这里写目录标题
-
- [flask 文件上传与接收](#flask 文件上传与接收)
- [argparse 不从命令行调用参数](#argparse 不从命令行调用参数)
- python解压压缩包
- python将文件如jpg保存到指定文件夹报错
- 一团糟
flask 文件上传与接收
文件流接收
1、前端传来的对象是二进制文件流,有两种方法保存本地。
(1)通过open()方法将文件流写入保存
(2)直接用调用 file.save() 方法保存传来的文件流:
flask应答(接收请求(文件、数据)
python
from flask import Flask,request
app = Flask(__name__)
@app.route('/upload',methods = ['POST'])
def file_receive():
# 获取文件对象
file = request.files['file']
# 获取文件名
filename = file.filename
# file.save 也可保存传来的文件
# file.save(f'./{filename}')
with open(f'./{filename}','wb') as f:
f.write(file.stream.read())
return {'success':1}
if __name__ == '__main__':
app.run()
flask请求(上传文件)
测试该段代码的文件上传可以用requests实现,用open()创建一个二进制对象,传给后端:
python
import requests
def uploads():
url = 'http://127.0.0.1:5000/upload'
files = {'file':open('C:\\Users\\xxx\\Desktop\\push\\test.mp4','rb')}
r = requests.post(url,files = files)
print(r.text)
if __name__=="__main__":
uploads()
传递参数和文件
试过了,行不通request.data
为空,真是的
python
from flask import Flask,request
app = Flask(__name__)
@app.route('/upload',methods = ['POST'])
def file_receive():
# 获取文件对象
file = request.files['file']
# 获取参数body
body = request.data
filename = file.filename
# file.save 也可保存传来的文件
# file.save(f'./{filename}')
with open(f'./{filename}','wb') as f:
f.write(file.stream.read())
return {'success':1}
if __name__ == '__main__':
app.run()
requests 测试代码:
python
import requests
def uploads():
url = 'http://127.0.0.1:5000/upload'
body = {'info':'test'}
files = {'file':open('C:\\Users\\xxx\\Desktop\\push\\test.mp4','rb')}
r = requests.post(url,json = body,files = files)
print(r.text)
if __name__=="__main__":
uploads()
假设我们目前有一些文件,和参数需要通过POST发送到请求服务端,我们可以通过content type为multipart/form-data 来同时传入这两个参数。
准备参数
我们先设置需要传入的参数,这里 file_path 需要改成自己的文件
python
import requests
# 设置要上传的文件
file_path = "path/to/your/file" # 这里替换成文件目录
files = {
"file1": ("filename", open(file_path, "rb"))
}
# 设置要发送的JSON数据
params = {
'key1': 'value1',
'key2': 'value2'
}
编写service
在服务端要如何获取文件和JSON参数?我们首先要知道,通过如上方式传入数据,content-type是multipart/form-data 。所以我们在服务端应该使用 request.form.to_dict() 来获取表格里的参数内容。
我们新建一个命名为 service.py 的文件,写入一下脚本来启动命名为"upload-endpoint"的服务。我们这里服务没有做数据处理,只是把它们打印出来。
python
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/upload-endpoint', methods=['POST'])
def upload_endpoint():
try:
# 获取JSON数据
params = request.form.to_dict()
print(params)
# 获取上传的文件
file1 = request.files['file1']
print(file1)
# 处理JSON数据和文件
# 在这里,你可以根据需要对JSON数据和文件进行操作
# 例如,你可以保存文件到服务器,访问JSON数据等
# 返回一个响应
response_data = {
'message': 'JSON参数和文件已成功接收和处理'
}
return jsonify(response_data), 200
except Exception as e:
error_message = str(e)
return jsonify({'error': error_message}), 400
if __name__ == '__main__':
app.run(debug=True, port=5000)
请求
python
# 发送POST请求,同时传送JSON数据和文件
response = requests.post('http://127.0.0.1:5000/upload-endpoint', data=params, files=files)
如何用python request同时上传文件和JSON参数
argparse 不从命令行调用参数
1、设置default值
python
parser.add_argument('-f', '--config_file', dest='config_file', type=argparse.FileType(mode='r'))
改进如下
python
yaml_path='test.yaml'
