python判别性别的代码_表情识别与性别识别 实时识别模型附源代码 基于python的tensorflow与keras...

本模型可以检测7种表情，分别是：生气、厌恶、害怕、开心、难过、惊奇、中立

另外还可以检测性别

一、环境

建议安装 Anconda

系统环境建议：

windows、ubuntu

环境建议：

python 3.6

tensorflow-gpu 1.13

keras-gpu 2.24

所需包建议（版本影响不大）：

statistics

pandas== 0.19.1

numpy== 1.12.1

h5py== 2 .7.0

opencv-python==3.2.0

注意：如果运行中缺少了一谢基本包可直接pip安装即可

如：pip install scipy

如：pip install numpy -i https://pypi.tuna.tsinghua.edu.cn/simple

在anconda 中一键安装也是可以的哦！

二、代码文件：

我就不卖官子啦！直接放源代码文件咯（有环境可以直接运行的那种）！

代码文件中我放了大量的注释，对python有一点基础的很快就可以简单理解！链接：

三、代码文件解析

解压后有3个文件

第一个文件是存放模型的文件夹（可以自行切换模型）

第二个是基础模块文件夹

第三个是主文件，环境安装完成后可直接运行检测模型

主文件（emotion_sex_recognition.py）

from statistics import mode

import cv2

from keras.models import load_model

import numpy as np

from utils_base.datasets import get_labels

from utils_base.inference import detect_faces

from utils_base.inference import draw_text

from utils_base.inference import draw_bounding_box

from utils_base.inference import apply_offsets

from utils_base.inference import load_detection_model

from utils_base.preprocessor import preprocess_input

import threading

from urllib import parse,request

#这是数据传输模块，没有需要的时候不需要使用

def swop(emotion,name,sex,age,gl):

values={"id":name,"number":emotion}

data=parse.urlencode(values)

try:

url = 'http://127.0.0.1.ngrok.xiaomiqiu.cn/saw_2'

req = request.Request(url, data.encode(encoding='utf-8'))

print("服务器接入成功，数据成功传输!")

except:

print("服务器接入失败，数据将临时保存")

# 加载数据和图像的参数

detection_model_path = 'models/detection_models/haarcascade_frontalface_default.xml'

emotion_model_path = 'models/emotion_models/fer2013_mini_XCEPTION.110-0.65.hdf5'

gender_model_path = 'models/gender_models/simple_CNN.81-0.96.hdf5'

emotion_labels = get_labels('fer2013')

gender_labels = get_labels('imdb')

font = cv2.FONT_HERSHEY_SIMPLEX

# 定义形状的超参数

frame_window = 10

gender_offsets = (30, 60)

emotion_offsets = (20, 40)

# 加载模型

face_detection = load_detection_model(detection_model_path)

emotion_classifier = load_model(emotion_model_path, compile=False)

gender_classifier = load_model(gender_model_path, compile=False)

# 获取用于推理的输入模型形状

emotion_target_size = emotion_classifier.input_shape[1:3]

gender_target_size = gender_classifier.input_shape[1:3]

# 计算模式的起始列表

gender_window = []

emotion_window = []

#预先打开窗口

cv2.namedWindow('window_frame')

#连接网络摄像头识别

video_capture = cv2.VideoCapture(0)

while True:

bgr_image = video_capture.read()[1]

#图像模式的转换

gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)

rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)

#查找人脸

faces = detect_faces(face_detection, gray_image)

for face_coordinates in faces:

#获取坐标进行框选

x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)

rgb_face = rgb_image[y1:y2, x1:x2]

x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)

gray_face = gray_image[y1:y2, x1:x2]

#图像大小

try:

rgb_face = cv2.resize(rgb_face, (gender_target_size))

gray_face = cv2.resize(gray_face, (emotion_target_size))

except:

continue

#标准化

gray_face = preprocess_input(gray_face, False)

#扩展数组的形状

gray_face = np.expand_dims(gray_face, 0)

gray_face = np.expand_dims(gray_face, -1)

#计算相似度

emotion_prediction = emotion_classifier.predict(gray_face)

#寻找可能性最大的表情

emotion_probability = np.max(emotion_prediction)

emotion_label_arg = np.argmax(emotion_prediction)

print("表情种类：",emotion_label_arg)

emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))

emotion_text = emotion_labels[emotion_label_arg]

emotion_window.append(emotion_text)

rgb_face = np.expand_dims(rgb_face, 0)

rgb_face = preprocess_input(rgb_face, False)

#相似度

gender_prediction = gender_classifier.predict(rgb_face)

