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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 | |
|  | |
| # Modelo | |
| YOLO es una familia de modelos de detecci贸n 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 ejecuci贸n de GPU | |
| ---------------------------------------------------------------------- | |
| YOLOv5 Gradio demo | |
| *Author: Ultralytics LLC and Gradio* | |
| # C贸digo | |
| """ | |
| #!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 | |
| import os | |
| import boto3 | |
| from botocore.exceptions import NoCredentialsError | |
| import tempfile | |
| import io | |
| 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') | |
| aws_access_key_id = os.environ['aws_access_key_id'] | |
| aws_secret_access_key = os.environ['aws_secret_access_key'] | |
| region = os.environ['region'] | |
| #Lista Json y Dataframe | |
| def removeStr(string): | |
| return string.replace(" ", "") | |
| def listJSON(a,b,c,d,e,f,resImg): | |
| 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+','+resImg+'}' | |
| 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 | |
| #Imagen temporal guardada en upload_file | |
| def tempFileJSON(img_file): | |
| temp = tempfile.NamedTemporaryFile(mode="wb") | |
| with temp as jpg: | |
| jpg.write(img_file) | |
| print(jpg.name) | |
| uf = upload_file(jpg.name) | |
| return uf | |
| # Envio de imagenes a S3 | |
| def upload_file(file_name, bucket=None, object_name=None): | |
| """Upload a file to an S3 bucket | |
| :param file_name: File to upload | |
| :param bucket: Bucket to upload to | |
| :param object_name: S3 object name. If not specified then file_name is used | |
| :return: Json if file was uploaded, else False | |
| """ | |
| # If S3 object_name was not specified, use file_name | |
| if object_name is None: | |
| object_name = os.path.basename(file_name+".jpg") | |
| if bucket is None: | |
| bucket = 'oceanapp' | |
| s3_client = boto3.client('s3',aws_access_key_id=aws_access_key_id,aws_secret_access_key=aws_secret_access_key) | |
| aws_region = boto3.session.Session().region_name | |
| # Upload the file | |
| try: | |
| with open(file_name, "rb") as f: | |
| response = s3_client.upload_fileobj(f, bucket, object_name) | |
| s3_url = f"https://{bucket}.s3.amazonaws.com/{object_name}" | |
| stado = '"url_details":[{"statusCode":200, "s3_url":"'+s3_url+'"}]' | |
| print(s3_url) | |
| except FileNotFoundError: | |
| print("The file was not found") | |
| return False | |
| except NoCredentialsError as e: | |
| logging.error(e) | |
| return False | |
| return stado | |
| # Model | |
| model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, autoshape=True) # local model o google colab | |
| def yolo(size, iou, conf, im): | |
| try: | |
| '''Wrapper fn for gradio''' | |
| # gain | |
| g = (int(size) / max(im.size)) | |
| # resize | |
| if(max(im.size)>900 and g>0.4): | |
| g=0.3 | |
| if(max(im.size)>2000 and g>0.4): | |
| g=0.1 | |
| im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) | |
| model.iou = iou | |
| model.conf = conf | |
| # inference | |
| results2 = model(im) | |
| # updates results.imgs with boxes and labels | |
| results2.render() | |
| results3 = str(results2) | |
| #Transformando la img en bytes | |
| pil_im = Image.fromarray(results2.ims[0]) | |
| b = io.BytesIO() | |
| pil_im.save(b, 'jpeg') | |
| im_bytes = b.getvalue() | |
| fileImg = tempFileJSON(im_bytes) | |
| lista = listJSON(results3[0:9], results3[11:18] ,results3[19:21],results3[22:32], results3[34:36], results3[36:45], fileImg) | |
| lista2 = arrayLista(results3[19:21],results3[22:32], results3[34:36], 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="Tama帽o 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="Identificaci贸n con Yolov5") | |
| out3 = gr.outputs.Dataframe(label="Descripci贸n", headers=['Cantidad','Especie']) | |
| out4 = gr.outputs.JSON(label="JSON") | |
| #-------------- Text----- | |
| title = 'OceanApp' | |
| description = """ | |
| <p> | |
| <center> | |
| <p>Sistema para el reconocimiento de las especies en la pesca acompa帽ante de cerco, utilizando redes neuronales convolucionales para una empresa del sector pesquero en los puertos de callao y paracas.</p> | |
| <p><b>Nota</b>: Este modelo solo acepta imagenes de <b>Lobos marinos</b> o <b>Pelicanos</b> proporcionados por empresas peruanas.</p> | |
| <center> | |
| <img src="https://i.pinimg.com/564x/3e/b8/f7/3eb8f7c348dffd7b3dffcafe81fbf2a6.jpg" alt="logo" width="250"/> | |
| </center> | |
| </center> | |
| </p> | |
| """ | |
| article ="<p style='text-align: center'><a href='' target='_blank'>Para mas info, clik para ir al white paper</a></p><p style='text-align: center'><a href='https://colab.research.google.com/drive/1j0T8gdLIa0X8fzkIgFpXDoU27BF49RUz?usp=sharing' target='_blank'>Google Colab Demo</a></p><p style='text-align: center'><a href='https://github.com/MssLune/OceanApp-Model' 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,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) | |
| """ |