Update app.py

app.py

@@ -1,38 +1,31 @@
-# Maximize CPU usage
-import multiprocessing
-import cv2
-
-# Get the number of CPU cores
-cpu_cores = multiprocessing.cpu_count()
-
-# Set OpenCV to use all available cores
-cv2.setNumThreads(cpu_cores)
-
-# Print the number of threads being used (optional)
-print(f"OpenCV using {cv2.getNumThreads()} threads out of {cpu_cores} available cores")
-
-##############
 import cv2
 import gradio as gr
 import numpy as np
 from PIL import Image, ImageDraw
 from ultralytics import YOLO
-from ultralytics.utils.plotting import Annotator, colors
 import logging
-import math
+import threading
+import queue
+import time
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Global variables to store line coordinates and line equation
+# Global variables for line coordinates and line equation
 start_point = None
 end_point = None
-line_params = None  # Stores (slope, intercept) of the line
+line_params = None  # Stores (slope, intercept, start_point, end_point)
 
 # Low-resolution for inference
 LOW_RES = (320, 180)
 
+# Frame queue for processed frames
+frame_queue = queue.Queue(maxsize=30)  # Adjust queue size based on memory constraints
+
+# Thread control flag
+processing_active = True
+
 def extract_first_frame(stream_url):
     """
     Extracts the first available frame from the IP camera stream and returns it as a PIL image.
@@ -63,46 +56,27 @@ def update_line(image, evt: gr.SelectData):
     """
     global start_point, end_point, line_params
 
-    # If it's the first click, set the start point and show it on the image
     if start_point is None:
         start_point = (evt.index[0], evt.index[1])
-
-        # Draw the start point on the image
         draw = ImageDraw.Draw(image)
-        draw.ellipse(
-            (start_point[0] - 5, start_point[1] - 5, start_point[0] + 5, start_point[1] + 5),
-            fill="blue", outline="blue"
-        )
-
+        draw.ellipse((start_point[0] - 5, start_point[1] - 5, start_point[0] + 5, start_point[1] + 5), fill="blue", outline="blue")
         return image, f"Line Coordinates:\nStart: {start_point}, End: None"
 
-    # If it's the second click, set the end point and draw the line
     end_point = (evt.index[0], evt.index[1])
-
-    # Calculate the slope (m) and intercept (b) of the line: y = mx + b
     if start_point[0] != end_point[0]:  # Avoid division by zero
         slope = (end_point[1] - start_point[1]) / (end_point[0] - start_point[0])
         intercept = start_point[1] - slope * start_point[0]
-        line_params = (slope, intercept, start_point, end_point)  # Store slope, intercept, and points
+        line_params = (slope, intercept, start_point, end_point)
     else:
-        # Vertical line (special case)
         line_params = (float('inf'), start_point[0], start_point, end_point)
 
-    # Draw the line and end point on the image
     draw = ImageDraw.Draw(image)
     draw.line([start_point, end_point], fill="red", width=2)
-    draw.ellipse(
-        (end_point[0] - 5, end_point[1] - 5, end_point[0] + 5, end_point[1] + 5),
-        fill="green", outline="green"
-    )
+    draw.ellipse((end_point[0] - 5, end_point[1] - 5, end_point[0] + 5, end_point[1] + 5), fill="green", outline="green")
 
-    # Return the updated image and line info
     line_info = f"Line Coordinates:\nStart: {start_point}, End: {end_point}\nLine Equation: y = {line_params[0]:.2f}x + {line_params[1]:.2f}"
-
-    # Reset the points for the next interaction
     start_point = None
     end_point = None
-
     return image, line_info
 
 def reset_line():
@@ -115,182 +89,96 @@ def reset_line():
     line_params = None
     return None, "Line reset. Click to draw a new line."
 
