semvec/pypi-packages huggingface dataset:
py-iec62056 1.1.1.1
Package for communicate with iec62056-21 Meters
Requires Python: >=3.7
Keywords: python, iiot, protocols

# Python IEC62056 Meter Tools

## How to use ?

### Install

```shell
pip install py-iec62056
```

### Create SerialClient

```python
client = SerialClient(
    baudrate=19200,
    port="COM3",
    transport="serial",
    parity="E",
    bytesize=7,
    stopbits=1
)
```

### Read A meter identification

This return an identification message from the meter.
You must set the ack_stop to True if you don't want to meter to give the default table.

```python
result = client.read_tariff_identification("5987893", ack_stop=True)
```

### Read Table dataset

This will return a list of DataSet instance.
Raise error if Timeout.

```python
result = client.request(meter_address="5987893", table=7, timeout=30)
assert isinstance(result, TariffResponse)
for dataset in result.data:
    logging.info(f"{dataset}")
```
semantic-sfm 0.0.2
x segmentation on photogrammetry point cloud
Requires Python: >=3.6

# semantic_SfM
Instance segmentation for SfM point cloud. 

## Installation
Option 1 (recommended): 
```
git clone https://github.com/ZhiangChen/semantic_SfM.git
cd semantic_SfM/semantic_SfM/ssfm/
pip3 install .
```

Option 2:
```
pip3 install ssfm
```

**Hardware requirements**: This project utilizes the Segment Anything Model (SAM) for panoptic segmentation. GPUs are not required for SAM but are recommended to expedite inference speed. The other parts of this project use only CPUs. As a point cloud will be stored in memory, memory usage depends on the point cloud size. 

## SSfM Architecture
[files.py](./semantic_SfM/ssfm/files.py) provides utility functions for file operation, such as point cloud and image files.

[image_segmentation.py](./semantic_SfM/ssfm/image_segmentation.py) allows to select deep learning models for instance segmentation and panoptic segmentation. 

[probabilistic_projection.py](./semantic_SfM/ssfm/probabilistic_projection.py) projects point clouds to images and creates a probablistic semantics.

[object_registration.py](./semantic_SfM/ssfm/object_registration.py) registers objects (instances and stuffs) in point clouds.



## Support Platforms and Models
This repository supports SfM products from the following platforms: 
- [WebODM](https://opendronemap.org/webodm/)
- [Agisoft](https://www.agisoft.com/)

Refer to the [docs/projection_tutorial.md](docs/projection_tutorial.md) to obtain point clouds and camera parameters from WebODM or Agisoft. The point clouds should use the format of [.las](https://laspy.readthedocs.io/en/latest/intro.html). 

For image segmentation, we have used Segment Anything Model from Meta: 
- [SAM](https://github.com/facebookresearch/segment-anything)


## Tutorials
1. Projecting Point Clouds onto Image Planes: [docs/projection_tutorial.md](docs/projection_tutorial.md)

2. Data Structure and Algorithms for Object Registration: [docs/object_registration.md](docs/object_registration.md)

3. Running a Complete Workflow Example from Scratch: [semantic_SfM/ssfm/workflow.ipynb](semantic_SfM/ssfm/workflow.ipynb)