zo1_core.py

#!/usr/bin/env python3
"""

ZO-1 Network Analysis Tool - Core Functions

Core analysis logic and classes for ZO-1 network quantification

"""

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, Circle
import cv2
from PIL import Image
import pandas as pd
from io import StringIO, BytesIO
import base64
import traceback
import os
from pathlib import Path

# Cellpose imports
from cellpose import models
from skimage.segmentation import find_boundaries

# PyTorch for GPU detection
import torch

# AI validation imports
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture

# Global variables for state management
global_state = {
    'img_gray': None,
    'masks': None,
    'membrane_mask': None,
    'quantifier': None,
    'analysis_geometry': 'Circles (RIS - recommended)',
    'image_basename': None
}

# Set matplotlib backend for non-interactive use
plt.switch_backend('Agg')

def _sanitize_hits_xy(hits_like):
    """Return a clean N×2 int32 array of hit coordinates from arbitrary nested inputs."""
    try:
        arr = np.asarray(hits_like, dtype=object)
        # Fast path: already a proper 2D numeric array
        if isinstance(arr, np.ndarray) and arr.ndim == 2 and arr.shape[1] == 2 and arr.dtype != object:
            return arr.astype(np.int32, copy=False)
        # Build list of valid coordinate pairs
        cleaned = []
        for item in arr:
            try:
                if isinstance(item, (list, tuple, np.ndarray)) and len(item) == 2:
                    y_val, x_val = item
                    if np.isscalar(y_val) and np.isscalar(x_val):
                        cleaned.append([int(y_val), int(x_val)])
            except Exception:
                continue
        return np.asarray(cleaned, dtype=np.int32).reshape(-1, 2) if cleaned else np.zeros((0, 2), dtype=np.int32)
    except Exception:
        return np.zeros((0, 2), dtype=np.int32)

def create_visualization(img_gray, masks, quantifier, analysis_geometry, show_contours=False, show_rectangles=True, show_cross_sections=True):
    """Create visualization with overlays"""
    if img_gray is None or masks is None:
        return None
    
    fig, ax = plt.subplots(1, 1, figsize=(10, 8))
    
    # Main image with overlays - ensure same dimensions
    if img_gray.shape != masks.shape:
        img_gray_resized = cv2.resize(img_gray, (masks.shape[1], masks.shape[0]), interpolation=cv2.INTER_LINEAR)
    else:
        img_gray_resized = img_gray
        
    ax.imshow(img_gray_resized, cmap='gray')
    
    if analysis_geometry == "Circles (RIS - recommended)":
        ax.set_title('ZO-1 Network with RIS Analysis Overlays', fontsize=14, fontweight='bold')
    else:
        ax.set_title('ZO-1 Network with TiJOR Analysis Overlays', fontsize=14, fontweight='bold')
    
    ax.axis('off')
    
    # Draw contours if requested
    if show_contours and global_state['membrane_mask'] is not None:
        validated_membrane_mask = global_state['membrane_mask']
        # Ensure same dimensions
        if validated_membrane_mask.shape != img_gray_resized.shape:
            validated_membrane_mask = cv2.resize(validated_membrane_mask, (img_gray_resized.shape[1], img_gray_resized.shape[0]), interpolation=cv2.INTER_NEAREST)
        
        # Contours with 2-pixel thickness
        # Use yellow contours for both RIS and TiJOR
        ax.contour(validated_membrane_mask, [0.5], colors='yellow', linewidths=2, alpha=0.7)
    
    # Draw analysis overlays based on geometry type
    if analysis_geometry == "Circles (RIS - recommended)" and quantifier and hasattr(quantifier, 'results'):
        # Draw concentric circles and scatter hits for RIS analysis
        if show_rectangles and 'radii' in quantifier.results:
            center_x = img_gray.shape[1] / 2
            center_y = img_gray.shape[0] / 2
            radii = quantifier.results['radii']
            colors = plt.cm.Blues(np.linspace(0.3, 1, len(radii)))
            
            for i, r in enumerate(radii):
                circle = plt.Circle((center_x, center_y), r, 
                                  linewidth=2, edgecolor=colors[i], 
                                  facecolor='none', linestyle='--', alpha=0.7)
                ax.add_patch(circle)
        
