python-OpenCV-人臉、眼睛,微笑檢測 Fu Xianjun. All Rights Reserved.
這裡寫目錄标題
- 一.人臉識别
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- 二.案例
一.人臉識别
人臉識别(face recognition system),是基于人的臉部特征資訊進行身份識别的一種生物識别技術。用錄影機或攝像頭采集含有人臉的圖像或視訊流,并自動在圖像中檢測和跟蹤人臉,進而對檢測到的人臉進行臉部識别的一系列相關技術,通常也叫做人像識别、面部識别。
二.案例
import cv2
import numpy as np
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_smile.xml')
cap = cv2.VideoCapture(0)
width=1280
height=960
cap.set(cv2.CAP_PROP_FRAME_WIDTH,width)#設定圖像寬度
cap.set(cv2.CAP_PROP_FRAME_HEIGHT,height)#設定圖像高度
fgbg = cv2.createBackgroundSubtractorMOG2(
history=500, varThreshold=100, detectShadows=False)#基于自适應混合高斯背景模組化的背景減除法,去除幹擾
cnt=1
while(1):
# get a frame
ret, frame = cap.read()
# show a frame
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5,0) #把灰階圖檔傳給haar進行灰階處理,傳回值是人臉左上角坐标,寬度和高度
#1.3為縮放比例,預設為1.1即每次搜尋視窗依次擴大10%
#5為構成檢測目标的相鄰矩形的最小個數
#0為flag,表示使用表認知,會使用Canny邊緣檢測來排除邊緣過多或過少的區域
for(x, y, w, h) in faces:
img = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
roi_gray = gray[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray, 1.8, 5,0)
roi_color = img[y:y + h, x:x + w]
for(ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
smiles = smile_cascade.detectMultiScale(roi_gray, scaleFactor = 1.16,\
minNeighbors= 65, minSize=(25,25), \
flags = cv2.CASCADE_SCALE_IMAGE)
for (ex,ey,ew,eh) in smiles:
# 畫出微笑框,紅色(BGR色彩體系),畫筆寬度為1
cv2.rectangle(roi_gray, (ex,ey), (ex+ew,ey+eh), (0,0,255), 1)
cv2.putText(img, "smile", (x,y-7), 3, 1.2, (0,0,225), 2, cv2.LINE_AA)
#cv2.LINE_AA 為抗鋸齒,這樣看起來會非常平滑
#cv2.imwrite(f"img{cnt}.png",img)
cnt+=1
cv2.imshow("camera", frame)
if cv2.waitKey(5) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
import cv2
import numpy as np
cap = cv2.VideoCapture("redone.mp4")
while(cap.isOpened()):
ret, frame = cap.read()
HSV = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)#把BGR圖像轉換為HSV格式
Lower = np.array([156, 43, 46])#要識别顔色的下限
Upper = np.array([180, 255, 255])#要識别的顔色的上限
#mask是把HSV圖檔中在顔色範圍内的區域變成白色,其他區域變成黑色
mask = cv2.inRange(HSV, Lower, Upper)
yu = cv2.bitwise_and(frame,frame,mask=mask)
cv2.imshow("frame",yu)
if cv2.waitKey(25) == ord("q"):
break
cap.release()#釋放攝像頭的資源
cv2.destroyAllWindows()