parser.add_argument('-f', '--config_file', dest='config_file',type=argparse.FileType(mode='r'),default=yaml_path)
2、"从命令行传入的参数".split()
现在很多python代码使用parser解析输入参数, 我们如果想要在IDE里(如pycharm)分析源代码,不可能每一次都使用命令行进行,因此这里面使用了一个技巧,即源程序在定义完入口命令行参数后,使用了args = parser.parse_args() 来接送实际使用命令行时的输入,我们这里把这句代码替换为:
args= parser.parse_args("从命令行传入的参数".split())
python
args = parser.parse_args("--input ../example_graphs/karate.adjlist --output ./output".split())
str="--input .../example_graphs/karate.adjlist"
args = parser.parse_args(str.split())
就报错AttributeError: 'str' object has no attribute 'spilt'
可以使用第三种方式
args = parser.parse_args(【'--input',str】)
Pycham不用命令行传入参数
3、['--input','内容']
Python 中使用 argparse 解析命令行参数 | Linux 中国
有一些第三方库用于命令行解析,但标准库 argparse 与之相比也毫不逊色。
无需添加很多依赖,你就可以编写带有实用参数解析功能的漂亮命令行工具。
Python 中的参数解析
使用 argparse 解析命令行参数时,第一步是配置一个 ArgumentParser 对象。这通常在全局模块内完成,因为单单_配置_一个解析器没有副作用。
python
import argparse
PARSER = argparse.ArgumentParser()
ArgumentParser 中最重要的方法是 .add_argument(),它有几个变体。默认情况下,它会添加一个参数,并期望一个值。
python
PARSER.add_argument("--value")
查看实际效果,调用 .parse_args():
python
PARSER.parse_args(["--value", "some-value"])
Namespace(value='some-value')
也可以使用 = 语法:
python
PARSER.parse_args(["--value=some-value"])
Namespace(value='some-value')
为了缩短在命令行输入的命令,你还可以为选项指定一个短"别名":
python
PARSER.add_argument("--thing", "-t")
可以传入短选项:
PARSER.parse_args("-t some-thing".split())
Namespace(value=None, thing='some-thing')
或者长选项:
PARSER.parse_args("--thing some-thing".split())
Namespace(value=None, thing='some-thing')
类型
有很多类型的参数可供你使用。除了默认类型,最流行的两个是布尔类型和计数器。布尔类型有一个默认为 True 的变体和一个默认为 False 的变体。
PARSER.add_argument("--active", action="store_true")
PARSER.add_argument("--no-dry-run", action="store_false", dest="dry_run")
PARSER.add_argument("--verbose", "-v", action="count")
除非显式传入 --active,否则 active 就是 False。dry-run 默认是 True,除非传入 --no-dry-run。无值的短选项可以并列。
传递所有参数会导致非默认状态:
PARSER.parse_args("--active --no-dry-run -vvvv".split())
Namespace(value=None, thing=None, active=True, dry_run=False, verbose=4)
默认值则比较单一:
PARSER.parse_args("".split())
Namespace(value=None, thing=None, active=False, dry_run=True, verbose=None)
子命令
经典的 Unix 命令秉承了"一次只做一件事,并做到极致",但现代的趋势把"几个密切相关的操作"放在一起。
git、podman 和 kubectl 充分说明了这种范式的流行。argparse 库也可以做到:
MULTI_PARSER = argparse.ArgumentParser()
subparsers = MULTI_PARSER.add_subparsers()
get = subparsers.add_parser("get")
get.add_argument("--name")
get.set_defaults(command="get")
search = subparsers.add_parser("search")
search.add_argument("--query")
search.set_defaults(command="search")
MULTI_PARSER.parse_args("get --name awesome-name".split())
Namespace(name='awesome-name', command='get')
MULTI_PARSER.parse_args("search --query name~awesome".split())
Namespace(query='name~awesome', command='search')`
程序架构
使用 argparse 的一种方法是使用下面的结构:
python
## my_package/__main__.py
import argparse
import sys
from my_package import toplevel
parsed_arguments = toplevel.PARSER.parse_args(sys.argv[1:])
toplevel.main(parsed_arguments)
## my_package/toplevel.py
PARSER = argparse.ArgumentParser()
## .add_argument, etc.
def main(parsed_args):
...
