Human Body Detection Program In Python OpenCV
In the previous article, we posted about a simple face detection program using Python OpenCV. In this article, you will learn how to detect human full body using the Python OpenCV library.
There are a variety of computer applications that identify human body in digital images, like - pedestrian crossing, criminal identification, healthcare and so on. The detection program allows us to identify and locate objects. It is very important in area of research, where the detected object can be count, accurately determined. The Python OpenCV library functions are mainly aimed at real-time computer vision. In this article, we are going to develop a Pedestrian detection program. The OpenCV library has inbuilt methods to detect pedestrians.
For pedestrian or human body detection program, we need the following modules-
- OpenCV (cv2)
Install OpenCV Module
OpenCV stands for Open Source Computer Vision Library. It is a free, open source library that is used for computer vision. It provides good support for the Machine Learning, Face Recognition, Deep Learning, etc.
The given command installs the OpenCV module with the pip tool. This command also installs some other supporting modules of OpenCV.
pip install opencv-contrib-python
The following code import the OpenCV module-
Install imutils module
The Imutils is used for basic image processing, such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV. It provides a series of convenient functions to perform operations.
pip install imutils
On successful installation, it returns the following-
Successfully built imutils Installing collected packages: imutils Successfully installed imutils-0.5.3
Use the following to import this library-
Read and Resize Image
After module installation, we first need to read and resize the image. We will use the imread() function to read the image.
image = cv2.imread('zebracrossing.jpg')
The loaded image can be of any size. So, we will have to resize this using resize() method of imutils module. If you do not want to resize the image then you can skip this code-
image = imutils.resize(image, width=min(500, image.shape))
HOG (Histogram of Oriented Gradients) is an object detector used to detect objects in computer vision and image processing. The technique counts the occurrence of gradient orientation in localized portions of an image. The cv.HOGDescriptor() method creates the HOG descriptor. The hog.setSVMDetector() method sets coefficients for the linear SVM classifier.
hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
Next, detect all the regions that have a human body inside it. The hog.detectMultiScale() method detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
(humans, _) = hog.detectMultiScale(image, winStride=(5, 5), padding=(3, 3), scale=1.21)
Next, we will draw a rectangle region surrounding each human body of the image-
for (x, y, w, h) in humans: cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
Complete Code: Human Body Detection Source Code
We hope, the above code explanation help you to understand the code flow. Here, we have merged the above code chunks.
import cv2 import imutils # Initializing the HOG person hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) # Reading the Image image = cv2.imread('zebracrossing.jpg') # Resizing the Image image = imutils.resize(image, width=min(500, image.shape)) # Detecting all humans (humans, _) = hog.detectMultiScale(image, winStride=(5, 5), padding=(3, 3), scale=1.21) # getting no. of human detected print('Human Detected : ', len(humans)) # Drawing the rectangle regions for (x, y, w, h) in humans: cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2) # Displaying the output Image cv2.imshow("Image", image) cv2.waitKey(0) cv2.destroyAllWindows()
The above code returns output something like this-
(env) c:\python37\Scripts\projects>pedestrain.py Humans Detected : 3
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