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app.py

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+
+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
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Global variables to store line coordinates and line equation
+start_point = None
+end_point = None
+line_params = None  # Stores (slope, intercept) of the line
+
+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 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"
+        )
+
+        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
+    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"
+    )
+
+    # 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():
+    """
+    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 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
+    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 process_video(confidence_threshold=0.5, selected_classes=None, stream_url=None):
+    """
+    Processes the IP camera stream to count objects of the selected classes crossing the line.
+    """
+    global line_params
+
+    errors = []
+
+    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.")
+
+    if errors:
+        return None, "\n".join(errors)
+
+    logger.info("Connecting to the IP camera stream...")
+    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():
+        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
+
+                        # Clear the dictionary if it gets too large
+                        if len(crossed_objects) > max_tracked_objects:
+                            crossed_objects.clear()
+
+        # Visualize the results with bounding boxes, masks, and IDs
+        annotated_frame = results[0].plot()
+
+        # Draw the angled line on the frame
+        draw_angled_line(annotated_frame, line_params, color=(0, 255, 0), thickness=2)
+
+        # 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)
+
+        # Yield the annotated frame to Gradio
+        yield annotated_frame, ""
+
+    cap.release()
+    logger.info("Stream processing completed.")
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("<center><h1><u>Fast Real-time Object Detection & Tracking with High-Res Output</u></h1></center>")
+    gr.Markdown("<center><h2> <u>Detect and count objects crossing a line with Yolo11n </u></h2></center>")
+    
+   
+   
+    # Step 1: Enter the IP Camera Stream URL
+    # gr.Markdown("### Step 0: Enter the IP Camera Stream URL")
+    stream_url = gr.Textbox(label="Enter IP Camera Stream URL", value="https://s86.ipcamlive.com/streams/56bajygtsxwuzdmte/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 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])
+
+        # Reset the line (optional)
+        # gr.Markdown("### Step 4: Reset the Line (Optional)")
+        # reset_button = gr.Button("Reset Line")
+        # reset_button.click(reset_line, inputs=None, 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
+        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 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])
+
+# Launch the interface
+demo.launch(debug=True)

requirements.txt

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+
+ultralytics
+opencv-python
+gradio
+torch
+lap>=0.5.12

yolo11n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ebbc80d4a7680d14987a577cd21342b65ecfd94632bd9a8da63ae6417644ee1
+size 5613764