#找出相似度最高的性别

gender_label_arg = np.argmax(gender_prediction)

print("性别：",gender_label_arg)

gender_text = gender_labels[gender_label_arg]

gender_window.append(gender_text)

#长度限制

if len(gender_window) > frame_window:

emotion_window.pop(0)

gender_window.pop(0)

#获取名称

try:

emotion_mode = mode(emotion_window)

gender_mode = mode(gender_window)

except:

continue

#框的颜色

if emotion_text == 'angry':

color = emotion_probability * np.asarray((255, 0, 0))

elif emotion_text == 'sad':

color = emotion_probability * np.asarray((0, 0, 255))

elif emotion_text == 'happy':

color = emotion_probability * np.asarray((255, 255, 0))

elif emotion_text == 'surprise':

color = emotion_probability * np.asarray((0, 255, 255))

else:

color = emotion_probability * np.asarray((0, 255, 0))

color = color.astype(int)

color = color.tolist()

#图像上绘制边框。

draw_bounding_box(face_coordinates, rgb_image, color)

gender_mode=gender_mode+" "+""

#打印在图片上

draw_text(face_coordinates, rgb_image, gender_mode,

color, 0, -20, 1, 1)

#显示识别相似度百分比

emotion_probability=int(emotion_probability*100)

emotion_mode=emotion_mode+" "+str(emotion_probability)+"%"

#显示视频帧率

fps = "fps:"+str(video_capture.get(cv2.CAP_PROP_FPS))

cv2.putText(rgb_image, fps, (5, 25), cv2.FONT_HERSHEY_DUPLEX, 0.9, (0, 255, 0), 0)

draw_text(face_coordinates, rgb_image, emotion_mode,

color, 0, -45, 1, 1)

#输出部分数据

demo=fps+"--"+emotion_mode+"--"+gender_mode

print(demo)

#开启多线程传输数据，默认屏蔽

# thread1=threading.Thread(target=swop,args=(emotion_label_arg,j, gender_label_arg, 18, emotion_probability))

# thread1.start()

bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)

bgr_image = cv2.resize(bgr_image, dsize=(1280, 720))

cv2.imshow('window_frame', bgr_image)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

video_capture.release()

cv2.destroyAllWindows()

import cv2

import matplotlib.pyplot as plt

import numpy as np

from keras.preprocessing import image

#装载模型

def load_detection_model(model_path):

detection_model = cv2.CascadeClassifier(model_path)

return detection_model

#查找脸部

def detect_faces(detection_model, gray_image_array):

return detection_model.detectMultiScale(gray_image_array, 1.2, 4)

#3个参数，第一个参数是查找的图像

#第2个是脸部最小的大小

#第3个是检测到几次才确定是人脸，与准确率直接挂钩

#画出脸部矩形

def draw_bounding_box(face_coordinates, image_array, color):

x, y, w, h = face_coordinates

cv2.rectangle(image_array, (x, y), (x + w, y + h), color, 2)

#确定脸的矩形位置

def apply_offsets(face_coordinates, offsets):

x, y, width, height = face_coordinates

x_off, y_off = offsets

return (x - x_off, x + width + x_off, y - y_off, y + height + y_off)

#文字打印在图片上

def draw_text(coordinates, image_array, text, color, x_offset=0, y_offset=0,

font_scale=2, thickness=2):

x, y = coordinates[:2]

cv2.putText(image_array, text, (x + x_offset, y + y_offset),

cv2.FONT_HERSHEY_SIMPLEX,

font_scale, color, thickness, cv2.LINE_AA)

#颜色

def get_colors(num_classes):

colors = plt.cm.hsv(np.linspace(0, 1, num_classes)).tolist()

colors = np.asarray(colors) * 255

return colors

#表情序号对应的表情

def get_labels(dataset_name):

if dataset_name == 'fer2013':

return {0: 'angry', 1: 'disgust', 2: 'fear', 3: 'happy',

4: 'sad', 5: 'surprise', 6: 'neutral'}

elif dataset_name == 'imdb':

return {0: 'woman', 1: 'man'}

elif dataset_name == 'KDEF':

return {0: 'AN', 1: 'DI', 2: 'AF', 3: 'HA', 4: 'SA', 5: 'SU', 6: 'NE'}

else:

raise Exception('Invalid dataset name')

import numpy as np

from scipy.misc import imread, imresize

def preprocess_input(x, v2=True):

x = x.astype('float32')

x = x / 255.0

if v2:

x = x - 0.5

x = x * 2.0

return x

#标准化

我是站在巨人的肩膀上的：