Python





```python
import requests  # 发送HTTP 请求模块
import json  # json 解析模块
# cv2 转 base64
import base64
import cv2
import urllib
def encode_image(filename):
    """
    编码图片
    :param filename: str 本地图片文件名
    :return: str 编码后的字符串
        eg:
        src="data:image/gif;base64,R0lGODlhMwAxAIAAAAAAAP///
            yH5BAAAAAAALAAAAAAzADEAAAK8jI+pBr0PowytzotTtbm/DTqQ6C3hGX
            ElcraA9jIr66ozVpM3nseUvYP1UEHF0FUUHkNJxhLZfEJNvol06tzwrgd
            LbXsFZYmSMPnHLB+zNJFbq15+SOf50+6rG7lKOjwV1ibGdhHYRVYVJ9Wn
            k2HWtLdIWMSH9lfyODZoZTb4xdnpxQSEF9oyOWIqp6gaI9pI1Qo7BijbF
            ZkoaAtEeiiLeKn72xM7vMZofJy8zJys2UxsCT3kO229LH1tXAAAOw=="
    """
    # 1、文件读取
    ext = filename.split(".")[-1]
    with open(filename, "rb") as f:
        img = f.read()
    # 2、base64编码
    data = base64.b64encode(img).decode()
    # 3、图片编码字符串拼接
    src = "data:image/{ext};base64,{data}".format(ext=ext, data=data)
    return src
png_uri = encode_image(r"picture\444.png")
url = 'http://smoking.market.alicloudapi.com/ai_image_detect/ai_smoking/v1'
# 请求头
headers = {
    # 根据API的要求，定义相对应的Content - Type
    "Content-Type": "application/x-www-form-urlencoded; charset=utf-8",
    # 权限指定 APPCODE
    'Authorization': 'APPCODE 6ee3b7d95c9d4dce9ab38df723ec7efd'
}
a = "data:image/png;base64,"
r = requests.post(url, headers=headers, data={"IMAGE": png_uri})
print(r.text)
```


参考 ：https://blog.csdn.net/mouday/article/details/93489508

Python base64 阿里云 API 市场 的 requests 请求

（1）用POST方法传输；





（2）用requests.post的json参数传是可以的，直接给，requests.post自动转成json；

（3）用requests.post的data参数传也是可以的，给成json字符串；

（4）POST的请求头部可给可不给，会自动识别，但是接口有自己特殊的请求头部的时候一定得给，比如下图：

![在这里插入图片描述](/static/img/images/00310.png)

写法1
```python
import requests
res = requests.post(url="http://0.0.0.0:8001/items/1",
                    json={
                        "name": "Foo",
                        "description": "A very nice Item",
                        "price": 35.4,
                        "tax": 3.2,
                    },
                    headers={"Content-Type": "application/json; charset=UTF-8"}
                    )
print(res.text)
```

写法2
```python
import json
import requests
res = requests.post(url="http://0.0.0.0:8001/items/1",
                    data=json.dumps({
                        "name": "Foo",
                        "description": "A very nice Item",
                        "price": 35.4,
                        "tax": 3.2,
                    }),
                    headers={"Content-Type": "application/json; charset=UTF-8"}
                    )
print(res.text)
```

