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SeptAlfauzan
add: model from training with hyper parameter epoch=120, batch=32 for SCB dataset and my own custom dataset
713f67e
import gradio as gr | |
from PIL import Image | |
import torch | |
from ultralyticsplus import YOLO, render_result | |
def launch( | |
image: gr.Image = None, | |
image_size: gr.Slider = 640, | |
conf_threshold: gr.Slider = 0.4, | |
iou_threshold: gr.Slider = 0.50, | |
): | |
try: | |
model_path = "./models/student-behaviour-best.pt" | |
model = YOLO( | |
"./student-behaviour-test-deploy/models/OWN-DATASET-640-e120-b32-best.pt" | |
) | |
# pil_image = Image.fromarray(image) | |
results = model.predict( | |
image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size | |
) | |
box = results[0].boxes | |
# print(box) | |
render = render_result(model=model, image=image, result=results[0]) | |
return render | |
except Exception as e: | |
print("error", e) | |
return "./download.jpeg" | |
inputs = [ | |
gr.Image(type="filepath", label="Input Image"), | |
gr.Slider(minimum=256, maximum=1280, value=640, step=32, label="Image Size"), | |
gr.Slider( | |
minimum=0.0, maximum=1.0, value=0.4, step=0.1, label="Confidence Threshold" | |
), | |
gr.Slider(minimum=0.0, maximum=1.0, value=0.4, step=0.1, label="IOU Threshold"), | |
] | |
outputs = gr.Image(type="filepath", label="Output Result") | |
iface = gr.Interface(fn=launch, inputs=inputs, outputs=outputs) | |
iface.launch() | |