Spaces:
Runtime error
Runtime error
File size: 2,337 Bytes
0a5ad3d 578200d 0a5ad3d e10d68b 0a5ad3d 7ebf87a 578200d 0a5ad3d 578200d 0a5ad3d 578200d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
import gradio as gr
from ultralyticsplus import YOLO
def yolov8_inference(
image: gr.inputs.Image = None,
model_path = "eeshawn11/naruto_hand_seal_detection",
conf_threshold: gr.inputs.Slider = 0.50,
iou_threshold: gr.inputs.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.conf = conf_threshold
model.iou = iou_threshold
results = model.predict(image, return_outputs=True)
object_prediction_list = []
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]
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']
inputs = [
# gr.inputs.Image(type="filepath", label="Input Image"),
gr.Image(source="upload", type="pil", label="Image Upload", interactive=True),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.5, step=0.05, label="Confidence Threshold"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs = gr.outputs.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() |