import cv2 import gradio as gr import numpy as np from PIL import Image, ImageDraw from ultralytics import YOLO import logging import threading import queue import time # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Global variables for line coordinates and line equation start_point = None end_point = None 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. """ logger.info("Attempting to extract the first frame from the stream...") cap = cv2.VideoCapture(stream_url) if not cap.isOpened(): logger.error("Error: Could not open stream.") return None, "Error: Could not open stream." ret, frame = cap.read() cap.release() if not ret: logger.error("Error: Could not read the first frame.") return None, "Error: Could not read the first frame." # Convert the frame to a PIL image frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) pil_image = Image.fromarray(frame_rgb) logger.info("First frame extracted successfully.") return pil_image, "First frame extracted successfully." def update_line(image, evt: gr.SelectData): """ Updates the line based on user interaction (click and drag). """ global start_point, end_point, line_params if start_point is None: start_point = (evt.index[0], evt.index[1]) 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") return image, f"Line Coordinates:\nStart: {start_point}, End: None" end_point = (evt.index[0], evt.index[1]) 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) else: line_params = (float('inf'), start_point[0], start_point, end_point) 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") line_info = f"Line Coordinates:\nStart: {start_point}, End: {end_point}\nLine Equation: y = {line_params[0]:.2f}x + {line_params[1]:.2f}" start_point = None end_point = None return image, line_info def reset_line(): """ Resets the line coordinates. """ global start_point, end_point, line_params start_point = None end_point = None line_params = None return None, "Line reset. Click to draw a new line." 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 x1, y1, x2, y2 = box 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 return intersection_count >= 2 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): 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]) 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)) 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) model = YOLO(model="yolo11n.pt") crossed_objects = {} while processing_active and cap.isOpened(): ret, frame = cap.read() if not ret: break # 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) # 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 # 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) # Add frame to the queue if not frame_queue.full(): frame_queue.put(annotated_frame) cap.release() 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 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("

Real-time monitoring, object tracking, and line-crossing detection for CCTV camera streams.

") 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) # Step 1: Extract the first frame from the stream gr.Markdown("### Step 1: Click on the frame to draw a line, the objects crossing it would be counted in real-time.") first_frame, status = extract_first_frame(stream_url.value) if first_frame is None: gr.Markdown(f"**Error:** {status}") else: 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") class_names = list(model.names.values()) selected_classes = gr.CheckboxGroup(choices=class_names, label="Select Classes to Detect") # 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 = gr.Image(label="Processed Frame", streaming=True) error_box = gr.Textbox(label="Errors/Warnings", interactive=False) # Event listener for processing the video 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)