前言
本文是利用训练好的模型 快速运行人脸识别与人脸关键点识别
分为两部分:利用mctnn的tensorflow模型,和利用opencv自带的级联分类器进行人脸检测
目录
cascade opencv 识别
模型下载:https://github.com/opencv/opencv/tree/master/data/haarcascades
或者在安装完python-opencv的盘里搜这几个文件
classifier_face = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
classifier_eye = cv2.CascadeClassifier("haarcascade_eye.xml")
classifier_mouth=cv2.CascadeClassifier("haarcascade_mcs_mouth.xml")
# !/usr/bin/env python
# coding=utf-8
import os
import numpy
from PIL import Image, ImageDraw
import cv2
#created by chenfenyu 2018.3.20
playCapture = cv2.VideoCapture(0)
#获取外接摄像头
#eye = cv2.imread("eye.png")
#mouth = cv2.imread("mouth.png")
size = (int(playCapture.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5), int(playCapture.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5))
#获取摄像头返回的宽和高
#fourcc = cv2.VideoWriter_fourcc(*'mp4v')
#video = cv2.VideoWriter("/Users/funny/Downloads/a.avi", fourcc, 5, size)
classifier_face = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
classifier_eye = cv2.CascadeClassifier("haarcascade_eye.xml")
classifier_mouth=cv2.CascadeClassifier("haarcascade_mcs_mouth.xml")
while playCapture.isOpened():
success, img = playCapture.read()
if success:
'''第一个参数ret的值为True或False，代表有没有读到图片
第二个参数是frame，是当前截取一帧的图片
'''
faceRects_face = classifier_face.detectMultiScale(img, 1.2, 2, cv2.CASCADE_SCALE_IMAGE, (20, 20))
#检测器：detectMultiScale参数（图像，每次缩小图像的比例，匹配成功所需要的周围矩形框的数目，检测的类型，匹配物体的大小范围）
key = cv2.waitKey(1)
#键盘等待
if len(faceRects_face) > 0:
#检测到人脸
for faceRect_face in faceRects_face:
x, y, w, h = faceRect_face
#获取图像x起点,y起点,宽，高
h1=int(float(h/1.5))
#截取人脸区域高度的一半位置，以精确识别眼睛的位置
intx=int(x)
inty=int(y)
intw=int(w)
inth=int(h)
#转换类型为int，方便之后图像截取
my = int(float(y + 0.7 * h))
#截取人脸区域下半部分左上角的y起点，以精确识别嘴巴的位置
mh = int(0.4 * h)
#截取人脸区域下半部分高度，以精确识别嘴巴的位置
img_facehalf = img[inty:(inty+h1), intx:intx+intw]
img_facehalf_bottom = img[my:(my + mh), intx:intx + intw]
'''img获取坐标为，【y,y+h之间（竖）：x,x+w之间(横)范围内的数组】
img_facehalf是截取人脸识别到区域上半部分
img_facehalf_bottom是截取人脸识别到区域下半部分
'''
cv2.rectangle(img, (int(x), my), (int(x) + int(w), my + mh), (0, 255, 0), 2, 0)
'''矩形画出区域 rectangle参数（图像，左顶点坐标(x,y)，右下顶点坐标（x+w,y+h），线条颜色，线条粗细）
画出人脸识别下部分区域，方便定位
'''
faceRects_mouth = classifier_mouth.detectMultiScale(img_facehalf_bottom, 1.1, 1, cv2.CASCADE_SCALE_IMAGE, (5, 20))
#嘴巴检测器
if len(faceRects_mouth) > 0:
for faceRect_mouth in faceRects_mouth:
xm1, ym1, wm1, hm2 = faceRect_mouth
cv2.rectangle(img_facehalf_bottom, (int(xm1), int(ym1)), (int(xm1) + int(wm1), int(ym1) + int(hm2)), (0,0, 255), 2, 0)
try:
img_mx = cv2.resize(mouth, (wm1, hm2), interpolation=cv2.INTER_CUBIC)
except:
pass
#if key == ord('z'):
# img[my+ym1:(my+ym1+hm2), intx+xm1:(intx + xm1+wm1)] = img_mx
cv2.rectangle(img, (int(x), int(y)), (int(x) + int(w), int(y) + int(h1)), (0, 255, 0), 2, 0)
# 画出人脸识别上部分区域，方便定位
faceRects_eye = classifier_eye.detectMultiScale(img_facehalf, 1.2, 2, cv2.CASCADE_SCALE_IMAGE, (20, 20))
#检测器识别眼睛
if len(faceRects_eye) > 0:
#检测到眼睛后循环
eye_tag = []
#定义一个列表存放两只眼睛坐标
for faceRect_eye in faceRects_eye:
x1, y1, w1, h2 = faceRect_eye
cv2.rectangle(img_facehalf, (int(x1), int(y1)), (int(x1) + int(w1), int(y1) + int(h2)), (0, 255, 0), 2, 0)
#画出眼睛区域
a = ((inty+y1),(inty+y1 + h2), (intx+x1),(intx+x1 + w1))
#定义a变量获取眼睛坐标，现在img顶点位置已经改变，需要加上intx和inty的值才可以
eye_tag.append(a)
#通过append存入数组a中
n_eyetag = numpy.array(eye_tag)
