semvec/pypi-packages huggingface dataset:
yolov5windowdetect 0.11
Capture window - run yolov5 - show results (Windows only)
License: MIT
Keywords: yolov5,yolo,screenshot,pandas,dataframe


#### Capture window - run yolov5 - show results

```python
pip install yolov5windowdetect
```

### Update 2022/10/25

#### Run multiple detection models

```python
winca = (
    Yolov5WindowDetect(
        pt_file=r"C:\Users\Gamer\anaconda3\envs\dfdir\yolov5\runs\train\playerbutton7\weights\best.pt",
        repo_or_dir="./yolov5",
        model="custom",
        source="local",
    )
    .add_models(
        pt_file=r"C:\Users\Gamer\anaconda3\envs\dfdir\yolov5\runs\train\playerbutton6\weights\best.pt",
        repo_or_dir="./yolov5",
        model="custom",
        source="local",
    )
    .get_hwnd_window(window_title_regex=r"[Bb]lue[sS]tacks.*")
    .take_screenshot_and_run_yolov(
        confidence_thresh=0.3,
        show_results=True,
        quit_key="q",
        sleep_time=0.04,
        rununtilstopped=True,
    )
)
```

```python
from yolov5windowdetect import Yolov5WindowDetect
winca = (
    Yolov5WindowDetect(
        pt_file=r"C:\Users\.....\best.pt",
        repo_or_dir="./yolov5",
        model="custom",
        source="local",
    )
    .get_hwnd_window(window_title_regex=r"[Bb]lue[Ss]tacks.*")
    .take_screenshot_and_run_yolov(
        confidence_thresh=0.3,
        show_results=True,
        quit_key="q",
        sleep_time=0.04,
        rununtilstopped=True,
    )
)

# window capture -> don't show results, only results as df
winca.get_hwnd_window(
    window_title_regex=r"[Bb]lue[Ss]tacks.*"
).take_screenshot_and_run_yolov(
    confidence_thresh=0.3, show_results=False, rununtilstopped=False,
).get_results_as_df()


# adb capture
winca.get_adb_window(
    adb_path=r"C:\ProgramData\adb\adb.exe", adb_serial="localhost:5555",
).take_screenshot_and_run_yolov(
    confidence_thresh=0.3, show_results=False, rununtilstopped=False,
).get_results_as_df()
```

#### Example

<div align="left">
      <a href="https://www.youtube.com/watch?v=-jXqL39Tf5w">
         <img src="https://img.youtube.com/vi/-jXqL39Tf5w/0.jpg" style="width:100%;">
      </a>
</div>
torchmimic 0.0.6
MIMIC Benchmark in PyTorch.
License: MIT
Requires Python: ~=3.6

# MIMIC Benchmark in PyTorch

MIMIC Benchmark reimplemented in PyTorch. For examples and documentation checkout:  https://torchmimic.readthedocs.io/en/latest/

## Installation

To install, run:

```
pip install torchmimic
```

## Data

This library contains PyTorch datasets that will load your existing MIMIC Benchmark data. To use those datasets, just import the data you want from `torchmimic.data`:

```
from torchmimic.data import DecompensationDataset
from torchmimic.data import IHMDataset
from torchmimic.data import LOSDataset
from torchmimic.data import PhenotypingDataset
from torchmimic.data import MultitaskDataset
```

## Models

We've included some of the models used in the benchmark paper for reproduction and comparison with your own methods. Current models include:

- Standard LSTM

You can access these models from `torchmimic.models`:

```
from torchmimic.models import StandardLSTM
```

## Benchmarks

Each of the benchmarks can be found in `torchmimic.benchmarks`:

```
from torchmimic.benchmarks import DecompensationBenchmark
from torchmimic.benchmarks import IHMBenchmark
from torchmimic.benchmarks import LOSBenchmark
from torchmimic.benchmarks import PhenotypingBenchmark
from torchmimic.benchmarks import MultitaskBenchmark
```

At a minimum, the benchmark need a model to be trained. You can create the model from one of our existing ones or you can create your own.


## Weights and Biases Support

This library has built-in logging of model configurations and key metrics using the Weights and Biases library.


## TODOs

### Benchmarks

- [x] Phenotyping
- [x] Length of Stay
- [x] Decompensation
- [x] In Hospital Mortality
- [ ] Multitask

### Models
- [x] Standard LSTM
- [ ] Standard LSTM + Deep Supervision
- [ ] Logistic Regression
- [ ] Channelwise LSTM
- [ ] Channelwise LSTM + Deep Supervision
- [ ] Multitask LSTM
- [ ] Multitask Channelwise LSTM

### Testing

- [ ] Test data for GitHub Workflow