How To Amplify Colors Of Image Filter

Designing Image Filters using OpenCV (Function 1)

Learn to make photo filters like the ones present in epitome processing Softwares like Domicile Photoshop using OpenCV in python.

We all accept edited our images to perhaps better the white balance, increment warmth, use unlike filters or even to crop out a person we don't similar. Have you ever wondered how all the software available did this or how nosotros could do this ourselves with code? If so then you lot are in the right place. In this article and it's second office we will leverage the power of OpenCV to edit our images in as many as xiv ways and another article, for creating frames for our images. Don't worry because this does not include the usual stuff like sharpening, cropping and blurring the images only instead filters like summer, 60TVS, splash, cartooning, embossing, pencil sketch, sepia and many more where most of them are inspired by Abode Photoshop Limited itself.

Bright

To increase the brightness of our image we can use HSV (Hue, Saturation, and Value) color space also known every bit HSB (Hue, Saturation, and Brightness) which is closer to how humans perceive color. In Opencv their maximum values of Hue range from 0–180 and of Saturation and Brightness from 0–255.

This prototype shows different colors obtained by changing the values of HSV. Hue is changed from pinnacle to lesser while Saturation and Effulgence change from left to correct. The right side of the image is much brighter than the middle part. And then to increase the effulgence of our images we demand to increment the Saturation and Brightness.

In a nutshell in the lawmaking we convert the image to HSV color infinite, modify the blazon to bladder to prevent loss later on multiplying, upscale channels ane and 2, catechumen all pixels values greater than 255 to 255, convert the type back to integer, and finally convert information technology back to BGR color space used in OpenCV before displaying it.

Details Enhancement

Well, this is sharpening and know I said that this article won't consist of sharpening but the just reason I have included this is to evidence the difference between a predefined role cv2.detailEnhance against the normal sharpening washed in prototype processing using a kernel and to show the brilliant results, we get using it.

Capsize

This was the simplest to implement. I am sure all of us have used this filter in one case in our lives and have been scared seeing our faces turn blue and our hair white. All nosotros got to do is to invert the values of pixels i.e. decrease each pixel value past 255. In OpenCV, nosotros use cv2.bitwise_not for this task because inverting the $.25 is subtracting it by 255.

Summer

We acquaintance summer with warmth and saturation where the impact of the red channel is more than than the blue channel. So that is exactly what we are going to exercise in it. We are going to utilize the gamma function for this. Have a look at the graph below to get an idea about its working.

Gamma office values for different values of gamma.

For values of gamma to a higher place one, the values are increased and for below i, the pixel values are decreased. It is implemented in OpenCV by creating a lookup table and using cv2.LUT() . The values of the Scarlet aqueduct of BGR and Saturation of HSV is increased by taking a value of gamma greater than 1 and the values of the Blueish aqueduct is decreased by taking a value less than 1. You can play around with different values of gamma to see different results of summertime filter or fifty-fifty brand a trackbar for information technology.

Winter

Now that we accept created a summer filter information technology is easy to create a winter filter which is the inverse of it using gamma office only. We acquaintance winters with coldness and gloom. And then we need to increment the values of the bluish aqueduct, decrease those of red channel and saturation using the advisable values of gamma.

Daylight

To implement daylight filter we demand to increase the light and this tin exist achieved in a color infinite called HLS (Hue, Lightness, and Saturation). All we need to exercise is to convert the image to HLS colour space increase the value of the Lightness channel past multiplying it by some value greater than 1 and convert it back to BGR.

60TVs

In the 60s in that location used to blackness and white televisions and there was a lot of noise due to loss of data. To create this filter we convert our prototype to grayscale and then randomly induce a lot of noise by adding or subtracting a random number and make certain to stay inside our limit of 0 to 255.

High Contrast

The loftier contrast filter in Habitation Photoshop Express is a grayscale filter where the histogram of the image is stretched to the edges. This tin can again be achieved using cv2.LUT and we will create a lookup table using np.interp which is used to obtain piecewise linear interpolation between data points.

Figure showing the values of the table created.

This is it from this article. Come across you lot in the second part, where we will implement the remaining filters which are fifty-fifty ameliorate than the first ones.