# do stuff with parsed_args
在这种情况下,使用 python -m my_package 运行。或者,你可以在包安装时使用 console_scprits 入口点。
总结
argparse 模块是一个强大的命令行参数解析器,还有很多功能没能在这里介绍。它能实现你想象的一切。
python解压压缩包
如果是从前端上传的zip,只想将解压后的文件夹存在服务器中,那么先解压再保存(保存之后才存在文件路径),可以将前端输入的zip文件
python
现在我们直接使用上一步产生的 spam.zip 文件内容,首先假定输入为字节数据,然后窥探其中每一个条目的文件信息与内容
import zipfile
import io
import os
def read_zipfiles(path, folder=''):
for member in path.iterdir():
filename = os.path.join(folder, member.name)
if member.is_file():
print(filename, ':', member.read_text()) # member.read_bytes()
else:
read_zipfiles(member, filename)
with open('spam.zip', 'rb') as myzip:
zip_data = myzip.read()
with zipfile.ZipFile(io.BytesIO(zip_data)) as zip_file:
read_zipfiles(zipfile.Path(zip_file))
python
# 处理压缩文件
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) # 压缩文件保存在项目路径下
local_dir = os.path.join(base_dir, '11') # 新创建一个路径,用来放压缩后的文件
hh = os.path.join(base_dir, filename) # 这个是找到压缩文件路径-------C:/Code/haha.zip
print(hh)
print(local_dir)
shutil.unpack_archive(filename=hh, extract_dir=local_dir)# 把文件保存在刚刚设定好的路径下
os.remove(hh) # 最后把压缩文件删除
python将文件如jpg保存到指定文件夹报错
dst = open(dst, "wb")
python
```python
from PIL import Image
import os
# 打开图片
image = Image.open('example.jpg')
# 保存图片到指定文件夹
if not os.path.exists('new_folder'):
os.makedirs('new_folder')
image.save('new_folder/example_new.jpg')
上述代码中,使用os模块创建一个新的文件夹new_folder,并将图片保存到这个文件夹中。
我的代码报错
python - IO错误: Errno 13 Permission denied for specific files
些许类似,没明白
一团糟
python
if suffix.lower() in ['jpg', 'png', 'jpeg']:
# uploaded_file.save(image_folder + uploaded_file.filename.split('.')[-2])
# image_folder = image_folder + uploaded_file.filename.split('.')[-2]
save_path=image_folder + uploaded_file.filename.split('.')[-2]
