image-processing | Image Processing with Python | Computer Vision library

Edit: As it has been pointed out, first two sections of this answer are completely wrong.

TLDR: This might not be feasible without a backend server first fetching the video data.

If you check the MDN tutorial you followed, the video object passed to texImage2D is actually an MP4 video. However, in your script, the video object you have access to (document.getElementsByTagName("video")[0]) is just a DOM object. You don't have the actual video data. And it is not easy to get access to that for YouTube. The YouTube player do not fetch the video data in one shot, rather the YouTube streaming server makes sure to stream chunks of the video. I am not absolutely sure on this, but I think it'll be very difficult to work around this if your goal is to have a real time video effects. I found some discussion on this (link1, link2) which might help.

That being said, there are some issues in your code from WebGL perspective. Ideally the code you have should be showing a blue rectangle as that is the texture data you are creating, instead of the initial glClearColor color. And after the video starts to play, it should switch to the video texture (which will show as black due to the issue I have explained above).

I think it is due to the way you had setup your position data and doing clip space calculation in the shader. That can be skipped to directly send normalized device coordinate position data. Here is the updated code, with some cleaning up to make it shorter, which behaves as expected:

You can use a crate such as anyhow to bubble your events up and handle them as needed.

Alternatively, you can write your own trait and implement it on Result.

First thing you should do is to switch from an ANN/MLP to a shallow/very simple convolutional neural network.

You can have a look here on TensorFlow's official website. (https://www.tensorflow.org/tutorials/images/cnn).

The last layer's definition, the optimizer, loss function and metrics are correct!

You only need a more powerful network to be able to learn on your dataset, hence the suitability of CNN in case of image processing.

Once you have a baseline established (based on the tutorial above), you can start playing around with the hyperparameters.

For future generations, here is how to only back and forward warp pixels within the bbox of the warped corner points, as @Micka suggested.

here banner is the grass image, and banner_coords_2d are the corners of the red region on image, which is meme-man.

How can I modify my original code for the original image to accurately measure only the relevant squares by using the following criteria for finding squares:

Your code only accepts contours that are exactly square. You need to have a "squaredness" factor and then determine some acceptable threshold.

The "squaredness" factor is h/w if w > h else w/h. The closer that value to one, the more square the rectangle is. Then you can accept only rectangles with a factor of .9 or higher (or whatever works best).

In general, why is a black background so much more beneficial than a white background in using the cv2.rectangle() function?

The contour finding algorithm that OpenCV uses is actually:

Suzuki, S. and Abe, K., Topological Structural Analysis of Digitized Binary Images by Border Following. CVGIP 30 1, pp 32-46 (1985)

In your case, the algorithm might just have picked up the contours just fine, but you have set the RETR_EXTERNAL flag, which will cause OpenCV to only report the outermost contours. Try changing it to RETR_LIST.

Find the OpenCV docs with regards to contour finding here: https://docs.opencv.org/master/d9/d8b/tutorial_py_contours_hierarchy.html

There is, to put it mildly, an easier way to do this.

An MutableImage is one you can mutate (change in place) - Images are immutable by default. You'll need some kind of monad that allows that though (see the documentation - there are a few including ST and IO).

To get an MutableImage you can use thawImage - then you can work (get/set) pixels with readPixel and writePixel - after you can freezeImage again to get back an immutable Image

If you want to know how you can rotate images you can check the source code of rotateLeft :

The residual error is the sum of the residuals at each point, so of course it grows with the number of points. However, for an unbiased algorithm such as the Gold Standard one, and a given level of i.i.d. noise, the curve flattens because the contribution of each additional point to the sum counts less and less toward the total.

Operations like filters are defined in separate repository:

https://gitlab.gnome.org/GNOME/gegl

this particular filter is defined here:

https://gitlab.gnome.org/GNOME/gegl/-/blob/master/operations/common/snn-mean.c