semvec/pypi-packages huggingface dataset:
hacca 0.0.5
A short description of your package
Keywords: hacca

# **haCCA: Multi-module Integrating of spatial transcriptomes and metabolomes.**

haCCA, a workflow utilizing high Correlated feature pairs combined with a modified spatial morphological alignment to ensure high resolution and accuracy of spot-to-spot data integration of spatial transcriptomes and metabolomes.

[![PyPI version](https://badge.fury.io/py/hacca.svg)](https://badge.fury.io/py/haCCA)

![haCCA](./asset/hacca_workflow.png)

## Installation

```python
pip install hacca
```

## Usage

```python
from hacca import *

a_h5ad = sc.read_h5ad(os.path.join('/path/to/a.h5ad'))
b_prime_h5ad = sc.read_h5ad(os.path.join('/path/to/b_prime.h5ad'))

# construct Data object from a and b_prime
# Data is a triplet of (X: np.ndarray, D: np.ndarray, Label: Optional[np.ndarray]), where X is the feature matrix, D is the spatial matrix that contains the location information, and Label is an optional array that contains the cluster information.

a = Data(X=a_h5ad.X.toarray(), D=a_h5ad.obsm['spatial'])
b_prime = Data(X=b_prime_h5ad.X.toarray(), D=b_prime_h5ad.obsm['spatial'])

# Infer b_predict from (a, b_prime) using the following alignment methods
# manual_gross_alignment | icp_3d_alignment | direct_alignment
# b_predict contains aligned feature from a and samples from b_prime

_b_prime = hacca.manual_gross_alignment(a, b_prime)
_a, _b_prime = hacca.icp_3d_alignment(a, _b_prime)
b_predict = hacca.direct_alignment(_a, _b_prime)
```

## Examples
You can refer to the [examples](./example) folder for more detailed usage.
irtk 0.1.8
Inverse-Rendering Toolkit
Requires Python: >=3.8

# irtk
[![PyPI version](https://badge.fury.io/py/irtk.svg?kill_cache=1)](https://badge.fury.io/py/irtk)
[![documentation](https://github.com/uci-rendering/inv-render-toolkit/actions/workflows/documentation.yml/badge.svg)](https://uci-rendering.github.io/irtk/)

`irtk` stands for **I**nverse-**R**endering **T**ool**k**it. It is a simple framework for conducting inverse rendering experiments. Since there are a lot differentiable renderers in development, mastering them all and migrating between them can be a headache. `irtk` provides a simple scene representation based on `pytorch` that can be rendered by **connectors** that connect to different differentiable renderers and get the graident of the scene parameters. Migrating between renderers is just a matter of changing the connector. 

## Installation
`irtk` depends on `pytorch`. Since it is widely used and some projects might have specific requirements to it, we leave its installation to the user. 

After installing `pytorch`, you can install `irtk` via
```
pip install irtk
```
iteach-toolkit 0.0.3
The iteach toolkit package includes the dhyolo model, designed to detect doors and handles in images.

# iTeach Toolkit Package 🛠️

## Overview 🔍
The `iTeach_package` is a toolkit designed for running object detection using the **DH-YOLO** model, specifically for identifying doors and handles in images. This package provides easy-to-use command-line tools for performing inference with a pre-trained DH-YOLO model.

## Model Checkpoints 📥
Pretrained model checkpoints can be downloaded from [this link](https://utdallas.box.com/v/DHYOLO-Pretrained-Checkpoints).

## Installation ⚙️
To install the package, use `pip`:

```bash
pip install iteach_toolkit
```

## Usage 🖥️

Below is an example of how to use the package for running inference on an image.

```python
import os
from PIL import Image as PILImg
from iteach_toolkit.DHYOLO import DHYOLODetector

# Set up paths
os.system("wget https://huggingface.co/spaces/IRVLUTD/DH-YOLO/resolve/main/test_imgs/jpad-irvl-test.jpg")
image_path = "./jpad-irvl-test.jpg"

model_path = "/path/to/yolov5_model.pt"

# Initialize the DHYOLODetector class
dhyolo = DHYOLODetector(model_path)

# Perform prediction on the image
orig_image, detections = dhyolo.predict(image_path, conf_thres=0.7, iou_thres=0.7, max_det=1000)

# Plot the bounding boxes on the original image
orig_image, image_with_bboxes = dhyolo.plot_bboxes(attach_watermark=True)

# Convert the image (with bounding boxes) from a NumPy array to a PIL Image for display.
pil_img_with_bboxes = PILImg.fromarray(image_with_bboxes)

# Plot the image
pil_img_with_bboxes.show()
```

## License 📜

This project is licensed under the MIT License.

## BibTex 📚
Please cite ***iTeach*** if it helps your work or research 🙌:
```bibtex
@misc{padalunkal2024iteach,
    title={iTeach: Interactive Teaching for Robot Perception using Mixed Reality},
    author={Jishnu Jaykumar P and Cole Salvato and Vinaya Bomnale and Jikai Wang and Yu Xiang},
    year={2024},
    eprint={2410.09072},
    archivePrefix={arXiv},
    primaryClass={cs.RO}
}
```