如：pip install scipy 

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

from statistics import modeimport cv2
from keras.models import load_model
import numpy as npfrom 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_inputimport 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+"  "+"<No entry>"#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_modeprint(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_coordinatescv2.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_coordinatesx_off, y_off = offsetsreturn (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) * 255return 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, imresizedef preprocess_input(x, v2=True):x = x.astype('float32')x = x / 255.0if v2:x = x - 0.5x = x * 2.0return x
#标准化