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import gradio as gr
import torch
from PIL import Image
from story import story_model
# Images
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update
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
r= results.pred[0].numpy().T
#probs = results.pred[0].numpy().T[-1]
res = [(results.names[x].lower(),p) for x,p in zip(r[-1].astype(int),r[-2])]
f_res = story_model(res)
#results.render() # updates results.imgs with boxes and labels
#return Image.fromarray(results.imgs[0])
return ','.join(f_res)
inputs = gr.inputs.Image(type='pil', label="Original Image")
#outputs = gr.outputs.Image(type="pil", label="Output Image")
outputs = gr.outputs.Textbox(type="str", label="Output Story")
title = "YOLOv5"
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://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
examples = [['zidane.jpg'], ['bus.jpg']]
iface = gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(cache_examples=True,enable_queue=True)
iface.launch() |