
After completing this lecture , the students will be able to read any image file on their computer and display it on a titled window on the screen.
This lecture introduces the 2 functions :
In this lecture the students learn how to convert a colour image into a gray image and write the gray image into a file on the computer for viewing later.
The following functions are introduced in this lecture :
In this lecture, the concept of channels is demystified. The 3 Channels of an RGB image are clearly explained and then the RGB image is decomposed into its constituent ( Red ,Green , & Blue ) channels respectively using the split() function. Next we merge the separate channels obtained using the merge() function, to reproduce the original image.
The key takeaways : To visualise that an RGB image (colour image) has 3 Channels , while a GrayscaleImage has just one channel.
This lecture introduces the concept of subplots and explains why they are so important. The video also explains the difference in conventions followed by matplotlib package and the cv2 library, when reading OR displaying an RGB image.
This video demonstrates, how to read and display a picture , from the provided url.
Histograms tell us a lot about an image. This lecture uses a grayscale picture as a sample and explains its histogram plot , detailing each aspect of the plot and the information it conveys.
After going through this lecture, the student will be more adept in analysing images solely based on its histograms.
Learn to compute and plot color histograms for an RGB image, using 256 bins per channel in Python with OpenCV, and visualize red, green, and blue distributions.
Learn to draw lines, circles, and rectangles using OpenCV with Python, adjusting start and end points, radius, color, and thickness, while preserving the original image by copying it first.
Read an image, compute its center pivot, build the rotation matrix with getRotationMatrix2D, and apply warpAffine to rotate the image by 45 degrees in OpenCV and Python.
This video will serve as an introduction for the Snipping utility project.
This fun project will help students experiment hands-on, utilising all the knowledge they have gained till this section.
Develop an interactive snipping tool in OpenCV with Python that uses mouse events to select a crop region, draw a bounding rectangle, and crop the image.
Apply a median filter to remove salt and pepper noise, improving a grainy image and illustrating how this technique recovers details in degraded images.
Explore how the median filter removes salt-and-pepper noise by evaluating a pixel's neighborhood. See how 3x3, 5x5, 7x7, and 9x9 kernels trade noise reduction for sharpness in OpenCV.
In this video, the students will attempt to perform thresholding without using the cv2.threshold() function.
Explore adaptive thresholding in OpenCV with Python, tuning block size and constants. Compare results to global thresholding and reduce false positives with smoothing for clearer images.
Apply adaptive thresholding to an image, then replace white pixels with random colors while keeping black pixels intact on a three-plane RGB canvas.
In this video the students will learn two superimpose one image on top of another image. This is a fun exercise , but the concept of this lesson is very important and is used in many applications.
Students can use this idea to make a photo watermarking tool in python.
This is the introduction to the videos section. Students are taught, how to access the webcam using code, and how to release the webcam.
In this video we will learn how to change colourspaces in a live video feed using users choice of colourspace.
In this video we will show, how to track an object in realtime using the object's colour.
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