app.py

# Install required libraries
#pip install gradio opencv-python-headless

# Download YOLO files
wget -nc https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg
wget -nc https://pjreddie.com/media/files/yolov3.weights
wget -nc https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names

import gradio as gr
import cv2
import numpy as np

def count_people(video_path):
    # Load YOLO model
    net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
    
    # Load class names
    with open('coco.names', 'r') as f:
        classes = [line.strip() for line in f.readlines()]
    
    # Open video
    cap = cv2.VideoCapture(video_path)
    
    frame_count = 0
    total_people_count = 0
    people_per_frame = []
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        
        height, width, _ = frame.shape
        
        # Create blob from frame
        blob = cv2.dnn.blobFromImage(frame, 1/255.0, (416, 416), swapRB=True, crop=False)
        net.setInput(blob)
        
        # Get output layer names
        output_layers_names = net.getUnconnectedOutLayersNames()
        
        # Forward pass
        layer_outputs = net.forward(output_layers_names)
        
        # Lists to store detected people
        boxes = []
        confidences = []
        
        # Process detections
        for output in layer_outputs:
            for detection in output:
                scores = detection[5:]
                class_id = np.argmax(scores)
                confidence = scores[class_id]
                
                # Check if detected object is a person
                if classes[class_id] == 'person' and confidence > 0.5:
                    # Object detected
                    center_x = int(detection[0] * width)
                    center_y = int(detection[1] * height)
                    w = int(detection[2] * width)
                    h = int(detection[3] * height)
                    
                    # Rectangle coordinates
                    x = int(center_x - w/2)
                    y = int(center_y - h/2)
                    
                    boxes.append([x, y, w, h])
                    confidences.append(float(confidence))
        
        # Apply non-maximum suppression
        indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
        
        # Count people in this frame
        people_in_frame = len(indexes)
        people_per_frame.append(people_in_frame)
        total_people_count += people_in_frame
        
        frame_count += 1
    
    # Release resources
    cap.release()
    
    # Prepare analytics
    return {
        'Total Frames Processed': frame_count,
        'Total People Detected': total_people_count,
        'Average People Per Frame': round(np.mean(people_per_frame), 2),
        'Max People in a Single Frame': int(np.max(people_per_frame))
    }

# Define Gradio interface
def analyze_video(video_file):
    result = count_people(video_file)
    result_str = "\n".join([f"{key}: {value}" for key, value in result.items()])
    return result_str

# Gradio UI
interface = gr.Interface(
    fn=analyze_video,
    inputs=gr.Video(label="Upload Video"),
    outputs=gr.Textbox(label="People Counting Results"),
    title="YOLO-based People Counter",
    description="Upload a video to detect and count people using YOLOv3."
)

# Launch Gradio app
interface.launch()