        # Plot crossing points (hits) if requested
        if show_cross_sections and 'hits_xy' in quantifier.results:
            hits = _sanitize_hits_xy(quantifier.results['hits_xy'])
            if hits.size > 0 and hits.ndim == 2 and hits.shape[1] == 2:
                ax.scatter(hits[:, 1], hits[:, 0],  # Note: y, x order for matplotlib
                          c='red', s=30, alpha=1.0, edgecolors='darkred', linewidth=2,
                          label=f'Crossings ({hits.shape[0]})')
                ax.legend(loc='upper right', fontsize=10)
    
    elif quantifier and hasattr(quantifier, 'results') and 'rectangle_sizes' in quantifier.results:
        # Draw rectangles and cross-sections for TiJOR analysis
        center_x = img_gray.shape[1] / 2
        center_y = img_gray.shape[0] / 2
        colors = plt.cm.Reds(np.linspace(0.3, 1, len(quantifier.results['rectangle_sizes'])))
        
        for i, size in enumerate(quantifier.results['rectangle_sizes']):
            # Force scalar float to avoid inhomogeneous shape issues
            try:
                size_val = float(np.asarray(size).reshape(-1)[0])
            except Exception:
                continue
            half_side = size_val / 2.0
            
            if show_rectangles:
                rect = Rectangle(
                    (float(center_x - half_side), float(center_y - half_side)),
                    float(size_val), float(size_val),
                    linewidth=2,
                    edgecolor=colors[i],
                    facecolor='none',
                    linestyle='--',
                    alpha=0.7
                )
                ax.add_patch(rect)
        
        # Plot TiJOR cross-section points
        if show_cross_sections and 'hits_xy' in quantifier.results:
            hits = _sanitize_hits_xy(quantifier.results['hits_xy'])
            if hits.size > 0 and hits.ndim == 2 and hits.shape[1] == 2:
                ax.scatter(hits[:, 1], hits[:, 0],  # Note: y, x order for matplotlib
                          c='red', s=25, alpha=1.0, edgecolors='darkred', linewidth=1.5,
                          label=f'Cross-sections ({hits.shape[0]})', zorder=10)
                ax.legend(loc='upper right', fontsize=10)
    
    # Convert plot to numpy array for Gradio
    buf = BytesIO()
    fig.savefig(buf, format='png', dpi=150, bbox_inches='tight')
    buf.seek(0)
    
    # Convert to PIL Image then to numpy array
    pil_image = Image.open(buf)
    numpy_array = np.array(pil_image)
    
    plt.close(fig)
    buf.close()
    
    return numpy_array

def create_visualization_with_masks(img_gray, masks):
    """Create visualization showing the segmentation masks"""
    if img_gray is None or masks is None:
        return None
    
    fig, ax = plt.subplots(1, 1, figsize=(10, 8))
    
    # Show original image
    ax.imshow(img_gray, cmap='gray')
    
    # Overlay masks with different colors
    if masks.max() > 0:
        # Create colored mask overlay
        colored_masks = np.zeros((*masks.shape, 3), dtype=np.uint8)
        for i in range(1, int(masks.max()) + 1):
            mask = (masks == i)
            color = np.random.randint(0, 255, 3)
            colored_masks[mask] = color
        
        # Overlay with transparency
        ax.imshow(colored_masks, alpha=0.3)
    
    ax.set_title('ZO-1 Segmentation Results', fontsize=14, fontweight='bold')
    ax.axis('off')
    
    # Convert to numpy array for Gradio
    buf = BytesIO()
    fig.savefig(buf, format='png', dpi=150, bbox_inches='tight')
    buf.seek(0)
    
    # Convert to PIL Image then to numpy array
    pil_image = Image.open(buf)
    numpy_array = np.array(pil_image)
    
    plt.close(fig)
    buf.close()
    
    return numpy_array

def validate_contours_with_ai(contours, image, method="K-means clustering", dilation_pixels=4):
    """Validate Cellpose contours using AI-powered methods"""
    try:
        print(f"[validate_contours_with_ai] method={method}, image.shape={image.shape}, image.dtype={image.dtype}")
    except Exception:
        pass
    if method == "K-means clustering":
        pixels = image.reshape(-1, 1).astype(np.float32)
        kmeans = KMeans(n_clusters=2, n_init=10, max_iter=300, random_state=42)
        labels = kmeans.fit_predict(pixels)
        
