semvec/pypi-packages huggingface dataset:
pointcloud-extraction 0.0.1.2
package consisting of the scripts for operating on the point cloud / photogrammetric data
Requires Python: >=3.8

## @geospatial-pipelines/data_preparation:

This package provides the tools and utils for the first stage of the operation after fetching data: allowing users to do operations of the current raw dataset of pointcloud (.las, .laz, .pcd) for operations like: 
    - Cropping (2D/3D cropping)
    - conversion of the coordinates 
    - combination of point clouds
    - conversion across various formats.

## installation and setup:
- Install from the pip directory: `pip install pointcloud_extraction_toolkit`.
- Or via the source by cloning the whole [geospatial-pipeline]() and then installing the file via the `pip install -r requirements.txt`.


## Various packages:

| package_file | remarks |
| ------------- | ------------- |
| cameras.utils | consist of methods to use the API across colmap / neuralangelo for photogrammetry |
| pdal.pipeline_generation | scripts to generate the pipeline for PDAL to do necessary transformations |
| cropping | Script to crop the given portion that you want to fetch. |
| threed_pointcloud | script that integrates the [open3D](https://www.open3d.org) for 3D data cropping at microlevel | 


## API's :


There are colab tutorials in `test/` folder that explain the various usecase, but now try to fetch the 

1. Import the cameras package for the photogrammetry pipeline processing to fetch the pointcloud
> Important: You need to setup colmap before this package in order to work.
```python

from pointcloud_extraction_toolkit.cameras.utils import ColmapDataParsing
import os
colmap_progressing = ColmapDataParsing(filepath="demo.mp4", output_dir="./demo_output")

colmap_progressing.convert_photo_to_video(downsampling_rate=5)


## also fetch the image metadata for the algorithm / reviewer in order to showcase the details.


## now fetching the image metadata from the given details in order to later on do the required transformation on the specific frame <> pose basis if needed.
files = './output/imgs'

data_info = {}

for filename in os.listdir(files):
    full_path = os.path.join(files, filename)
    imagemetadata = colmap_progressing.get_image_metadata(full_path)
    data_info[filename].append(imagemetadata)

## and finally the colmap transformation

await colmap_processing.colmap_transformation()

## in the results you seem to see some of the outputs are not compatible with the alignment then run the following method to fix and rerun colmap_transformation().
## analyze_colmap_images(self,camera_bin_path, transform_file, camera_depth, coordinates_adjust = ["0", "0", "0", "1"] )


colmap_processing.analyze_colmap_images(camera_bin_path= files + "stereo/camera.bin" , transform_file= files + "transforms.json", camera_depth = "", coordinates_adjust = [] )


```

2. Tutorial for 3D data processing directly:
siconv 0.1.2.9
The siconv Python package simplifies the conversion between Sinhala and Singlish languages. Whether you're dealing with Sinhala text that needs transformation into Singlish or vice versa, siconv offers a user-friendly solution.
Requires Python: >=3.6

# Simplify Sinhala and Singlish Language Conversion with `siconv` Python Package

Unlock efficient and accurate language conversion between Sinhala and Singlish effortlessly using the `siconv` Python package. Whether you need to transform Sinhala text to Singlish or vice versa, `siconv` offers a seamless and user-friendly solution.

## Key Features:

- **Sinhala to Singlish Conversion:** Easily convert Sinhala text to Singlish.
- **Singlish to Sinhala Conversion:** Effortlessly transform Singlish text to Sinhala.
- **Efficient and Accurate Algorithms:** `siconv` incorporates efficient and accurate language conversion algorithms for reliable results.

## Installation:

Install the package effortlessly using pip:

```bash
pip install siconv
```

## Usage Example:

```python
from siconv import sinhala_to_singlish, singlish_to_sinhala

# Example Usage
sinhala_text = "ආයුබෝවන්"
converted_singlish = sinhala_to_singlish(sinhala_text)
print("Sinhala to Singlish:", converted_singlish)

singlish_text = "ayubowan"
converted_sinhala = singlish_to_sinhala(singlish_text)
print("Singlish to Sinhala:", converted_sinhala)
```

## Advanced Applications:

### 1. Fine-tuning Language Models:
Utilize `siconv` to preprocess training data for fine-tuning large language models (LLMs) specifically tailored for Sinhala or Singlish language tasks.

### 2. Building NLP Applications:
Incorporate `siconv` into the development of Sinhala and Singlish applications, such as chatbots, sentiment analysis, or language translation, ensuring seamless text conversion.

### 3. Tokenization:
Enhance custom tokenization processes with `siconv` for accurate and consistent tokenization of Sinhala and Singlish text across diverse language-specific tasks.

Empower your projects with the versatility of `siconv`, not only streamlining language conversion but also contributing to the broader landscape of natural language processing in Sinhala and Singlish.