cleanup and files added

qt_app_pyside1/ui/widgets/detection_overlay_widget.py

@@ -0,0 +1,171 @@
+"""
+Detection Overlay Widget - Displays object detection bounding boxes and labels
+"""
+
+from PySide6.QtWidgets import QWidget, QLabel, QVBoxLayout, QHBoxLayout, QFrame
+from PySide6.QtCore import Qt, Signal, QRect, QPoint, QTimer
+from PySide6.QtGui import QPainter, QPen, QBrush, QColor, QFont
+import json
+
+class DetectionOverlayWidget(QWidget):
+    """
+    Widget for displaying object detection overlays on video frames
+    
+    Features:
+    - Bounding box visualization
+    - Class labels and confidence scores
+    - Tracking IDs for multi-object tracking
+    - Color-coded detection classes
+    - Customizable overlay styles
+    """
+    
+    # Signals
+    detection_clicked = Signal(dict)  # Emitted when a detection is clicked
+    
+    def __init__(self, parent=None):
+        super().__init__(parent)
+        
+        self.detections = []
+        self.class_colors = {
+            'person': QColor(255, 0, 0),      # Red
+            'bicycle': QColor(0, 255, 0),     # Green
+            'car': QColor(0, 0, 255),         # Blue
+            'motorcycle': QColor(255, 255, 0), # Yellow
+            'bus': QColor(255, 0, 255),       # Magenta
+            'truck': QColor(0, 255, 255),     # Cyan
+            'traffic_light': QColor(255, 165, 0), # Orange
+            'stop_sign': QColor(128, 0, 128),  # Purple
+        }
+        
+        self.show_labels = True
+        self.show_confidence = True
+        self.show_tracking_id = True
+        self.overlay_opacity = 0.8
+        
+        # Make widget transparent for overlay
+        self.setAttribute(Qt.WA_TransparentForMouseEvents, False)
+        self.setStyleSheet("background-color: transparent;")
+    
+    def set_detections(self, detections):
+        """
+        Set detection results to display
+        
+        Args:
+            detections: List of detection dictionaries with format:
+                {
+                    'bbox': [x1, y1, x2, y2],
+                    'class': 'person',
+                    'confidence': 0.95,
+                    'track_id': 123 (optional)
+                }
+        """
+        self.detections = detections
+        self.update()  # Trigger repaint
+    
+    def set_overlay_options(self, show_labels=True, show_confidence=True, 
+                          show_tracking_id=True, opacity=0.8):
+        """Configure overlay display options"""
+        self.show_labels = show_labels
+        self.show_confidence = show_confidence
+        self.show_tracking_id = show_tracking_id
+        self.overlay_opacity = opacity
+        self.update()
+    
+    def add_class_color(self, class_name, color):
+        """Add or update color for a detection class"""
+        self.class_colors[class_name] = color
+    
+    def paintEvent(self, event):
+        """Paint detection overlays"""
+        if not self.detections:
+            return
+        
+        painter = QPainter(self)
+        painter.setRenderHint(QPainter.Antialiasing)
+        
+        # Set up font for labels
+        font = QFont("Arial", 10, QFont.Bold)
+        painter.setFont(font)
+        
+        for detection in self.detections:
+            self._draw_detection(painter, detection)
+    
+    def _draw_detection(self, painter, detection):
+        """Draw a single detection"""
+        # Extract detection info
+        bbox = detection.get('bbox', [0, 0, 100, 100])
+        class_name = detection.get('class', 'unknown')
+        confidence = detection.get('confidence', 0.0)
+        track_id = detection.get('track_id', None)
+        
+        x1, y1, x2, y2 = bbox
+        
+        # Get color for this class
+        color = self.class_colors.get(class_name, QColor(128, 128, 128))
+        
+        # Set up pen for bounding box
+        pen = QPen(color, 3)
+        painter.setPen(pen)
+        
+        # Draw bounding box
+        rect = QRect(int(x1), int(y1), int(x2-x1), int(y2-y1))
+        painter.drawRect(rect)
+        
+        # Prepare label text
+        label_parts = []
+        if self.show_labels:
+            label_parts.append(class_name)
+        if self.show_confidence:
+            label_parts.append(f"{confidence:.2f}")
+        if self.show_tracking_id and track_id is not None:
+            label_parts.append(f"ID:{track_id}")
+        
+        if not label_parts:
+            return
+        
+        label_text = " | ".join(label_parts)
+        
+        # Calculate label background
+        metrics = painter.fontMetrics()
+        label_width = metrics.horizontalAdvance(label_text) + 10
+        label_height = metrics.height() + 6
+        
+        # Draw label background
+        label_rect = QRect(int(x1), int(y1-label_height), label_width, label_height)
+        painter.fillRect(label_rect, color)
+        
+        # Draw label text
+        painter.setPen(QPen(QColor(255, 255, 255), 1))
+        text_rect = QRect(int(x1+5), int(y1-label_height+3), label_width-10, label_height-6)
+        painter.drawText(text_rect, Qt.AlignLeft | Qt.AlignVCenter, label_text)
+    
+    def mousePressEvent(self, event):
+        """Handle mouse clicks on detections"""
+        if event.button() == Qt.LeftButton:
+            click_pos = event.pos()
+            
+            # Check if click is within any detection bbox
+            for detection in self.detections:
+                bbox = detection.get('bbox', [0, 0, 100, 100])
+                x1, y1, x2, y2 = bbox
+                
+                if x1 <= click_pos.x() <= x2 and y1 <= click_pos.y() <= y2:
+                    self.detection_clicked.emit(detection)
+                    break
+    
+    def clear_detections(self):
+        """Clear all detections"""
+        self.detections = []
+        self.update()
+    
+    def get_detection_count(self):
+        """Get number of current detections"""
+        return len(self.detections)
+    
+    def get_class_counts(self):
+        """Get count of detections by class"""
+        counts = {}
+        for detection in self.detections:
+            class_name = detection.get('class', 'unknown')
+            counts[class_name] = counts.get(class_name, 0) + 1
+        return counts