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import gradio as gr
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
from ultralyticsplus import YOLO
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
"""
model = YOLO(f'{model_path}.pt')
# set model parameters
model.overrides['conf'] = conf_threshold # NMS confidence threshold
model.overrides['iou'] = iou_threshold # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
results = model.predict(image, imgsz=image_size, return_outputs=True)
object_prediction_list = []
for _, image_results in enumerate(results):
if len(image_results)!=0:
image_predictions_in_xyxy_format = image_results['det']
for pred in image_predictions_in_xyxy_format:
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 = read_image(image)
output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
return output_image['image']
inputs = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Dropdown(["yolov8n", "yolov8m", "yolov8l", "yolov8x"],
default="yolov8m", label="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 = "State-of-the-Art YOLO Models for Object detection"
# examples = [['demo_01.jpg', 'yolov8n', 640, 0.25, 0.45], ['demo_02.jpg', 'yolov8l', 640, 0.25, 0.45], ['demo_03.jpg', 'yolov8x', 1280, 0.25, 0.45]]
demo_app = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True) |