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import gradio as gr
import torch
from ultralyticsplus import YOLO, render_result
from PIL import Image
import os
def yolov8_func(image,
image_size,
conf_thresold=0.4,
iou_thresold=0.50):
# Load the YOLOv8 model
model_path = "best.pt"
model = YOLO(model_path)
# Make predictions
result = model.predict(image, conf=conf_thresold, iou=iou_thresold, imgsz=image_size)
# Access object detection results
boxes = result[0].boxes
num_boxes = len(boxes)
# Print object detection details (optional)
print("Object type: ", boxes.cls)
print("Confidence: ", boxes.conf)
print("Coordinates: ", boxes.xyxy)
print(f"Number of bounding boxes: {num_boxes}")
# Categorize based on number of boxes (detections) and provide recommendations
if num_boxes > 10:
severity = "Worse"
recommendation = "It is recommended to see a dermatologist and start stronger acne treatment."
elif 5 <= num_boxes <= 10:
severity = "Medium"
recommendation = "You should follow a consistent skincare routine with proper cleansing and moisturizing."
else:
severity = "Good"
recommendation = "Your skin looks good! Keep up with your current skincare routine."
print(f"Acne condition: {severity}")
print(f"Recommendation: {recommendation}")
# Render the result (with bounding boxes/labels)
render = render_result(model=model, image=image, result=result[0])
predicted_image_save_path = "predicted_image.jpg"
render.save(predicted_image_save_path)
return predicted_image_save_path, f"Acne condition: {severity}", recommendation
# Define inputs for the Gradio app
inputs = [
gr.Image(type="filepath", label="Input Image"),
gr.Slider(minimum=320, maximum=1280, step=32, value=640, label="Image Size"),
gr.Slider(minimum=0, maximum=1, step=0.05, value=0.15, label="Confidence Threshold"),
gr.Slider(minimum=0, maximum=1, step=0.05, value=0.2, label="IOU Threshold")
]
# Use a Row layout to align the textboxes for condition and recommendation
output_image = gr.Image(type="filepath", label="Output Image")
acne_condition = gr.Textbox(label="Acne Condition")
recommendation = gr.Textbox(label="Recommendation")
# Define the layout using Rows and Columns
outputs = [
output_image,
gr.Row([acne_condition, recommendation])
]
# Set the title of the Gradio app
title = "YOLOv8: An Object Detection for Acne"
# Create the Gradio interface
yolo_app = gr.Interface(fn=yolov8_func,
inputs=inputs,
outputs=outputs,
title=title)
# Launch the app
yolo_app.launch(debug=True) |