semvec/pypi-packages huggingface dataset:
micropython-googlesheet 0.0.5
Update or append the data to Google Sheet, or get the data on Google Sheet. by using HTTP to execute the Google Apps Script API compatible with ESP32.
License: MIT
Keywords: googlesheet, esp32, micropython

MicroPython-GoogleSheet
=======================

Update or append the data to Google Sheet, or get the data on Google
Sheet. by using HTTP to execute the Google Apps Script API. Compatible
with ESP32.

Full Documentation
==================

Github: https://github.com/PerfecXX/MicroPython-GoogleSheet

Quick Example
=============

.. code:: python

   # Import Library 
   from ggsheet import MicroGoogleSheet
   from network import WLAN,STA_IF

   # Network Creadential 
   ssid = "Change_SSID"    
   password = "Change_Password"

   # Connect to Network
   sta_if = WLAN(STA_IF)
   sta_if.active(True)
   if not sta_if.isconnected():
       print("Connecting to wifi: ", ssid)
       sta_if.connect(ssid, password)
       while not sta_if.isconnected():
           pass
   print("Connection successful")

   # Google Sheet Credential 
   google_sheet_url = "https://docs.google.com/spreadsheets/d/xxxxxxxxx/edit#gid=0"
   google_sheet_name = "Sheet1"
   google_app_deployment_id = "xxxxxxxx"

   # Create Instance 
   ggsheet = MicroGoogleSheet(google_sheet_url,google_sheet_name)
   ggsheet.set_DeploymentID(google_app_deployment_id)

   # Update the data to a specific cell (Row,Column,Data)
   ggsheet.updateCell(1,1,"Hello this is my first data")

   # Get the data from a specific cell (Row,Column)
   print(ggsheet.getCell(1,1))

   # Delete the data from a specific cell (Row,Column)
   ggsheet.deleteCell(1,1)

   # Append the data to a specific row (Row, Data List)
   ggsheet.appendRow(1,[1,2,3,"Row 1 Appended!"])

   # Update the data in a specific row (Row, Data List) 
   ggsheet.updateRow(1,[3,2,1,"Row 1 Updated!"])

   # Get all of the data from a specific row (Row)
   ggsheet.getRow(1)

   # Delete the data in a specific row (Row)
   ggsheet.deleteRow(1)

   # Append the data to a specific column (Column, Data List)
   ggsheet.appendColumn(1,[1,2,3,"Column 1 Appended!"])

   # Update the data to a specific column (Column, Data List)
   ggsheet.updateColumn(1,[3,2,1,"Column 1 Updated!"])

   # Get all of the data from a specific column (Column)
   ggsheet.getColumn(1)

   # Delete the data in a specific column (Column)
   ggsheet.deleteColumn(1)
yolov9-onnx 0.8.0
Run YOLOv9 MIT model with ONNX Runtime
License: MIT

# ONNX YOLOv9 MIT Object Detection
 Python scripts performing object detection using the YOLOv9 MIT model in ONNX.
 
![!ONNX YOLOv9 Object Detection](https://github.com/user-attachments/assets/a4237b6e-53f1-4c51-be3a-bd8369c1991c)

> [!CAUTION]
> I skipped adding the pad to the input image when resizing, which might affect the accuracy of the model if the input image has a different aspect ratio compared to the input size of the model. Always try to get an input size with a ratio close to the input images you will use.

## Requirements

 * Check the **requirements.txt** file.
 * For ONNX, if you have a NVIDIA GPU, then install the **onnxruntime-gpu**, otherwise use the **onnxruntime** library.

## Installation [![PyPI](https://img.shields.io/pypi/v/yolov9-onnx?color=2BAF2B)](https://pypi.org/project/yolov9-onnx/)

```bash
pip install yolov9-onnx
```
Or, clone this repository:
```bash
git clone https://github.com/ibaiGorordo/ONNX-YOLOv9-MIT-Object-Detection.git
cd ONNX-YOLOv9-MIT-Object-Detection
pip install -r requirements.txt
```
### ONNX Runtime
For Nvidia GPU computers:
`pip install onnxruntime-gpu`

