Python 3 利用 Dlib 19.7 實(shí)現(xiàn)攝像頭人臉檢測特征點(diǎn)標(biāo)定

0.引言

利用python開發(fā)，借助Dlib庫捕獲攝像頭中的人臉，進(jìn)行實(shí)時(shí)特征點(diǎn)標(biāo)定；

圖1 工程效果示例(gif)

圖2 工程效果示例(靜態(tài)圖片)

(實(shí)現(xiàn)比較簡單，代碼量也比較少，適合入門或者興趣學(xué)習(xí)。)

1.開發(fā)環(huán)境

python:　　3.6.3

dlib:　　　 19.7

OpenCv, numpy

import dlib # 人臉識(shí)別的庫dlib

import numpy as np # 數(shù)據(jù)處理的庫numpy

import cv2 # 圖像處理的庫OpenCv

2.源碼介紹

其實(shí)實(shí)現(xiàn)很簡單，主要分為兩個(gè)部分：攝像頭調(diào)用+人臉特征點(diǎn)標(biāo)定

2.1 攝像頭調(diào)用

介紹下opencv中攝像頭的調(diào)用方法；

利用 cap = cv2.VideoCapture(0) 創(chuàng)建一個(gè)對象；

(具體可以參考官方文檔)

# 2018-2-26

# By TimeStamp

# cnblogs: http://www.cnblogs.com/AdaminXie

"""

cv2.VideoCapture(), 創(chuàng)建cv2攝像頭對象/ open the default camera

Python: cv2.VideoCapture() →

Python: cv2.VideoCapture(filename) →

filename – name of the opened video file (eg. video.avi) or image sequence (eg. img_%02d.jpg, which will read samples like img_00.jpg, img_01.jpg, img_02.jpg, ...)

Python: cv2.VideoCapture(device) →

device – id of the opened video capturing device (i.e. a camera index). If there is a single camera connected, just pass 0.

"""

cap = cv2.VideoCapture(0)

"""

cv2.VideoCapture.set(propId, value)，設(shè)置視頻參數(shù);

propId:

CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds.

CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.

CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file: 0 - start of the film, 1 - end of the film.

CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.

CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.

CV_CAP_PROP_FPS Frame rate.

CV_CAP_PROP_FOURCC 4-character code of codec.

CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.

CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .

CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.

CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).

CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).

CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).

CV_CAP_PROP_HUE Hue of the image (only for cameras).

CV_CAP_PROP_GAIN Gain of the image (only for cameras).

CV_CAP_PROP_EXPOSURE Exposure (only for cameras).

CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.

CV_CAP_PROP_WHITE_BALANCE_U The U value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)

CV_CAP_PROP_WHITE_BALANCE_V The V value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)

CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)

CV_CAP_PROP_ISO_SPEED The ISO speed of the camera (note: only supported by DC1394 v 2.x backend currently)

CV_CAP_PROP_BUFFERSIZE Amount of frames stored in internal buffer memory (note: only supported by DC1394 v 2.x backend currently)

value: 設(shè)置的參數(shù)值/ Value of the property

"""

cap.set(3, 480)

"""

cv2.VideoCapture.isOpened(), 檢查攝像頭初始化是否成功 / check if we succeeded

返回true或false

"""

cap.isOpened()

"""

cv2.VideoCapture.read([imgage]) -> retval,image, 讀取視頻 / Grabs, decodes and returns the next video frame

返回兩個(gè)值：

一個(gè)是布爾值true/false，用來判斷讀取視頻是否成功/是否到視頻末尾

圖像對象，圖像的三維矩陣

"""

flag, im_rd = cap.read()

2.2 人臉特征點(diǎn)標(biāo)定

調(diào)用預(yù)測器“shape_predictor_68_face_landmarks.dat”進(jìn)行68點(diǎn)標(biāo)定，這是dlib訓(xùn)練好的模型，可以直接調(diào)用進(jìn)行人臉68個(gè)人臉特征點(diǎn)的標(biāo)定；

2.3 源碼

實(shí)現(xiàn)的方法比較簡單：

利用 cv2.VideoCapture() 創(chuàng)建攝像頭對象，然后利用 flag, im_rd = cv2.VideoCapture.read() 讀取攝像頭視頻，im_rd就是視頻中的一幀幀圖像；

然后就類似于單張圖像進(jìn)行人臉檢測，對這一幀幀的圖像im_rd利用dlib進(jìn)行特征點(diǎn)標(biāo)定，然后繪制特征點(diǎn)；

你可以按下s鍵來獲取當(dāng)前截圖，或者按下q鍵來退出攝像頭；

# 2018-2-26

# By TimeStamp

# cnblogs: http://www.cnblogs.com/AdaminXie

# github: https://github.com/coneypo/Dlib_face_detection_from_camera

import dlib #人臉識(shí)別的庫dlib

import numpy as np #數(shù)據(jù)處理的庫numpy

import cv2 #圖像處理的庫OpenCv

# dlib預(yù)測器

detector = dlib.get_frontal_face_detector()

predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

# 創(chuàng)建cv2攝像頭對象

cap = cv2.VideoCapture(0)

# cap.set(propId, value)

# 設(shè)置視頻參數(shù)，propId設(shè)置的視頻參數(shù)，value設(shè)置的參數(shù)值

cap.set(3, 480)

# 截圖screenshoot的計(jì)數(shù)器

cnt = 0

# cap.isOpened() 返回true/false 檢查初始化是否成功

while(cap.isOpened()):

# cap.read()

# 返回兩個(gè)值：

# 一個(gè)布爾值true/false，用來判斷讀取視頻是否成功/是否到視頻末尾

# 圖像對象，圖像的三維矩陣

flag, im_rd = cap.read()

# 每幀數(shù)據(jù)延時(shí)1ms，延時(shí)為0讀取的是靜態(tài)幀

k = cv2.waitKey(1)

# 取灰度

img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)

# 人臉數(shù)rects

rects = detector(img_gray, 0)

#print(len(rects))

# 待會(huì)要寫的字體

font = cv2.FONT_HERSHEY_SIMPLEX

# 標(biāo)68個(gè)點(diǎn)

if(len(rects)!=0):

# 檢測到人臉

for i in range(len(rects)):

landmarks = np.matrix([[p.x, p.y] for p in predictor(im_rd, rects[i]).parts()])

for idx, point in enumerate(landmarks):

# 68點(diǎn)的坐標(biāo)

pos = (point[0, 0], point[0, 1])

# 利用cv2.circle給每個(gè)特征點(diǎn)畫一個(gè)圈，共68個(gè)

cv2.circle(im_rd, pos, 2, color=(0, 255, 0))

# 利用cv2.putText輸出1-68

cv2.putText(im_rd, str(idx + 1), pos, font, 0.2, (0, 0, 255), 1, cv2.LINE_AA)

cv2.putText(im_rd, "faces: "+str(len(rects)), (20,50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)

else:

# 沒有檢測到人臉

cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)

# 添加說明

im_rd = cv2.putText(im_rd, "s: screenshot", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)

im_rd = cv2.putText(im_rd, "q: quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)

# 按下s鍵保存

if (k == ord('s')):

cnt+=1

cv2.imwrite("screenshoot"+str(cnt)+".jpg", im_rd)

# 按下q鍵退出

if(k==ord('q')):

break

# 窗口顯示

cv2.imshow("camera", im_rd)

# 釋放攝像頭

cap.release()

# 刪除建立的窗口

cv2.destroyAllWindows()

如果對您有幫助，歡迎在GitHub上star本項(xiàng)目。

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