        cluster_centers = kmeans.cluster_centers_.flatten()
        foreground_cluster = np.argmax(cluster_centers)
        ai_mask = (labels == foreground_cluster).reshape(image.shape).astype(np.uint8) * 255
        
    elif method == "Gaussian Mixture Model (GMM)":
        pixels = image.reshape(-1, 1).astype(np.float32)
        gmm = GaussianMixture(n_components=2, n_init=10, max_iter=300, random_state=42)
        labels = gmm.fit_predict(pixels)
        
        cluster_centers = gmm.means_.flatten()
        foreground_cluster = np.argmax(cluster_centers)
        ai_mask = (labels == foreground_cluster).reshape(image.shape).astype(np.uint8) * 255
        
    else:  # Otsu
        otsu_threshold, _ = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        ai_mask = (image > otsu_threshold).astype(np.uint8) * 255
    
    # Dilate mask for tolerance
    kernel = np.ones((dilation_pixels, dilation_pixels), np.uint8)
    dilated_mask = cv2.dilate(ai_mask, kernel, iterations=1)
    
    # Combine with contours
    validated_mask = np.logical_and(contours > 0, dilated_mask > 0).astype(np.uint8)
    
    return validated_mask

def run_segmentation_only(img_gray, diam, scale, enable_contour_validation, validation_method):
    """Run only the AI-powered segmentation step"""
    if img_gray is None:
        return None, None, "No image provided"
    
    try:
        print(f"[run_segmentation_only] img_gray.shape={img_gray.shape}, dtype={img_gray.dtype}, diam={diam}, scale={scale}, enable_validation={enable_contour_validation}, method={validation_method}")
        # Initialize Cellpose model
        # Use GPU if available, fallback to CPU
        device = 'cuda' if torch.cuda.is_available() else 'cpu'
        cp_model = models.CellposeModel(gpu=(device=='cuda'), model_type='cyto')
        print(f"🔧 Using device: {device}")
        if device == 'cuda':
            print(f"🚀 GPU: {torch.cuda.get_device_name(0)}")
        else:
            print("⚠️  Running on CPU - this will be slower")
        
        # Downsample if requested
        h, w = img_gray.shape
        if scale < 1.0:
            small = cv2.resize(img_gray, (int(w*scale), int(h*scale)), interpolation=cv2.INTER_AREA)
            diam_small = max(1, int(diam*scale))
        else:
            small = img_gray
            diam_small = diam
        
        # Run segmentation
        masks_small, flows, styles = cp_model.eval(
            small,
            diameter=diam_small,
            channels=[0, 0],
            flow_threshold=0.4,
            batch_size=4,
            resample=True,
            augment=True
        )
        
        # Upsample masks if needed
        if scale < 1.0:
            masks = cv2.resize(masks_small.astype(np.uint8), (w, h), interpolation=cv2.INTER_NEAREST)
        else:
            masks = masks_small
        
        # Create membrane mask for network analysis
        contours = find_boundaries(masks, mode='inner')
        
        # Apply AI-powered validation
        if enable_contour_validation:
            membrane_mask = validate_contours_with_ai(contours, img_gray, validation_method, 4)
        else:
            membrane_mask = contours.astype(np.uint8)
        
        # Update global state
        global_state['masks'] = masks
        global_state['membrane_mask'] = membrane_mask
        
        guidance = (
            f"Segmentation complete! Found {int(masks.max())} cells.\n"
            "If not happy with segmentation, adjust the Cell Diameter estimate and rerun.\n"
            "If satisfied, proceed to the Analysis tab."
        )
        return masks, membrane_mask, guidance
        
    except Exception as e:
        return None, None, f"Segmentation failed: {str(e)}"

class ZO1TiJORQuantifier:
    """TiJOR quantifier for rectangular analysis"""
    
    def __init__(self, initial_size=10, max_size=100, num_steps=10, min_distance=5):
        self.initial_size = initial_size
        self.max_size = max_size
        self.num_steps = num_steps
        self.min_distance = min_distance
        self.results = {}
    
    def analyze(self, membrane_mask):
        """Analyze membrane network using expanding rectangles"""
        if membrane_mask is None:
            return False
        
