Exercise #11 Chroma key

For this exercise, we made a parody of an anime opening song using chromakeying. The output is as shown.

That is all.

Exercise #10 Color Tracking

For this exercise, we take a ROI on a webcam, detect the color, and track all colors that are the same or atleast within its threshold. Also, if the ROI should see black, The tracking is reset.

As its hard, no images are shown as reference.

The algorithm is as such:
1. First, make the ROI.
2. Save the ROI to get the colors on the region. Convert RGB to HSV and get the hue.
3. Compute the range and, using it, apply it to be tracked using inRange() function.
4. For tracking, code given can be used.

That is all.

Exercise #9 Background Subtraction

For this exercise, we are tasked to subtract the background of any clip. We used this.

First, we get all the frames of the video into a folder.
Then, taking the sequence of images, we convert them to gray values and get the median of a certain pixel from all frames. once the median is chosen, repeat for all pixels. Doing that, we will get the background of the image.
Next, using the background we got, all frames are again converted to its gray value.
Finally, subtracting the background, as long as its within the threshold, we will get the foreground for all the frames and compile.

That is all.

Exercise #8 Pseudo Coloring (False Coloring)

For this exercise, we were tasked to re-color the bodies of water on the following set of weather images.

After re-coloring, the outputs derived are:

We will use the first image for reference while doing this exercise.

First, get the grayscale and hsv counterparts of the image.

Using the function inRange(), get a binarized image where the bodies of water is white.

Using that on the original image, as long as it is white, recolor the the pixel to the desirable scale of your choosing. Depending on the intensity of the blues, recolor the image. The output is seen at the top.

That is all.

Exercise #4 Binarization and Canny Edge Detection.

For this exercise.

First, we binarized a bunch of images.
The following images are used for binarization.

For this set of images, we set thresholds for themselves for better binarization. As using one code for all is not an easy task for this one, we opted to hardcode it, making it so that each image use their own bits of the code.

The following are the output.

Next, for Adaptive thresholding, we used this sample image.

For its algorithm, we used the mean of the neighbors of the pixel of interest. Using it, we got this binarized image.

For detecting edges, we used a blur function built in in OpenCV. We also used Sobel and Canny Edge Detectors that are built in on the images.

That is all.

Exercise 1: Image Transition

The goal of the exercise is to transition one image to the other using a limited number of sequences.
The image used are:

That is all.