The objective of this Python project is to build the Human Detection and Counting System through webcam or you can give your own video or images.
PROJECT PREREQUISITES
This Python project requires you to have basic knowledge of python programming and the OpenCV library. We will need the following libraries:
OpenCV: A strong library used for machine learning
Imutils: For Image Processing
Numpy: Used for Scientific Computing. Image is stored in a numpy array.
Argparse: Used to give input in command line.
To install the required libraries, run the following code in your terminal.
pip install opencv-python
pip install imutils
pip install numpy
DOWNLOAD REAL-TIME HUMAN DETECTION PROJECT CODE Please download the source code of the project with the link given below. https://github.com/khnbilal/Real-Time-Human-Detection Histogram of Oriented Gradient Descriptor
HOG is a feature descriptor used in computer vision and image processing for the purpose of object detection. This is one of the most popular techniques for object detection, to our fortune, OpenCV has already been implemented in an efficient way to combine the HOG Descriptor algorithm with Support Vector Machine or SVM.
1. Import the libraries: Run the following code in your Terminal.
import cv2
import imutils
import numpy as np
import argparse
2. Create a model which will detect Humans:
As discussed earlier, We will use HOGDescriptor with SVM already implemented in OpenCV. Below code will do this work:
HOGCV = cv2.HOGDescriptor()
HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
cv2.HOGDescriptor_getDefaultPeopleDetector() calls the pre-trained model for Human detection of OpenCV and then we will feed our support vector machine with it. 3. Detect() method:
Video: A video combines a sequence of images to form a moving picture. We call these images as frame. So in general we will detect the person in the frame. And show it one after another that it looks like a video.
That is exactly what our Detect() method will do. It will take a frame to detect a person in it. It will make a box around a person and show the frame and return the frame with person bounded by a green box.
def detect(frame):
bounding_box_cordinates, weights = HOGCV.detectMultiScale(frame, winStride = (4, 4), padding = (8, 8), scale = 1.03)
person = 1
for x,y,w,h in bounding_box_cordinates:
cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 2)
cv2.putText(frame, f'person {person}', (x,y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 1)
person += 1
cv2.putText(frame, 'Status : Detecting ', (40,40), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)
cv2.putText(frame, f'Total Persons : {person-1}', (40,70), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)
cv2.imshow('output', frame)
return frameEverything will be done by detectMultiScale(). It returns 2-tuple.
List containing Coordinates of bounding Box of person. Coordinates are in form X, Y, W, H. Where x,y are starting coordinates of box and w, h are width and height of box respectively.
Confidence Value that it is a person.
Now, We have our detect method. Let’s Create a Detector. 4. Human Detector() method:
There are two ways of getting Video.
Web Camera
Path of file stored
In this computer vision project, we can take images also. So our method will check if a path is given then search for the video or image in the given path and operate. Otherwise, it will open the webcam.
def humanDetector(args):
image_path = args["image"]
video_path = args['video']
if str(args["camera"]) == 'true' : camera = True
else : camera = False
writer = None
if args['output'] is not None and image_path is None:
writer = cv2.VideoWriter(args['output'],cv2.VideoWriter_fourcc(*'MJPG'), 10, (600,600))
if camera:
print('[INFO] Opening Web Cam.')
detectByCamera(ouput_path,writer)
elif video_path is not None:
print('[INFO] Opening Video from path.')
detectByPathVideo(video_path, writer)
elif image_path is not None:
print('[INFO] Opening Image from path.')
detectByPathImage(image_path, args['output'])5. Detect ByCamera() method:
def detectByCamera(writer):
video = cv2.VideoCapture(0)
print('Detecting people...')
while True:
check, frame = video.read()
frame = detect(frame)
if writer is not None:
writer.write(frame)
key = cv2.waitKey(1)
if key == ord('q'):
break
video.release()
cv2.destroyAllWindows()v2.VideoCapture(0) passing 0 in this function means we want to record from a webcam. video.read() reads the frame by frame. It returns a check which is True if this was able to read a frame otherwise False. Now, For each Frame, we will call detect() method. Then we write the frame in our output file.
6. Detect By PathVideo() method:
This method is very similar to the previous method except we will give a path to the Video. First, we will check if the video on the provided path is found or not.
Note – A full path must be given.
def detectByPathVideo(path, writer):
video = cv2.VideoCapture(path)
check, frame = video.read()
if check == False:
print('Video Not Found. Please Enter a Valid Path (Full path of Video Should be Provided).')
return
print('Detecting people...')
while video.isOpened():
#check is True if reading was successful
check, frame = video.read()
if check:
frame = imutils.resize(frame , width=min(800,frame.shape[1]))
frame = detect(frame)
if writer is not None:
writer.write(frame)
key = cv2.waitKey(1)
if key== ord('q'):
break
else:
break
video.release()
cv2.destroyAllWindows()
def detectByCamera(writer):
video = cv2.VideoCapture(0)
print('Detecting people...')
while True:
check, frame = video.read()
frame = detect(frame)
if writer is not None:
writer.write(frame)
key = cv2.waitKey(1)
if key == ord('q'):
break
video.release()
cv2.destroyAllWindows()The implementation is similar to the previous function except for each frame we will check that it successfully reads the frame or not. At the end when the frame is not read we will end the loop. 7. Detect By Pathimage() method: This method is used if a person needs to be detected from an image.
def detectByPathImage(path, output_path):
image = cv2.imread(path)
image = imutils.resize(image, width = min(800, image.shape[1]))
result_image = detect(image)
if output_path is not None:
cv2.imwrite(output_path, result_image)
cv2.waitKey(0)
cv2.destroyAllWindows()8. Argparse() method
The function argparse() simply parses and returns as a dictionary the arguments are passed through your terminal to our script. There will be three arguments within the parser:
Image: The path to the image file inside your system
Video: The path to the Video file inside your system
Camera: A variable that if set to ‘true’ will call the cameraDetect() method.
def argsParser():
arg_parse = argparse.ArgumentParser()
arg_parse.add_argument("-v", "--video", default=None, help="path to Video File ")
arg_parse.add_argument("-i", "--image", default=None, help="path to Image File ")
arg_parse.add_argument("-c", "--camera", default=False, help="Set true if you want to use the camera.")
arg_parse.add_argument("-o", "--output", type=str, help="path to optional output video file")
args = vars(arg_parse.parse_args())
return argsMAIN FUNCTION: We have reached the end of our project.
if __name__ == "__main__":
HOGCV = cv2.HOGDescriptor()
HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
args = argsParser()
humanDetector(args)Instead of declaring our model above, we can declare it in our main function.
Run the Human Detection Project:
To run the human detection project, please run below-mentioned commands as per requirements
1. To give video file as input:
python main.py -v ‘Path_to_video’
2. To give image file as input:
python main.py -i ‘Path_to-image’
3. To use the camera:
python main.py -c True
4. To save the output:
Python main.py -c True -o ‘file_name’
Summary
In this computer vision project, we have learned how to create a people counter using HOG and OpenCV to generate an efficient people counter. We developed the project where you can supply the input as: video, image, or even live camera. This is an intermediate level project, which will surely help you in mastering python and deep learning libraries.
If you have any questions, feel free to leave it in the comment section. Want to get in touch?, You can connect with me on Instagram and LinkedIN Thanks for reading!😄 🙌
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