        # Generate rectangle sizes as percentage of image size
        img_size = min(membrane_mask.shape)  # Use smaller dimension
        min_size = img_size * self.initial_size / 100
        max_size = img_size * self.max_size / 100
        sizes = np.linspace(min_size, max_size, self.num_steps).astype(np.float32)
        self.results['rectangle_sizes'] = sizes
        try:
            print(f"[TiJOR.analyze] img_size={img_size:.2f}, sizes.shape={sizes.shape}, sizes[:3]={sizes[:3] if sizes.size>=3 else sizes}")
        except Exception:
            pass
        
        # Calculate cross-sections for each size
        tijor_values = []
        cross_section_counts = []
        filtered_cross_section_counts = []
        all_hits_xy = []  # Store all intersection points
        
        center_y, center_x = np.array(membrane_mask.shape) / 2
        
        for size in sizes:
            half_side = size / 2
            
            # Define rectangle boundaries
            y1 = max(0, int(center_y - half_side))
            y2 = min(membrane_mask.shape[0], int(center_y + half_side))
            x1 = max(0, int(center_x - half_side))
            x2 = min(membrane_mask.shape[1], int(center_x + half_side))
            
            # Create rectangle boundary mask (2-pixel thickness like RIS) without reduce()
            y, x = np.ogrid[:membrane_mask.shape[0], :membrane_mask.shape[1]]
            tolerance = 2
            # Outer rectangle (expanded by tolerance)
            within_outer = (
                (x >= (x1 - tolerance)) & (x <= (x2 + tolerance)) &
                (y >= (y1 - tolerance)) & (y <= (y2 + tolerance))
            )
            # Inner rectangle (shrunk by tolerance)
            within_inner = (
                (x > (x1 + tolerance)) & (x < (x2 - tolerance)) &
                (y > (y1 + tolerance)) & (y < (y2 - tolerance))
            )
            # Boundary is the ring between outer and inner
            boundary_mask = within_outer & (~within_inner)
            
            # Find intersections: membrane pixels near rectangle boundary (boolean-safe)
            intersection_region = (membrane_mask > 0) & boundary_mask
            
            # Apply minimum separation filtering (like RIS)
            total_cross_sections = 0
            if np.any(intersection_region):
                y_coords, x_coords = np.where(intersection_region)
                
                # Filter points with minimum separation
                filtered_points = []
                for y_coord, x_coord in zip(y_coords, x_coords):
                    # Check if this point is far enough from existing points
                    is_unique = True
                    for existing_y, existing_x in filtered_points:
                        distance = np.sqrt((y_coord - existing_y)**2 + (x_coord - existing_x)**2)
                        if distance < self.min_distance:
                            is_unique = False
                            break
                    
                    if is_unique:
                        yi = int(y_coord)
                        xi = int(x_coord)
                        filtered_points.append([yi, xi])
                        all_hits_xy.append([yi, xi])
                
                total_cross_sections = len(filtered_points)
            
            cross_section_counts.append(total_cross_sections)
            filtered_count = total_cross_sections
            
            filtered_cross_section_counts.append(filtered_count)
            
            # Calculate TiJOR (crossings per pixel length; rectangle perimeter)
            perimeter = 4.0 * float(size)
            tijor = (filtered_count / perimeter) if perimeter > 0 else 0.0
            tijor_values.append(float(tijor))
        
        self.results['tijor_values'] = np.asarray(tijor_values, dtype=np.float32)
        self.results['cross_section_counts'] = np.asarray(cross_section_counts, dtype=np.int32)
        self.results['filtered_cross_section_counts'] = np.asarray(filtered_cross_section_counts, dtype=np.int32)
        try:
            print(f"[TiJOR.analyze] crossings={self.results['cross_section_counts']}, tijor_values={self.results['tijor_values']}")
        except Exception:
            pass
        