Python requests post application/json

修改video_path = 'test1.mp4' 这一句即可，源码地址：https://github.com/hongcyu/char_painting





```python
import cv2
from PIL import Image, ImageFont, ImageDraw
import os
from cv2 import VideoWriter, VideoWriter_fourcc, imread, resize
# =========================
# coding:UTF-8
# 视频转字符画含音频version-1
# 参考1：https://blog.csdn.net/mp624183768/article/details/81161260
# 参考2：https://blog.csdn.net/qq_42820064/article/details/90958577
# 参考3：https://blog.csdn.net/zj360202/article/details/79026891
# =========================
def get_char(r, g, b, alpha=256):
    ascii_char = list(r"#TONGHUIgogoggoWGPZ*@$C&998798213123123128?3345462I1>;\';][.../]'\]!:-;. ")
    # ascii_char = list("#RMNHQODBWGPZ*@$C&98?32I1>!:-;. ")
    # ascii_char = list("$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\|()1{}[]?-_+~<>i!lI;:oa+>!:+. ")
    if alpha == 0:
        return ''
    length = len(ascii_char)
    gray = int(0.2126 * r + 0.7152 * g + 0.0722 * b)
    unit = (256.0 + 1) / len(ascii_char)
    return ascii_char[int(gray / unit)]
# 将视频转换为图片 并进行计数，返回总共生成了多少张图片！
def video_to_pic(vp):
    # vp = cv2.VideoCapture(video_path)
    number = 0
    if vp.isOpened():
        r, frame = vp.read()
        if not os.path.exists('cache_pic'):
            os.mkdir('cache_pic')
        os.chdir('cache_pic')
    else:
        r = False
    while r:
        number += 1
        cv2.imwrite(str(number) + '.jpg', frame)
        r, frame = vp.read()
    print('\n由视频一共生成了{}张图片！'.format(number))
    os.chdir("..")
    return number
def img_to_char(image_path, raw_width, raw_height, task):
    width = int(raw_width / 6)
    height = int(raw_height / 15)
    im = Image.open(image_path).convert('RGB')  # 必须以RGB模式打开
    im = im.resize((width, height), Image.NEAREST)
    txt = ''
    color = []
    for i in range(height):
        for j in range(width):
            pixel = im.getpixel((j, i))
            color.append((pixel[0], pixel[1], pixel[2]))  # 将颜色加入进行索引
            if len(pixel) == 4:
                txt += get_char(pixel[0], pixel[1], pixel[2], pixel[3])
            else:
                txt += get_char(pixel[0], pixel[1], pixel[2])
        txt += '\n'
        color.append((255, 255, 255))
    im_txt = Image.new("RGB", (raw_width, raw_height), (255, 255, 255))
    dr = ImageDraw.Draw(im_txt)
    # font = ImageFont.truetype('consola.ttf', 10, encoding='unic') #改为这个字体会让图片比例改变
    font = ImageFont.load_default().font
    x, y = 0, 0
    font_w, font_h = font.getsize(txt[1])
    font_h *= 1.37  # 调整字体大小
    for i in range(len(txt)):
        if (txt[i] == '\n'):
            x += font_h
            y = -font_w
        dr.text((y, x), txt[i], fill=color[i])
        y += font_w
    os.chdir('cache_char')
    im_txt.save(str(task) + '.jpg')
    os.chdir("..")
    return 0
def star_to_char(number, save_pic_path):
    if not os.path.exists('cache_char'):
        os.mkdir('cache_char')
    img_path_list = [save_pic_path + r'/{}.jpg'.format(i) for i in range(1, number + 1)]  # 生成目标图片文件的路径列表
    task = 0
    for image_path in img_path_list:
        img_width, img_height = Image.open(image_path).size  # 获取图片的分辨率
        task += 1
        img_to_char(image_path, img_width, img_height, task)
        print('{}/{} is finished.'.format(task, number))
    print('=======================')
    print('All image was finished!')
    print('=======================')
    return 0
def process_bar(percent, start_str='', end_str='', total_length=0):
    # 进度条
    bar = ''.join("■ " * int(percent * total_length)) + ''
    bar = '\r' + start_str + bar.ljust(total_length) + ' {:0>4.1f}%|'.format(percent * 100) + end_str
    print(bar, end='', flush=True)
def jpg_to_video(char_image_path, FPS):
    video_fourcc = VideoWriter_fourcc(*"MP42")  # 设置视频编码器,这里使用使用MP42编码器,可以生成更小的视频文件
    char_img_path_list = [char_image_path + r'/{}.jpg'.format(i) for i in range(1, number + 1)]  # 生成目标字符图片文件的路径列表
    char_img_test = Image.open(char_img_path_list[1]).size  # 获取图片的分辨率
    if not os.path.exists('video'):
        os.mkdir('video')
    video_writter = VideoWriter('video/new_char_video.avi', video_fourcc, FPS, char_img_test)
    sum = len(char_img_path_list)
    count = 0
    for image_path in char_img_path_list:
        img = cv2.imread(image_path)
        video_writter.write(img)
        end_str = '100%'
        count = count + 1
        process_bar(count / sum, start_str='', end_str=end_str, total_length=15)
    video_writter.release()
    print('\n')
    print('=======================')
    print('The video is finished!')
    print('=======================')
if __name__ == '__main__':
    video_path = 'test1.mp4'
    save_pic_path = 'cache_pic'
    save_charpic_path = 'cache_char'
    vp = cv2.VideoCapture(video_path)
    number = video_to_pic(vp)
    FPS = vp.get(cv2.CAP_PROP_FPS)
    star_to_char(number, save_pic_path)
    vp.release()
    jpg_to_video(save_charpic_path, FPS)
```

还是有人不会用，下面具体一点。前提是你会python，你需要有python环境。

安装程序的依赖包：
```
pip install opencv-python pillow
```

把代码弄进去，同级目录下加入一个test1.mp4，然后直接运行代码。

![在这里插入图片描述](/static/img/images/00311.png)

