Update app.py

app.py

@@ -1,102 +1,299 @@
+import gradio as gr
 import cv2
 import numpy as np
 import pandas as pd
 import pydicom
-import gradio as gr
+import io
 import os
+from PIL import Image
+import tempfile
 
-# Global variables to mimic the local functionality
-results = []
-image_display = None
-circle_diameter = 9
-zoom_factor = 1.0
-
-# Function to load DICOM files
-def load_dicom(file_path):
-    dicom_data = pydicom.dcmread(file_path)
-    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
-    pixel_spacing = dicom_data.PixelSpacing
-    return image, dicom_data, pixel_spacing
-
-# Function to analyze ROI
-def analyze_roi(image, pixel_spacing, x, y, circle_diameter):
-    mask = np.zeros_like(image, dtype=np.uint8)
-    y_indices, x_indices = np.ogrid[:image.shape[0], :image.shape[1]]
-    distance_from_center = np.sqrt((x_indices - x)**2 + (y_indices - y)**2)
-    mask[distance_from_center <= circle_diameter / 2] = 1
-    pixels = image[mask == 1]
-
-    # Calculate metrics
-    area_mm2 = np.sum(mask) * (float(pixel_spacing[0])**2)
-    mean = np.mean(pixels)
-    stddev = np.std(pixels)
-    min_val = np.min(pixels)
-    max_val = np.max(pixels)
-
-    return {
-        "Area (mm²)": f"{area_mm2:.3f}",
-        "Mean": f"{mean:.3f}",
-        "StdDev": f"{stddev:.3f}",
-        "Min": f"{min_val:.3f}",
-        "Max": f"{max_val:.3f}"
-    }
-
-# Gradio callback function to simulate the local analyzer
-def process_image(file, x, y, zoom, diameter):
-    global results, image_display, circle_diameter, zoom_factor
-
-    # Load the DICOM image
-    image, dicom_data, pixel_spacing = load_dicom(file.name)
-    image_display = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
-    
-    # Adjust zoom and diameter
-    zoom_factor = float(zoom)
-    circle_diameter = int(diameter)
-
-    # Analyze the clicked point
-    x_original = int(float(x) / zoom_factor)
-    y_original = int(float(y) / zoom_factor)
-    analysis_result = analyze_roi(image, pixel_spacing, x_original, y_original, circle_diameter)
-
-    # Append to results
-    results.append(analysis_result)
-    return analysis_result
-
-# Gradio app definition
-def reset_results():
-    global results
-    results = []
-    return "Results cleared!"
-
-with gr.Blocks() as app:
-    gr.Markdown("## DICOM Analyzer - Matches Local Features")
-    
-    with gr.Row():
-        file_input = gr.File(label="Upload DICOM File", type="filepath")
-        x_input = gr.Number(label="X Coordinate (px)", value=50)
-        y_input = gr.Number(label="Y Coordinate (px)", value=50)
-        zoom_input = gr.Number(label="Zoom Factor", value=1.0)
-        diameter_input = gr.Number(label="Circle Diameter (px)", value=9)
-    
-    output_text = gr.Textbox(label="Analysis Results")
-    clear_button = gr.Button("Clear Results")
-
-    with gr.Row():
-        analyze_button = gr.Button("Analyze")
-        reset_button = gr.Button("Reset")
-
-    analyze_button.click(
-        process_image,
-        inputs=[file_input, x_input, y_input, zoom_input, diameter_input],
-        outputs=output_text
-    )
+class DicomAnalyzer:
+    def __init__(self):
+        self.results = []
+        self.circle_diameter = 9
+        self.zoom_factor = 1.0
+        self.current_image1 = None
+        self.current_image2 = None
+        self.dicom_data1 = None
+        self.dicom_data2 = None
+        self.image_display1 = None
+        self.image_display2 = None
+        self.marks1 = []
+        self.marks2 = []
+
+    def load_dicom(self, file):
+        try:
+            if file is None:
+                return None, None, None
+                
+            dicom_data = pydicom.dcmread(file.name)
+            image = dicom_data.pixel_array.astype(np.float32)
+            
+            # Apply rescale slope and intercept
+            rescale_slope = getattr(dicom_data, 'RescaleSlope', 1)
+            rescale_intercept = getattr(dicom_data, 'RescaleIntercept', 0)
+            image = (image * rescale_slope) + rescale_intercept
+            
+            # Store original image for analysis
+            original_image = image.copy()
+            
+            # Normalize for display
+            image_display = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
+            
+            # Convert to BGR for visualization
+            if len(image_display.shape) == 2:
+                image_display = cv2.cvtColor(image_display, cv2.COLOR_GRAY2BGR)
+            
+            return original_image, image_display, dicom_data
+        except Exception as e:
+            print(f"Error loading DICOM file: {str(e)}")
+            return None, None, None
+
+    def analyze_point(self, image, dicom_data, x, y):
+        try:
+            # Create a circular mask
+            mask = np.zeros_like(image, dtype=np.uint8)
+            y_indices, x_indices = np.ogrid[:image.shape[0], :image.shape[1]]
+            distance_from_center = np.sqrt((x_indices - x)**2 + (y_indices - y)**2)
+            mask[distance_from_center <= self.circle_diameter / 2] = 1
+
+            # Extract pixel values within the circle
+            pixels = image[mask == 1]
+
+            # Calculate metrics
+            area_pixels = np.sum(mask)
+            pixel_spacing = float(dicom_data.PixelSpacing[0])
+            area_mm2 = area_pixels * (pixel_spacing**2)
+            mean = np.mean(pixels)
+            stddev = np.std(pixels)
+            min_val = np.min(pixels)
+            max_val = np.max(pixels)
+
+            return {
+                'Area (mm²)': f"{area_mm2:.3f}",
+                'Mean': f"{mean:.3f}",
+                'StdDev': f"{stddev:.3f}",