        # Safely create hits_xy array
        if len(all_hits_xy) > 0:
            try:
                self.results['hits_xy'] = np.asarray(all_hits_xy, dtype=np.int32).reshape(-1, 2)
            except Exception:
                valid_pairs = []
                for pair in all_hits_xy:
                    if isinstance(pair, (list, tuple, np.ndarray)) and len(pair) == 2:
                        y_val, x_val = pair
                        if np.isscalar(y_val) and np.isscalar(x_val):
                            valid_pairs.append([int(y_val), int(x_val)])
                self.results['hits_xy'] = np.asarray(valid_pairs, dtype=np.int32).reshape(-1, 2) if len(valid_pairs) > 0 else np.zeros((0, 2), dtype=np.int32)
        else:
            self.results['hits_xy'] = np.zeros((0, 2), dtype=np.int32)
        
        return True
    
    def _filter_by_distance(self, coords, min_distance):
        """Filter coordinates by minimum distance"""
        if len(coords) <= 1:
            return coords
        
        filtered = [coords[0]]
        for coord in coords[1:]:
            distances = [np.linalg.norm(coord - f) for f in filtered]
            if min(distances) >= min_distance:
                filtered.append(coord)
        
        return np.array(filtered)
    
    def get_summary_stats(self):
        """Get summary statistics"""
        if not self.results:
            return {}
        
        tijor_values = self.results['tijor_values']
        filtered_counts = self.results['filtered_cross_section_counts']
        
        return {
            'mean_tijor': np.mean(tijor_values),
            'std_tijor': np.std(tijor_values),
            'max_tijor': np.max(tijor_values),
            'min_tijor': np.min(tijor_values),
            'total_cross_sections': np.sum(filtered_counts),
            'mean_cross_sections_per_rectangle': np.mean(filtered_counts)
        }

class ZO1RISQuantifier:
    """Circular (Sholl-style) crossings-per-circumference for ZO-1 networks"""
    
    def __init__(self, packing_factor=1.5, min_radius_percent=10, max_radius_percent=80, num_circles=15, min_separation=5):
        self.kappa = float(packing_factor)
        self.min_radius_percent = float(min_radius_percent)
        self.max_radius_percent = float(max_radius_percent)
        self.num_circles = int(num_circles)
        self.min_separation = int(min_separation)
        self.results = {}
    
    def analyze(self, membrane_mask, d_eff_pixels, scale_factor=1.0):
        """Analyze membrane network using concentric circles with proper intersection detection"""
        if membrane_mask is None:
            return False
        
        # Calculate reference density
        d_ref = self.kappa / float(d_eff_pixels)
        
        # Generate radii for concentric circles with percentage-based spacing
        img_size = min(membrane_mask.shape) / 2  # Half of smaller dimension
        min_radius = img_size * self.min_radius_percent / 100
        max_radius = img_size * self.max_radius_percent / 100
        radii = np.linspace(min_radius, max_radius, self.num_circles)
        self.results['radii'] = radii
        
        # Calculate crossings for each radius using proper intersection detection
        crossings = []
        hits_xy = []
        center_y, center_x = np.array(membrane_mask.shape) / 2
        
        for radius in radii:
            # Create circular mask
            y, x = np.ogrid[:membrane_mask.shape[0], :membrane_mask.shape[1]]
            circle_mask = (x - center_x)**2 + (y - center_y)**2 <= radius**2
            
            # Find membrane pixels near the circle (within 2 pixels tolerance)
            circle_boundary = np.logical_and(
                (x - center_x)**2 + (y - center_y)**2 <= (radius + 2)**2,
                (x - center_x)**2 + (y - center_y)**2 >= (radius - 2)**2
            )
            
            # Count intersections: membrane pixels near circle boundary (boolean-safe)
            intersection_region = (membrane_mask > 0) & circle_boundary
            
            # Apply minimum separation filtering to avoid counting clustered pixels
            if np.sum(intersection_region > 0) > 0:
                y_coords, x_coords = np.where(intersection_region > 0)
                
                # Filter points with minimum separation
                filtered_points = []
                for y_coord, x_coord in zip(y_coords, x_coords):
                    # Check if this point is far enough from existing points
                    is_unique = True
                    for existing_y, existing_x in filtered_points:
                        distance = np.sqrt((y_coord - existing_y)**2 + (x_coord - existing_x)**2)
                        if distance < self.min_separation:
                            is_unique = False
                            break
                    
                    if is_unique:
                        filtered_points.append([y_coord, x_coord])
                
                crossing_count = len(filtered_points)
                crossings.append(crossing_count)
                
                # Store filtered hit coordinates for visualization
                hits_xy.extend(filtered_points)
            else:
                crossings.append(0)
        
        self.results['crossings'] = np.array(crossings)
        
