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import gradio as gr
import torch
from sahi.classification import ImageClassification
from sahi.utils.cv import visualize_object_predictions, read_image
from ultralyticsplus import YOLO
def yolov8_inference(
image: gr.Image = None,
model_path: gr.Dropdown = None,
image_size: gr.Slider = 640,
conf_threshold: gr.Slider = 0.25,
iou_threshold: gr.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
"""
model = YOLO(model_path)
model.overrides['conf'] = conf_threshold
model.overrides['iou']= iou_threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000
# observe results
top_class_index = torch.argmax(results[0].probs).item()
Class = model.names[top_class_index]
print(Class)
inputs = [
gr.Image(type="filepath", label="Input Image"),
gr.Dropdown(["foduucom/Tyre-Quality-Classification-AI"],
default="foduucom/Tyre-Quality-Classification-AI", label="Model"),
gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs = gr.Image(type="filepath", label="Output Image")
title = "AI-Powered Tire Quality Inspection: YOLOv8s Enhanced Classification"
Space Description:"""
Welcome to our 🤖 AI-Powered Tire Quality Inspection Space – a cutting-edge solution harnessing the capabilities of YOLOv8s to revolutionize 🚗 tire quality control processes.
"""
About This Space: """
This interactive platform empowers you to classify tires with unparalleled precision, utilizing a fine-tuned YOLOv8s model 🎯 specifically developed for identifying defects in tire manufacturing. By submitting an image of a tire, you can instantly determine whether it meets the rigorous quality standards required in the industry, helping to ensure safety and reliability in automotive products.
"""
examples = [['samples/1.jpeg', 'foduucom/thermal-image-object-detection', 640, 0.25, 0.45], ['samples/2.jpg', 'foduucom/thermal-image-object-detection', 640, 0.25, 0.45]]
demo_app = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
description=description,
examples=examples,
cache_examples=True,
theme='huggingface',
)
demo_app.queue().launch(debug=True) |