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import numpy as np
import gradio as gr
from ultralytics import YOLO
import tempfile
import cv2
def inference(image, video, model_id, image_size, conf_threshold):
if model_id == "yolov10n-obb":
model = YOLO("pretrained/yolov10n-obb.pt")
elif model_id == "yolov10s-640-obb":
model = YOLO("pretrained/yolov10s-640-obb.pt")
elif model_id == "yolov10s-obb":
model = YOLO("pretrained/yolov10s-obb.pt")
elif model_id == "yolov10m-obb":
model = YOLO("pretrained/yolov10m-obb.pt")
elif model_id == "yolov10b-obb":
model = YOLO("pretrained/yolov10b-obb.pt")
elif model_id == "yolov10l-obb":
model = YOLO("pretrained/yolov10l-obb.pt")
elif model_id == "yolov10x-obb":
model = YOLO("pretrained/yolov10x-obb.pt")
if image:
results = model.predict(source=image, imgsz=image_size, conf=conf_threshold, device="cpu")
annotated_image = results[0].plot()
return annotated_image[:, :, ::-1], None
else:
video_path = tempfile.mktemp(suffix=".webm")
with open(video_path, "wb") as f:
with open(video, "rb") as g:
f.write(g.read())
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
output_video_path = tempfile.mktemp(suffix=".webm")
out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp90'), fps, (frame_width, frame_height))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold)
annotated_frame = results[0].plot()
out.write(annotated_frame)
cap.release()
out.release()
return None, output_video_path
def inference_for_examples(image, model_path, image_size, conf_threshold):
annotated_image, _ = inference(image, None, model_path, image_size, conf_threshold)
return annotated_image
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Image", visible=True)
video = gr.Video(label="Video", visible=False)
input_type = gr.Radio(
choices=["Image", "Video"],
value="Image",
label="Input Type",
)
model_id = gr.Dropdown(
label="Model",
choices=[
"yolov10n-obb",
"yolov10s-640-obb",
"yolov10s-obb",
"yolov10m-obb",
"yolov10b-obb",
"yolov10l-obb",
"yolov10x-obb",
],
value="yolov10n-obb",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.25,
)
inferBtn = gr.Button(value="Detect")
with gr.Column():
output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)
output_video = gr.Video(label="Annotated Video", visible=False)
def update_visibility(input_type):
image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
return image, video, output_image, output_video
input_type.change(
fn=update_visibility,
inputs=[input_type],
outputs=[image, video, output_image, output_video],
)
def run_inference(image, video, model_id, image_size, conf_threshold, input_type):
if input_type == "Image":
return inference(image, None, model_id, image_size, conf_threshold)
else:
return inference(None, video, model_id, image_size, conf_threshold)
inferBtn.click(
fn=run_inference,
inputs=[image, video, model_id, image_size, conf_threshold, input_type],
outputs=[output_image, output_video],
)
gr.Examples(
examples=[
[
"test_images/P0024.jpg",
"yolov10n-obb",
1024,
0.25,
],
[
"test_images/P0035.jpg",
"yolov10n-obb",
1024,
0.25,
],
[
"test_images/P0121.jpg",
"yolov10n-obb",
1024,
0.25,
],
[
"test_images/P0180.jpg",
"yolov10n-obb",
1024,
0.25,
],
[
"test_images/P0279.jpg",
"yolov10n-obb",
1024,
0.25,
],
[
"test_images/P2112.jpg",
"yolov10n-obb",
1024,
0.25,
],
],
fn=inference_for_examples,
inputs=[
image,
model_id,
image_size,
conf_threshold,
],
outputs=[output_image],
cache_examples='lazy',
)
gradio_app = gr.Blocks()
with gradio_app:
gr.Markdown(
"""
# YOLOv10 - OBB (Oriented Bounding Box)
for more detail description about this model, please visit [here](https://github.com/hamhanry/YOLOv10-OBB)
"""
)
with gr.Row():
with gr.Column():
app()
if __name__ == '__main__':
gradio_app.queue()
gradio_app.launch() |