OpenCV 101 and Instagram Filters
By: Rahul Banerjee
Tags: Image Processing, Computer Vision, Artificial Intelligence, Opencv, Instagram Filter

OpenCV 101 and Instagram Filters

Photo by Janis Fasel on Unsplash

OpenCV is a library primarily built for computer vision. You do not need to be a pro in image processing to build a few simple image filters like Instagram’s sepia effect, Emboss effect, etc. We will be going over the following.

How is an Image stored?

Convolution and how Filters work

Reading and Displaying Images

Reading VideoCaptures and using your WebCam

Some Filters and Image manipulation

How is an Image Stored?

Photo by Greyson Joralemon on Unsplash

Each image is made of pixels. The number of pixels in an image is determined by the width and height of the image. The above image is 3776 x 2832, and therefore it has 3776 x 2832 pixels in total. For black and white images, the image will have only one channel, but for a colored image, like the one above, there will be three channels, namely Red, Blue, and Green.

O penCV stores the pixels in BGR Format.

All the channels will merge to form the final image. Therefore to answer the question, how is the image stored? It is stored in the form of a 3D array with shape (2832, 3776, 3). A black image will have a 3D array with all 0, while a white image will have a 3D array with all 256.

Convolution and How filters work?

Convolution is essentially breaking the image matrix into small parts and laying the filter matrix on top of it. We then process it and calculate the output. Some Filter matrices are listed below. The values for these matrices take years of research. Instagram filters are basically combinations of such low-level filters, such as blurs, contrast changes, color changes, etc.

I have linked a video by Computerphile below. It goes more in-depth about Convolution and examples of how filters work.

OpenCV already provides us with functions to apply blur, edge detection, and a few more filters. For certain other filters like sepia, we will have to develop the matrix and use kernel convolution.

Install OpenCV

pip install opencv

To import the library, type.

import cv2

All the functions we discuss will be part of the cv2 package.

Reading and Displaying Images

img = cv2.imread('mainImage.jpg')

imread() takes in the path to the image as the parameter and returns the pixel values

cv2.imshow('Image Display' , img)

cv2.waitKey(5000)

imshow() displays the image in a new window. The first parameter is the name of the window and the second parameter is the image object containing the pixel values. By default, the window closes right after it pops up, and therefore we can not see it. To solve the problem, we manually add a wait time. waitKey() takes in the number of milliseconds to wait before automatically closing the window. If you pass a value of 0, the window will be displayed for infinity milliseconds, i.e., forever until you close it manually.

Reading VideoCaptures and using your WebCam

cap = cv2.VideoCapture("testvideo.mp4")

We use VideoCapture() to read videos and use the webcam. It is pretty similar to imread(). It takes in a path to the video and reads it. To use your webcam, the parameter will be needed to be set to 0. This gives it access to your default webcam.

webCam = cv2.VideoCapture(0)

You can also set the width and height of the window using your webcam. You will need to know the parameter id to set the value. We will use the set() function.

webCam.set(3 ,500) # id 3 is width

webCam.set(4,500) # id 4 is height

webCam.set(10, 100) #id 10 is brightness

A video is basically a collection of images in a sequence; therefore, we will need a while loop and imread() function to display the video. The same method can be used to display the webcam as well.

while True:

try:

success , img = cap.read()

cv2.imshow("Video", img)

if cv2.waitKey(10) == ord('q'):

break

except:

print('Video End')

break

The value passed to waitkey() dictates the speed of your video. The higher the number, the slower the speed. If you put 0, which translates to an infinite wait time, you will only be able to see the first frame. The if statement essentially checks if the user presses the ‘q’ key. If the ‘q’ key is pressed, the window closes, and then we exit the while loop.

Common Filters

Original Image

import cv2



img = cv2.imread('ogImage.jpg')

print(img.shape)

cv2.imshow('Original Picture' , img)

cv2.waitKey(0)

Photo by Roman Synkevych on Unsplash

The shape of the image is

(427, 640, 3)

The pixels are store in BGR format.

GrayScale Image

img_gray = cv2.cvtColor(img , cv2.COLOR_BGR2GRAY)

cv2.imshow('GrayScale' , img_gray)

Photo by Roman Synkevych on Unsplash (Gray Scale)

Blur Image

img_blur = cv2.GaussianBlur(img , (15,15) , 0)

cv2.imshow('Blur Image', img_blur)

Photo by Roman Synkevych on Unsplash (Blur with Kernel Size 15)

The greater the Kernel Size, the more blurred the picture will be

img_blur = cv2.GaussianBlur(img , (25,25) , 0)

cv2.imshow('Blur Image', img_blur)

Photo by Roman Synkevych on Unsplash (Blur with Kernel Size 25)

Brightness

img_bright = cv2.convertScaleAbs(img, beta=100)

cv2.imshow('Less Bright Image', img_bright)

As the value of beta increases, the picture becomes brighter. To decrease brightness, use negative values.

Photo by Roman Synkevych on Unsplash (Beta = 100)

img_bright = cv2.convertScaleAbs(img, beta=-50)

cv2.imshow('Less Bright Image', img_bright)

Photo by Roman Synkevych on Unsplash (Beta = 100)

Edge Detection

img_canny = cv2.Canny(img , 100 ,100)

cv2.imshow('Edge Detection Image', img_canny)

Photo by Roman Synkevych on Unsplash (Beta = 100)

Emboss Effect

kernel = np.array([[0, -1, -1], [1, 0, -1], [1, 1, 0]])

img_sharpen = cv2.filter2D(img, -1, kernel)

cv2.imshow('Embossed Image', img_sharpen)

Photo by Roman Synkevych on Unsplash (Embossed)

Image Sharpening

https://towardsdatascience.com/python-opencv-building-instagram-like-image-filters-5c482c1c5079

kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])

img_sharpen = cv2.filter2D(img, -1, kernel)

cv2.imshow('Sharpened Image', img_sharpen)

Photo by Roman Synkevych on Unsplash (Sharpened)

Sepia Effect

kernel = np.array([[0.272, 0.534, 0.131],

[0.349, 0.686, 0.168],

[0.393, 0.769, 0.189]])

img_sepia = cv2.filter2D(img, -1, kernel)

cv2.imshow('Sharpened Image', img_sepia)

Photo by Roman Synkevych on Unsplash (Sepia Effect)

Image Manipulation

Image Resize

img_resize = cv2.resize(img,(200,200))

cv2.imshow('Resized Image', img_resize)

cv2.waitKey(0)

Photo by Roman Synkevych on Unsplash (Resized)

Image Crop

img_cropped = img[0:256 , 0:256]

cv2.imshow('Cropped Image', img_cropped)

Photo by Roman Synkevych on Unsplash (Cropped)

Since the image is stored as a matrix, we can just slice it to crop the image.

Resources

Let me know if you find any more cool filters or functions. I’d love to try them out! 😃