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import gradio as gr
from ultralyticsplus import YOLO, render_result
from ultralytics.yolo.utils.plotting import Annotator
def yolov8_inference(
image: gr.Image = None,
model_path = "eeshawn11/naruto_hand_seal_detection",
conf_threshold: gr.Slider = 0.50,
iou_threshold: gr.Slider = 0.45,
):
"""
YOLOv8 inference function
Args:
image: Input image
model_path: Path to the model
conf_threshold: Confidence threshold
iou_threshold: IOU threshold
Returns:
Rendered image
"""
# model = YOLO(model_path)
model = YOLO("ultralyticsplus/yolov8s")
model.conf = conf_threshold
model.iou = iou_threshold
# results = model.predict(image, return_outputs=True)
results = model.predict(image)
# object_prediction_list = []
# annotator = Annotator(image)
# for _, image_results in enumerate(results):
# if len(image_results)!=0:
# image_predictions_in_xyxy_format = image_results['det']
# for pred in image_predictions_in_xyxy_format:
# x1, y1, x2, y2 = (
# int(pred[0]),
# int(pred[1]),
# int(pred[2]),
# int(pred[3]),
# )
# bbox = [x1, y1, x2, y2]
# score = pred[4]
# category_name = model.model.names[int(pred[5])]
# category_id = pred[5]
# annotator.box_label(bbox, f"{category_name} {score}")
# object_prediction = ObjectPrediction(
# bbox=bbox,
# category_id=int(category_id),
# score=score,
# category_name=category_name,
# )
# object_prediction_list.append(object_prediction)
# image = read_image(image)
# output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
# return output_image['image']
# return annotator.result()
render = render_result(model=model, image=image, result=results[0])
return render
inputs = [
# gr.inputs.Image(type="filepath", label="Input Image"),
gr.Image(source="upload", type="pil", label="Image Upload", interactive=True),
gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Confidence Threshold"),
gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
]
outputs = gr.Image(type="filepath", label="Output Image")
title = "Naruto Hand Seal Detection with YOLOv8"
myapp = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
)
myapp.queue()
myapp.launch() |