import os
import requests
import base64
import json
from pprint import pprint
import time
import io
from io import BytesIO
import cv2
import numpy as np
from PIL import Image
import glob

# client_id 为官网获取的AK， client_secret 为官网获取的SK
api_key = 'dYxpyYmrXloe8x75j40k67xM'
secret_key = 'QlzEqWmVG420yQBohEG1mqqQncZxyTT1'

class Traffic_flowRecognizer(object):
def __init__(self, api_key, secret_key):
self.access_token = self._get_access_token(api_key=api_key,secret_key=secret_key)
self.API_URL = 'https://aip.baidubce.com/rest/2.0/image-classify/v1/body_tracking' + '?access_token=' \
+ self.access_token
# 获取token
@staticmethod
def _get_access_token(api_key, secret_key):
api = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials' \
'&client_id={}&client_secret={}'.format(api_key, secret_key)
rp = requests.post(api)
if rp.ok:
rprp_json = rp.json()
print(rprp_json['access_token'])
return rprp_json['access_token']
else:
print('=>Error in get access token!')

def get_result(self, params):
headers = {'content-type': 'application/x-www-form-urlencoded'}
print("params=",params)
rp = requests.post(self.API_URL, data=params,headers=headers)

if rp.ok:
print('=>Success! got result: ')
rprp_json = rp.json()
pprint(rprp_json)
return rprp_json
else:
print('=>Error! token invalid or network error!')
print(rp.content)
return None
# 人流量统计
def detect(self):
###对视频进行抽帧后，抽帧频率5fps，连续读取图片
WSI_MASK_PATH = 'output' # 存放图片的文件夹路径
paths = glob.glob(os.path.join(WSI_MASK_PATH, '*.jpg'))
paths.sort()
data_list = []
c = 1
for path in paths:
print("path=",path)
f = open(path, 'rb')
img_str = base64.b64encode(f.read()).decode()
print("img_str=",img_str)
data_list.append(img_str)
params = {"dynamic": "true", "area": "1,210,179,169,179,120,1,120", "case_id": c, "case_init": "false",
"image": data_list, "show": "true"}
# params = {"area": "1,1,719,1,719,719,1,719", "case_id": 1, "case_init": "false", "dynamic": "true",
# "image": data_list,'show': 'true'}
tic = time.clock()
# json_str = json.dumps(params)
# print("json_str=",json_str)
rp_json = self.get_result(params)
print("rp_json=",rp_json)
toc = time.clock()
print('单次处理时长: ' + '%.2f' % (toc - tic) + ' s')
img_b64encode = rp_json['image']
img_b64decode = base64.b64decode(img_b64encode) # base64解码
# 显示检测结果图片
# image = io.BytesIO(img_b64decode)
# img = Image.open(image)
# img.show()
# 存储检测结果图片
file = open('out/' + str(c) + '.jpg', 'wb')
file.write(img_b64decode)
file.close()
c = c + 1

if __name__ == '__main__':
recognizer = Traffic_flowRecognizer(api_key, secret_key)
recognizer.detect()

程序报错：

24.d51903c9edea56761ea6992c17f658ef.2592000.1616655046.282335-23611612
path= output\0001.jpg
img_str= 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
params= {'dynamic': 'true', 'area': '1,210,179,169,179,120,1,120', 'case_id': 1, 'case_init': 'false', 'image': 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'], 'show': 'true'}
=>Success! got result:
{'error_code': 282004,
'error_msg': 'invalid parameter(s)',
'log_id': 5483178101645919287}
Traceback (most recent call last):
rp_json= {'log_id': 5483178101645919287, 'error_code': 282004, 'error_msg': 'invalid parameter(s)'}
单次处理时长: 0.33 s
File "C:/伟东资料/python应用开发大纲/人体分析06/code/python_code/one/body_tracking_dynamic_demo07.py", line 91, in
recognizer.detect()
File "C:/伟东资料/python应用开发大纲/人体分析06/code/python_code/one/body_tracking_dynamic_demo07.py", line 76, in detect
img_b64encode = rp_json['image']
KeyError: 'image'