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import torch | |
import gradio as gr | |
from huggingface_hub import hf_hub_download | |
from PIL import Image | |
REPO_ID = "rgp/yolov5-street-view-detection" | |
FILENAME = "best.pt" | |
yolov5_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) | |
model = torch.hub.load('ultralytics/yolov5', 'custom', path=yolov5_weights, force_reload=True) # local repo | |
def object_detection(im, size=640): | |
results = model(im) # inference | |
#results.print() # print results to screen | |
#results.show() # display results | |
#results.save() # save as results1.jpg, results2.jpg... etc. | |
results.render() # updates results.imgs with boxes and labels | |
return Image.fromarray(results.ims[0]) | |
title = "Identificação de Pedestres e meios de locomoção nas ruas" | |
description = """Esse modelo é uma pequena demonstração baseada em uma análise de cerca de 680 imagens.""" | |
input = gr.inputs.Image(shape=(640, 640), image_mode="RGB", source="upload", label="Imagem", optional=False) | |
output = gr.outputs.Image(type="pil", label="Output Image") | |
examples = [["sample_images/IMG_0125.jpg"], ["sample_images/IMG_0129.jpg"], ["sample_images/IMG_0157.jpg"], ["sample_images/IMG_0158.jpg"], ["sample_images/IMG_012.jpg"]] | |
gr.Interface( | |
fn=object_detection, | |
inputs=input, | |
outputs=output, | |
title=title, | |
description=description, | |
examples=examples | |
).launch(debug=True) |