Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,840 Bytes
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import spaces
import supervision as sv
import PIL.Image as Image
from ultralytics import YOLO
import gradio as gr
import torch
model_filenames = [
"yolo11n.pt",
"yolo11s.pt",
"yolo11m.pt",
"yolo11l.pt",
"yolo11x.pt"
]
box_annotator = sv.BoxAnnotator()
category_dict = {
0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
}
@spaces.GPU
def yolo_inference(image, model_id, conf_threshold, iou_threshold, max_detection):
model = YOLO(model_id)
results = model(source=image, imgsz=640, iou=iou_threshold, conf=conf_threshold, verbose=False, max_det=max_detection)[0]
detections = sv.Detections.from_ultralytics(results)
labels = [
f"{category_dict[class_id]} {confidence:.2f}"
for class_id, confidence in zip(detections.class_id, detections.confidence)
]
annotated_image = box_annotator.annotate(image, detections=detections, labels=labels)
return annotated_image
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Image", interactive=True)
model_id = gr.Dropdown(
label="Model",
choices=model_filenames,
value=model_filenames[0] if model_filenames else "",
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.25,
)
iou_threshold = gr.Slider(
label="IoU Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.45,
)
max_detection = gr.Slider(
label="Max Detection",
minimum=1,
maximum=300,
step=1,
value=1,
)
yolov_infer = gr.Button(value="Detect Objects")
with gr.Column():
output_image = gr.Image(type="pil", label="Annotated Image", interactive=False)
yolov_infer.click(
fn=yolo_inference,
inputs=[
image,
model_id,
conf_threshold,
iou_threshold,
max_detection,
],
outputs=[output_image],
)
gr.Examples(
examples=[
[
"zidane.jpg",
"yolo11s.pt",
0.25,
0.45,
300,
],
[
"bus.jpg",
"yolo11m.pt",
0.25,
0.45,
300,
],
[
"yolo_vision.jpg",
"yolo11x.pt",
0.25,
0.45,
300,
],
],
fn=yolo_inference,
inputs=[
image,
model_id,
conf_threshold,
iou_threshold,
max_detection,
],
outputs=[output_image],
cache_examples="lazy",
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
Yolo11
</h1>
""")
with gr.Row():
with gr.Column():
app()
gradio_app.launch() |