python mtnn 口罩检测
2021/4/10 20:41:35
本文主要是介绍python mtnn 口罩检测,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
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
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