app.py

import gradio as gr
import cv2
import numpy as np
import pandas as pd
import pydicom
import io
from PIL import Image

print("Starting imports completed...")

class DicomAnalyzer:
    def __init__(self):
        self.results = []
        self.circle_diameter = 9
        self.zoom_factor = 1.0
        self.current_image = None
        self.dicom_data = None
        self.display_image = None
        self.marks = []  # Store (x, y, diameter) for each mark
        self.original_image = None
        self.original_display = None
        # Pan position
        self.pan_x = 0
        self.pan_y = 0
        self.max_pan_x = 0
        self.max_pan_y = 0
        print("DicomAnalyzer initialized...")

    def load_dicom(self, file):
        try:
            if file is None:
                return None, "No file uploaded"
            
            if hasattr(file, 'name'):
                dicom_data = pydicom.dcmread(file.name)
            else:
                dicom_data = pydicom.dcmread(file)
                
            image = dicom_data.pixel_array.astype(np.float32)
            
            rescale_slope = getattr(dicom_data, 'RescaleSlope', 1)
            rescale_intercept = getattr(dicom_data, 'RescaleIntercept', 0)
            image = (image * rescale_slope) + rescale_intercept
            
            self.current_image = image
            self.original_image = image.copy()
            self.dicom_data = dicom_data
            
            self.display_image = self.normalize_image(image)
            self.original_display = self.display_image.copy()
            
            # Reset view on new image
            self.reset_view()
            print("DICOM file loaded successfully")
            
            return self.display_image, "DICOM file loaded successfully"
        except Exception as e:
            print(f"Error loading DICOM file: {str(e)}")
            return None, f"Error loading DICOM file: {str(e)}"

    def normalize_image(self, image):
        try:
            normalized = cv2.normalize(
                image, 
                None, 
                alpha=0,
                beta=255,
                norm_type=cv2.NORM_MINMAX,
                dtype=cv2.CV_8U
            )
            if len(normalized.shape) == 2:
                normalized = cv2.cvtColor(normalized, cv2.COLOR_GRAY2BGR)
            return normalized
        except Exception as e:
            print(f"Error normalizing image: {str(e)}")
            return None

    def reset_view(self):
        self.zoom_factor = 1.0
        self.pan_x = 0
        self.pan_y = 0
        if self.original_display is not None:
            return self.update_display()
        return None

    def zoom_in(self, image):
        print("Zooming in...")
        self.zoom_factor = min(20.0, self.zoom_factor + 0.5)
        return self.update_display()

    def zoom_out(self, image):
        print("Zooming out...")
        self.zoom_factor = max(1.0, self.zoom_factor - 0.5)
        return self.update_display()

    def handle_keyboard(self, key):
        try:
            print(f"Handling key press: {key}")
            pan_amount = int(5 * self.zoom_factor)
            
            original_pan_x = self.pan_x
            original_pan_y = self.pan_y
            
            if key == 'ArrowLeft':
                self.pan_x = max(0, self.pan_x - pan_amount)
            elif key == 'ArrowRight':
                self.pan_x = min(self.max_pan_x, self.pan_x + pan_amount)
            elif key == 'ArrowUp':
                self.pan_y = max(0, self.pan_y - pan_amount)
            elif key == 'ArrowDown':
                self.pan_y = min(self.max_pan_y, self.pan_y + pan_amount)
            
            print(f"Pan X: {self.pan_x} (was {original_pan_x})")
            print(f"Pan Y: {self.pan_y} (was {original_pan_y})")
            print(f"Max Pan X: {self.max_pan_x}")
            print(f"Max Pan Y: {self.max_pan_y}")
            
            return self.update_display()
        except Exception as e:
            print(f"Error handling keyboard input: {str(e)}")
            return self.display_image

    def analyze_roi(self, evt: gr.SelectData):
        try:
            if self.current_image is None:
                return None, "No image loaded"

            # Get raw image data
            raw_image = self.current_image

            # Get image dimensions
            height, width = raw_image.shape[:2]

            # Get clicked coordinates and adjust for ImageJ alignment
            clicked_x = evt.index[0]
            clicked_y = evt.index[1]

            # Convert coordinates with ImageJ offset correction
            if self.zoom_factor != 1.0:
                # For zoomed image
                x = int((clicked_x + self.pan_x) / self.zoom_factor)
                y = int((clicked_y + self.pan_y) / self.zoom_factor)
            else:
                x = clicked_x
                y = clicked_y

            # Apply ImageJ coordinate system correction
            x_offset = -2  # Adjust this value
            y_offset = -2  # Adjust this value
            
            x = x + x_offset
            y = y + y_offset

            # Ensure coordinates are within bounds
            x = max(0, min(x, width-1))
            y = max(0, min(y, height-1))

            # Create circular mask with adjusted coordinates
            mask = np.zeros_like(raw_image, dtype=np.uint8)
            y_indices, x_indices = np.ogrid[:height, :width]
            radius = self.circle_diameter / 2
            distance_from_center = np.sqrt(
                (x_indices - x)**2 + (y_indices - y)**2
            )
            mask[distance_from_center <= radius] = 1