-def intersect(A, B, C, D):
-    """
-    Determines if two line segments AB and CD intersect.
-    """
-    def ccw(A, B, C):
-        return (C[1] - A[1]) * (B[0] - A[0]) - (B[1] - A[1]) * (C[0] - A[0])
-
-    def on_segment(A, B, C):
-        if min(A[0], B[0]) <= C[0] <= max(A[0], B[0]) and min(A[1], B[1]) <= C[1] <= max(A[1], B[1]):
-            return True
-        return False
-
-    # Check if the line segments intersect
-    ccw1 = ccw(A, B, C)
-    ccw2 = ccw(A, B, D)
-    ccw3 = ccw(C, D, A)
-    ccw4 = ccw(C, D, B)
-
-    if ((ccw1 * ccw2 < 0) and (ccw3 * ccw4 < 0)):
-        return True
-    elif ccw1 == 0 and on_segment(A, B, C):
-        return True
-    elif ccw2 == 0 and on_segment(A, B, D):
-        return True
-    elif ccw3 == 0 and on_segment(C, D, A):
-        return True
-    elif ccw4 == 0 and on_segment(C, D, B):
-        return True
-    else:
-        return False
-
 def is_object_crossing_line(box, line_params):
     """
     Determines if an object's bounding box is fully intersected by the user-drawn line.
     """
     _, _, line_start, line_end = line_params
-
-    # Get the bounding box coordinates
     x1, y1, x2, y2 = box
-
-    # Define the four edges of the bounding box
-    box_edges = [
-        ((x1, y1), (x2, y1)),  # Top edge
-        ((x2, y1), (x2, y2)),  # Right edge
-        ((x2, y2), (x1, y2)),  # Bottom edge
-        ((x1, y2), (x1, y1))   # Left edge
-    ]
-
-    # Count the number of intersections between the line and the bounding box edges
+    box_edges = [((x1, y1), (x2, y1)), ((x2, y1), (x2, y2)), ((x2, y2), (x1, y2)), ((x1, y2), (x1, y1))]
     intersection_count = 0
     for edge_start, edge_end in box_edges:
         if intersect(line_start, line_end, edge_start, edge_end):
             intersection_count += 1
-
-    # Only count the object if the line intersects the bounding box at least twice
     return intersection_count >= 2
 
-def draw_angled_line(image, line_params, color=(0, 255, 0), thickness=2):
-    """
-    Draws the user-defined line on the frame.
-    """
-    _, _, start_point, end_point = line_params
-    cv2.line(image, start_point, end_point, color, thickness)
-
-def detect_and_draw(frame):
-    """
-    Processes the frame in low resolution and scales the results back to high resolution.
-    """
-    # Create low-res copy
-    low_res_frame = cv2.resize(frame, LOW_RES)
-    
-    # Perform detection on the low-res frame
-    results = model(low_res_frame, verbose=False)
-
-    # Calculate scaling factors for bounding boxes
-    scale_x = frame.shape[1] / LOW_RES[0]
-    scale_y = frame.shape[0] / LOW_RES[1]
-
-    # Draw bounding boxes on the high-res frame
-    for detection in results[0].boxes.data:
-        x1, y1, x2, y2, conf, cls = detection
-        # Scale bounding box coordinates to high-res
-        x1, y1, x2, y2 = int(x1 * scale_x), int(y1 * scale_y), int(x2 * scale_x), int(y2 * scale_y)
-        label = f"{results[0].names[int(cls)]} {conf:.2f}"
-        # Draw the bounding box and label on the high-res frame
-        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
-        cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
-
-    return frame
-
-def process_video(confidence_threshold=0.5, selected_classes=None, stream_url=None):
+def intersect(A, B, C, D):
     """
-    Processes the IP camera stream to count objects of the selected classes crossing the line.
+    Determines if two line segments AB and CD intersect.
     """
-    global line_params
-
-    errors = []
+    def ccw(A, B, C):
+        return (C[1] - A[1]) * (B[0] - A[0]) - (B[1] - A[1]) * (C[0] - A[0])
 
-    if line_params is None:
-        errors.append("Error: No line drawn. Please draw a line on the first frame.")
-    if selected_classes is None or len(selected_classes) == 0:
-        errors.append("Error: No classes selected. Please select at least one class to detect.")
-    if stream_url is None or stream_url.strip() == "":
-        errors.append("Error: No stream URL provided.")
+    def on_segment(A, B, C):
+        return min(A[0], B[0]) <= C[0] <= max(A[0], B[0]) and min(A[1], B[1]) <= C[1] <= max(A[1], B[1])
 
-    if errors:
-        return None, "\n".join(errors)
+    ccw1 = ccw(A, B, C)
+    ccw2 = ccw(A, B, D)
+    ccw3 = ccw(C, D, A)
+    ccw4 = ccw(C, D, B)
+    return ((ccw1 * ccw2 < 0) and (ccw3 * ccw4 < 0)) or (ccw1 == 0 and on_segment(A, B, C)) or (ccw2 == 0 and on_segment(A, B, D)) or (ccw3 == 0 and on_segment(C, D, A)) or (ccw4 == 0 and on_segment(C, D, B))
 
-    logger.info("Connecting to the IP camera stream...")
+def process_frames(stream_url, confidence_threshold, selected_classes):
+    """
+    Processes frames in a separate thread and adds them to the frame queue.
+    """
+    global processing_active, frame_queue
     cap = cv2.VideoCapture(stream_url)
-    if not cap.isOpened():
-        errors.append("Error: Could not open stream.")
-        return None, "\n".join(errors)
-
     model = YOLO(model="yolo11n.pt")
     crossed_objects = {}
-    max_tracked_objects = 1000  # Maximum number of objects to track before clearing
 
-    logger.info("Starting to process the stream...")
-    while cap.isOpened():
+    while processing_active and cap.isOpened():
         ret, frame = cap.read()
         if not ret:
-            errors.append("Error: Could not read frame from the stream.")
             break
 
-        # Perform object tracking with confidence threshold
-        results = model.track(frame, persist=True, conf=confidence_threshold)
-
-        if results[0].boxes.id is not None:
-            track_ids = results[0].boxes.id.int().cpu().tolist()
-            clss = results[0].boxes.cls.cpu().tolist()
-            boxes = results[0].boxes.xyxy.cpu()
-            confs = results[0].boxes.conf.cpu().tolist()
-
-            for box, cls, t_id, conf in zip(boxes, clss, track_ids, confs):
-                if conf >= confidence_threshold and model.names[cls] in selected_classes:
-                    # Check if the object crosses the line
-                    if is_object_crossing_line(box, line_params) and t_id not in crossed_objects:
-                        crossed_objects[t_id] = True
+        # Perform detection on low-res frame
+        low_res_frame = cv2.resize(frame, LOW_RES)
+        results = model.track(low_res_frame, persist=True, conf=confidence_threshold)
 
-                        # Clear the dictionary if it gets too large
-                        if len(crossed_objects) > max_tracked_objects:
-                            crossed_objects.clear()
+        # Scale bounding boxes to high-res
+        scale_x = frame.shape[1] / LOW_RES[0]
+        scale_y = frame.shape[0] / LOW_RES[1]
+        for detection in results[0].boxes.data:
+            x1, y1, x2, y2, conf, cls = detection
+            x1, y1, x2, y2 = int(x1 * scale_x), int(y1 * scale_y), int(x2 * scale_x), int(y2 * scale_y)
+            if is_object_crossing_line((x1, y1, x2, y2), line_params):
+                crossed_objects[results[0].boxes.id.int().cpu().tolist()[0]] = True
 
-        # Visualize the results with bounding boxes, masks, and IDs
-        annotated_frame = detect_and_draw(frame)
+        # Draw bounding boxes and line on the frame
+        annotated_frame = results[0].plot()
+        if line_params:
+            draw_angled_line(annotated_frame, line_params, color=(0, 255, 0), thickness=2)
 
-        # Draw the angled line on the frame
-        draw_angled_line(annotated_frame, line_params, color=(0, 255, 0), thickness=2)
+        # Add frame to the queue
+        if not frame_queue.full():
+            frame_queue.put(annotated_frame)
 
-        # Display the count on the frame with a modern look
-        count = len(crossed_objects)
-        (text_width, text_height), _ = cv2.getTextSize(f"COUNT: {count}", cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
-
-        # Calculate the position for the middle of the top
-        margin = 10  # Margin from the top
-        x = (annotated_frame.shape[1] - text_width) // 2  # Center-align the text horizontally
-        y = text_height + margin  # Top-align the text
-
-        # Draw the black background rectangle
-        cv2.rectangle(annotated_frame, (x - margin, y - text_height - margin), (x + text_width + margin, y + margin), (0, 0, 0), -1)
-
-        # Draw the text
-        cv2.putText(annotated_frame, f"COUNT: {count}", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+    cap.release()
 
-        # Yield the annotated frame to Gradio
-        yield annotated_frame, ""
+def draw_angled_line(image, line_params, color=(0, 255, 0), thickness=2):
+    """
+    Draws the user-defined line on the frame.
+    """
+    _, _, start_point, end_point = line_params
+    cv2.line(image, start_point, end_point, color, thickness)
 
-    cap.release()
-    logger.info("Stream processing completed.")
+def display_frames():
+    """
+    Displays frames from the queue at a consistent frame rate.
+    """
+    while processing_active:
+        if not frame_queue.empty():
+            frame = frame_queue.get()
+            yield cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), ""
+        else:
+            time.sleep(0.03)  # Wait for the next frame
 
 # Define the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("<h1>Real-time monitoring, object tracking, and line-crossing detection for CCTV camera streams.</h1></center>")
     gr.Markdown("## https://github.com/SanshruthR/CCTV_SENTRY_YOLO11")
-    
+
     # Step 1: Enter the IP Camera Stream URL
     stream_url = gr.Textbox(label="Enter IP Camera Stream URL", value="https://s104.ipcamlive.com/streams/68idokwtondsqpmkr/stream.m3u8", visible=False)
 
@@ -300,33 +188,33 @@ with gr.Blocks() as demo:
     if first_frame is None:
         gr.Markdown(f"**Error:** {status}")
     else:
-        # Image component for displaying the first frame
         image = gr.Image(value=first_frame, label="First Frame of Stream", type="pil")
-
         line_info = gr.Textbox(label="Line Coordinates", value="Line Coordinates:\nStart: None, End: None")
         image.select(update_line, inputs=image, outputs=[image, line_info])
 
         # Step 2: Select classes to detect
         gr.Markdown("### Step 2: Select Classes to Detect")
-        model = YOLO(model="yolo11n.pt")  # Load the model to get class names
-        class_names = list(model.names.values())  # Get class names
+        model = YOLO(model="yolo11n.pt")
+        class_names = list(model.names.values())
         selected_classes = gr.CheckboxGroup(choices=class_names, label="Select Classes to Detect")
 
-        # Step 3: Adjust confidence threshold 
+        # Step 3: Adjust confidence threshold
         gr.Markdown("### Step 3: Adjust Confidence Threshold (Optional)")
         confidence_threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, label="Confidence Threshold")
 
         # Process the stream
         process_button = gr.Button("Process Stream")
-
-        # Output image for real-time frame rendering
         output_image = gr.Image(label="Processed Frame", streaming=True)
-
-        # Error box to display warnings/errors
         error_box = gr.Textbox(label="Errors/Warnings", interactive=False)
 
         # Event listener for processing the video
-        process_button.click(process_video, inputs=[confidence_threshold, selected_classes, stream_url], outputs=[output_image, error_box])
+        process_button.click(
+            fn=lambda: (setattr(globals(), "processing_active", True), threading.Thread(target=process_frames, args=(stream_url.value, confidence_threshold.value, selected_classes.value)).start()),
+            outputs=None
+        )
+
+        # Display frames
+        demo.load(display_frames, inputs=None, outputs=[output_image, error_box], every=0.03)
 
 # Launch the interface
 demo.launch(debug=True)