就能得到效果视频了：

![在这里插入图片描述](/static/img/images/00312.gif)

Python 井川里予 当程序员看多了 程序 生成 字符画

参考：[https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html)





3080Ti 好像只能用CUDA 11 ，最终我装了CUDA 11.1。

# 1 显卡驱动安装

这个是越高版本越好，因为向下兼容CUDA。安装支持你显卡的最高版本即可！

搜索驱动并安装：

[https://www.nvidia.com/Download/index.aspx?lang=en-us](https://www.nvidia.com/Download/index.aspx?lang=en-us)

![在这里插入图片描述](/static/img/images/00313.png)

![在这里插入图片描述](/static/img/images/00314.png)

# 2 CUDA安装

参考看这里：[https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)

从 CUDA 11.0 开始，由于 CUDA 库（例如 cuFFT 和 CUB）中的 C++11 要求，推荐的最低 GCC 编译器至少为 GCC 6。

如何升级gcc：

```python
# 教程 https://www.cnblogs.com/jixiaohua/p/11732225.html
sudo yum install centos-release-scl  # 安装centos-release-scl  红帽自带工具让原始gcc和你新安装的共存
sudo yum install devtoolset-8-gcc*  #安装devtoolset，注意，如果想安装7.*版本的，就改成devtoolset-7-gcc*，以此类推
scl enable devtoolset-8 bash  # 激活对应的devtoolset，所以你可以一次安装多个版本的devtoolset，需要的时候用下面这条命令切换到对应的版本
gcc -v # 查看版本
# 避免下次重启终端后还是会恢复到gcc4.8.5,所以：
cd /usr/bin/  # 去这个目录
mv /usr/bin/gcc /usr/bin/gcc-4.8.5  # 备份原有gcc
ln -s /opt/rh/devtoolset-8/root/bin/gcc /usr/bin/gcc   # 软链接
mv /usr/bin/g++ /usr/bin/g++-4.8.5    # 备份  没有的话可以不管
ln -s /opt/rh/devtoolset-8/root/bin/g++ /usr/bin/g++   # 软链接  没有的话可以不管
gcc --version  # 查看版本
g++ --version  # 查看版本
```



查看你的centos系统内核版本，应该满足下面图里的要求：
```python
uname -srm
输出结果：
Linux 3.10.0-957.12.2.el7.x86_64 x86_64
3 - 内核版本.
10 - 主修订版本.
0-957 - 次要修订版本.
12 - 补丁版本.
```

![在这里插入图片描述](/static/img/images/00315.png)




下载cuda的run文件：

[https://developer.nvidia.com/cuda-toolkit-archive](https://developer.nvidia.com/cuda-toolkit-archive)

![在这里插入图片描述](/static/img/images/00316.png)


linux下载与安装很方便：

```python
wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run
sudo sh cuda_11.1.0_455.23.05_linux.run
```

安装时不用选择选卡驱动，其他的选上然后安装即可。

成功安装后看到：

```python
(base) [root@localhost ~]# sh cuda_11.1.0_455.23.05_linux.run
===========
= Summary =
===========
Driver:   Not Selected
Toolkit:  Installed in /usr/local/cuda-11.1/
Samples:  Installed in /root/, but missing recommended libraries
Please make sure that
 -   PATH includes /usr/local/cuda-11.1/bin
 -   LD_LIBRARY_PATH includes /usr/local/cuda-11.1/lib64, or, add /usr/local/cuda-11.1/lib64 to /etc/ld.so.conf and run ldconfig as root
To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-11.1/bin
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least .00 is required for CUDA 11.1 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
    sudo <CudaInstaller>.run --silent --driver
Logfile is /var/log/cuda-installer.log
```



所以增加环境变量

```python
vim /etc/profile  # 进入文件写env变量
# 在最后添加三句话env
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.1/lib64
export PATH=$PATH:/usr/local/cuda-11.1/bin
export CUDA_HOME=$CUDA_HOME:/usr/local/cuda-11.1
# 也可以这样添加
export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64
export PATH=$PATH:/usr/local/cuda-11.1/bin
export CUDA_HOME=/usr/local/cuda-11.1
source /etc/profile # 生效env变量
```


此时就能查看CUDA版本：

```python
(torch111_k) [root@localhost ~]# nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Tue_Sep_15_19:10:02_PDT_2020
Cuda compilation tools, release 11.1, V11.1.74
Build cuda_11.1.TC455_06.29069683_0
```

关于安装CUDA出现的`WARNING: Incomplete installation!`提示：别管他！



# 3 cuDNN

参考看这里：[https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html)