+                'Min': f"{min_val:.3f}",
+                'Max': f"{max_val:.3f}"
+            }
+        except Exception as e:
+            print(f"Error analyzing point: {str(e)}")
+            return None
+
+    def draw_circle(self, image, x, y, is_image1=True):
+        try:
+            image_copy = image.copy()
+            
+            # Draw all previous marks
+            marks = self.marks1 if is_image1 else self.marks2
+            for mark_x, mark_y in marks:
+                cv2.circle(image_copy, 
+                          (int(mark_x), int(mark_y)), 
+                          int(self.circle_diameter/2), 
+                          (0, 255, 255), 
+                          1, 
+                          lineType=cv2.LINE_AA)
+            
+            # Draw new mark
+            cv2.circle(image_copy, 
+                      (int(x), int(y)), 
+                      int(self.circle_diameter/2), 
+                      (0, 255, 255), 
+                      1, 
+                      lineType=cv2.LINE_AA)
+            
+            # Store new mark
+            if is_image1:
+                self.marks1.append((x, y))
+            else:
+                self.marks2.append((x, y))
+                
+            return image_copy
+        except Exception as e:
+            print(f"Error drawing circle: {str(e)}")
+            return image
+
+    def process_image1(self, file):
+        image, image_display, dicom_data = self.load_dicom(file)
+        self.current_image1 = image
+        self.image_display1 = image_display
+        self.dicom_data1 = dicom_data
+        return image_display
+
+    def process_image2(self, file):
+        image, image_display, dicom_data = self.load_dicom(file)
+        self.current_image2 = image
+        self.image_display2 = image_display
+        self.dicom_data2 = dicom_data
+        return image_display
+
+    def handle_click1(self, evt: gr.SelectData):
+        if self.current_image1 is None:
+            return self.image_display1, "Please load Image 1 first"
+        
+        try:
+            x, y = evt.index
+            marked_image = self.draw_circle(self.image_display1, x, y, is_image1=True)
+            self.image_display1 = marked_image
+            
+            results = self.analyze_point(self.current_image1, self.dicom_data1, x, y)
+            if results:
+                results['Image'] = "Image 1"
+                results['Point'] = f"({x}, {y})"
+                self.results.append(results)
+                
+            return self.image_display1, self.format_results()
+        except Exception as e:
+            print(f"Error in handle_click1: {str(e)}")
+            return self.image_display1, f"Error: {str(e)}"
+
+    def handle_click2(self, evt: gr.SelectData):
+        if self.current_image2 is None:
+            return self.image_display2, "Please load Image 2 first"
+        
+        try:
+            x, y = evt.index
+            marked_image = self.draw_circle(self.image_display2, x, y, is_image1=False)
+            self.image_display2 = marked_image
+            
+            results = self.analyze_point(self.current_image2, self.dicom_data2, x, y)
+            if results:
+                results['Image'] = "Image 2"
+                results['Point'] = f"({x}, {y})"
+                self.results.append(results)
+                
+            return self.image_display2, self.format_results()
+        except Exception as e:
+            print(f"Error in handle_click2: {str(e)}")
+            return self.image_display2, f"Error: {str(e)}"
+
+    def format_results(self):
+        if not self.results:
+            return "No results yet"
+        df = pd.DataFrame(self.results)
+        return df.to_string()
+
+    def clear_results(self):
+        self.results = []
+        self.marks1 = []
+        self.marks2 = []
+        if self.current_image1 is not None:
+            self.image_display1 = cv2.cvtColor(
+                cv2.normalize(self.current_image1, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8),
+                cv2.COLOR_GRAY2BGR
+            )
+        if self.current_image2 is not None:
+            self.image_display2 = cv2.cvtColor(
+                cv2.normalize(self.current_image2, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8),
+                cv2.COLOR_GRAY2BGR
+            )
+        return "Results cleared", self.image_display1, self.image_display2
+
+    def add_blank_row(self):
+        self.results.append({
+            'Image': '',
+            'Point': '',
+            'Area (mm²)': '',
+            'Mean': '',
+            'StdDev': '',
+            'Min': '',
+            'Max': ''
+        })
+        return self.format_results()
+
+    def update_circle_diameter(self, value):
+        self.circle_diameter = value
+        return f"Circle diameter set to {value}"
+
+    def save_results(self):
+        try:
+            if not self.results:
+                return None, "No results to save"
+                
+            df = pd.DataFrame(self.results)
+            
+            # Create temporary file
+            temp_dir = tempfile.gettempdir()
+            temp_file = os.path.join(temp_dir, "analysis_results.xlsx")
+            
+            # Save to Excel
+            df.to_excel(temp_file, index=False, engine='openpyxl')
+            
+            return temp_file, "Results saved successfully. Click to download."
+        except Exception as e:
+            print(f"Error saving results: {str(e)}")
+            return None, f"Error saving results: {str(e)}"
+
+def create_interface():
+    analyzer = DicomAnalyzer()
     
