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import gradio as gr
import torch
from PIL import Image
import os
cwd = os.getcwd()
# !pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio
# Images
torch.hub.download_url_to_file('https://i.imgur.com/Qi6I5Yf.png', 's1000_50_metre.jpg')
torch.hub.download_url_to_file('https://i.imgur.com/kATwqiq.png', 's1000_100_metre.jpg')
torch.hub.download_url_to_file('https://i.imgur.com/UhtsKNx.png', 's1000_150_metre.jpg')
torch.hub.download_url_to_file('https://i.imgur.com/xXnXoEX.png', 's1000_200_metre.jpg')
torch.hub.download_url_to_file('https://i.imgur.com/SU5yPAS.png', 's1000_250_metre.jpg')
# Model
#model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update
model = torch.hub.load(cwd+'/yolov5', 'custom', path=cwd+'/saved_model/s1000_best.pt', source='local') # local model
def yolo(im, size=640):
g = (size / max(im.size)) # gain
im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize
results = model(im) # inference
results.render() # updates results.imgs with boxes and labels
return Image.fromarray(results.imgs[0])
inputs = gr.inputs.Image(type='pil', label="Original Image")
outputs = gr.outputs.Image(type="pil", label="Output Image")
title = "S1000 Detection"
description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use."
article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes " \
"simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, " \
"and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> |" \
"<a href='https://apps.apple.com/app/id1452689527'>iOS App</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
'''path_folder = cwd+'/datasets/s1000/'
examples = [[path_folder+'s1000_50_metre.jpg'], [path_folder+'s1000_100_metre.jpg'],[path_folder+'s1000_150_metre.jpg'],[path_folder+'s1000_200_metre.jpg'],[path_folder+'s1000_250_metre.jpg']]'''
examples = [['s1000_50_metre.jpg'], ['s1000_100_metre.jpg'],['s1000_150_metre.jpg'],['s1000_200_metre.jpg'],['s1000_250_metre.jpg']]
gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(
debug=True)
'''
git init
git config user.name bulentsofttech
git config user.email bulent.softtech@gmail.com
git add *
git commit -m "WriteCommit"
git push origin master
''' |