官方的demo文档没有随着版本而更新，导致 坑太多，特写了一下。

1. 部署版本
测试成功的版本（经历有限，只测试出一种能用的）：

ubuntu 20 （最初是centos7，一直试了不行，后改为Ubuntu系统）
python 3.8.10 （如果装不好报各种缺包的错误）
pip 20.0.2
paddlepaddle 1.8.5 （刚开始装的2.X的试了不行）
paddlehub 1.7.0 按照官网demo选的最低版本

2. 部署过程
把上面的版本安装好后，可是启动，这个按照官方demo 文档来

• 2.1 上传参数
  实际的上传数据json数据，即图片要使用base64编码。

img1 = cv2_to_base64(cv2.imread("C:/Users/Lenovo/Pictures/2.jpg"))
data = {'images': [img1]}

• 2.2 request header

headers = {"Content-type": "application/json"}

• 2.2 request url

 url = "http://121.220.131.209:8866/predict/deeplabv3p_xception65_humanseg"

• 2.2 response
  响应数据是下面这样的：

{'msg': '', 'results': [{'data': 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'status': '0'}

因此其解析的图片数据是在data上：

    r = requests.post(url=url, headers=headers, data=json.dumps(data))
    result=r.json();
    img=result["results"][0]
    with open("2.jpg", "wb") as fp:
        fp.write(base64.b64decode(img["data"].split(',')[-1]))

(附完整代码)：

# coding: utf8
import requests
import json
import cv2
import base64


def cv2_to_base64(image):
    data = cv2.imencode('.jpg', image)[1]
    return base64.b64encode(data.tostring()).decode('utf8')


if __name__ == '__main__':

    # files = [("image", (open(item, "rb"))) for item in file_list]
    # 获取图片的base64编码格式
    img1 = cv2_to_base64(cv2.imread("C:/Users/Lenovo/Pictures/2.jpg"))
    # img2 = cv2_to_base64(cv2.imread("../../../../docs/imgs/woman_mask.jpg"))
    data = {'images': [img1]}
    # 指定content-type
    headers = {"Content-type": "application/json"}
    # 发送HTTP请求
    url = "http://121.220.131.209:8866/predict/deeplabv3p_xception65_humanseg"
    r = requests.post(url=url, headers=headers, data=json.dumps(data))
    result=r.json();
    print(result)
    img=result["results"][0]
    with open("2-1.jpg", "wb") as fp:
        fp.write(base64.b64decode(img["data"].split(',')[-1]))