python mtnn 口罩检测
作者：互联网
该项目主要是使用mtcnn网络检测到人脸，然后将人脸截取训练进而判断是否戴口罩。
本文最后有该项目下载链接，如有需要可自行前往下载！
下载完以后只需要运行这个文件即可。
运行以后会出现这样的界面：
然后只需要选择需要检测的图片或者视频即可效果如下所示：
下面是mtcnn人脸检测的网络代码：
import cv2
import numpy as np
import tensorflow as tf
import utils.utils as utils
from keras.layers import (Activation, Conv2D, Dense, Flatten, Input, MaxPool2D,
Permute, Reshape)
from keras.layers.advanced_activations import PReLU
from keras.models import Model, Sequential
#-----------------------------#
# 粗略获取人脸框
# 输出bbox位置和是否有人脸
#-----------------------------#
def create_Pnet(weight_path):
inputs = Input(shape=[None, None, 3])
x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(inputs)
x = PReLU(shared_axes=[1,2],name='PReLU1')(x)
x = MaxPool2D(pool_size=2)(x)
x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1,2],name='PReLU2')(x)
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1,2],name='PReLU3')(x)
classifier = Conv2D(2, (1, 1), activation='softmax', name='conv4-1')(x)
# 无激活函数，线性。
bbox_regress = Conv2D(4, (1, 1), name='conv4-2')(x)
model = Model([inputs], [classifier, bbox_regress])
model.load_weights(weight_path, by_name=True)
return model
#-----------------------------#
# mtcnn的第二段
# 精修框
#-----------------------------#
def create_Rnet(weight_path):
inputs = Input(shape=[24, 24, 3])
# 24,24,3 -> 22,22,28 -> 11,11,28
x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(inputs)
x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)
# 11,11,28 -> 9,9,48 -> 4,4,48
x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
x = MaxPool2D(pool_size=3, strides=2)(x)
# 4,4,48 -> 3,3,64
x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
# 3,3,64 -> 64,3,3
x = Permute((3, 2, 1))(x)
x = Flatten()(x)
# 576 -> 128
x = Dense(128, name='conv4')(x)
x = PReLU( name='prelu4')(x)
# 128 -> 2
classifier = Dense(2, activation='softmax', name='conv5-1')(x)
# 128 -> 4
bbox_regress = Dense(4, name='conv5-2')(x)
model = Model([inputs], [classifier, bbox_regress])
model.load_weights(weight_path, by_name=True)
return model
#-----------------------------#
# mtcnn的第三段
# 精修框并获得五个点
#-----------------------------#
def create_Onet(weight_path):
inputs = Input(shape = [48,48,3])
# 48,48,3 -> 46,46,32 -> 23,23,32
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(inputs)
x = PReLU(shared_axes=[1,2],name='prelu1')(x)
x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
# 23,23,32 -> 21,21,64 -> 10,10,64
x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1,2],name='prelu2')(x)
x = MaxPool2D(pool_size=3, strides=2)(x)
# 8,8,64 -> 4,4,64
x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1,2],name='prelu3')(x)
x = MaxPool2D(pool_size=2)(x)
# 4,4,64 -> 3,3,128
x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
x = PReLU(shared_axes=[1,2],name='prelu4')(x)
# 3,3,128 -> 128,12,12
x = Permute((3,2,1))(x)
x = Flatten()(x)
# 1152 -> 256
x = Dense(256, name='conv5') (x)
x = PReLU(name='prelu5')(x)
# 256 -> 2
classifier = Dense(2, activation='softmax',name='conv6-1')(x)
# 256 -> 4
bbox_regress = Dense(4,name='conv6-2')(x)
# 256 -> 10
landmark_regress = Dense(10,name='conv6-3')(x)
model = Model([inputs], [classifier, bbox_regress, landmark_regress])
model.load_weights(weight_path, by_name=True)
return model
class mtcnn():
def __init__(self):
self.Pnet = create_Pnet('model_data/pnet.h5')
self.Rnet = create_Rnet('model_data/rnet.h5')
self.Onet = create_Onet('model_data/onet.h5')
def detectFace(self, img, threshold):
#-----------------------------#
# 归一化
#-----------------------------#
copy_img = (img.copy() - 127.5) / 127.5
