backup.py

import gradio as gr
import numpy as np
import cv2
from sahi.predict import get_sliced_prediction
from sahi import AutoDetectionModel
from PIL import Image
import plotly.graph_objects as go
import torch
import spaces

device = "cuda:0" if torch.cuda.is_available() else "cpu"


class Detection:

    def __init__(self):
        # Set the model path and confidence threshold
        yolov8_model_path = "./model/best.pt"  # Update to your model path

        # Initialize the AutoDetectionModel
        self.model = AutoDetectionModel.from_pretrained(
            model_type='yolov8',
            model_path=yolov8_model_path,
            confidence_threshold=0.3,
            device='cpu'  # Change to 'cuda:0' if you are using a GPU
        )

    def detect_from_image(self, image):
        # Perform sliced prediction with SAHI
        results = get_sliced_prediction(
            image=image,
            detection_model=self.model,
            slice_height=256,
            slice_width=256,
            overlap_height_ratio=0.2,
            overlap_width_ratio=0.2,
            postprocess_type='NMS',
            postprocess_match_metric='IOU',
            postprocess_match_threshold=0.1,
            postprocess_class_agnostic=True,
        )

        # Retrieve COCO annotations
        coco_annotations = results.to_coco_annotations()
        return coco_annotations

    def draw_annotations(self, image, annotations):
        """Draw bounding boxes on the image based on COCO annotations using OpenCV."""
        # Define colors for each category in BGR (OpenCV uses BGR format)
        category_styles = {
            'Nicks': {'color': (255, 60, 60), 'thickness': 2},     # Nicks (Red)
            'Dents': {'color': (255, 148, 156), 'thickness': 2},   # Dents (Light Red)
            'Scratches': {'color': (255, 116, 28), 'thickness': 2}, # Scratches (Orange)
            'Pittings': {'color': (255, 180, 28), 'thickness': 2}   # Pittings (Yellow)
        }

        for annotation in annotations:
            bbox = annotation['bbox']  # Extract the bounding box
            category_name = annotation['category_name']
            score = annotation.get('score', 0)  # Extract confidence score, default to 0 if not present

            # Get color and thickness for the current category
            style = category_styles.get(category_name, {'color': (255, 0, 0), 'thickness': 2})  # Default to red if not found
            
            # Draw rectangle
            cv2.rectangle(image, 
                        (int(bbox[0]), int(bbox[1])), 
                        (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])), 
                        style['color'], 
                        style['thickness'])
            
            # Prepare text with category and confidence score
            text = f"{category_name}: {score:.2f}"  # Format the score to two decimal places
            
            # Put category text with score
            cv2.putText(image, 
                        text, 
                        (int(bbox[0]), int(bbox[1] - 10)),  # Position above the rectangle
                        cv2.FONT_HERSHEY_SIMPLEX, 
                        0.5, 
                        style['color'], 
                        2)

        return image


    def generate_individual_graphs(self, annotations):
        """Generate individual area distribution histograms for each defect category."""
        # Dictionary to hold areas for each category
        category_areas = {
            'Nicks': [],
            'Dents': [],
            'Scratches': [],
            'Pittings': []
        }

        # Populate the category_areas dictionary
        for annotation in annotations:
            category_name = annotation['category_name']
            area = annotation['bbox'][2] * annotation['bbox'][3]  # Width * Height
            if category_name in category_areas:
                category_areas[category_name].append(area)

        # Create individual area distribution histograms for each ctegory
        individual_graphs = {}
        for category in ['Nicks', 'Dents', 'Scratches', 'Pittings']:
            areas = category_areas[category]
            fig = go.Figure()
            if areas:  # Check if there are areas to plot
                # Create a histogram and store the frequencies
                histogram_data = go.Histogram(
                    x=areas,
                    name=category,
                    marker_color=self.get_color(category),  # Use associated color
                    opacity=1,
                    nbinsx=10  # Number of bins
                )
                fig.add_trace(histogram_data)

                # Get the frequencies and edges for swapping axes
                frequencies = histogram_data.y
                edges = histogram_data.x

