Spaces:
Sleeping
Sleeping
File size: 4,081 Bytes
d7ba2cb 98b8558 d7ba2cb 98b8558 d7ba2cb 98b8558 d7ba2cb 98b8558 d7ba2cb 98b8558 d7ba2cb 98b8558 d7ba2cb 98b8558 19cae31 98b8558 19cae31 98b8558 |
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 |
import gradio as gr
import spaces
from huggingface_hub import hf_hub_download
class YOLOv9App:
def __init__(self):
self.gradio_app = gr.Blocks()
def download_models(self, model_id):
hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir="./")
return f"./{model_id}"
@spaces.GPU
def yolov9_inference(self, img_path, model_id, image_size, conf_threshold, iou_threshold):
"""
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.
"""
# Import YOLOv9
import yolov9
# Load the model
model_path = self.download_models(model_id)
model = yolov9.load(model_path, device="cpu")
# Set model parameters
model.conf = conf_threshold
model.iou = iou_threshold
# Perform inference
results = model(img_path, size=image_size)
# Optionally, show detection bounding boxes on image
output = results.render()
return output[0]
def app(self):
with gr.Blocks():
with gr.Row():
with gr.Column():
img_path = gr.Image(type="filepath", label="Image")
model_path = gr.Dropdown(
label="Model",
choices=[
"gelan-c.pt",
"gelan-e.pt",
"yolov9-c.pt",
"yolov9-e.pt",
],
value="gelan-e.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="Submit")
with gr.Column():
output_numpy = gr.Image(type="numpy", label="Output")
yolov9_infer.click(
fn=self.yolov9_inference,
inputs=[
img_path,
model_path,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_numpy],
)
def launch(self):
with self.gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv9 Base Model
</h1>
"""
)
gr.HTML(
"""
<h3 style='text-align: center'>
Aplicação para ajudar nos resgates do RS
</h3>
"""
)
with gr.Row():
with gr.Column():
self.app()
self.gradio_app.launch(debug=True)
# Criação e execução da aplicação
if __name__ == "__main__":
yolov9_app = YOLOv9App()
yolov9_app.launch()
|