from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense, Permute
from keras.layers.advanced_activations import PReLU
from keras.models import Model, Sequential
import tensorflow as tf
import numpy as np
import utils.utils as utils
import cv2
#-----------------------------#
# 粗略获取人脸框
# 输出bbox位置和是否有人脸
#-----------------------------#
def create_Pnet(weight_path):
# h,w
input = Input(shape=[None, None, 3])
# h,w,3 -> h/2,w/2,10
x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1,2],name='PReLU1')(x)
x = MaxPool2D(pool_size=2)(x)
# h/2,w/2,10 -> h/2,w/2,16
x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
x = PReLU(shared_axes=[1,2],name='PReLU2')(x)
# h/2,w/2,32
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
x = PReLU(shared_axes=[1,2],name='PReLU3')(x)
# h/2, w/2, 2
classifier = Conv2D(2, (1, 1), activation='softmax', name='conv4-1')(x)
# 无激活函数，线性。
# h/2, w/2, 4
bbox_regress = Conv2D(4, (1, 1), name='conv4-2')(x)
model = Model([input], [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):
input = Input(shape=[24, 24, 3])
# 24,24,3 -> 11,11,28
x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)
# 11,11,28 -> 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 -> 2 128 -> 4
classifier = Dense(2, activation='softmax', name='conv5-1')(x)
bbox_regress = Dense(4, name='conv5-2')(x)
model = Model([input], [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):
input = Input(shape = [48,48,3])
# 48,48,3 -> 23,23,32
x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
x = PReLU(shared_axes=[1,2],name='prelu1')(x)
x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
# 23,23,32 -> 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 -> 128,3,3
x = Permute((3,2,1))(x)
# 1152 -> 256
x = Flatten()(x)
x = Dense(256, name='conv5') (x)
x = PReLU(name='prelu5')(x)
# 鉴别
# 256 -> 2 256 -> 4 256 -> 10
classifier = Dense(2, activation='softmax',name='conv6-1')(x)
bbox_regress = Dense(4,name='conv6-2')(x)
landmark_regress = Dense(10,name='conv6-3')(x)
model = Model([input], [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 = scale_img.reshape(1, *scale_img.shape)
ouput = self.Pnet.predict(inputs)
out.append(ouput)
image_num = len(scales)
rectangles = []
for i in range(image_num):
# 有人脸的概率
cls_prob = out[i][0][0][:,:,1]
# 其对应的框的位置
roi = out[i][1][0]
# 取出每个缩放后图片的长宽
out_h, out_w = cls_prob.shape
out_side = max(out_h, out_w)
# 解码过程
rectangle = utils.detect_face_12net(cls_prob, roi, out_side, 1 / scales[i], origin_w, origin_h, threshold[0])
rectangles.extend(rectangle)
# for i in range(len(rectangles)):
# bbox = rectangles[i]
# crop_img = img[int(bbox[1]):int(bbox[3]),int(bbox[0]):int(bbox[2])]
# if bbox[3]-bbox[1]>80:
# cv2.imshow("crop_img",crop_img)
# cv2.waitKey(0)
# 进行非极大抑制
rectangles = 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:
crop_img = copy_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img, (24, 24))
predict_24_batch.append(scale_img)
predict_24_batch = np.array(predict_24_batch)
out = self.Rnet.predict(predict_24_batch)
# 可信度
cls_prob = out[0]
cls_prob = np.array(cls_prob)
# 如何调整某一张图片对应的rectangle
roi_prob = out[1]
roi_prob = np.array(roi_prob)
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:
crop_img = copy_img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]
scale_img = cv2.resize(crop_img, (48, 48))
predict_batch.append(scale_img)
predict_batch = np.array(predict_batch)
output = self.Onet.predict(predict_batch)
cls_prob = output[0]
roi_prob = output[1]
pts_prob = output[2]
rectangles = utils.filter_face_48net(cls_prob, roi_prob, pts_prob, rectangles, origin_w, origin_h, threshold[2])
return rectangles