# # uploaded_file.save(save_path + uploaded_file.filename)
# image_folder = image_folder + uploaded_file.filename.split('.')[-2]
# uploaded_file.save('./images/hhh/'+ uploaded_file.filename)
# image_folder = image_folder + 'hhh/'
# save_path=image_folder + uploaded_file.filename.split('.')[-2]+'/'+ uploaded_file.filename.split('.')[-2]+'.'
# print(save_path)
# uploaded_file.save(save_path + suffix.lower())
# image_folder = image_folder + uploaded_file.filename.split('.')[-2]
print(uploaded_file.filename)
print(type(uploaded_file.filename))
save_path=os.path.join(save_path, uploaded_file.filename)
print(save_path)
with open(uploaded_file.filename, 'wb') as f:
print('222')
f.write(uploaded_file)
print('111')
python
# from paddleocr import PaddleOCR
import os
import sys
import importlib
__dir__ = os.path.dirname(__file__)
sys.path.append(os.path.join(__dir__, ''))
import cv2
import logging
import numpy as np
from pathlib import Path
# import base64
# from io import BytesIO
from PIL import Image
def _import_file(module_name, file_path, make_importable=False):
spec = importlib.util.spec_from_file_location(module_name, file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
if make_importable:
sys.modules[module_name] = module
return module
tools = _import_file(
'tools', os.path.join(__dir__, 'tools/__init__.py'), make_importable=True)
ppocr = importlib.import_module('ppocr', 'paddleocr')
ppstructure = importlib.import_module('ppstructure', 'paddleocr')
from ppocr.utils.logging import get_logger
from tools.infer import predict_system
from ppocr.utils.utility import check_and_read, get_image_file_list, alpha_to_color, binarize_img
from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url
from tools.infer.utility import draw_ocr, str2bool, check_gpu
from ppstructure.utility import init_args, draw_structure_result
from ppstructure.predict_system import StructureSystem, save_structure_res, to_excel
logger = get_logger()
__all__ = [
'PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result',
'save_structure_res', 'download_with_progressbar', 'to_excel'
]
SUPPORT_DET_MODEL = ['DB']
VERSION = '2.7.0.3'
SUPPORT_REC_MODEL = ['CRNN', 'SVTR_LCNet']
BASE_DIR = os.path.expanduser("~/.paddleocr/")
DEFAULT_OCR_MODEL_VERSION = 'PP-OCRv4'
SUPPORT_OCR_MODEL_VERSION = ['PP-OCR', 'PP-OCRv2', 'PP-OCRv3', 'PP-OCRv4']
DEFAULT_STRUCTURE_MODEL_VERSION = 'PP-StructureV2'
SUPPORT_STRUCTURE_MODEL_VERSION = ['PP-Structure', 'PP-StructureV2']
MODEL_URLS = {
'OCR': {
'PP-OCRv4': {
'det': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_infer.tar',
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar',
},
'ml': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_infer.tar'
}
},
'rec': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/english/en_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/en_dict.txt'
},
'korean': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/korean_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/japan_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/ta_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/te_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/ka_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/arabic_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv4/multilingual/devanagari_PP-OCRv4_rec_infer.tar',
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
},
},
'cls': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
}
},
},
'PP-OCRv3': {
'det': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar',
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar',
},
'ml': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_infer.tar'
}
},
'rec': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/en_dict.txt'
},
'korean': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
},
},
'cls': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
}
},
},
'PP-OCRv2': {
'det': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar',
},
},
'rec': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
}
},
'cls': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
}
},
},
'PP-OCR': {
'det': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
},
'structure': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
}
},
'rec': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/en_dict.txt'
},
'french': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/french_dict.txt'
},
'german': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/german_dict.txt'
},
'korean': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
},
'structure': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
'dict_path': 'ppocr/utils/dict/table_dict.txt'
}
},
'cls': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
}
},
}
},
'STRUCTURE': {
'PP-Structure': {
'table': {
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
}
}
},
'PP-StructureV2': {
'table': {
'en': {
'url':
'https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar',
'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
},
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar',
'dict_path': 'ppocr/utils/dict/table_structure_dict_ch.txt'
}
},
'layout': {
'en': {
'url':
'https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar',
'dict_path':
'ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt'
},
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar',
'dict_path':
'ppocr/utils/dict/layout_dict/layout_cdla_dict.txt'
}
}
}
}
}
def parse_args(mMain=True):
import argparse
parser = init_args()
parser.add_help = mMain
parser.add_argument("--lang", type=str, default='ch')
parser.add_argument("--det", type=str2bool, default=True)
parser.add_argument("--rec", type=str2bool, default=True)
parser.add_argument("--type", type=str, default='ocr')
parser.add_argument(
"--ocr_version",
type=str,
choices=SUPPORT_OCR_MODEL_VERSION,
default='PP-OCRv4',
help='OCR Model version, the current model support list is as follows: '
'1. PP-OCRv4/v3 Support Chinese and English detection and recognition model, and direction classifier model'
'2. PP-OCRv2 Support Chinese detection and recognition model. '
'3. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.'
)
parser.add_argument(
"--structure_version",
type=str,
choices=SUPPORT_STRUCTURE_MODEL_VERSION,
default='PP-StructureV2',
help='Model version, the current model support list is as follows:'
' 1. PP-Structure Support en table structure model.'
