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# 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()
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