# import gradio as gr
# import spaces
# from huggingface_hub import hf_hub_download
# # Import YOLOv9
# import yolov9
# # def download_models(model_id):
# # hf_hub_download("SakshiRathi77/void-space-detection/weights", filename=f"{model_id}", local_dir=f"./")
# # return f"./{model_id}"
# def download_models(model_id):
# hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir=f"./")
# return f"./{model_id}"
# def yolov9_inference(img_path, 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.
# """
# # Load the model
# model_path = download_models()
# # model = yolov9.load("./best.pt")
# # 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():
# with gr.Blocks():
# with gr.Row():
# with gr.Column():
# img_path = gr.Image(type="filepath", label="Image")
# 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],
# )
# gradio_app = gr.Blocks()
# with gradio_app:
# gr.HTML(
# """
#
# YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
#
# """)
# gr.HTML(
# """
#
# Follow me for more!
#
# """)
# with gr.Row():
# with gr.Column():
# app()
# gradio_app.launch(debug=True)
# make sure you have the following dependencies
# import gradio as gr
# import torch
# from torchvision import transforms
# from PIL import Image
# # Load the YOLOv9 model
# model_path = "best.pt" # Replace with the path to your YOLOv9 model
# model = torch.load(model_path)
# # Define preprocessing transforms
# preprocess = transforms.Compose([
# transforms.Resize((640, 640)), # Resize image to model input size
# transforms.ToTensor(), # Convert image to tensor
# ])
# # Define a function to perform inference
# def detect_void(image):
# # Preprocess the input image
# image = Image.fromarray(image)
# image = preprocess(image).unsqueeze(0) # Add batch dimension
# # Perform inference
# with torch.no_grad():
# output = model(image)
# # Post-process the output if needed
# # For example, draw bounding boxes on the image
# # Convert the image back to numpy array
# # and return the result
# return output.squeeze().numpy()
# # Define Gradio interface components
# input_image = gr.inputs.Image(shape=(640, 640), label="Input Image")
# output_image = gr.outputs.Image(label="Output Image")
# # Create Gradio interface
# gr.Interface(fn=detect_void, inputs=input_image, outputs=output_image, title="Void Detection App").launch()
import gradio as gr
import spaces
from huggingface_hub import hf_hub_download
def download_models(model_id):
hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir=f"./")
return f"./{model_id}"
def yolov9_inference(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 = download_models(model_id)
model = yolov9.load(model_path)
# 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():
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-c.pt",
],
value="yolov9-c.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],
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
""")
gr.HTML(
"""
""")
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True)