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Social Distancing using YOLOv3 – Object Detection – with source code – fun project – 2022

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So guys guys guys, here come one of the most awaited projects, Social Distancing using YOLOv3 and OpenCV. In this project, we performed object detection on a camera’s live feed or video to check if Social Distancing is being followed or not in a locality. So without any further due, Let’s do it…

Create a conda environment and install the required libraries

conda create -n sd python=3.9
conda activate sd
pip install opencv-python numpy

Code for Social Distancing project…

import cv2
import numpy as np
import random
import os
from PIL import Image
import time

net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

distance_thres = 50

cap = cv2.VideoCapture('data/humans.mp4')

def dist(pt1,pt2):
    try:
        return ((pt1[0]-pt2[0])**2 + (pt1[1]-pt2[1])**2)**0.5
    except:
        return

layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
print('Output layers',output_layers)

_,frame = cap.read()

fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter('output.avi', fourcc, 30,(frame.shape[1], frame.shape[0]), True)


ret = True
while ret:

    ret, img = cap.read()
    if ret:
        height, width = img.shape[:2]

        blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)

        net.setInput(blob)
        outs = net.forward(output_layers)

        confidences = []
        boxes = []
        
        for out in outs:
            for detection in out:
                scores = detection[5:]
                class_id = np.argmax(scores)
                if class_id!=0:
                    continue
                confidence = scores[class_id]
                if confidence > 0.3:
                    center_x = int(detection[0] * width)
                    center_y = int(detection[1] * height)

                    w = int(detection[2] * width)
                    h = int(detection[3] * height)
                    x = int(center_x - w / 2)
                    y = int(center_y - h / 2)

                    boxes.append([x, y, w, h])
                    confidences.append(float(confidence))

        indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

        persons = []
        person_centres = []
        violate = set()

        for i in range(len(boxes)):
            if i in indexes:
                x,y,w,h = boxes[i]
                persons.append(boxes[i])
                person_centres.append([x+w//2,y+h//2])

        for i in range(len(persons)):
            for j in range(i+1,len(persons)):
                if dist(person_centres[i],person_centres[j]) <= distance_thres:
                    violate.add(tuple(persons[i]))
                    violate.add(tuple(persons[j]))
        
        v = 0
        for (x,y,w,h) in persons:
            if (x,y,w,h) in violate:
                color = (0,0,255)
                v+=1
            else:
                color = (0,255,0)
            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)
            cv2.circle(img,(x+w//2,y+h//2),2,(0,0,255),2)

        cv2.putText(img,'No of Violations : '+str(v),(15,frame.shape[0]-10),cv2.FONT_HERSHEY_SIMPLEX,1,(0,126,255),2)
        writer.write(img)
        cv2.imshow("Image", img)
    
    if cv2.waitKey(1) == 27:
        break

cap.release()
cv2.destroyAllWindows()
https://machinelearningprojects.net/wp-content/uploads/2022/10/Social-Distancing-using-YOLOv3.mp4

Download yolov3.weights

Download the source code…

Do let me know if there’s any query regarding Social Distancing or object detection by contacting me on email or LinkedIn.

So this is all for this blog folks, thanks for reading it and I hope you are taking something with you after reading this and till the next time ?…

Read my previous post: DOCUMENT SCANNER USING OPENCV 

Check out my other machine learning projectsdeep learning projectscomputer vision projectsNLP projectsFlask projects at machinelearningprojects.net.

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