# -*- coding: utf-8 -*-
"""Deploy OceanApp demo.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1j0T8gdLIa0X8fzkIgFpXDoU27BF49RUz?usp=sharing
# 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
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 gradio as gr
import pandas as pd
import torch
import logging
import json
import re
from PIL import Image
# Images
torch.hub.download_url_to_file('https://i.pinimg.com/564x/18/0b/00/180b00e454362ff5caabe87d9a763a6f.jpg', 'ejemplo1.jpg')
torch.hub.download_url_to_file('https://i.pinimg.com/564x/3b/2f/d4/3b2fd4b6881b64429f208c5f32e5e4be.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 removeStr(string):
return string.replace(" ", "")
def listJSON(a,b,c,d,e,f):
x = re.findall("obo mar", d)
y = re.findall("elica", d)
z = re.findall("elica", f)
if x:
d = 'Lobo marino'
if y:
d = 'Pelicano'
if z:
f = 'Pelicano'
if(d=='Lobo marino' or d=='Pelicano'):
if d =='Pelicano\nSp' or d =='Pelicano\nS':
d = 'Pelicano'
if f!='Pelicano':
strlista = '"detail":[{"quantity":"'+str(removeStr(c))+'","description":"'+str(d)+'"}]'
else:
strlista = '"detail":[{"quantity":"'+str(removeStr(c))+'","description":"'+str(d)+'"},{"quantity":"'+str(removeStr(e))+'","description":"'+str(f)+'"}]'
strlist = '{"image":"'+str(removeStr(a))+'","size":"'+str(removeStr(b))+'",'+strlista+'}'
json_string = json.loads(strlist)
return json_string
def arrayLista(a,b,c,d):
x = re.findall("obo mar", b)
y = re.findall("elica", b)
z = re.findall("elica", d)
if x:
b = 'Lobo marino'
if y:
b = 'Pelicano'
if z:
d = 'Pelicano'
if(b=='Lobo marino' or b=='Pelicano'):
strlist =[]
strlist2 =[]
strlist.append(removeStr(a))
strlist.append(b)
if d=='Pelicano':
strlist2.append(removeStr(c))
strlist2.append(d)
strlista = [strlist,strlist2]
df = pd.DataFrame(strlista,columns=['Cantidad','Especie'])
return df
def yolo(size, iou, conf, im):
try:
'''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
results3 = str(results2)
lista = listJSON(results3[0:9], results3[11:18] ,results3[19:21],results3[22:32], results3[35:37], results3[37:45])
lista2 = arrayLista(results3[19:21],results3[22:32], results3[35:37], results3[37:45])
return Image.fromarray(results2.ims[0]), lista2, lista
except Exception as e:
logging.error(e, exc_info=True)
#------------ 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="Identificación con Yolov5")
out3 = gr.outputs.Dataframe(label="Descripción", headers=['Cantidad','Especie'])
out4 = gr.outputs.JSON(label="JSON")
#-------------- Text-----
title = 'OceanApp'
description = """
Sistema para el reconocimiento de las especies en la pesca acompañante de cerco, utilizando redes neuronales convolucionales para una empresa del sector pesquero en los puertos de callao y paracas.
Nota: Este modelo solo acepta imagenes de Lobos marinos o Pelicanos proporcionados por empresas peruanas.
"""
article ="Para mas info, clik para ir al white paper
Google Colab Demo
Repo Github
"
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,out3,out4], title=title, description=description, article=article, examples=examples,theme="huggingface", analytics_enabled=False).launch(
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
[](https://zenodo.org/badge/latestdoi/264818686)
"""