        # Safely create hits_xy array
        if len(hits_xy) > 0:
            try:
                self.results['hits_xy'] = np.asarray(hits_xy, dtype=np.int32).reshape(-1, 2)
            except Exception:
                # Fallback: filter only valid pairs
                valid_pairs = [(int(y), int(x)) for y, x in hits_xy if isinstance(y, (int, np.integer)) and isinstance(x, (int, np.integer))]
                self.results['hits_xy'] = np.asarray(valid_pairs, dtype=np.int32).reshape(-1, 2) if valid_pairs else np.zeros((0, 2), dtype=np.int32)
        else:
            self.results['hits_xy'] = np.zeros((0, 2), dtype=np.int32)
        
        # Calculate RIS metrics as crossings per pixel length, averaged over all circles
        radii = self.results['radii']
        circle_lengths = 2.0 * np.pi * np.maximum(radii, 1e-6)
        crossings_array = self.results['crossings'].astype(np.float32)
        densities = crossings_array / circle_lengths  # crossings per pixel length
        self.results['crossings_per_length'] = densities
        
        if len(densities) > 0:
            d_mean = float(np.mean(densities))
            d_peak = float(np.max(densities))
            ris = (d_mean / d_ref) if d_ref > 0 else 0.0
            ris_peak = (d_peak / d_ref) if d_ref > 0 else 0.0
            self.results['RIS'] = float(ris)
            self.results['RIS_peak'] = float(ris_peak)
            self.results['d_mean'] = d_mean
            self.results['d_peak'] = d_peak
            self.results['d_ref'] = float(d_ref)
        
        return True
    
    def get_summary_stats(self):
        """Get summary statistics for RIS analysis"""
        if not self.results:
            return {}
        
        crossings = self.results.get('crossings', np.array([]))
        densities = self.results.get('crossings_per_length', np.array([]))
        return {
            'RIS': self.results.get('RIS', np.nan),
            'RIS_peak': self.results.get('RIS_peak', np.nan),
            'd_mean': self.results.get('d_mean', np.nan),  # mean crossings per pixel length
            'd_peak': self.results.get('d_peak', np.nan),  # peak crossings per pixel length
            'd_ref': self.results.get('d_ref', np.nan),
            'total_crossings': float(np.sum(crossings)) if crossings.size else 0.0,
            'mean_crossings_per_px': float(np.mean(densities)) if densities.size else np.nan,
            'packing_factor': self.kappa
        }

def run_analysis(analysis_geometry, initial_size, max_size, num_steps, min_distance, packing_factor, min_radius_percent, max_radius_percent, num_circles, min_separation, show_contours=True, show_rectangles=True, show_cross_sections=True):
    """Run the selected analysis method"""
    if global_state['membrane_mask'] is None:
        return "No membrane mask available. Please run segmentation first.", None, None
    
    try:
        # Persist current analysis mode for downstream export/reporting
        global_state['analysis_geometry'] = analysis_geometry
        print(f"[run_analysis] mode={analysis_geometry}, init_size={initial_size}, max_size={max_size}, steps={num_steps}, min_dist={min_distance}, kappa={packing_factor}, minR%={min_radius_percent}, maxR%={max_radius_percent}, circles={num_circles}, min_sep={min_separation}")
        if analysis_geometry == "Circles (RIS - recommended)":
            # RIS analysis with user-configurable parameters
            quantifier = ZO1RISQuantifier(
                packing_factor=packing_factor,
                min_radius_percent=min_radius_percent,
                max_radius_percent=max_radius_percent,
                num_circles=num_circles,
                min_separation=min_separation
            )
            success = quantifier.analyze(global_state['membrane_mask'], 20, scale_factor=1.0)
            
            if success:
                global_state['quantifier'] = quantifier
                summary = quantifier.get_summary_stats()
                print(f"[run_analysis][RIS] summary keys={list(summary.keys())}")
                
                # Create results display
                ris = summary.get('RIS', np.nan)
                ris_peak = summary.get('RIS_peak', np.nan)
                mean_crossings = summary.get('d_mean', np.nan)
                total_crossings = summary.get('total_crossings', np.nan)
                d_ref = summary.get('d_ref', np.nan)
                results_text = f"""

