Social Distancing using YOLOv3 – Object Detection – with source code – fun project – 2023

Abhishek Sharma

2 years ago

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…

Table of Contents

Create a conda environment and install the required libraries

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

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()

Line 1-6 – Importing required libraries.
Line 8-10 – Read the Yolo files and enable Cuda.
Line 12 – Set the distance threshold to 50 pixels.
Line 14 – Instantiate the VideoCapture object which will help us in reading frames from the video file.
Line 16-20 – A simple distance function that calculates the distance between two coordinates on the plane.
Line 22 – Get a list of all layer names in the network.
Line 23 – Get output layers.

Line 26 – Read a frame from the video just to get the height and width of it.
Line 28-29 – We will be saving our results in a video output also as shown below using VideoWriter.
Line 32-33 – Let’s start the loop.
Line 35 – Start reading from the input video.
Line 36- If the cam object is returning something then the ret will be True.
Line 37 – Extract image height and width.
Line 38 – Create a blob of shape 416X416 from the image.
Line 41 – Give this blob as input to the net using cv2.dnn.blobFromImage.
Line 42 – Get output from output layers.
Line 47 – Traverse in all the outputs from that frame.
Line 48 – Now traverse in all the detections.
Line 49 – There are 85 points in the detection array. The first four indices are for coordinates of the box and indexes starting from 5 to 85 are for class confidences.
Line 50 – Get the class id by getting the index of that element that has the highest score.
Line 51-52 – If the class_id is not 0 (person), continue. Because our main purpose in this use case is just to detect persons.
Line 53 – Get the confidence score.
Line 54 – If the confidence is greater than 30%, proceed further.
Line 55-56 – Calculate the center x and center y points.
Line 58-61 – Calculate the x,y,w, and h of the bounding box.
Line 63 – Append this Bounding Box in our boxes list.
Line 64 – Append confidences in the confidences list.
Line 66 – Here we are performing non-maximum suppression of bounding boxes using cv2.dnn.NMSBoxes. It will return a list of indexes containing a list of those indexes which we have to consider.
Line 72-76 – Now traverse in all boxes and take only those boxes whose index is in the indexes list. And append-only these relevant boxes in the person list. Also, append the box centers in the person_centres array.
Line 79-83 – Now traverse in person_centres array and find all those person centers who are violating social distancing norms of 50 pixels. We will pass these person_centres points in the distance function and check the distances between all the persons. We will add these violating persons to the violate array.
Line 85-93 – Draw a red box around the persons who are violating the social distancing norms and a green box around those who are not violating them.
Line 95 – Show the number of violations on the screen.
Line 96 – Save the output in video form.
Line 97 – Showing the output.
Line 99-100 – If anyone hits the ESC key, break the code.
Line 102-103 – Release the VideoCapture object and destroy all open windows.