semvec/pypi-packages huggingface dataset:
student-imgutils 0.3.7
A library for simple and esily editing an image in python
Requires Python: >=3.6

# imgutils

A library for quickly and easily making small edits to images

## Features
- Rotate image: function "rotate", insert str (image) and int (angle of rotation)
- Invert image horizontaly: function "flip", insert str (image)
- Add saturation: function "saturate", insert str (image) and int (saturation value)
- Add blur: function "blurring", insert str (image)
- Add sharpen: function "sharpening", insert str (image)
- Add color filter: function "color_filter", insert str (image), float (filter stenght, 1.0 to 0.0), and int value for the colors (red, green, blue)
- Add grayscale: function "gray_scale", insert str (image)
- Add color border: function "borderer", insert str (image), int (border size), and int value for the colors (red, green, blue)
- Collect image data (returns three str with the image format, size and mode): function "image_data_get", insert (image)
constrained-sdd 0.1.6
A Python package for working with constrained Stanford Drone Dataset.
Requires Python: >=3.7

# Constrained Stanford Drone Dataset
[![Python application](https://github.com/april-tools/constrained-sdd/actions/workflows/python-app.yml/badge.svg)](https://github.com/april-tools/constrained-sdd/actions/workflows/python-app.yml)

This repository contains an annotated version of the Stanford Drone Dataset[1]. We segmented the first 50 images from SDD and drew polygons for buildings, obstacles and offroad. Our trajectories only follow the roads/walking paths.

# Installation

Using this dataset is really easy! First, you can install this package using pypi:
```bash
 pip install constrained-sdd
```
then just use the provided class to download the dataset and load it into memory:
```python
import sdd.constrained_sdd as csdd

img_id = 0
folder = "data/sdd"
sdd = csdd.ConstrainedStanfordDroneDataset(0, sdd_data_path=folder, download=True)
train, val, test = sdd.get_dataset()
```
this creates a dataset with a list of trajectories in train/val/test, e.g. useful for position prediction.

You can also create the trajectory-prediction task via:
```python
train, val, test = sdd.get_trajectory_prediction_dataset(window_size, sampling_rate)
```


## Citation

Leander Kurscheidt, Paolo Morettin, Roberto Sebastiani, Andrea Passerini, Antonio Vergari, A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction, arXiv:2503.19466

## Examples

![Image 12](imgs/img12.png)
![Image 2](imgs/img2.png)
![Image 0](imgs/img0.png)
![Image 18](imgs/img18.png)

An overview over all the images can be seen in `analysis/viz_data.ipynb`.

## Further Details

We annotate the first 50 pictures (scenes in the original dataset) with trajectories of the Stanford Drone Dataset[1], as extracted by P2T[2]. We select all trajectories following the road and delete obvious errors, like projecting a trajectory that slightly touches a building outwards and deleting trajectories that suddenly jump around. We annotate using three classes: Building, Obstacle and Offroad, which forms our constraint-set. Additionally, we have annotated the entrance of a building to differentiate trajectories that enter a building from those who not, but this is unused. All trajectories in our dataset are constraint-abiding.


[1]  A. Robicquet, A. Sadeghian, A. Alahi, S. Savarese, Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes in European Conference on Computer Vision (ECCV), 2016. 
[2] Nachiket Deo, Mohan M. Trivedi, Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans, 2021
sscs_assn4 0.1.0
Python rekor monitor assignment for sscs
Requires Python: >=3.10

# Python Rekor Monitor 

This project is a simple Rekor log monitor written in python. You can verify that an artifact has been signed and not modified since signing, get the latest checkpoint, and verify consistency between earlier logs and later logs. 

### Usage

Get latest checkpoint:
    python3 main.py -c

Verify inclusion of an artifact:
    python3 main.py --inclusion LOG_INDEX --artifact ARTIFACT_FILEPATH

Verify consistency of the most recent checkpoint and an earlier checkpoint:
    python3 main.py --consistency --tree-id TREE_ID --tree-size TREE_SIZE --root-hash ROOT_HASH

### Maintainers and Contributors
Just me for now! Jess Ermi - je2230 on github