app.py

# -*- coding: utf-8 -*-
"""Deploy Barcelo demo.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1FxaL8DcYgvjPrWfWruSA5hvk3J81zLY9

![   ](https://www.vicentelopez.gov.ar/assets/images/logo-mvl.png)

# Modelo

YOLO es una familia de modelos de detección de objetos a escala compuesta entrenados en COCO dataset, e incluye una funcionalidad simple para Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.


## Gradio Inferencia

![](https://i.ibb.co/982NS6m/header.png)

Este Notebook se acelera opcionalmente con un entorno de ejecución de GPU


----------------------------------------------------------------------

 YOLOv5 Gradio demo

*Author: Ultralytics LLC and Gradio*

# Código
"""

#!pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio # install dependencies

import os
import gradio as gr
import torch
from PIL import Image

HF_TOKEN = os.getenv("HF_TOKEN")
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "demoIAZIKA-flags")

# Images
torch.hub.download_url_to_file('https://i.pinimg.com/originals/7f/5e/96/7f5e9657c08aae4bcd8bc8b0dcff720e.jpg', 'ejemplo1.jpg')
torch.hub.download_url_to_file('https://i.pinimg.com/originals/c2/ce/e0/c2cee05624d5477ffcf2d34ca77b47d1.jpg', 'ejemplo2.jpg')

# Model
#model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update

#model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')  # local model  o google colab
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, autoshape=True)  # local model  o google colab
#model = torch.hub.load('path/to/yolov5', 'custom', path='/content/yolov56.pt', source='local')  # local repo


def yolo(size, iou, conf, im):
    '''Wrapper fn for gradio'''
    g = (int(size) / max(im.size))  # gain
    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize

    model.iou = iou
    
    model.conf = conf

   
    results2 = model(im)  # inference
   
    results2.render()  # updates results.imgs with boxes and labels
    return Image.fromarray(results2.ims[0])

#------------ Interface-------------



in1 = gr.inputs.Radio(['640', '1280'], label="Tamaño de la imagen", default='640', type='value')
in2 = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.45, label='NMS IoU threshold')
in3 = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.50, label='Umbral o  threshold')
in4 = gr.inputs.Image(type='pil', label="Original Image")

out2 = gr.outputs.Image(type="pil", label="YOLOv5")
#-------------- Text-----
title = 'Trampas Barceló'
description = """
<p>
<center>
Sistemas de Desarrollado por Subsecretaría de Innovación del Municipio de Vicente López. Advertencia solo usar fotos provenientes de las trampas Barceló, no de celular o foto de internet.
<img src="https://www.vicentelopez.gov.ar/assets/images/logo-mvl.png" alt="logo" width="250"/>
</center>
</p>
"""
article ="<p style='text-align: center'><a href='https://docs.google.com/presentation/d/1T5CdcLSzgRe8cQpoi_sPB4U170551NGOrZNykcJD0xU/edit?usp=sharing' target='_blank'>Para mas info, clik para ir al white paper</a></p><p style='text-align: center'><a href='https://drive.google.com/drive/folders/1owACN3HGIMo4zm2GQ_jf-OhGNeBVRS7l?usp=sharing ' target='_blank'>Google Colab Demo</a></p><p style='text-align: center'><a href='https://github.com/Municipalidad-de-Vicente-Lopez/Trampa_Barcelo' target='_blank'>Repo Github</a></p></center></p>"
          
examples = [['640',0.45, 0.75,'ejemplo1.jpg'], ['640',0.45, 0.75,'ejemplo2.jpg']]

iface = gr.Interface(yolo, 
                        inputs=[in1, in2, in3, in4], 
                        outputs=out2, title=title, 
                        description=description, 
                        article=article, 
                        examples=examples,
                        theme="huggingface", 
                        analytics_enabled=False,
                        allow_flagging="manual",
                        flagging_options=["correcto", "incorrecto", "casi correcto", "error", "otro"],
                        flagging_callback=hf_writer).launch(
    enable_queue=True,
    debug=True)

iface.launch()

"""For YOLOv5 PyTorch Hub inference with **PIL**, **OpenCV**, **Numpy** or **PyTorch** inputs please see the full [YOLOv5 PyTorch Hub Tutorial](https://github.com/ultralytics/yolov5/issues/36).


## Citation

[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686)
"""