In this post, we will provide some examples of what you can do with OpenCV.
Blending Images in OpenCV
We will give a walk-through example of how we can blend images using Python OpenCV. Below we represent the target and the filter images.
import cv2 # Two images img1 = cv2.imread('target.jpg') img2 = cv2.imread('filter.png') # OpenCV expects to get BGR images, so we will convert from BGR to RGB img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB) img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB) # Resize the Images. In order to blend them, the two images # must be of the same shape img1 =cv2.resize(img1,(620,350)) img2 =cv2.resize(img2,(620,350)) # Now, we can blend them, we need to define the weight (alpha) of the target image # as well as the weight of the filter image # in our case we choose 80% target and 20% filter blended = cv2.addWeighted(src1=img1,alpha=0.8,src2=img2,beta=0.2,gamma=0) # finally we can save the image. Now we need to convert it from RGB to BGR cv2.imwrite('Blending.png',cv2.cvtColor(blended, cv2.COLOR_RGB2BGR))
Processing Images in OpenCV
We will show how you can apply image processing with OpenCV in Python. We will work with this image obtained from Unsplash.
How to Blur the Image
import cv2 import numpy as np import matplotlib.pyplot as plt %matplotlib inline img = cv2.imread('panda.jpeg') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) blurred_img = cv2.blur(img,ksize=(20,20)) cv2.imwrite("blurredpanda.jpg", blurred_img)
How to apply Sobel Operator
img = cv2.imread('panda.jpeg',0) sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5) sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5) cv2.imwrite("sobelx_panda.jpg", sobelx) cv2.imwrite("sobely_panda.jpg", sobely)
How to apply a Threshold to an Image
We can also binarize the images.
img = cv2.imread('panda.jpeg',0) ret,th1 = cv2.threshold(img,100,255,cv2.THRESH_BINARY) fig = plt.figure(figsize=(12,10)) plt.imshow(th1,cmap='gray')
Face Detection In OpenCV
We will discuss about how we can apply Face Detection using OpenCV. We go straightforward with a practical reproducible example.
The logic it the following: We get the image from the URL (or from the hard disk). We convert it to an
numpy array and then to a gray scale. Then by applying the proper CascadeClassifier we get the bounding boxes of the faces. Finally, using PIllow (or even OpenCV) we can draw the boxes on the initial image.
import cv2 as cv import numpy as np import PIL from PIL import Image import requests from io import BytesIO from PIL import ImageDraw # I have commented out the cat and eye cascade. Notice that the xml files are in the opencv folder that you have downloaded and installed # so it is good a idea to write the whole path face_cascade = cv.CascadeClassifier('C:\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_default.xml') #cat_cascade = cv.CascadeClassifier('C:\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalcatface.xml') #eye_cascade = cv.CascadeClassifier('C:\\opencv\\build\\etc\\haarcascades\\haarcascade_eye.xml') URL = "https://images.unsplash.com/photo-1525267219888-bb077b8792cc?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1050&q=80" response = requests.get(URL) img = Image.open(BytesIO(response.content)) img_initial = img.copy() # convert it to np array img = np.asarray(img) gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray) # And lets just print those faces out to the screen #print(faces) drawing=ImageDraw.Draw(img_initial) # For each item in faces, lets surround it with a red box for x,y,w,h in faces: # That might be new syntax for you! Recall that faces is a list of rectangles in (x,y,w,h) # format, that is, a list of lists. Instead of having to do an iteration and then manually # pull out each item, we can use tuple unpacking to pull out individual items in the sublist # directly to variables. A really nice python feature # # Now we just need to draw our box drawing.rectangle((x,y,x+w,y+h), outline="red") display(img_initial)
The initial Image was this one:
And then after drawing the Bounding Boxes we got:
As we can see, we managed to get correctly the four faces BUT we discovered also a “ghost” behind the window…
Crop the faces to separate images
We can also crop the faces to separate images
for x,y,w,h in faces: img_initial.crop((x,y,x+w,y+h)) display(img_initial.crop((x,y,x+w,y+h)))
For example, the first face that we get is:
Notice: In case you wanted to read the image from the hard disk you could simply type the following three lines:
# read image from the PC initial_img=Image.open('my_image.jpg') img = cv.imread('my_image.jpg') gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
Face Detection Videos In OpenCV
This post is a practical example of how we can use OpenCV with Python for detecting faces in a video. In a previous post, we explained how to apply Object Detection in Tensorflow and Face Detection in OpenCV. Generally, the Computer Vision and the Object Detection is a hot topic in Artificial Intelligence. Think for instance the autonomous vehicles which must detect continuously many different objects around them (pedestrians, other vehicles, signs etc).
How to Record a Video of Face Detection
In the following example, we apply a Face Detection with our USB Camera and we write the video to an
.mp4 file. As you can see, the OpenCV is able to detect the face, and when it is hiding behind the hands the OpenCV is losing it.
import cv2 # change your path to the one where the haarcascades/haarcascade_frontalface_default.xml is face_cascade = cv2.CascadeClassifier('../DATA/haarcascades/haarcascade_frontalface_default.xml') cap = cv2.VideoCapture(0) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # MACOS AND LINUX: *'XVID' (MacOS users may want to try VIDX as well just in case) # WINDOWS *'VIDX' writer = cv2.VideoWriter('myface.mp4', cv2.VideoWriter_fourcc(*'XVID'),25, (width, height)) while True: ret, frame = cap.read(0) frame = detect_face(frame) writer.write(frame) cv2.imshow('Video Face Detection', frame) # escape button to close it c = cv2.waitKey(1) if c == 27: break cap.release() writer.release() cv2.destroyAllWindows()
The Output of the Computer Vision Code
Few lines of code are needed to record this video with dynamic face detection. If you run the above block of code you will get a similar video (Of course with a different face