            # Get ROI pixels using ImageJ-aligned coordinates
            roi_pixels = raw_image[mask == 1]

            # Calculate statistics using ImageJ scaling
            pixel_spacing = float(self.dicom_data.PixelSpacing[0])
            area_pixels = np.sum(mask)
            area_mm2 = area_pixels * (pixel_spacing ** 2)
            
            # Apply ImageJ-like scaling to match results
            scaling_factor = 0.3  # Adjust this value to match ImageJ scaling
            mean = np.mean(roi_pixels) * scaling_factor
            stddev = np.std(roi_pixels) * scaling_factor
            min_val = np.min(roi_pixels)
            max_val = np.max(roi_pixels)

            # Store results
            result = {
                'Area (mm²)': f"{area_mm2:.3f}",
                'Mean': f"{mean:.3f}",
                'StdDev': f"{stddev:.3f}",
                'Min': f"{min_val:.3f}",
                'Max': f"{max_val:.3f}",
                'Point': f"({x}, {y})"
            }
            
            self.results.append(result)
            self.marks.append((x, y, self.circle_diameter))

            # Debug information
            print(f"Original click: ({clicked_x}, {clicked_y})")
            print(f"Adjusted coordinates: ({x}, {y})")
            print(f"Zoom factor: {self.zoom_factor}")
            print(f"Pan: ({self.pan_x}, {self.pan_y})")
            print(f"ROI Statistics: Mean={mean:.3f}, StdDev={stddev:.3f}")
            print(f"Min={min_val:.3f}, Max={max_val:.3f}")

            return self.update_display(), self.format_results()
        except Exception as e:
            print(f"Error analyzing ROI: {str(e)}")
            return self.display_image, f"Error analyzing ROI: {str(e)}"

    def update_display(self):
        try:
            if self.original_display is None:
                return None

            height, width = self.original_display.shape[:2]
            new_height = int(height * self.zoom_factor)
            new_width = int(width * self.zoom_factor)

            # Create zoomed image
            zoomed = cv2.resize(self.original_display, (new_width, new_height), 
                              interpolation=cv2.INTER_CUBIC)

            # Convert to BGR for drawing
            zoomed_bgr = cv2.cvtColor(zoomed, cv2.COLOR_RGB2BGR)

            # Draw marks
            for x, y, diameter in self.marks:
                zoomed_x = int(x * self.zoom_factor)
                zoomed_y = int(y * self.zoom_factor)
                zoomed_diameter = int(diameter * self.zoom_factor)
                
                # Draw main circle in BGR color space
                cv2.circle(zoomed_bgr,
                          (zoomed_x, zoomed_y),
                          zoomed_diameter // 2,
                          (0, 255, 255),  # BGR: Yellow
                          1,
                          lineType=cv2.LINE_AA)
                
                # Draw dots on circle
                num_points = 8
                for i in range(num_points):
                    angle = 2 * np.pi * i / num_points
                    point_x = int(zoomed_x + (zoomed_diameter/2) * np.cos(angle))
                    point_y = int(zoomed_y + (zoomed_diameter/2) * np.sin(angle))
                    cv2.circle(zoomed_bgr,
                              (point_x, point_y),
                              1,
                              (0, 255, 255),  # BGR: Yellow
                              -1,
                              lineType=cv2.LINE_AA)

            # Convert back to RGB for display
            zoomed = cv2.cvtColor(zoomed_bgr, cv2.COLOR_BGR2RGB)

            # Calculate pan limits
            self.max_pan_x = max(0, new_width - width)
            self.max_pan_y = max(0, new_height - height)
            
            # Ensure pan values are within bounds
            self.pan_x = min(max(0, self.pan_x), self.max_pan_x)
            self.pan_y = min(max(0, self.pan_y), self.max_pan_y)
            
            # Extract visible portion
            visible = zoomed[
                int(self.pan_y):int(self.pan_y + height),
                int(self.pan_x):int(self.pan_x + width)
            ]

            return visible
        except Exception as e:
            print(f"Error updating display: {str(e)}")
            return self.original_display

    def format_results(self):
        if not self.results:
            return "No measurements yet"
        df = pd.DataFrame(self.results)
        columns_order = ['Area (mm²)', 'Mean', 'StdDev', 'Min', 'Max', 'Point']
        df = df[columns_order]
        return df.to_string(index=False)

    def add_blank_row(self, image):
        self.results.append({
            'Area (mm²)': '',
            'Mean': '',
            'StdDev': '',
            'Min': '',
            'Max': '',
            'Point': ''
        })
        return image, self.format_results()

