Delete visualization_helper.py

visualization_helper.py

@@ -1,147 +0,0 @@
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-from typing import Any, List, Dict, Tuple, Optional
-import io
-from PIL import Image
-
-class VisualizationHelper:
-    """Helper class for visualizing detection results"""
-    
-    @staticmethod
-    def visualize_detection(image: Any, result: Any, color_mapper: Optional[Any] = None,
-                            figsize: Tuple[int, int] = (12, 12),
-                            return_pil: bool = False) -> Optional[Image.Image]:
-        """
-        Visualize detection results on a single image
-        
-        Args:
-            image: Image path or numpy array
-            result: Detection result object
-            color_mapper: ColorMapper instance for consistent colors
-            figsize: Figure size
-            return_pil: If True, returns a PIL Image object
-            
-        Returns:
-            PIL Image if return_pil is True, otherwise displays the plot
-        """
-        if result is None:
-            print('No data for visualization')
-            return None
-            
-        # Read image if path is provided
-        if isinstance(image, str):
-            img = cv2.imread(image)
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        else:
-            img = image
-            if len(img.shape) == 3 and img.shape[2] == 3:
-                # Check if BGR format (OpenCV) and convert to RGB if needed
-                if isinstance(img, np.ndarray):
-                    # Assuming BGR format from OpenCV
-                    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        
-        # Create figure
-        fig, ax = plt.subplots(figsize=figsize)
-        ax.imshow(img)
-        
-        # Get bounding boxes, classes and confidences
-        boxes = result.boxes.xyxy.cpu().numpy()
-        classes = result.boxes.cls.cpu().numpy()
-        confs = result.boxes.conf.cpu().numpy()
-        
-        # Get class names
-        names = result.names
-        
-        # Create a default color mapper if none is provided
-        if color_mapper is None:
-            # For backward compatibility, fallback to a simple color function
-            from matplotlib import colormaps
-            cmap = colormaps['tab10']
-            def get_color(class_id):
-                return cmap(class_id % 10)
-        else:
-            # Use the provided color mapper
-            def get_color(class_id):
-                hex_color = color_mapper.get_color(class_id)
-                # Convert hex to RGB float values for matplotlib
-                hex_color = hex_color.lstrip('#')
-                return tuple(int(hex_color[i:i+2], 16) / 255 for i in (0, 2, 4)) + (1.0,)
-        
-        # Draw detection results
-        for box, cls, conf in zip(boxes, classes, confs):
-            x1, y1, x2, y2 = box
-            cls_id = int(cls)
-            cls_name = names[cls_id]
-            
-            # Get color for this class
-            box_color = get_color(cls_id)
-            
-            # Add text label with colored background
-            ax.text(x1, y1 - 5, f'{cls_name}: {conf:.2f}',
-                    color='white', fontsize=10,
-                    bbox=dict(facecolor=box_color[:3], alpha=0.7))
-            
-            # Add bounding box
-            ax.add_patch(plt.Rectangle((x1, y1), x2-x1, y2-y1, 
-                                    fill=False, edgecolor=box_color[:3], linewidth=2))
-        
-        ax.axis('off')
-        # ax.set_title('Detection Result')
-        plt.tight_layout()
-        
-        if return_pil:
-            # Convert plot to PIL Image
-            buf = io.BytesIO()
-            fig.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
-            buf.seek(0)
-            pil_img = Image.open(buf)
-            plt.close(fig)
-            return pil_img
-        else:
-            plt.show()
-            return None
-    
-    @staticmethod
-    def create_summary(result: Any) -> Dict:
-        """
-        Create a summary of detection results
-        
-        Args:
-            result: Detection result object
-            
-        Returns:
-            Dictionary with detection summary statistics
-        """
-        if result is None:
-            return {"error": "No detection result provided"}
-            
-        # Get classes and confidences
-        classes = result.boxes.cls.cpu().numpy().astype(int)
-        confidences = result.boxes.conf.cpu().numpy()
-        names = result.names
-        
-        # Count detections by class
-        class_counts = {}
-        for cls, conf in zip(classes, confidences):
-            cls_name = names[int(cls)]
-            if cls_name not in class_counts:
-                class_counts[cls_name] = {"count": 0, "confidences": []}
-                
-            class_counts[cls_name]["count"] += 1
-            class_counts[cls_name]["confidences"].append(float(conf))
-            
-        # Calculate average confidence for each class
-        for cls_name, stats in class_counts.items():
-            if stats["confidences"]:
-                stats["average_confidence"] = float(np.mean(stats["confidences"]))
-                stats.pop("confidences")  # Remove detailed confidences list to keep summary concise
-        
-        # Prepare summary
-        summary = {
-            "total_objects": len(classes),
-            "class_counts": class_counts,
-            "unique_classes": len(class_counts)
-        }
-        
-        return summary