🔵 **RIS Analysis Results**

RIS Score: {ris:.4f}

RIS Peak: {ris_peak:.4f}

Mean Crossings/px length: {mean_crossings:.4f}

Total Crossings: {total_crossings:.0f}

Reference Density (per px length): {d_ref:.4f}

Parameters: κ={packing_factor:.1f}, Min Radius={min_radius_percent}%, Max Radius={max_radius_percent}%, Circles={num_circles}, Min Sep={min_separation}px

                """
                
                # Create visualization
                viz = create_visualization(
                    global_state['img_gray'], 
                    global_state['masks'], 
                    quantifier, 
                    analysis_geometry,
                    show_contours=show_contours,
                    show_rectangles=show_rectangles,
                    show_cross_sections=show_cross_sections
                )
                
                return results_text, viz, summary
            else:
                return "RIS analysis failed.", None, None
                
        else:
            # TiJOR analysis
            quantifier = ZO1TiJORQuantifier(initial_size, max_size, num_steps, min_distance)
            success = quantifier.analyze(global_state['membrane_mask'])
            
            if success:
                global_state['quantifier'] = quantifier
                # Defensive normalization of results to avoid shape errors downstream
                try:
                    if 'hits_xy' in quantifier.results:
                        quantifier.results['hits_xy'] = _sanitize_hits_xy(quantifier.results['hits_xy'])
                    if 'rectangle_sizes' in quantifier.results:
                        sizes_arr = np.asarray(quantifier.results['rectangle_sizes']).reshape(-1)
                        quantifier.results['rectangle_sizes'] = sizes_arr.astype(np.float32, copy=False)
                except Exception:
                    quantifier.results['hits_xy'] = np.zeros((0, 2), dtype=np.int32)
                summary = quantifier.get_summary_stats()
                print(f"[run_analysis][TiJOR] sizes.shape={quantifier.results.get('rectangle_sizes', np.array([])).shape}, hits.shape={quantifier.results.get('hits_xy', np.zeros((0,2))).shape}, summary keys={list(summary.keys())}")
                
                # Create results display
                mean_tijor = float(summary.get('mean_tijor', np.nan))
                total_cross_sections = float(summary.get('total_cross_sections', np.nan))
                cells_detected = int(global_state['masks'].max()) if global_state['masks'] is not None else 0
                results_text = f"""

📊 **TiJOR Analysis Results**

Mean TiJOR: {mean_tijor:.4f}

Total Cross-sections: {total_cross_sections:.0f}

Cells Detected: {cells_detected}

                """
                
                # Create visualization
                viz = create_visualization(
                    global_state['img_gray'], 
                    global_state['masks'], 
                    quantifier, 
                    analysis_geometry,
                    show_contours=show_contours,
                    show_rectangles=show_rectangles,
                    show_cross_sections=show_cross_sections
                )
                
                return results_text, viz, summary
            else:
                return "TiJOR analysis failed.", None, None
                
    except Exception as e:
        tb = traceback.format_exc()
        print(f"[run_analysis][EXCEPTION] {e}\n{tb}")
        return f"Analysis failed: {str(e)}\n\nTraceback:\n{tb}", None, None

def process_image(image, cell_diameter, scale_factor, enable_validation, validation_method):
    """Process uploaded image and run segmentation"""
    if image is None:
        return "No image uploaded", None, None
    
    try:
        # Accept either numpy array or filepath; store basename for exports
        if isinstance(image, str):
            try:
                global_state['image_basename'] = Path(image).stem
            except Exception:
                global_state['image_basename'] = 'results'
            # Read with unchanged flag to preserve bit depth
            img = cv2.imread(image, cv2.IMREAD_UNCHANGED)
            if img is None:
                return "Failed to read image from path", None, None
            # Convert to grayscale
            if len(img.shape) == 3:
                img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            else:
                img_gray = img
        else:
            # Numpy array path
            global_state['image_basename'] = global_state.get('image_basename') or 'results'
            # Handle different image formats and data types (especially TIFF)
            if len(image.shape) == 3:
                # Convert to RGB first, then grayscale
                if image.shape[2] == 4:  # RGBA
                    image = image[:, :, :3]  # Remove alpha channel
                img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
            else:
                img_gray = image
        
