一、靜態的圖像人臉檢測

import numpy as np

import cv2 as cv

path = 'haarcascade_frontalface_default.xml'

face_cascade = cv.CascadeClassifier(path)

path = 'haarcascade_eye.xml'

eye_cascade = cv.CascadeClassifier(path)

# 靜態圖像人臉檢測

img = cv.imread('test.jpg')

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

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

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

cv.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:

cv.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

cv.imshow('img',img)

cv.waitKey(0)

cv.destroyAllWindows()

二、視頻人臉實時檢測及保存

# 攝像頭動態人臉檢測 及 視頻保存

import numpy as np

import cv2 as cv

path = 'haarcascade_frontalface_default.xml'

face_cascade = cv.CascadeClassifier(path)

path = 'haarcascade_eye.xml'

eye_cascade = cv.CascadeClassifier(path)

#1.來自視頻圖像

# cap = cv.VideoCapture('/Users/admin/opencv-4.0.0/samples/data/vtest.avi')

#2. 來自攝像頭

cap = cv.VideoCapture(0)

print(cap.isOpened())

count = 0

# 視頻保存的參數設置

sz = (int(cap.get(cv.CAP_PROP_FRAME_WIDTH)),

int(cap.get(cv.CAP_PROP_FRAME_HEIGHT)))

fps = 5

#fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')

#fourcc = cv2.VideoWriter_fourcc('m', 'p', 'e', 'g')

fourcc = cv.VideoWriter_fourcc(*'mpeg')

## open and set props

vout = cv.VideoWriter()

vout.open('output2.mp4',fourcc,fps,sz,True)

while(True):

count += 1

ret, img = cap.read()

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

# cv.imshow('FRAME', gray)

# cv.imwrite('FRAME_%d.png'%count, gray)

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

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

cv.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:

cv.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,255),2)

cv.imshow('img',img)

vout.write(img)

if cv.waitKey(1) & 0xFF == ord('q'):

break

cap.release()

vout.release()

cv.destroyAllWindows()

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