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Running
on
Zero
File size: 4,289 Bytes
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import gradio as gr
from detect_strongsort import run
import os
import threading
should_continue = True
def yolov9_inference(model_id, image_size, conf_threshold, iou_threshold, img_path=None, vid_path=None):
global should_continue
img_extensions = ['.jpg', '.jpeg', '.png', '.gif'] # Add more image extensions if needed
vid_extensions = ['.mp4', '.avi', '.mov', '.mkv'] # Add more video extensions if needed
input_path = None
if img_path is not None:
_, img_extension = os.path.splitext(img_path)
if img_extension.lower() in img_extensions:
input_path = img_path
elif vid_path is not None:
_, vid_extension = os.path.splitext(vid_path)
if vid_extension.lower() in vid_extensions:
input_path = vid_path
output_path = run(yolo_weights=model_id, imgsz=(image_size,image_size), conf_thres=conf_threshold, iou_thres=iou_threshold, source=input_path, device='cpu', strong_sort_weights = "osnet_x0_25_msmt17.pt", hide_conf= True)
# Assuming output_path is the path to the output file
_, output_extension = os.path.splitext(output_path)
if output_extension.lower() in img_extensions:
output_image = output_path # Load the image file here
output_video = None
elif output_extension.lower() in vid_extensions:
output_image = None
output_video = output_path # Load the video file here
return output_image, output_video, output_path
def inference(model_id, image_size, conf_threshold, iou_threshold, img_path=None, vid_path=None):
global should_continue
should_continue = True
output_image, output_video, output_path = yolov9_inference(model_id, image_size, conf_threshold, iou_threshold, img_path, vid_path)
return output_image, output_video, output_path
def stop_processing():
global should_continue
should_continue = False
return "Stop..."
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
gr.HTML("<h2>Input Parameters</h2>")
img_path = gr.File(label="Image")
vid_path = gr.File(label="Video")
model_id = gr.Dropdown(
label="Model",
choices=[
"last_best_model.pt",
"best_model-converted.pt"
],
value="./last_best_model.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")
stop_button = gr.Button(value="Stop")
with gr.Column():
gr.HTML("<h2>Output</h2>")
output_image = gr.Image(type="numpy",label="Output Image")
output_video = gr.Video(label="Output Video")
output_path = gr.Textbox(label="Output path")
yolov9_infer.click(
fn=inference,
inputs=[
model_id,
image_size,
conf_threshold,
iou_threshold,
img_path,
vid_path
],
outputs=[output_image, output_video, output_path],
)
stop_button.click(stop_processing)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv9: Real-time Object Detection
</h1>
""")
css = """
body {
background-color: #f0f0f0;
}
h1 {
color: #4CAF50;
}
"""
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True)
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