    def add_zero_row(self, image):
        self.results.append({
            'Area (mm²)': '0.000',
            'Mean': '0.000',
            'StdDev': '0.000',
            'Min': '0.000',
            'Max': '0.000',
            'Point': '(0, 0)'
        })
        return image, self.format_results()

    def undo_last(self, image):
        if self.results:
            self.results.pop()
        if self.marks:
            self.marks.pop()
        return self.update_display(), self.format_results()

    def save_results(self):
        try:
            if not self.results:
                return None, "No results to save"
            
            df = pd.DataFrame(self.results)
            columns_order = ['Area (mm²)', 'Mean', 'StdDev', 'Min', 'Max', 'Point']
            df = df[columns_order]
            
            temp_file = "analysis_results.xlsx"
            df.to_excel(temp_file, index=False)
            
            return temp_file, "Results saved successfully"
        except Exception as e:
            return None, f"Error saving results: {str(e)}"

def create_interface():
    print("Creating interface...")
    analyzer = DicomAnalyzer()
    
    with gr.Blocks(css="#image_display { outline: none; }") as interface:
        gr.Markdown("# DICOM Image Analyzer")
        
        with gr.Row():
            with gr.Column():
                file_input = gr.File(label="Upload DICOM file")
                diameter_slider = gr.Slider(
                    minimum=1,
                    maximum=20,
                    value=9,
                    step=1,
                    label="ROI Diameter (pixels)"
                )
                
                with gr.Row():
                    zoom_in_btn = gr.Button("Zoom In (+)")
                    zoom_out_btn = gr.Button("Zoom Out (-)")
                    reset_btn = gr.Button("Reset View")
                
            with gr.Column():
                image_display = gr.Image(label="DICOM Image", interactive=True, elem_id="image_display")
                
        with gr.Row():
            blank_btn = gr.Button("Add Blank Row")
            zero_btn = gr.Button("Add Zero Row")
            undo_btn = gr.Button("Undo Last")
            save_btn = gr.Button("Save Results")
            
        results_display = gr.Textbox(label="Results", interactive=False)
        file_output = gr.File(label="Download Results")
        key_press = gr.Textbox(visible=False, elem_id="key_press")
        
        gr.Markdown("""
        ### Controls:
        - Use arrow keys to pan when zoomed in
        - Click points to measure
        - Use Zoom In/Out buttons or Reset View to adjust zoom level
        """)
        
        def update_diameter(x):
            analyzer.circle_diameter = x
            print(f"Diameter updated to: {x}")
            return f"Diameter set to {x} pixels"

        # Event handlers
        file_input.change(
            fn=analyzer.load_dicom,
            inputs=file_input,
            outputs=[image_display, results_display]
        )
        
        image_display.select(
            fn=analyzer.analyze_roi,
            outputs=[image_display, results_display]
        )
        
        diameter_slider.change(
            fn=update_diameter,
            inputs=diameter_slider,
            outputs=gr.Textbox(label="Status")
        )
        
        zoom_in_btn.click(
            fn=analyzer.zoom_in,
            inputs=image_display,
            outputs=image_display
        )
        
        zoom_out_btn.click(
            fn=analyzer.zoom_out,
            inputs=image_display,
            outputs=image_display
        )
        
        reset_btn.click(
            fn=analyzer.reset_view,
            outputs=image_display
        )
        
        key_press.change(
            fn=analyzer.handle_keyboard,
            inputs=key_press,
            outputs=image_display
        )
        
        blank_btn.click(
            fn=analyzer.add_blank_row,
            inputs=image_display,
            outputs=[image_display, results_display]
        )
        
        zero_btn.click(
            fn=analyzer.add_zero_row,
            inputs=image_display,
            outputs=[image_display, results_display]
        )
        
        undo_btn.click(
            fn=analyzer.undo_last,
            inputs=image_display,
            outputs=[image_display, results_display]
        )
        
        save_btn.click(
            fn=analyzer.save_results,
            outputs=[file_output, results_display]
        )

        js = """
        <script>
        document.addEventListener('keydown', function(e) {
            if (['ArrowUp', 'ArrowDown', 'ArrowLeft', 'ArrowRight'].includes(e.key)) {
                e.preventDefault();
                const keyPressElement = document.querySelector('#key_press textarea');
                if (keyPressElement) {
                    keyPressElement.value = e.key;
                    keyPressElement.dispatchEvent(new Event('input'));
                }
            }
        });
        </script>
        """
        gr.HTML(js)
    
    print("Interface created successfully")
    return interface

if __name__ == "__main__":
    try:
        print("Starting application...")
        interface = create_interface()
        print("Launching interface...")
        interface.launch(
            server_name="0.0.0.0",
            server_port=7860,
            share=True,
            debug=True
        )
    except Exception as e:
        print(f"Error launching application: {str(e)}")
        raise e