下载链接：[https://developer.nvidia.com/zh-cn/cudnn](https://developer.nvidia.com/zh-cn/cudnn)

需要注册登陆才能下载，下载与CUDA版本对应的版本：

![在这里插入图片描述](/static/img/images/00317.png)

安装很简单：

```python
tar -zxvf cudnn-11.1-linux-x64-v8.0.5.39.tgz # 解压后就是cuda文件夹
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include 
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 
sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
```

验证cudnn安装成功：

```python
# CUDA 10.2
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
vim test.py
```

```python
import torch
# 若正常则静默
a = torch.tensor(1.)
# 若正常则静默
print(a.cuda())
# 若正常则返回 tensor(1., device='cuda:0')
from torch.backends import cudnn
# 若正常则静默
print(cudnn.is_available())
# 若正常则返回 True
print(cudnn.is_acceptable(a.cuda()))
# 若正常则返回 True
```


# 总的来说

```shell
#!/bin/bash
cd /data
scp -r root@10.10.14.36:/data/datagpu/ /data
sudo yum install wget vim -y
sudo yum install centos-release-scl -y # 安装centos-release-scl  红帽自带工具让原始gcc和你新安装的共存
sudo yum install devtoolset-8-gcc*  -y  #安装devtoolset，注意，如果想安装7.*版本的，就改成devtoolset-7-gcc*，以此类推
scl enable devtoolset-8 bash  # 激活对应的devtoolset，所以你可以一次安装多个版本的devtoolset，需要的时候用下面这条命令切换到对应的版本
gcc -v # 查看版本
cd /data/datagpu/
sh cuda_11.1.0_455.23.05_linux.run
echo "export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64" >>/etc/profile
echo "export PATH=$PATH:/usr/local/cuda-11.1/bin" >>/etc/profile
echo "export CUDA_HOME=/usr/local/cuda-11.1" >>/etc/profile
source /etc/profile
nvcc -V
tar -zxvf cudnn-11.1-linux-x64-v8.0.5.39.tgz # 解压后就是cuda文件夹
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include 
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 
sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
sh Miniconda3-py38_4.9.2-Linux-x86_64.sh
rm -rf ~/.condarc
vim ~/.condarc
channels:
  - defaults
show_channel_urls: true
channel_alias: https://mirrors.bfsu.edu.cn/anaconda
default_channels:
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/main
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/free
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/r
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/pro
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.bfsu.edu.cn/anaconda/cloud
  msys2: https://mirrors.bfsu.edu.cn/anaconda/cloud
  bioconda: https://mirrors.bfsu.edu.cn/anaconda/cloud
  menpo: https://mirrors.bfsu.edu.cn/anaconda/cloud
  pytorch: https://mirrors.bfsu.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.bfsu.edu.cn/anaconda/cloud
source ~/.bashrc
conda create -n torch python=3.8
source ~/.bashrc
conda activate torch
conda install -y pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
python test.py
```

Linux CentOS7 深度学习环境 安装 CUDA Cudnn 验证安装成功 3080Ti 显卡 pytorch验证

Python





```python
yum whatprovides */lspci
yum install pciutils
```

这样就能：

```python
lspci  | grep -i vga
```

没有可用软件包 lspci yum install pciutils

Python





```python
img = cv2.imread(os.path.join(filepath, name))
dst = cv2.resize(img, (img.shape[1]//4, img.shape[0]//4))
cv2.imwrite(os.path.join(aimpath, "image" + str(num).zfill(6)) + ".jpg", dst)
```

Python opencv 图像缩放

Python tsfresh 自动计算大量的时间序列特征

[https://zhuanlan.zhihu.com/p/99069186](https://zhuanlan.zhihu.com/p/99069186)

LightGBM 决策树 gradient boosting framework

图片文件如果相同，md5是一样的。于是利用此进行图片去重。





```python
import os
import hashlib
def get_md5(file):
    file = open(file, 'rb')
    md5 = hashlib.md5(file.read())
    file.close()
    md5_values = md5.hexdigest()
    return md5_values
file_path =  r"D:\Users\Administrator\Desktop\smokingsig\yolov5k\images"
os.chdir(file_path)
file_list = os.listdir(file_path)
md5_list = []
for file in file_list:
    md5 = get_md5(file)
    if md5 not in md5_list:
        md5_list.append(md5)
    else:
        os.remove(file)
```

