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import gradio as gr
import torch
from ultralytics import YOLO
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions
import cv2
from utils import attempt_download_from_hub
# Images
torch.hub.download_url_to_file('https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg', 'highway.jpg')
torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.jpg')
torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
def yolov8_inference(
image: gr.inputs.Image = None,
model_path: gr.inputs.Dropdown = None,
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 0.45,
):
"""
YOLOv8 inference function
Args:
image: Input image
model_path: Path to the model
image_size: Image size
conf_threshold: Confidence threshold
iou_threshold: IOU threshold
Returns:
Rendered image
"""
hf_model_path = attempt_download_from_hub(model_path)
model = YOLO(hf_model_path)
model.conf = conf_threshold
model.iou = iou_threshold
prediction = model.predict(image, imgsz=image_size)
object_prediction_list = []
for _, image_predictions_in_xyxy_format in enumerate(prediction):
for pred in image_predictions_in_xyxy_format.cpu().detach().numpy():
x1, y1, x2, y2 = (
int(pred[0]),
int(pred[1]),
int(pred[2]),
int(pred[3]),
)
bbox = [x1, y1, x2, y2]
score = pred[4]
category_name = model.model.names[int(pred[5])]
category_id = pred[5]
object_prediction = ObjectPrediction(
bbox=bbox,
category_id=int(category_id),
score=score,
category_name=category_name,
)
object_prediction_list.append(object_prediction)
image = cv2.imread(image)
save_path = 'output.jpg'
output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
output_image = cv2.imwrite(save_path, output_image["image"])
return save_path
inputs = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Dropdown(["kadirnar/yolov8n-v8.0", "kadirnar/yolov8m-v8.0", "kadirnar/yolov8l-v8.0", "kadirnar/yolov8x-v8.0", "kadirnar/yolov8x6-v8.0"], label="먼저 모델을 선택해주세요 first choose Model"),
gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "Ultralytics YOLOv8: State-of-the-Art YOLO Models"
examples = [['highway.jpg', 'kadirnar/yolov8m-v8.0', 640, 0.25, 0.45], ['highway1.jpg', 'kadirnar/yolov8l-v8.0', 640, 0.25, 0.45], ['small-vehicles1.jpeg', 'kadirnar/yolov8x-v8.0', 1280, 0.25, 0.45]]
demo_app = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
live=True,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)
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