人脸检测预测年龄性别

    # -*- coding: utf-8 -*-
    # -- coding: UTF-8 --
    # coding = UTF-8
    # -*- coding: utf-8 -*-
    import base64
    import matplotlib.pyplot as plt
    import cv2
    import pandas as pd
    from PIL import Image
    from io import BytesIO
    from PIL import Image, ImageDraw, ImageFont
    import numpy as np
    from aip import AipFace
    import time
    import dlib
    import threading
    def frame2base64(frame):
    img = Image.fromarray(frame)  # 将每一帧转为Image
    output_buffer = BytesIO()  # 创建一个BytesIO
    img.save(output_buffer, format='JPEG')  # 写入output_buffer
    byte_data = output_buffer.getvalue()  # 在内存中读取
    base64_data = base64.b64encode(byte_data)  # 转为BASE64
    return base64_data  # 转码成功 返回base64编码
    def face_detection(face_data,im):
    for i in face_data['result']['face_list']:
        left = max(int(i['location']['left']),0)
        top = max(int(i['location']['top']),0)
        width = int(i['location']['width'])
        height = int(i['location']['height'])
        gender = str(i['gender']['type'])
        age = str(i['age'])
        cv2.rectangle(im, (left, top), (left+width , top+height),(0,0,255),2)
        cv2.putText(im,gender+age, (left+10, top+10), cv2.FONT_HERSHEY_SIMPLEX,
                    0.7, (0, 255, 0), 1, cv2.LINE_AA)
    return im
    
    video_capture = cv2.VideoCapture(0)
    
    while True :
    ret, frame = video_capture.read()
    frame = cv2.flip(frame, 1)
    if cv2.waitKey(1) == ord('Q'):
        break
    APP_ID = 'test1'
    API_KEY = '9OWZWm5tMrecGxYm4hSNbtYL'
    SECRET_KEY = 'Y7Qw0dWX4u1B0WM76tiTrnM4tv17pvMQ'
    imageType = 'BASE64'
    client = AipFace(APP_ID, API_KEY, SECRET_KEY)
    resized_image = cv2.resize(frame, (0, 0), fx=1, fy=1)
    base64_2 = frame2base64(resized_image)
    base64_2 = str(base64_2)
    options = {}
    options["max_face_num"] = 10
    options["face_type"] = "LIVE"
    options["liveness_control"] = "NORMAL"
    options["face_field"] = "age,gender,beauty,"
    face_data = client.detect(base64_2, imageType, options)
    print face_data
    if face_data['error_msg'] == 'SUCCESS':
        frame = face_detection(face_data, resized_image)
    cv2.imshow('fda', frame)