' 2. PP-StructureV2 Support ch and en table structure model.')
for action in parser._actions:
if action.dest in [
'rec_char_dict_path', 'table_char_dict_path', 'layout_dict_path'
]:
action.default = None
if mMain:
return parser.parse_args()
else:
inference_args_dict = {}
for action in parser._actions:
inference_args_dict[action.dest] = action.default
return argparse.Namespace(**inference_args_dict)
def parse_lang(lang):
latin_lang = [
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr',
'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl',
'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv',
'sw', 'tl', 'tr', 'uz', 'vi', 'french', 'german'
]
arabic_lang = ['ar', 'fa', 'ug', 'ur']
cyrillic_lang = [
'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', 'ava',
'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
]
devanagari_lang = [
'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', 'gom',
'sa', 'bgc'
]
if lang in latin_lang:
lang = "latin"
elif lang in arabic_lang:
lang = "arabic"
elif lang in cyrillic_lang:
lang = "cyrillic"
elif lang in devanagari_lang:
lang = "devanagari"
assert lang in MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION][
'rec'], 'param lang must in {}, but got {}'.format(
MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION]['rec'].keys(), lang)
if lang == "ch":
det_lang = "ch"
elif lang == 'structure':
det_lang = 'structure'
elif lang in ["en", "latin"]:
det_lang = "en"
else:
det_lang = "ml"
return lang, det_lang
def get_model_config(type, version, model_type, lang):
if type == 'OCR':
DEFAULT_MODEL_VERSION = DEFAULT_OCR_MODEL_VERSION
elif type == 'STRUCTURE':
DEFAULT_MODEL_VERSION = DEFAULT_STRUCTURE_MODEL_VERSION
else:
raise NotImplementedError
model_urls = MODEL_URLS[type]
if version not in model_urls:
version = DEFAULT_MODEL_VERSION
if model_type not in model_urls[version]:
if model_type in model_urls[DEFAULT_MODEL_VERSION]:
version = DEFAULT_MODEL_VERSION
else:
logger.error('{} models is not support, we only support {}'.format(
model_type, model_urls[DEFAULT_MODEL_VERSION].keys()))
sys.exit(-1)
if lang not in model_urls[version][model_type]:
if lang in model_urls[DEFAULT_MODEL_VERSION][model_type]:
version = DEFAULT_MODEL_VERSION
else:
logger.error(
'lang {} is not support, we only support {} for {} models'.
format(lang, model_urls[DEFAULT_MODEL_VERSION][model_type].keys(
), model_type))
sys.exit(-1)
return model_urls[version][model_type][lang]
def img_decode(content: bytes):
np_arr = np.frombuffer(content, dtype=np.uint8)
return cv2.imdecode(np_arr, cv2.IMREAD_UNCHANGED)
def check_img(img):
if isinstance(img, bytes):
img = img_decode(img)
if isinstance(img, str):
# download net image
if is_link(img):
download_with_progressbar(img, 'tmp.jpg')
img = 'tmp.jpg'
image_file = img
img, flag_gif, flag_pdf = check_and_read(image_file)
if not flag_gif and not flag_pdf:
with open(image_file, 'rb') as f:
img_str = f.read()
img = img_decode(img_str)
if img is None:
try:
buf = BytesIO()
image = BytesIO(img_str)
im = Image.open(image)
rgb = im.convert('RGB')
rgb.save(buf, 'jpeg')
buf.seek(0)
image_bytes = buf.read()
data_base64 = str(base64.b64encode(image_bytes),
encoding="utf-8")
image_decode = base64.b64decode(data_base64)
img_array = np.frombuffer(image_decode, np.uint8)
img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
except:
logger.error("error in loading image:{}".format(image_file))
return None
if img is None:
logger.error("error in loading image:{}".format(image_file))
return None
if isinstance(img, np.ndarray) and len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
return img
class PaddleOCR(predict_system.TextSystem):
def __init__(self, **kwargs):
"""
paddleocr package
args:
