# -*- 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 re import json import numpy as np import pandas as pd import gradio as gr import torch from PIL import Image # Images torch.hub.download_url_to_file('https://huggingface.co/spaces/Municipalidad-de-Vicente-Lopez/Trampas_Barcelo/resolve/main/2024-03-11T10-50-27.jpg', 'ejemplo1.jpg') torch.hub.download_url_to_file('https://i.pinimg.com/originals/c2/ce/e0/c2cee05624d5477ffcf2d34ca77b47d1.jpg', 'ejemplo2.jpg') # model = torch.hub.load('ultralytics/yolov9', 'custom', path='best.pt', force_reload=True, autoshape=True, trust_repo=True) model = torch.hub.load('/content/yolov9', 'custom', path='/content/yolov9/best.pt', source='local', force_reload=True, autoshape=True) #model = torch.hub.load('yolov9', 'custom', path='best.pt', source='local', force_reload=True, autoshape=True)  # load on CPU # Model class YOLODetect(): def __init__(self, modelo): self.modelo = modelo def predecir(self, img, imgsz=640, conf=0.5, iou=0.40): # iou float 0.7 umbral de intersección sobre unión (IoU) para NMS # conf float 0.25 umbral de confianza del objeto para la detección self.modelo.iou = iou self.modelo.conf = conf self.results = self.modelo(img) # inference return self.results def to_json(self): detail = [] for index, row in self.results_df.iterrows(): item = { "quantity": row['Cantidad'], "description": row['Especie'] } detail.append(item) data = { "image": self.results.files[0], "size": f"{self.results.s[2]}x{self.results.s[3]}", "detail": detail } return data def to_dataframe(self): labels_map = { 'Aedes': "Aedes", 'Mosquito': "Mosquitos", 'Mosca': "Moscas", } labels = list(labels_map.keys()) columns_name = {'class': 'Cantidad', 'name': 'Especie'} self.results_df = self.results.pandas().xyxy[0][['class','name']].groupby('name').count().reset_index().rename(columns=columns_name) self.results_df = pd.merge(pd.DataFrame(labels, columns=['Especie']), self.results_df, how='left', on='Especie').fillna(0) self.results_df['Cantidad'] = self.results_df['Cantidad'].astype(int) self.results_df['Especie'] = self.results_df['Especie'].map(labels_map) return self.results_df modelo_yolo = YOLODetect(model) 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.LANCZOS) # resize with antialiasing im = np.asarray(im, dtype=np.float32) resultado = modelo_yolo.predecir(im, imgsz=size, conf=conf, iou=iou) resultado.render() # updates results.imgs with boxes and labels resultado_df = modelo_yolo.to_dataframe() resultado_json = modelo_yolo.to_json() return Image.fromarray(resultado.ims[0]), resultado_df, resultado_json #------------ 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=0.25, 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="YOLOv9") out3 = gr.outputs.Dataframe(label="Cantidad_especie", headers=['Cantidad','Especie'], type="pandas") out4 = gr.outputs.JSON(label="JSON") #-------------- Text----- title = 'Trampas Barceló' description = """

Sistemas de Desarrollado por Subsecretaría de Modernización del Municipio de Vicente López. Advertencia solo usar fotos provenientes de las trampas Barceló, no de celular o foto de internet. logo

""" article ="

Para mas info, clik para ir al white paper

Google Colab Demo

Repo Github

" examples = [['640',0.25, 0.5,'ejemplo1.jpg'], ['640',0.25, 0.5,'ejemplo2.jpg']] iface = gr.Interface(yolo, inputs=[in1, in2, in3, in4], outputs=[out2,out3,out4], title=title, description=description, article=article, examples=examples, analytics_enabled=False, allow_flagging="manual", flagging_options=["Correcto", "Incorrecto", "Casi correcto", "Error", "Otro"], #flagging_callback=hf_writer ) #iface.queue() iface.launch(server_name="0.0.0.0", server_port=7860, enable_queue=True, debug=True) #iface.launch(server_name="0.0.0.0", server_port=7860, debug=True) """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) """