用python库face_recognition进行人脸识别

1.pip install opencv

2.pip install face_recognition

期间在安装依赖包dlib时遇到问题,解决见:  http://kissmett.iteye.com/blog/2409857

3.通过摄像头实时在获取的帧上进行人脸识别(较卡顿)

facerecognition.py

# -*- coding: UTF-8 -*- 
import face_recognition 
import cv2 
import os 
import ft2 
#中文支持,加载微软雅黑字体
ft = ft2.put_chinese_text('msyh.ttf')  
# 获取摄像头# 0（默认） 
video_capture = cv2.VideoCapture(0) 
# 加载待识别人脸图像并识别。 
basefacefilespath ="images"#faces文件夹中放待识别任务正面图,文件名为人名,将显示于结果中
baseface_titles=[] #图片名字列表
baseface_face_encodings=[] #识别所需人脸编码结构集
#读取人脸资源
for fn in os.listdir(basefacefilespath): #fn 人脸文件名
	baseface_face_encodings.append(face_recognition.face_encodings(face_recognition.load_image_file(basefacefilespath+"/"+fn))[0]) 
	fn=fn[:(len(fn)-4)] 
	baseface_titles.append(fn)# 
while True: 
	# 获取一帧视频 
	ret, frame = video_capture.read() 
	# 人脸检测,并获取帧中所有人脸编码
	face_locations = face_recognition.face_locations(frame) 
	face_encodings = face_recognition.face_encodings(frame, face_locations) 
	# 遍历帧中所有人脸编码 
	for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): 
		# 与baseface_face_encodings匹配否? 
		for i,v in enumerate(baseface_face_encodings): 
			match = face_recognition.compare_faces([v], face_encoding,tolerance=0.5) 
			name = "?" 
			if match[0]: 
				name = baseface_titles[i] 
				break 
		name=unicode(name,'gb2312')#gbk is also ok.
		print(name)
		# 围绕脸的框 
		cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) 
		# 框下的名字(即,匹配的图片文件名)
		cv2.rectangle(frame, (left, bottom), (right, bottom+35), (0, 0, 255), cv2.FILLED) 
		frame = ft.draw_text(frame, (left + 2, bottom + 12), name, 16,  (255, 255, 255))  
		# show结果图像 
		cv2.imshow('Video', frame) 
	# 按q退出 
	if cv2.waitKey(1) & 0xFF == ord('q'): 
		break 
# 释放摄像头中的流 
video_capture.release() 
cv2.destroyAllWindows()

 ft2.py 

# -*- coding: utf-8 -*-      
                                                              
import numpy as np
import freetype
import copy
import pdb

class put_chinese_text(object):
    def __init__(self, ttf):
        self._face = freetype.Face(ttf)

    def draw_text(self, image, pos, text, text_size, text_color):
        '''
        draw chinese(or not) text with ttf
        :param image:     image(numpy.ndarray) to draw text
        :param pos:       where to draw text
        :param text:      the context, for chinese should be unicode type
        :param text_size: text size
        :param text_color:text color
        :return:          image
        '''
        self._face.set_char_size(text_size * 64)
        metrics = self._face.size
        ascender = metrics.ascender/64.0

        #descender = metrics.descender/64.0
        #height = metrics.height/64.0
        #linegap = height - ascender + descender
        ypos = int(ascender)

        if not isinstance(text, unicode):
            text = text.decode('utf-8')
        img = self.draw_string(image, pos[0], pos[1]+ypos, text, text_color)
        return img

    def draw_string(self, img, x_pos, y_pos, text, color):
        '''
        draw string
        :param x_pos: text x-postion on img
        :param y_pos: text y-postion on img
        :param text:  text (unicode)
        :param color: text color
        :return:      image
        '''
        prev_char = 0
        pen = freetype.Vector()
        pen.x = x_pos << 6   # div 64
        pen.y = y_pos << 6

        hscale = 1.0
        matrix = freetype.Matrix(int(hscale)*0x10000L, int(0.2*0x10000L),\
                                 int(0.0*0x10000L), int(1.1*0x10000L))
        cur_pen = freetype.Vector()
        pen_translate = freetype.Vector()

        image = copy.deepcopy(img)
        for cur_char in text:
            self._face.set_transform(matrix, pen_translate)

            self._face.load_char(cur_char)
            kerning = self._face.get_kerning(prev_char, cur_char)
            pen.x += kerning.x
            slot = self._face.glyph
            bitmap = slot.bitmap

            cur_pen.x = pen.x
            cur_pen.y = pen.y - slot.bitmap_top * 64
            self.draw_ft_bitmap(image, bitmap, cur_pen, color)

            pen.x += slot.advance.x
            prev_char = cur_char

        return image

    def draw_ft_bitmap(self, img, bitmap, pen, color):
        '''
        draw each char
        :param bitmap: bitmap
        :param pen:    pen
        :param color:  pen color e.g.(0,0,255) - red
        :return:       image
        '''
        x_pos = pen.x >> 6
        y_pos = pen.y >> 6
        cols = bitmap.width
        rows = bitmap.rows

        glyph_pixels = bitmap.buffer

        for row in range(rows):
            for col in range(cols):
                if glyph_pixels[row*cols + col] != 0:
                    img[y_pos + row][x_pos + col][0] = color[0]
                    img[y_pos + row][x_pos + col][1] = color[1]
                    img[y_pos + row][x_pos + col][2] = color[2]


if __name__ == '__main__':
    # just for test
    import cv2

    line = '你好'
    img = np.zeros([300,300,3])

    color_ = (0,255,0) # Green
    pos = (3, 3)
    text_size = 24

    #ft = put_chinese_text('wqy-zenhei.ttc')
    ft = put_chinese_text('msyh.ttf')
    image = ft.draw_text(img, pos, line, text_size, color_)

    cv2.imshow('ss', image)
    cv2.waitKey(0)

将msyh.ttf 微软雅黑字体文件copy到同级目录,在同级images文件夹下,以人名命名正面脸图.

运行, python facerecognition.py 

卡顿,跳帧,

结果如图:

base faces

识别: