object and colour detection stream fix

App/pages/general_page.py

@@ -8,65 +8,67 @@
 from Algorithms.Objects.colour_detection import ColourRecognizer
 from Algorithms.Objects.object_detection import ObjectRecognizer
 from utils import load_stylesheet,close_event
+from PyQt5.QtCore import QTimer, Qt
 
 class GeneralDemoPage(QWidget):
-    """page used for general display of streams"""
+    """Page used for general display of streams"""
     def __init__(self, algorithm: str):
         super().__init__()
 
         self.algorithm = algorithm
         if self.algorithm == "colour":
             self.setWindowTitle("Colour Detection")
             self.recognizer = ColourRecognizer('Datasets/colour_ranges.csv')
-            instructions="Hold up one of the following colours:"
-            description=("The program detects colours in an image using predefined colour ranges. It"
-                            "first creates a mask to highlight areas that match each colour. Then, it"
-                            "finds the boundaries of these colour regions and draws outlines around them,"
-                            "making it easy to identify and count different colours in the video.")
+            instructions = "Hold up one of the following colours:"
+            description = ("The program detects colours in an image using predefined colour ranges. It "
+                           "first creates a mask to highlight areas that match each colour. Then, it "
+                           "finds the boundaries of these colour regions and draws outlines around them, "
+                           "making it easy to identify and count different colours in the video.")
 
         elif self.algorithm == "object":
             self.setWindowTitle("Object Detection")
-            instructions="Hold up an object to detect and classify"
+            instructions = "Hold up an object to detect and classify"
             self.recognizer = ObjectRecognizer('yolo11n.pt')
             description = ("YOLO is a fast object detection system that processes an image by dividing "
-              "it into a grid. Each grid cell predicts whether an object is present and, "
-              "if so, draws a box around it. The image passes through multiple layers of a "
-              "neural network, where early layers detect simple features like edges, while "
-              "deeper layers recognize complex patterns and object shapes. Each grid cell "
-              "outputs bounding boxes, confidence scores (how sure the model is about "
-              "the detection), and class labels. This allows YOLO to quickly "
-              "and accurately detect multiple objects in an image.")
-
-
-        # Main layout (horizontal layout for video right panel)
+                           "it into a grid. Each grid cell predicts whether an object is present and, "
+                           "if so, draws a box around it. The image passes through multiple layers of a "
+                           "neural network, where early layers detect simple features like edges, while "
+                           "deeper layers recognize complex patterns and object shapes. Each grid cell "
+                           "outputs bounding boxes, confidence scores (how sure the model is about "
+                           "the detection), and class labels. This allows YOLO to quickly "
+                           "and accurately detect multiple objects in an image.")
+
+        # Main layout
         main_layout = QHBoxLayout(self)
 
         # Video Stream Section (Left)
         self.video_label = QLabel()
         self.video_label.setObjectName("video_label")
+        self.video_label.setAlignment(Qt.AlignCenter)
         main_layout.addWidget(self.video_label, stretch=3)
 
         # Right Panel (Output and Description)
         right_panel = QVBoxLayout()
 
-        # Output Section (Top of Right Panel)
+        # Output Section
         self.output_label = QLabel(instructions)
         self.output_label.setObjectName("output_label")
+        self.output_label.setAlignment(Qt.AlignCenter)
         right_panel.addWidget(self.output_label, stretch=1)
 
-        # Description Section (Bottom of Right Panel)
+        # Description Section
         self.description_label = QLabel(description)
         self.description_label.setObjectName("description_label")
         self.description_label.setWordWrap(True)
+        self.description_label.setAlignment(Qt.AlignLeft | Qt.AlignTop)
         right_panel.addWidget(self.description_label, stretch=2)
 
-        # Add the right panel to the main layout
+        # Add right panel to the main layout
         main_layout.addLayout(right_panel, stretch=2)
-
-        # Set the main layout
         self.setLayout(main_layout)
 
-        load_stylesheet(self,'App\styles\general.qss')
+        # Load stylesheet
+        load_stylesheet(self, 'App/styles/general.qss')
 
         # Start video capture
         self.cap = cv2.VideoCapture(0)
@@ -75,38 +77,44 @@ def __init__(self, algorithm: str):
             sys.exit()
 
         self.failed_frames = 0  # Counter for consecutive failed frames
-        self.timer = self.startTimer(20)
 
-    def timer_event(self):
-        """handle timer event for capturing and processing frames"""
+        # Start Timer to Refresh Video Feed
+        self.timer = QTimer(self)
+        self.timer.timeout.connect(self.update_frame)
+        self.timer.start(20)  # 20ms interval
+
+    def update_frame(self):
+        """Capture and process frames for display"""
         ret, frame = self.cap.read()
         if not ret:
             self.failed_frames += 1
             print(f"Failed to grab frame {self.failed_frames} times.")
 
-            # Close the stream if the threshold is reached
             if self.failed_frames > 10:
                 print("Too many failed frames. Stopping the stream.")
-                self.close()  # Trigger close event
+                self.close()
             return
 
-        # Reset failed frames counter on successful frame capture
-        self.failed_frames = 0
+        self.failed_frames = 0  # Reset counter on successful capture
 
+        # Apply the selected algorithm
         if self.algorithm == "colour":
             processed_frame = self.recognizer.detect_and_draw(frame)
         elif self.algorithm == "object":
             processed_frame = self.recognizer.detect_and_draw(frame)
+        else:
+            processed_frame = frame  # Fallback to original frame
 
         # Convert processed frame to QImage
-        height, width = processed_frame.shape
-        bytes_per_line = 3 * width
+        height, width, channels = processed_frame.shape  # Ensure all dimensions are captured
+        bytes_per_line = channels * width
         qimg = QImage(processed_frame.data, width, height, bytes_per_line, QImage.Format_BGR888)
 
-        # Update the video frame (using QLabel's setPixmap)
-        pixmap = QPixmap.fromImage(qimg)
-        self.video_label.setPixmap(pixmap)
+        # Update QLabel with new frame
+        self.video_label.setPixmap(QPixmap.fromImage(qimg))
 
-    def call_close_event(self, event):
-        """handle close event to release resources"""
-        close_event(event,self)
+    def closeEvent(self, event):
+        """Handle window close event to release resources"""
+        self.cap.release()
+        self.timer.stop()
+        close_event(event, self)

utils.py

@@ -3,7 +3,10 @@ def load_stylesheet(window,styleSheetPath):
         with open(styleSheetPath, "r") as file:
             window.setStyleSheet(file.read())
 
-def close_event(event,recogniser):
-    """handle close event to release resources"""
-    recogniser.release()
+def close_event(event, widget):
+    """Handle cleanup when closing a page"""
+    if hasattr(widget, "cap"):  # Ensure widget has a video capture instance
+        widget.cap.release()
+    if hasattr(widget, "timer"):  # Stop the timer if it exists
+        widget.timer.stop()
     event.accept()