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from ultralyticsplus import YOLO, render_result | |
# set image | |
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' | |
def PPE(image): | |
# load model | |
model = YOLO('keremberke/yolov8m-protective-equipment-detection') | |
# set model parameters | |
model.overrides['conf'] = 0.25 # NMS confidence threshold | |
model.overrides['iou'] = 0.45 # NMS IoU threshold | |
model.overrides['agnostic_nms'] = False # NMS class-agnostic | |
model.overrides['max_det'] = 1000 # maximum number of detections per image | |
# perform inference | |
results = model.predict(image) | |
# observe results | |
print(results[0].boxes) | |
render = render_result(model=model, image=image, result=results[0]) | |
render.show() | |
import gradio as gr | |
def greet(name): | |
return "Hello " + name + "!!" | |
iface = gr.Interface(fn=PPE, inputs=image, outputs=PPE()) | |
iface.launch() |