                # Create a bar chart to swap the axes
                fig = go.Figure(data=[
                    go.Bar(
                        x=frequencies,  # Frequencies on x-axis
                        y=edges,  # Edges on y-axis
                        name=category,
                        marker_color=self.get_color(category),  # Use associated color
                        opacity=1
                    )
                ])
            else:  # Generate an empty graph if no areas
                fig.add_trace(go.Bar(x=[], y=[], name=category))  # Empty graph

            # Update layout with swapped axes
            fig.update_layout(
                title=f'Area Distribution of {category}',
                xaxis_title='Frequency',  # Frequency on x-axis
                yaxis_title='Area',       # Area on y-axis
                showlegend=True
            )
            individual_graphs[category] = fig

        return individual_graphs['Nicks'], individual_graphs['Dents'], individual_graphs['Scratches'], individual_graphs['Pittings']



    

    def generate_frequency_graph(self, annotations):
        """Generate a frequency bar chart for defect categories."""
        category_counts = {
            'Nicks': 0,
            'Dents': 0,
            'Scratches': 0,
            'Pittings': 0
        }

        # Count occurrences of each defect category
        for annotation in annotations:
            category_name = annotation['category_name']
            if category_name in category_counts:
                category_counts[category_name] += 1

        # Create a bar chart for frequency
        freq_chart = go.Figure()
        category_colors = {
            'Nicks': 'rgba(255, 60, 60, 0.7)',      # Red
            'Dents': 'rgba(255, 148, 156, 0.7)',    # Light Red
            'Scratches': 'rgba(255, 116, 28, 0.7)',  # Orange
            'Pittings': 'rgba(255, 180, 28, 0.7)'    # Yellow
        }

        for category, count in category_counts.items():
            freq_chart.add_trace(go.Bar(
                x=[category],
                y=[count],
                name=category,
                marker_color=category_colors.get(category, 'blue')  # Default to blue if not found
            ))

        freq_chart.update_layout(
            title='Frequency of Defects',
            xaxis_title='Defect Category',
            yaxis_title='Count',
            barmode='group'
        )

        return freq_chart
    

    def get_color(self, category_name):
        """Get the color associated with a category name."""
        category_styles = {
            'Nicks': 'rgba(255, 60, 60, 0.7)',       # Red
            'Dents': 'rgba(255, 148, 156, 0.7)',     # Light Red
            'Scratches': 'rgba(255, 116, 28, 0.7)',  # Orange
            'Pittings': 'rgba(255, 180, 28, 0.7)'    # Yellow
        }
        return category_styles.get(category_name, (255, 0, 0))  # Default to red if not found



detection = Detection()

def upload_image(image):
    """Process the uploaded image (if needed) and display it.""" 
    return image

@spaces.GPU
def apply_detection(image):
    """Run object detection on the uploaded image and return the annotated image.""" 
    # Convert image from PIL to NumPy array
    img = np.array(image)

    # Perform detection and get COCO annotations
    annotations = detection.detect_from_image(img)

    # Draw the annotations on the image using OpenCV
    annotated_image = detection.draw_annotations(img, annotations)

    # Convert back to PIL format for Gradio output
    return Image.fromarray(annotated_image), annotations

def generate_graphs_btn(annotations):
    """Generate interactive graphs from the annotations.""" 
    # Generate individual graphs for each defect category
    individual_graphs = detection.generate_individual_graphs(annotations)
    frequency_graph = detection.generate_frequency_graph(annotations)
    return individual_graphs

css = """

@import url('https://fonts.googleapis.com/css2?family=Ubuntu:wght@300;400;500;700&family=Montserrat:wght@700&family=Open+Sans&family=Poppins:wght@300;400;500;600;700;800&display=swap');

*{
    margin: 0;
    padding: 0;
    box-sizing: border-box;
    font-family: 'Ubuntu',sans-serif;
}

a{
    text-decoration: none;
    color: #000;
}


body{
    background-color: #fff;
}



header{
    padding: 0 80px;
    height: calc(100vh-80px);
    display: flex;
    align-items: center;
    justify-content: space-between;
}

header .left h1 {
    font-size: 80px;
    display: flex;
    justify-content: center;
    margin-top: 17rem;
    