相似度  计算量非常大，不太好用：
```python
# -*- encoding=utf-8 -*-
import cv2
import os
class dup_picture:
    def __init__(self):
        self.orb = cv2.ORB_create()
        self.bf = cv2.BFMatcher(cv2.NORM_HAMMING)
    def get_pic_desc(self, img1_path: str):
        img1 = cv2.imread(img1_path, cv2.IMREAD_GRAYSCALE)
        kp1, des1 = self.orb.detectAndCompute(img1, None)
        return des1
    def get_similary(self, des1, des2):
        # knn筛选结果
        matches = self.bf.knnMatch(des1, trainDescriptors=des2, k=2)
        # 查看最大匹配点数目
        good = [m for (m, n) in matches if m.distance < 0.75 * n.distance]
        if len(matches) == 0:
            return 0
        similary = len(good) / len(matches)
        return similary
if __name__ == '__main__':
    duptool = dup_picture()
    file_path = r"D:\PycharmProjects\downloadpicture\all_smoking_picture"
    file_list = os.listdir(file_path)
    file_list = list(filter(lambda x: x.endswith(".jpg"), file_list))  # must .jpg pic
    os.chdir(file_path)
    unique_list = [duptool.get_pic_desc(file_list[0]), ]
    for k,file in enumerate(file_list[1:]):
        print(k)
        des = duptool.get_pic_desc(file)
        if des is None:
            os.remove(file)
            continue
        for des_i in unique_list:
            if duptool.get_similary(des, des_i) > 0.5:
                os.remove(file)
                print(file)
                break
        else:  # Smooth and uninterrupted execution completed
            unique_list.append(des)
```


其余labels处理：

```python
import os
imagespath = r"D:\Users\Administrator\Desktop\smokingsig\yolov5k\images"
labelspath = r"D:\Users\Administrator\Desktop\yololabels"
img_list = os.listdir(imagespath)
lab_list = os.listdir(labelspath)
# ---- read label ----> if none label ----> delete label file
for name in lab_list:
    lfile = os.path.join(labelspath, name)
    if "image" in name:
        with open(lfile, "r") as f:
            res = f.readlines()
        if len(res) == 0:
            os.remove(lfile)
# ---- read label ----> if  label str too short ----> delete label file
for name in lab_list:
    lfile = os.path.join(labelspath, name)
    if "image" in name:
        with open(lfile, "r") as f:
            res = f.readlines()
        if len(res[0]) < 3:
            os.remove(lfile)
# ---- read images names ----> if have not label file ----> delete imgfile
for name in img_list:
    if name[:-4] + ".txt" not in lab_list:
        os.remove(os.path.join(imagespath, name))
```

Python 图片去重 图片文件去重 依据相似度去重 cv2.ORB

# 四种POST 请求方式：





[https://imququ.com/post/four-ways-to-post-data-in-http.html#toc-2](https://imququ.com/post/four-ways-to-post-data-in-http.html#toc-2)

Content-Type: application/x-www-form-urlencoded  最原始表单，参数单一，不能传文件。

Content-Type:multipart/form-data  改进后的表单，能传文件。

Content-Type: application/json  倾向于传json，json规范导致前后端传数据都喜欢用这个，数据生产和消费简单。

# FastAPI 中如何支持

[https://fastapi.tiangolo.com/zh/tutorial/schema-extra-example/](https://fastapi.tiangolo.com/zh/tutorial/schema-extra-example/)

把函数参数改为Body解析即可，而不是FORM解析。

```python
# -*- coding:utf-8 -*-
import traceback
import uvicorn
from typing import Optional
from fastapi import Body, FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI(
    title='FastAPI',
    description='FastAPI',
    version='1.0.0',
    docs_url='/docs',
    redoc_url='/redocs',
)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
class Item(BaseModel):
    name: str
    description: Optional[str] = None
    price: float
    tax: Optional[float] = None
@app.post("/items/{item_id}")
async def update_item(
        item_id: int,
        item: Item = Body(
            ...,
            example={
                "name": "Foo",
                "description": "A very nice Item",
                "price": 35.4,
                "tax": 3.2,
            },
        ),
):
    results = {"item_id": item_id, "item": item}
    return results
if __name__ == '__main__':
    uvicorn.run('fastapia:app', host='0.0.0.0', port=8001, reload=False, debug=False, workers=1)
```

# PostMan 测试

修改请求头

![在这里插入图片描述](/static/img/images/00319.png)

写raw的原生json字符串

![在这里插入图片描述](/static/img/images/00320.png)


```python
import requests
requests.post(url="http://0.0.0.0:8001/items/1",
              json={
                  "name": "Foo",
                  "description": "A very nice Item",
                  "price": 35.4,
                  "tax": 3.2,
              },
              )
```