**kwargs: other params show in paddleocr --help
"""
params = parse_args(mMain=False)
params.__dict__.update(**kwargs)
assert params.ocr_version in SUPPORT_OCR_MODEL_VERSION, "ocr_version must in {}, but get {}".format(
SUPPORT_OCR_MODEL_VERSION, params.ocr_version)
params.use_gpu = check_gpu(params.use_gpu)
if not params.show_log:
logger.setLevel(logging.INFO)
self.use_angle_cls = params.use_angle_cls
lang, det_lang = parse_lang(params.lang)
# init model dir
det_model_config = get_model_config('OCR', params.ocr_version, 'det',
det_lang)
params.det_model_dir, det_url = confirm_model_dir_url(
params.det_model_dir,
os.path.join(BASE_DIR, 'whl', 'det', det_lang),
det_model_config['url'])
rec_model_config = get_model_config('OCR', params.ocr_version, 'rec',
lang)
params.rec_model_dir, rec_url = confirm_model_dir_url(
params.rec_model_dir,
os.path.join(BASE_DIR, 'whl', 'rec', lang), rec_model_config['url'])
cls_model_config = get_model_config('OCR', params.ocr_version, 'cls',
'ch')
params.cls_model_dir, cls_url = confirm_model_dir_url(
params.cls_model_dir,
os.path.join(BASE_DIR, 'whl', 'cls'), cls_model_config['url'])
if params.ocr_version in ['PP-OCRv3', 'PP-OCRv4']:
params.rec_image_shape = "3, 48, 320"
else:
params.rec_image_shape = "3, 32, 320"
# download model if using paddle infer
if not params.use_onnx:
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
maybe_download(params.cls_model_dir, cls_url)
if params.det_algorithm not in SUPPORT_DET_MODEL:
logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
sys.exit(0)
if params.rec_algorithm not in SUPPORT_REC_MODEL:
logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
sys.exit(0)
if params.rec_char_dict_path is None:
params.rec_char_dict_path = str(
Path(__file__).parent / rec_model_config['dict_path'])
logger.debug(params)
# init det_model and rec_model
super().__init__(params)
self.page_num = params.page_num
def ocr(self,
img,
det=True,
rec=True,
cls=True,
bin=False,
inv=False,
alpha_color=(255, 255, 255)):
"""
OCR with PaddleOCR
args:
img: img for OCR, support ndarray, img_path and list or ndarray
det: use text detection or not. If False, only rec will be exec. Default is True
rec: use text recognition or not. If False, only det will be exec. Default is True
cls: use angle classifier or not. Default is True. If True, the text with rotation of 180 degrees can be recognized. If no text is rotated by 180 degrees, use cls=False to get better performance. Text with rotation of 90 or 270 degrees can be recognized even if cls=False.
bin: binarize image to black and white. Default is False.
inv: invert image colors. Default is False.
alpha_color: set RGB color Tuple for transparent parts replacement. Default is pure white.
"""
assert isinstance(img, (np.ndarray, list, str, bytes))
if isinstance(img, list) and det == True:
logger.error('When input a list of images, det must be false')
exit(0)
if cls == True and self.use_angle_cls == False:
logger.warning(
'Since the angle classifier is not initialized, it will not be used during the forward process'
)
img = check_img(img)
# for infer pdf file
if isinstance(img, list):
if self.page_num > len(img) or self.page_num == 0:
self.page_num = len(img)
imgs = img[:self.page_num]
else:
imgs = [img]
def preprocess_image(_image):
_image = alpha_to_color(_image, alpha_color)
if inv:
_image = cv2.bitwise_not(_image)
if bin:
_image = binarize_img(_image)
return _image
if det and rec:
ocr_res = []
for idx, img in enumerate(imgs):
img = preprocess_image(img)
dt_boxes, rec_res, _ = self.__call__(img, cls)
if not dt_boxes and not rec_res:
ocr_res.append(None)
continue
tmp_res = [[box.tolist(), res]
for box, res in zip(dt_boxes, rec_res)]
ocr_res.append(tmp_res)
return ocr_res
elif det and not rec:
