade object detection'
frame_width = 500

def selectFile():
    file_name =  filedialog.askopenfilename(initialdir = "./image",title = "Select file",filetypes = (("jpeg files","*.jpg"),("all files","*.*")))
    print('File name : ', file_name)
    read_image = cv2.imread(file_name)
    (height, width) = read_image.shape[:2]
    frameSize = int(sizeSpin.get())
    ratio = frameSize / width
    dimension = (frameSize, int(height * ratio))
    read_image = cv2.resize(read_image, dimension, interpolation = cv2.INTER_AREA)
    image = cv2.cvtColor(read_image, cv2.COLOR_BGR2RGB)
    image = Image.fromarray(image)
    imgtk = ImageTk.PhotoImage(image=image)
    detectAndDisplay(read_image)
    
def detectAndDisplay(frame):
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    frame_gray = cv2.equalizeHist(frame_gray)
    #-- Detect faces
    faces = face_cascade.detectMultiScale(frame_gray)
    for (x,y,w,h) in faces:
        
        center = (x + w//2, y + h//2)
        frame = cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 4)
        faceROI = frame_gray[y:y+h,x:x+w]
        #-- In each face, detect eyes
        eyes = eyes_cascade.detectMultiScale(faceROI)
        for (x2,y2,w2,h2) in eyes:
            eye_center = (x + x2 + w2//2, y + y2 + h2//2)
            radius = int(round((w2 + h2)*0.25))
            frame = cv2.circle(frame, eye_center, radius, (255, 0, 0 ), 4)
    #cv2.imshow('Capture - Face detection', frame)
    image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    image = Image.fromarray(image)
    imgtk = ImageTk.PhotoImage(image=image)
    detection.config(image=imgtk)
    detection.image = imgtk
    
#main
main = Tk()
main.title(title_name)
main.geometry()

read_image = cv2.imread("image/marathon_01.jpg")
(height, width) = read_image.shape[:2]
ratio = frame_width / width
dimension = (frame_width, int(height * ratio))
read_image = cv2.resize(read_image, dimension, interpolation = cv2.INTER_AREA)

image = cv2.cvtColor(read_image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
imgtk = ImageTk.PhotoImage(image=image)

face_cascade_name = '../opencv/data/haarcascades/haarcascade_frontalface_alt.xml'
eyes_cascade_name = '../opencv/data/haarcascades/haarcascade_eye_tree_eyeglasses.xml'

face_cascade = cv2.CascadeClassifier()
eyes_cascade = cv2.CascadeClassifier()

#-- 1. Load the cascades
if not face_cascade.load(cv2.samples.findFile(face_cascade_name)):
    print('--(!)Error loading face cascade')
    exit(0)
if not eyes_cascade.load(cv2.samples.findFile(eyes_cascade_name)):
    print('--(!)Error loading eyes cascade')
    exit(0)

label=Label(main, text=title_name)
label.config(font=("Courier", 18))
label.grid(row=0,column=0,columnspan=4)
sizeLabel=Label(main, text='Frame Width : ')                
sizeLabel.grid(row=1,column=0)
sizeVal  = IntVar(value=frame_width)
sizeSpin = Spinbox(main, textvariable=sizeVal,from_=0, to=2000, increment=100, justify=RIGHT)
sizeSpin.grid(row=1, column=1)
Button(main,text="File Select", height=2,command=lambda:selectFile()).grid(row=1, column=2, columnspan=2, sticky=(W, E))
detection=Label(main, image=imgtk)
detection.grid(row=2,column=0,columnspan=4)
detectAndDisplay(read_image)

main.mainloop()