Computer Vision

Add white text 'Hello CV' at position (50,100) with font scale 2 and thickness 2.

Create a white circle on black background and perform AND operation with another image.

Increase image brightness by 50 units.

Change the color of pixel at (100,100) to blue and display the image.

Split the image into its B, G, R components and display each channel.

Detect all green pixels in an image and display them in white.

Find and draw contours on a thresholded image.

Increase image contrast by 1.5 times.

Convert the image from BGR to RGB color space and display it.

Load an image and convert it to grayscale.

Create a 300x300 blank white image and display it.

Crop the image to show only the region from (100,100) to (300,300).

Apply dilation with 3x3 kernel to a binary image.

Draw a blue circle centered at (300,300) with radius 100 and thickness 3.

Draw a red line from (50,50) to (250,250) with thickness 3.

Draw a yellow triangle (3-sided polygon) with points (100,100), (300,100), (200,300).

Draw a green rectangle on the image from (50,50) to (200,200) with thickness 2.

Apply Canny edge detection to the image.

Apply erosion with 3x3 kernel to a binary image.

Flip the image horizontally (left-right).

Print the width, height and number of color channels of an image.

Apply 3x3 averaging filter to blur the image.

Blend two images with equal weights.

Apply Gaussian blur with 5x5 kernel to the image.

Concatenate two images horizontally and display the result.

Calculate and display histogram of a grayscale image.

Apply a circular mask to an image.

Calculate and print the moments of a binary image.

Create and display the negative of an image.

Add 20 pixel black border around the image.

Create and display a Gaussian pyramid with 4 levels.

Rotate the image by 45 degrees clockwise.

Shift the image 100 pixels right and 50 pixels down.

Apply perspective transformation to an image.

Load an image using OpenCV and display it in a window.

Apply 3x3 median filter to the image.

Resize the image to 200x200 pixels.

Load an image and save it as 'output.jpg'.

Subtract two images to find differences.

Convert image to grayscale and apply binary threshold at 127.