semvec/pypi-packages huggingface dataset:
countool 0.0.4
A small example package of calculator for params/macs
Requires Python: >=3.6


"""
This is a demo for cvisp, 开发板、相机等拍摄设备取到的RAW with metadata，可以直接用该工具包进行demosaic，得到RGB图像，以便于展示。
    - pip install cvisp
    - from cvisp.utils import Demosaic_RAW
    
"""

"""
三种模式，分别为：预览raw图，推理GDIP，预览demosaic（自用）
    run_demosaic(): return the demosaiced image, (HWC, u16, bmp)
    run_isp_with_meta(): return the rgb image , (HWC, u8, bmp)
    run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0'): return the output of       your          model, (HWC, u8, bmp) 
    
    完整传参说明：
    - Demosaic_RAW(mode : str = None, input_data_path : str = None, show_meta_data : bool = True, 
    show_image : bool = False, save_image : bool = True, result_output_dir : str = None):
        mode: defatult = 'simple_dm', choices = ['simple_dm', 'opencv_dm']
        input_data_path: raw图片路径，支持单张图片，如：'./test.dng'
        show_meta_data: show the meta data of the input data, default = True，你可以选择是否展示meta data
        show_image: show the output image, default = False，你可以选择是否展示输出图像
        save_image: save the output image, default = True，你可以选择是否保存输出图像，保存路径为result_output_dir
        result_output_dir: the output dir of the result image, default = './demosaic/'，你可以选择保存路径
"""

if __name__ == '__main__':
    for im in glob.glob(image_path_list + '*.dng'):
        demosaicd_raw = Demosaic_RAW(input_data_path = im).run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0')
        # rgb = Demosaic_RAW(input_data_path = im).run_isp_with_meta()
        # gdip_out = Demosaic_RAW(input_data_path = im).run_model(weight_path = './your.pt', model = GDIP(), device = 'cuda:0')
mod-docx 0.1.0
The purpose of this library is to serve as a reliable and simple library to reassemble docx documents after they have been treated using AI/machine learning text treatment.
Requires Python: >=3.7

# mod_docx
The purpose of this library is to serve as a reliable and simple library to reassemble docx documents after they have been treated using AI/machine learning text treatment.

## Example of library usage
import os

import sys

sys.path.insert(1, '/usr/lib/python3.11/site-packages')

import mod_docx


### [Step 0] Open the docx file as an XML object
#### Obtain the orignal docx file
fpath = ".../mod_docx/tests"

fichier = "test_document.docx"

docx_filename = os.path.join(fpath, fichier)
        
#### Create a class object for the name_of_file.class_name. Use the class object, to call the functions in the class.
md = mod_docx.mod_docx(docx_filename)

#### Convert the orignal docx file to XML
document_org = md.opendocx(docx_filename)


### [Step 1] Modify the document
#### [To be completed for next version] Modify the document as desired (ie: replace text, change style, change font)
doc_finale = document_org

### [Step 2] Save the changed XML document as a docx file
root_dir = f'{fpath}/'

desired_output_docx_name = 'test_document_out.docx'

md.savedocx_ver_fichier(docx_filename, root_dir, desired_output_docx_name, doc_finale)