#存放为ndarray数组类型，输入内容为[[x1 y1 x1+w y1+h][x1 y1 x1+w y1+h]...],后面会获取多维数组的下标来替换数值
#if len(faceRects_eye)==2:
# img_ex=cv2.resize(eye,(n_eyetag[0,1]-n_eyetag[0,0], n_eyetag[0,3]-n_eyetag[0,2]),interpolation=cv2.INTER_CUBIC)
# img_ex1 = cv2.resize(eye, (n_eyetag[1, 1] - n_eyetag[1, 0], n_eyetag[1, 3] - n_eyetag[1, 2]), interpolation=cv2.INTER_CUBIC)
#if key == ord('p'):
# img[n_eyetag[0,0]:n_eyetag[0,1],n_eyetag[0,2]:n_eyetag[0,3]]=img_ex
# img[n_eyetag[1, 0]:n_eyetag[1, 1], n_eyetag[1, 2]:n_eyetag[1, 3]] = img_ex1
#if len(faceRects_eye)==1:
# # 眼睛识别到一个时，替换图片
# img_ex = cv2.resize(eye, (n_eyetag[0, 1] - n_eyetag[0, 0], n_eyetag[0, 3] - n_eyetag[0, 2]), interpolation=cv2.INTER_CUBIC)
# if key == ord('p'):
# img[n_eyetag[0, 0]:n_eyetag[0, 1], n_eyetag[0, 2]:n_eyetag[0, 3]] = img_ex
#video.write(img)
cv2.imshow('video', img)
#显示图片，标题名字为video
cv2.resizeWindow('video',1280,720)
#调整窗口大小video为1280*720
if key == ord('q'):
#检测到键盘输入q，退出循环
break
#video.release()
#不再录制视频
cap.release()
cv2.destroyAllWindows()mtcnn识别
模型下载:https://github.com/imistyrain/MTCNN/blob/master/tensorflow/mtcnn.pb
import argparse,cv2
#from mtcnn import MTCNN
import tensorflow as tf
class MTCNN:
def __init__(self, model_path, min_size=40, factor=0.709, thresholds=[0.6, 0.7, 0.7]):
self.min_size = min_size
self.factor = factor
self.thresholds = thresholds
graph = tf.Graph()
with graph.as_default():
with open(model_path, 'rb') as f:
graph_def = tf.GraphDef.FromString(f.read())
tf.import_graph_def(graph_def, name='')
self.graph = graph
config = tf.ConfigProto(
allow_soft_placement=True,
intra_op_parallelism_threads=4,
inter_op_parallelism_threads=4)
config.gpu_options.allow_growth = True
self.sess = tf.Session(graph=graph, config=config)
def detect(self, img):
feeds = {
self.graph.get_operation_by_name('input').outputs[0]: img,
self.graph.get_operation_by_name('min_size').outputs[0]: self.min_size,
self.graph.get_operation_by_name('thresholds').outputs[0]: self.thresholds,
self.graph.get_operation_by_name('factor').outputs[0]: self.factor
}
fetches = [self.graph.get_operation_by_name('prob').outputs[0],
self.graph.get_operation_by_name('landmarks').outputs[0],
self.graph.get_operation_by_name('box').outputs[0]]
prob, landmarks, box = self.sess.run(fetches, feeds)
return box, prob, landmarks
def test_image(imgpath):
mtcnn = MTCNN('./mtcnn.pb')
img = cv2.imread(imgpath)
bbox, scores, landmarks = mtcnn.detect(img)
print('total box:', len(bbox))
for box, pts in zip(bbox, landmarks):
box = box.astype('int32')
img = cv2.rectangle(img, (box[1], box[0]), (box[3], box[2]), (255, 0, 0), 3)
pts = pts.astype('int32')
for i in range(5):
img = cv2.circle(img, (pts[i+5], pts[i]), 1, (0, 255, 0), 2)
cv2.imshow('image', img)
cv2.waitKey()
def test_camera(index=0):
mtcnn = MTCNN('./mtcnn.pb')
cap=cv2.VideoCapture(index)
while True:
ret,img=cap.read()
if not ret:
break
bbox, scores, landmarks = mtcnn.detect(img)
#print('total box:', len(bbox))
for box, pts in zip(bbox, landmarks):
box = box.astype('int32')
img = cv2.rectangle(img, (box[1], box[0]), (box[3], box[2]), (255, 0, 0), 3)
pts = pts.astype('int32')
for i in range(5):
img = cv2.circle(img, (pts[i+5], pts[i]), 1, (0, 255, 0), 2)
cv2.imshow('img', img)
key = cv2.waitKey(1)
if key ==ord('q'):
break
if __name__ == '__main__':
#test_image()
test_camera()参考:https://www.cnblogs.com/fightccc/p/8616068.html
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