        # Ensure image is 8-bit (TIFF files might be 16-bit or float)
        if img_gray.dtype != np.uint8:
            imin = float(img_gray.min())
            imax = float(img_gray.max())
            if imax > imin:
                # Normalize to 0-255 range
                img_gray = ((img_gray - imin) / (imax - imin) * 255).astype(np.uint8)
            else:
                # Flat image; return zeros to avoid NaNs
                img_gray = np.zeros_like(img_gray, dtype=np.uint8)
        
        global_state['img_gray'] = img_gray
        
        # Run segmentation
        masks, membrane_mask, message = run_segmentation_only(
            img_gray, cell_diameter, scale_factor, enable_validation, validation_method
        )
        
        if masks is not None:
            # Create initial visualization with masks
            viz = create_visualization_with_masks(img_gray, masks)
            return message, viz, masks
        else:
            return message, None, None
            
    except Exception as e:
        return f"Image processing failed: {str(e)}", None, None

def export_results(format_type):
    """Export analysis results; returns (text_content, file_path)."""
    if global_state['quantifier'] is None:
        return "No analysis results to export", None
    
    try:
        # Determine filename base and suffix
        base = global_state.get('image_basename') or 'results'
        suffix = '_RIS' if global_state.get('analysis_geometry') == "Circles (RIS - recommended)" else '_TiJOR'
        if format_type == "CSV":
            if global_state['analysis_geometry'] == "Circles (RIS - recommended)":
                # Export RIS results
                results_data = []
                if hasattr(global_state['quantifier'], 'results') and 'crossings' in global_state['quantifier'].results:
                    for i, crossing in enumerate(global_state['quantifier'].results['crossings']):
                        results_data.append({
                            'Circle': i+1,
                            'Crossings': int(crossing)
                        })
                
                df = pd.DataFrame(results_data)
                csv_data = df.to_csv(index=False)
                file_path = f"{base}{suffix}.csv"
                with open(file_path, 'w', newline='') as f:
                    f.write(csv_data)
                return csv_data, file_path
            else:
                # Export TiJOR results
                results_data = []
                if hasattr(global_state['quantifier'], 'results') and 'tijor_values' in global_state['quantifier'].results:
                    for i, (size, tijor) in enumerate(zip(
                        global_state['quantifier'].results['rectangle_sizes'],
                        global_state['quantifier'].results['tijor_values']
                    )):
                        results_data.append({
                            'Step': i+1,
                            'Size (px)': f'{size:.1f}',
                            'TiJOR': f'{tijor:.4f}'
                        })
                
                df = pd.DataFrame(results_data)
                csv_data = df.to_csv(index=False)
                file_path = f"{base}{suffix}.csv"
                with open(file_path, 'w', newline='') as f:
                    f.write(csv_data)
                return csv_data, file_path
        else:
            # Export as text report
            summary = global_state['quantifier'].get_summary_stats()
            
            if global_state['analysis_geometry'] == "Circles (RIS - recommended)":
                ris = summary.get('RIS', np.nan)
                ris_peak = summary.get('RIS_peak', np.nan)
                mean_crossings = summary.get('d_mean', np.nan)
                total_crossings = summary.get('total_crossings', np.nan)
                d_ref = summary.get('d_ref', np.nan)
                report = f"""ZO-1 RIS Network Analysis Report

{'='*50}

RIS Score: {ris:.4f}

RIS Peak: {ris_peak:.4f}

Mean Crossings/px length: {mean_crossings:.4f}

Total Crossings: {total_crossings:.0f}

Reference Density (per px length): {d_ref:.4f}

                """
            else:
                mean_tijor = summary.get('mean_tijor', np.nan)
                total_cross_sections = summary.get('total_cross_sections', np.nan)
                cells_detected = int(global_state['masks'].max()) if global_state['masks'] is not None else 0
                report = f"""ZO-1 TiJOR Network Analysis Report

{'='*50}

Mean TiJOR: {mean_tijor:.4f}

Total Cross-sections: {total_cross_sections:.0f}

Cells Detected: {cells_detected}

                """
            file_path = f"{base}{suffix}.txt"
            with open(file_path, 'w', encoding='utf-8') as f:
                f.write(report)
            return report, file_path
            
    except Exception as e:
        return f"Export failed: {str(e)}", None