ocr_res = []
for idx, img in enumerate(imgs):
img = preprocess_image(img)
dt_boxes, elapse = self.text_detector(img)
if not dt_boxes:
ocr_res.append(None)
continue
tmp_res = [box.tolist() for box in dt_boxes]
ocr_res.append(tmp_res)
return ocr_res
else:
ocr_res = []
cls_res = []
for idx, img in enumerate(imgs):
if not isinstance(img, list):
img = preprocess_image(img)
img = [img]
if self.use_angle_cls and cls:
img, cls_res_tmp, elapse = self.text_classifier(img)
if not rec:
cls_res.append(cls_res_tmp)
rec_res, elapse = self.text_recognizer(img)
ocr_res.append(rec_res)
if not rec:
return cls_res
return ocr_res
import json
import os
import io
import zipfile
import shutil
class Result:
def __init__(self, id, value):
self.id = id
self.value = value
def result_encoder(obj):
if isinstance(obj, Result):
return {'id': obj.id, 'PaddleOCR': obj.value}
return json.JSONEncoder.default(obj)
import paddle
paddle.disable_signal_handler() # 在2.2版本提供了disable_signal_handler接口
from flask import Flask, request
app = Flask(__name__)
@app.route('/OCR', methods=['GET','POST'])
def fun():
print(request.files)
uploaded_file = request.files['file']
if not uploaded_file:
return {'error': 'No file is provided'}
suffix = uploaded_file.filename.split('.')[-1] # 取得文件的后缀名
# #也可以根据文件的后缀名对文件类型进行过滤,如:
if suffix.lower() not in ['jpg', 'png', 'jpeg', 'zip']:
return {'error': 'The uploaded file type is invalid'}
image_folder = './images/'
if not os.path.exists(image_folder):
os.makedirs(image_folder)
if suffix.lower() in ['jpg', 'png', 'jpeg']:
# uploaded_file.save(image_folder + uploaded_file.filename.split('.')[-2])
# image_folder = image_folder + uploaded_file.filename.split('.')[-2]
save_path=image_folder + uploaded_file.filename.split('.')[-2]
if not os.path.exists(save_path):
os.makedirs(save_path)
uploaded_file.save(os.path.join(save_path, uploaded_file.filename))
image_folder = save_path
else:
zip_buffer = io.BytesIO(uploaded_file.read())
with zipfile.ZipFile(zip_buffer, 'r') as zip_ref:
zip_ref.extractall(image_folder) # 解压缩到指定的目标文件夹
save_path=image_folder + uploaded_file.filename.split('.')[-2]
# uploaded_file.save(image_folder+'/'+uploaded_file.filename)
# with zipfile.ZipFile('/data1/xyj/PaddleOCR/images/app_test.zip', 'r') as zip_ref:
# zip_ref.extractall(image_folder) # 解压缩到指定的目标文件夹
# image_folder = save_path
# with zipfile.ZipFile('/data1/xyj/PaddleOCR/images/app_test.zip', 'r') as zip_ref:
# for member in zip_ref.infolist():
# zip_ref.extract(member.filename, image_folder)
# image_folder = save_path
# shutil.unpack_archive('/data1/xyj/PaddleOCR/images/app_test.zip', image_folder, 'zip')
image_folder = save_path
# image_folder = "/data1/xyj/datasets/zh_test"
# image_folder = request.json['image_folder']
output_path = 'outputs/'
if not os.path.exists(output_path):
os.makedirs(output_path)
output_path = os.path.join(output_path, "zh_test_PaddleOCR.json")
if (os.path.exists(output_path)):
os.remove(output_path)
# Paddleocr目前支持的多语言语种可以通过修改lang参数进行切换
# 例如`ch`, `en`, `fr`, `german`, `korean`, `japan`
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
ans = {}
for filename in os.listdir(image_folder):
img_path = os.path.join(image_folder, filename)
result = ocr.ocr(img_path, cls=True)
for res in result:
outputs=''
if res is not None:
for line in res:
outputs=outputs+line[1][0]+' '
res = Result(filename, outputs)
with open(output_path, "a", encoding="utf8") as file:
json.dump(result_encoder(res), file, ensure_ascii=False, indent=4)
ans[filename] = outputs
# 将列表转换为 JSON 格式的字符串
json_data = json.dumps(ans, ensure_ascii=False)
# 将 JSON 字符串写入文件
with open("data.json", "w") as file:
file.write(json_data)
return {'result': json_data}
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5009)
app_ppocr.py