Add application files

app.py

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+# -*- 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
+
+![   ](https://i.pinimg.com/564x/3e/b8/f7/3eb8f7c348dffd7b3dffcafe81fbf2a6.jpg)
+
+# 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 gradio as gr
+import torch
+import logging
+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 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
+        return Image.fromarray(results2.ims[0])
+    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="YOLOv5")
+#-------------- Text-----
+title = 'OceanApp'
+description = """
+<p>
+<center>
+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.
+<img src="https://i.pinimg.com/564x/3e/b8/f7/3eb8f7c348dffd7b3dffcafe81fbf2a6.jpg" 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).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
+
+[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686)
+"""

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:388ea86a83bda4541be7e4185195588615482df9a9cd64bc0144d01ea01bb9c9
+size 14339573

requirements.txt

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+# pip install -r requirements.txt
+# base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python-headless
+Pillow
+PyYAML>=5.3.1
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.41.0
+# logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+# plotting ------------------------------------
+seaborn>=0.11.0
+pandas
+# export --------------------------------------
+# coremltools>=4.1
+# onnx>=1.9.0
+# scikit-learn==0.19.2  # for coreml quantization
+# extras --------------------------------------
+# Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
+# pycocotools>=2.0  # COCO mAP
+# albumentations>=1.0.3
+thop  # FLOPs computation