Face++ API实现手势识别系统 Face++ API实现手势识别系统设计（face mask）

'''
# -*- coding:utf-8 -*-
@author: TulLing
'''
import requests 
from json import JSONDecoder 
 
gesture_englist = ['big_v','fist','double_finger_up','hand_open','heart_d','index_finger_up','ok','phonecall','palm_up','rock','thumb_down','thumb_up','victory']
gesture_chinese = ["我最帅",
   "拳头，停下",
   "我发誓",
   "数字5",
   "比心",
   "数字1",
   "好的呢，OK",
   "打电话",
   "手心向上",
   "爱你，520",
   "差评，不好的",
   "好评，Good，很棒",
   "胜利，开心"]
# 将字典排序
def sort_dict(adict):
 return sorted(adict.items(),key= lambda item:item[1])
 
class Gesture(object):
 def __init__(self):
 self.http_url = 'https://api-cn.faceplusplus.com/humanbodypp/beta/gesture'
 self.key = '*****'
 self.secret = '******'
 self.data = {"api_key":self.key,"api_secret":self.secret}
 
 
 # 获取手势信息
 def get_info(self,files):
 response = requests.post(self.http_url,data=self.data,files=files)
 req_con = response.content.decode('utf-8')
 req_dict = JSONDecoder().decode(req_con)
 #print(req_dict)
 if('error_message' not in req_dict.keys()) and (len(req_dict['hands'])):
 # 获取
  hands_dict = req_dict['hands']
  #print(type(hands_dict))
  # 获取到手的矩形的字典
  gesture_rectangle_dict = hands_dict[0]['hand_rectangle']
  # 获取到手势的字典
  gesture_dict = hands_dict[0]['gesture']
  
  return gesture_dict,gesture_rectangle_dict
 else:
  return [],[];
 
 # 获取到手势文本信息
 def get_text(self,index):
 return gesture_chinese[index]
 
 # 获取到手势对应的概率
 def get_pro(self,gesture_dict,index):
 # print(gesture_dict)
 if(gesture_dict is None or gesture_dict == []):
  return 0
 return gesture_dict[gesture_englist[index]]
 
 # 获取到手势的位置
 def get_rectangle(self,gesture_rectangle_dict):
 if(gesture_rectangle_dict is None or gesture_rectangle_dict == []):
  return (0,0,0,0)
 x = gesture_rectangle_dict['top']
 y = gesture_rectangle_dict['left']
 width = gesture_rectangle_dict['width']
 height = gesture_rectangle_dict['height']
 return (x,y,width,height)

'''
# -*- coding:utf-8 -*-
@author: TulLing
'''
import sys
sys.path.append("../gesture/")
 
import os
import random
import cv2 as cv
import time
import LearnGesture
 
def gestureLearning():
 os.system("cls")
 print("进入学习手势模式！")
 print("我们有13个手势，来和我学吧！(每次结束后可以选择输入 Q\q 退出！)")
 while(True):
 pic_num = random.randint(0,12) # 生成显示的图片的编号(随机数： 0 - 13)
 print(pic_num)
 pic_path = '../gesture/pic/gesture' + str(pic_num) + ".jpg" # 生成图片路径
 
 pic = cv.imread(pic_path) # 加载图片
 pic = cv.resize(pic,(120,120))
 cv.imshow("PIC",pic) # 显示要学习的手势
 
 print("即将打开摄像头，你有5秒种的时间准备手势，5秒种保持手势！")
 write_path = "../gesture/pic/test.jpg"
 cap = cv.VideoCapture(1)
 while(True):
  _,frame = cap.read()
  cv.imshow("Frame",frame)
  key = cv.waitKey(10)
  if(key == ord('Q') or key == ord('q')):
  cv.imwrite(write_path,frame)
  cv.waitKey(200)
  cap.release()
  cv.destroyAllWindows()
  break
  
 # 此处应该有手势识别
 files = {"image_file":open(write_path,'rb')}
 gesture = LearnGesture.Gesture()
 
 # 获取到手势文本
 ge_text = gesture.get_text(pic_num)
 # 获取手势信息
 gesture_dict,gesture_rectangle_dict = gesture.get_info(files)
 # 获取手势的概率
 ge_pro = gesture.get_pro(gesture_dict,pic_num)
 # 获取到手势的坐标
 ge_rect = gesture.get_rectangle(gesture_rectangle_dict)
 print("您学习的手势是：",ge_text)
 print("相似度达到：",ge_pro)
 print("具体位置：",ge_rect)
 
 
 # print("一轮学习结束，是否继续学习？(Y/N)")
 # 退出程序，回到主菜单或者继续
 commend = input("一轮学习结束，是否继续学习？(Y/N)：")
 print(commend)
 
 if( commend == 'N' or commend == "n"):
  break
gestureLearning()