File size: 3,135 Bytes
e09ba31 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
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"
description ="""
🔥 Unveiling ThermalFoduu: Spot Objects with Thermal Vision! 🔍📸 Lost your keys in the dark? 🗝️🌑 ThermalFoduu's got you covered! Powered by Foduu AI, our app effortlessly detects objects in thermal images. No more blurry blobs – just pinpoint accuracy! 🦅🎯
Love the thermal world? Give us a thumbs up! 👍 Questions or suggestions? Contact us at info@foduu. Let's decode the thermal universe together! 📧🌡️
"""
🌟 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) |