Otherwise:
`pip install onnxruntime`

## ONNX model
- If the model file is not found in the models directory, it will be downloaded automatically from the [release page](https://github.com/ibaiGorordo/ONNX-YOLOv9-MIT-Object-Detection/releases/tag/0.1.0).
- Or, for exporting the models with a different input size, use the Google Colab notebook to convert the model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KPFC-BLL7i7kQJFYq-1JACFiTzPYyOsC?usp=sharing)
- **Available models**: 
  - **MIT:** v9-s_mit.onnx, v9-m_mit.onnx, v9-c_mit.onnx
  - **Official:** gelan-c.onnx, gelan-e.onnx, yolov9-c.onnx, yolov9-e.onnx

## Original YOLOv9 MIT model
The original YOLOv9 MIT model can be found in this repository: [YOLOv9 MIT Repository](https://github.com/WongKinYiu/YOLO)
- The License of the models is MIT license: [License](https://github.com/WongKinYiu/YOLO/blob/main/LICENSE)

## Usage
```python
import cv2
from yolov9 import YOLOv9, draw_detections

detector = YOLOv9("v9-c_mit.onnx")

img = cv2.imread("image.jpg")

class_ids, boxes, confidences = detector(img)

combined_img = draw_detections(img, boxes, confidences, class_ids)
cv2.imshow("Detections", combined_img)
cv2.waitKey(0)
```

## Examples

 * **Image inference**:
 ```
 python image_object_detection.py
 ```

 * **Webcam inference**:
 ```
 python webcam_object_detection.py
 ```

 * **Video inference**: https://youtu.be/X_XVkEqgCUM
 ```
 python video_object_detection.py
 ```

https://github.com/user-attachments/assets/71b3ef97-92ef-4ddb-a62c-5e52922a396d

## References:
* YOLOv9 MIT model: https://github.com/WongKinYiu/YOLO
* YOLOv9 model: https://github.com/WongKinYiu/yolov9
yolov10-onnx 0.3.0
Run YOLOv10 model with ONNX Runtime
License: MIT

# ONNX YOLOv10 Object Detection
 Python scripts performing object detection using the YOLOv10 model in ONNX.
 
![!ONNX YOLOv10 Object Detection](https://github.com/user-attachments/assets/a926d7d7-7ee5-474d-b90f-310ef874f0cb)

> [!CAUTION]
> I skipped adding the pad to the input image when resizing, which might affect the accuracy of the model if the input image has a different aspect ratio compared to the input size of the model. Always try to get an input size with a ratio close to the input images you will use.

## Requirements

 * Check the **requirements.txt** file.
 * For ONNX, if you have a NVIDIA GPU, then install the **onnxruntime-gpu**, otherwise use the **onnxruntime** library.

## Installation [![PyPI](https://img.shields.io/pypi/v/yolov10-onnx?color=2BAF2B)](https://pypi.org/project/yolov10-onnx/)

```bash
pip install yolov10-onnx
```
Or, clone this repository:
```bash
git clone https://github.com/ibaiGorordo/ONNX-YOLOv10-Object-Detection.git
cd ONNX-YOLOv10-Object-Detection
pip install -r requirements.txt
```
### ONNX Runtime
For Nvidia GPU computers:
`pip install onnxruntime-gpu`

Otherwise:
`pip install onnxruntime`

## ONNX model
- If the model file is not found in the models directory, it will be downloaded automatically from the [Official Repo](https://github.com/THU-MIG/yolov10/releases/tag/v1.1).
- **Available models**: yolov10n.onnx, yolov10s.onnx, yolov10m.onnx, yolov10b.onnx, yolov10l.onnx, yolov10x.onnx

## Original YOLOv10 model
The original YOLOv10 model can be found in this repository: https://github.com/THU-MIG/yolov10
- The License of the models is AGPL-3.0 license: https://github.com/THU-MIG/yolov10/blob/main/LICENSE

## Examples

 * **Image inference**:
 ```
 python image_object_detection.py
 ```

 * **Webcam inference**:
 ```
 python webcam_object_detection.py
 ```

 * **Video inference**: https://youtu.be/hz9PYZF4ax4
 ```
 python video_object_detection.py
 ```
![!yolov10_object_detection](https://github.com/user-attachments/assets/08872b1c-2009-4f8d-97a5-7b88f13ec887)


## References:
* YOLOv10 model: https://github.com/THU-MIG/yolov10