origin_h, origin_w, _ = copy_img.shape
#-----------------------------#
# 计算原始输入图像
# 每一次缩放的比例
#-----------------------------#
scales = utils.calculateScales(img)
out = []
#-----------------------------#
# 粗略计算人脸框
# pnet部分
#-----------------------------#
for scale in scales:
hs = int(origin_h * scale)
ws = int(origin_w * scale)
scale_img = cv2.resize(copy_img, (ws, hs))
inputs = np.expand_dims(scale_img, 0)
ouput = self.Pnet.predict(inputs)
#---------------------------------------------#
# 每次选取图像金字塔中的一张图片进行预测
# 预测结果也是一张图片的，
# 所以我们可以将对应的batch_size维度给消除掉
#---------------------------------------------#
ouput = [ouput[0][0], ouput[1][0]]
out.append(ouput)
rectangles = []
#-------------------------------------------------#
# 在这个地方我们对图像金字塔的预测结果进行循环
# 取出每张图片的种类预测和回归预测结果
#-------------------------------------------------#
for i in range(len(scales)):
#------------------------------------------------------------------#
# 为了方便理解，这里和视频上看到的不太一样
# 因为我们在上面对图像金字塔循环的时候就把batch_size维度给去掉了
#------------------------------------------------------------------#
cls_prob = out[i][0][:, :, 1]
roi = out[i][1]
#-------------------------------------#
# 取出每个缩放后图片的高宽
#-------------------------------------#
out_h, out_w = cls_prob.shape
out_side = max(out_h, out_w)
#-------------------------------------#
# 解码的过程
#-------------------------------------#
rectangle = utils.detect_face_12net(cls_prob, roi, out_side, 1 / scales[i], origin_w, origin_h, threshold[0])
rectangles.extend(rectangle)
#-------------------------------------#
# 进行非极大抑制
#-------------------------------------#
rectangles = np.array(utils.NMS(rectangles, 0.7))
if len(rectangles) == 0:
return rectangles
#-----------------------------#
# 稍微精确计算人脸框
# Rnet部分
#-----------------------------#
predict_24_batch = []
for rectangle in rectangles:
#------------------------------------------#
# 利用获取到的粗略坐标，在原图上进行截取
#------------------------------------------#
crop_img = copy_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
#-----------------------------------------------#
# 将截取到的图片进行resize，调整成24x24的大小
#-----------------------------------------------#
scale_img = cv2.resize(crop_img, (24, 24))
predict_24_batch.append(scale_img)
cls_prob, roi_prob = self.Rnet.predict(np.array(predict_24_batch))
#-------------------------------------#
# 解码的过程
#-------------------------------------#
rectangles = utils.filter_face_24net(cls_prob, roi_prob, rectangles, origin_w, origin_h, threshold[1])
if len(rectangles) == 0:
return rectangles
#-----------------------------#
# 计算人脸框
# onet部分
#-----------------------------#
predict_batch = []
for rectangle in rectangles:
#------------------------------------------#
# 利用获取到的粗略坐标，在原图上进行截取
#------------------------------------------#
crop_img = copy_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
#-----------------------------------------------#
# 将截取到的图片进行resize，调整成48x48的大小
#-----------------------------------------------#
scale_img = cv2.resize(crop_img, (48, 48))
predict_batch.append(scale_img)
cls_prob, roi_prob, pts_prob = self.Onet.predict(np.array(predict_batch))
#-------------------------------------#
# 解码的过程
#-------------------------------------#
rectangles = utils.filter_face_48net(cls_prob, roi_prob, pts_prob, rectangles, origin_w, origin_h, threshold[2])
return rectangles
该项目使用是tensorflow2.2.0的版本，下载以后如需配置深度学习环境可以联系作者。qq：1735375343
项目下载链接：下载链接
标签：口罩,name,img,python,#-----------------------------#,mtnn,strides,rectangles,Conv2 来源： https://blog.csdn.net/qq_45087786/article/details/115583114