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c79c7e9 b81e5e4 e50f601 b81e5e4 c79c7e9 d722369 c79c7e9 b81e5e4 e50f601 c79c7e9 e50f601 c79c7e9 b81e5e4 c79c7e9 b81e5e4 c79c7e9 1855600 c79c7e9 f34ddd2 c79c7e9 3bb85ff d722369 c79c7e9 d722369 c79c7e9 3bb85ff c79c7e9 | 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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | import gradio as gr
import os
import spaces
import yolov9
from huggingface_hub import hf_hub_download
@spaces.GPU
def yolov9_inference(img_path, model_id='YOLOv9-S_X_LOCO-converted.pt', img_size=640, conf_thres=0.3, iou_thres=0.7):
"""
Load a YOLOv9 model, configure it, perform inference on an image, and optionally adjust
the input size and apply test time augmentation.
:param model_path: Path to the YOLOv9 model file.
:param conf_threshold: Confidence threshold for NMS.
:param iou_threshold: IoU threshold for NMS.
:param img_path: Path to the image file.
:param size: Optional, input size for inference.
:return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying.
"""
model_path = download_models(model_id)
model = yolov9.load(model_path)
# Set model parameters
model.conf = conf_thres
model.iou = iou_thres
# Perform inference
results = model(img_path, size=img_size)
# Optionally, show detection bounding boxes on image
output = results.render()
return output[0]
def download_models(model_id):
hf_hub_download("KoniHD/LOCO-Detection", filename=f"{model_id}", local_dir=f"./",
token=os.getenv("HF_TOKEN"))
return f"./{model_id}"
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
img_path = gr.Image(type="filepath", label="Image")
model_path = gr.Dropdown(
label="Model",
choices=[
"YOLOv9-S_X_LOCO-converted.pt",
"YOLOv9-S_X_LOCO.pt",
"YOLOv9-E_X_LOCO-converted.pt",
"YOLOv9-E_X_LOCO.pt",
],
value="YOLOv9-E_X_LOCO-converted.pt",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.4,
)
iou_threshold = gr.Slider(
label="IoU Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.5,
)
yolov9_infer = gr.Button(value="Inference")
with gr.Column():
output_numpy = gr.Image(type="numpy",label="Output")
yolov9_infer.click(
fn=yolov9_inference,
inputs=[
img_path,
model_path,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_numpy],
)
gr.Examples(
examples=[
[
"data/forklift.jpg",
"YOLOv9-S_X_LOCO-converted.pt",
640,
0.3,
0.7,
],
[
"data/hall.jpg",
"YOLOv9-E_X_LOCO-converted.pt",
640,
0.4,
0.5,
],
],
fn=yolov9_inference,
inputs=[
img_path,
model_path,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_numpy],
cache_examples=True,
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
</h1>
""")
gr.HTML(
"""
<h3 style='text-align: center'>
Follow me for more!
<a href='https://twitter.com/konihd_7' target='_blank'>Twitter</a> | <a href='https://github.com/KoniHD' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/konstantin-zeck/' target='_blank'>Linkedin</a> | <a href='https://www.huggingface.co/KoniHD/' target='_blank'>HuggingFace</a>
</h3>
""")
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True) |