}

header .left span{
    font-size: 80px;
    color: #083484;
    display: flex;
    justify-content: center;

}
header .left .second-line{
    font-size: 80px;
    color: #083484;
    display: flex;
    justify-content: center;
    font-weight: 400;

}

header .left p{
    margin-top: 35px;
    font-stretch: ultra-condensed;
    color: #777;
    display: flex;
    justify-content: center;
    text-align: center;
    margin-bottom: 10px;
}

header .left a{
    display: flex;
    align-items: center;
    background: #083484;
    width: 150px;
    padding: 8px;
    border-radius: 60px;
}

header .left a i{
    background-color: #fff;
    font-size: 24px;
    border-radius: 50%;
    padding: 8px;
}

header .left a span{
    color: #fff;
    margin-left: 22px;
}

.container {
    padding:30px;
    text-align: center;
    overflow: auto;
    margin-top: 500px;
}

.sub-header {
    font-size: 4em;
    text-align: center;
    color: #083484;
    font-family: 'Montserrat',sans-serif;
}




"""



js_func = """
function refresh() {
    const url = new URL(window.location);

    if (url.searchParams.get('__theme') !== 'light') {
        url.searchParams.set('__theme', 'light');
        window.location.href = url.href;
    }
}

"""



# Gradio interface components
with gr.Blocks(css = css,js=js_func) as demo:

    gr.HTML("""             

    <header>
        <div class="left">
            <h1><span>OIS</span><br></h1>
            <span class="second-line">AI Detection Model</span>
            <p>
                The OIS AI Detection Model enhances manufacturing by using the powerful YOLOv11 algorithm on 
                a Raspberry Pi for real-time, on-device defect detection. It automates quality control,
                reduces human error, and minimizes downtime. With a user-friendly web interface, 
                the model enables offline swift defect identification, seamless integration into
                production, and improving both efficiency and product quality.
            </p>
        </div>

    </header>
            
    <section class="container">
    
        <p class="sub-header">OFFLINE DETECTION</p>
            
    </section>   
                
    """)
    

    with gr.Row():
        # Image Upload and Display in two columns
        with gr.Column():
            gr.Markdown("### Input")
            upload_image_component = gr.Image(type="pil", label="Select Image")

        with gr.Column():
            gr.Markdown("### Output")
            output_image_component = gr.Image(type="pil", label="Annotated Image")
            apply_detection_btn = gr.Button("Apply Detection")
            output_annotations = gr.State()  # Store annotations
            apply_detection_btn.click(apply_detection, inputs=upload_image_component, outputs=[output_image_component, output_annotations])


    # Row for the graphs
    with gr.Row():
        # Individual graphs for each defect category
        nicks_graph_component = gr.Plot(label="Nicks Area Distribution")
        dents_graph_component = gr.Plot(label="Dents Area Distribution")
        scratches_graph_component = gr.Plot(label="Scratches Area Distribution")
        pittings_graph_component = gr.Plot(label="Pittings Area Distribution")

    # Button to generate graphs
    with gr.Row():
        graph_btn = gr.Button("Generate Area Distribution Graphs")
        graph_btn.click(generate_graphs_btn, inputs=output_annotations, outputs=[
            nicks_graph_component, dents_graph_component, 
            scratches_graph_component, pittings_graph_component
        ])

    # Row for frequency graph
    with gr.Row():
        frequency_graph_component = gr.Plot(label="Defect Frequency Distribution")  # Frequency Graph

    # Row for frequency graph btn
    with gr.Row():
        freq_graph_btn = gr.Button("Generate Frequency Graph")
        freq_graph_btn.click(detection.generate_frequency_graph, 
                              inputs=output_annotations, 
                              outputs=frequency_graph_component)

# Launch the Gradio interface
demo.launch(share=True)