OpenCV 라이브러리를 jupyter에 설치 및 실습

 

이 자료는 한국인공지능협회에서 진행한 2021년 사업주 직업능력 개발훈련으로 BASIC AI(32시간)-박성주 강사의 온라인 교육내용을 참고한 것입니다. 

 

 

In [ ]:

# jupyter에서 외부에 있는 명령을 수행하기 위해서는 !를 사용한다.

 

주요 모듈 설치¶

In [2]:

!pip install opencv-python

 

Collecting opencv-python
  Downloading opencv_python-4.5.4.60-cp36-cp36m-win_amd64.whl (35.1 MB)
Requirement already satisfied: numpy>=1.13.3 in c:\programdata\anaconda3\envs\p36t19_beauty\lib\site-packages (from opencv-python) (1.19.5)
Installing collected packages: opencv-python
Successfully installed opencv-python-4.5.4.60

 

주요 및 추가 모듈 설치¶

In [3]:

!pip install opencv-contrib-python

 

Collecting opencv-contrib-python
  Downloading opencv_contrib_python-4.5.4.60-cp36-cp36m-win_amd64.whl (42.0 MB)
Requirement already satisfied: numpy>=1.13.3 in c:\programdata\anaconda3\envs\p36t19_beauty\lib\site-packages (from opencv-contrib-python) (1.19.5)
Installing collected packages: opencv-contrib-python
Successfully installed opencv-contrib-python-4.5.4.60

In [6]:

import cv2

In [7]:

cv2.__version__

Out[7]:

'4.5.4'

In [10]:

import numpy as np

In [ ]:

imgfile = 'imgs/test2.jpg'
img = cv2.imread(imgfile, cv2.IMREAD_COLOR)

cv2.imshow('수지', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

 

https://github.com/opencv/opencv/tree/master/data/haarcascades¶

In [ ]:

#haarcascade_frontalface_default.xml 다운로드

 

 

 

 

라이브러리 불러오기¶

In [ ]:

import numpy as np
import cv2

 

얼굴인식하는 라이브러리 불러오기¶

(캠이 있으면 바로 됨)¶

In [5]:

detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)

 

프로그램을 실행하여 얼굴인식하기¶

In [ ]:

while (True):
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = detector.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
 
    cv2.imshow('frame', img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
 
cap.release()
cv2.destroyAllWindows()

 

# Face_68point_Detection

In [1]:

import sys
import os
import dlib
import glob

In [5]:

predictor_path = "models/shape_predictor_68_face_landmarks.dat"
faces_folder_path = "imgs/test2.jpg"

In [7]:

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
win = dlib.image_window()

In [8]:

f = faces_folder_path

print("Processing file: {}".format(f))
img = dlib.load_rgb_image(f)

win.clear_overlay()
win.set_image(img)

dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
for k, d in enumerate(dets):
    print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
        k, d.left(), d.top(), d.right(), d.bottom()))
    shape = predictor(img, d)
    print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
                                              shape.part(1)))
    win.add_overlay(shape)

win.add_overlay(dets)
dlib.hit_enter_to_continue()

 

Processing file: imgs/test2.jpg
Number of faces detected: 1
Detection 0: Left: 167 Top: 266 Right: 390 Bottom: 489
Part 0: (172, 316), Part 1: (176, 346) ...

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