-    reset_button.click(
-        reset_results,
-        outputs=output_text
-    )
+    with gr.Blocks() as interface:
+        gr.Markdown("# CT DICOM Image Analyzer")
+        
+        with gr.Row():
+            with gr.Column():
+                file1 = gr.File(label="Upload first DICOM file")
+                image1 = gr.Image(label="Image 1", interactive=True, type="numpy")
+                file1.change(fn=analyzer.process_image1, inputs=file1, outputs=image1)
+            
+            with gr.Column():
+                file2 = gr.File(label="Upload second DICOM file")
+                image2 = gr.Image(label="Image 2", interactive=True, type="numpy")
+                file2.change(fn=analyzer.process_image2, inputs=file2, outputs=image2)
+        
+        with gr.Row():
+            circle_diameter = gr.Slider(
+                minimum=1, 
+                maximum=20, 
+                value=9, 
+                step=1, 
+                label="Circle Diameter"
+            )
+        
+        with gr.Row():
+            clear_btn = gr.Button("Clear Results")
+            blank_row_btn = gr.Button("Add Blank Row")
+            save_btn = gr.Button("Save Results")
+        
+        results = gr.Textbox(label="Results", interactive=False)
+        file_output = gr.File(label="Download Results")
+        status = gr.Textbox(label="Status")
+        
+        # Connect events
+        circle_diameter.change(
+            fn=analyzer.update_circle_diameter, 
+            inputs=circle_diameter, 
+            outputs=status
+        )
+        
+        image1.select(
+            fn=analyzer.handle_click1, 
+            outputs=[image1, results]
+        )
+        
+        image2.select(
+            fn=analyzer.handle_click2, 
+            outputs=[image2, results]
+        )
+        
+        clear_btn.click(
+            fn=analyzer.clear_results, 
+            outputs=[status, image1, image2]
+        )
+        
+        blank_row_btn.click(
+            fn=analyzer.add_blank_row, 
+            outputs=results
+        )
+        
+        save_btn.click(
+            fn=analyzer.save_results, 
+            outputs=[file_output, status]
+        )
+
+    return interface
 
-app.launch()
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch()