silhouette | source python library to drive the Silhouette

This is a perfect sample image that includes range of black objects.

And this code suppose to find every black plants

I have a setup with multiple cameras that all point towards the same scene. All cameras are calibrated to the same world coordinate system (i.e.: I know the location of all the cameras with respect to the origin of the world coordinate system). In each image from the cameras, I will detect objects in the scene (segmentation). My goal is to count all objects in the scene and I do not want to count an object twice as it will appear in multiple images. This means that if I detect an object in image A and I detect an object in image B, then I should be able to confirm that this is the same object or not. It should be possible to do this using the 3D info I have due to my calibrated cameras. I was thinking of the following:

• Voxel carving. I create silhouettes out of all images with the detected objects. I apply voxel carving and then count the unique number of clustered voxels I have. this will be the number of unique objects in the scene?

• I also thought about for example taking the center of the object and then casting a ray from it into the 3D world, this for each camera and then detecting if the lines cross each other (from different cameras). But this would be very error-prone as the objects might have a slightly different size/shape in each image and the center might be off. Also, the locations of the cameras are not 100% exact, which will result in the ray being off.

What would be a good approach to tackle this issue?

I'm using hierarchical clustering for my data. And I'd like to plot the Silhouette score across different number of cluster. I have searched a lot posts, and I found that I need to use pam for the clustering in order to plot the Silhouette score. I'm wodering if there is a way to plot based on hierarchical clustering result?

Here is a sample data:

so I'm trying to create an online game using Babylon.js but have run into a problem thats got me a little stumped so hoping someone here would be willing to help me out. Please bear with me on this one, i'm a complete newbie with babylon as i've only every worked with THREE.js. Right now my game consists of a scene compromising of multiple meshes with multiple users represented as avatars (created from basic circle geometry for the moment) loaded into an environment. What I want to do is highlight the outline of these avatars ONLY when they are occluded by any other object, meaning that when they are not occluded they look normal with no highlight but when behind an object their highlighted silhouette can be seen by others (including yourself as you can see your own avatar). This is very akin to effects used in many other video games (see example below).

Example of Effect

Thus far, based on some googling and forum browsing (Babylonjs outline through walls & https://forum.babylonjs.com/t/highlight-through-objects/8002/4) I've figured out how to highlight the outline of objects using Babylon.HighlighLayer and I know that i can render objects above others via RenderingGroups but I can't seem to figure out how to use them in conjunction to create the effect I want. The best i've managed to do is get the highlighted avatar render above everything but I need just the silhouette not the entire mesh. I'm also constrained by the fact that my scene has many meshes in it that are loaded dynamically and i'm also trying to keep things as optimal as possible. Can't afford to use very computationally expensive procedures.

Anybody know of the best way to approach this? Would greatly appreciate any advice or assistance you can provide.Thanks!

In the following code segment, I am iterating over ardene.section1.items. I have logged this array to the console to verify that it does exist. The "BEFORE FOR LOOP" test is printed to the screen but the "INSIDE FOR LOOP" test is never printed. And I get and NG0901 error which Angular tells me is a ngDoCheck error.

I'm using the following data set to perform a cluster analysis on categorical data - link to data set - using the following packages in R:

I have the following code in which first I read the dataset. Then I apply k-means clustering to the dataset. I want to modify the code using a for loop. I have three clusters. I want to plot them using for loop also calculate the mean of the first column of every cluster. I also want them to be using a single for loop instead of manually written code. How can I do that? Can anybody help me with that?

Dataset

I have a dataset the has 10 different groups and sales for 3 weeks in a year. I am trying to run a clustering algorithm that clusters each of the groups based on how many items are present in each group. Basically, I want to treat each group differently.

I tried a manual approach and set the clusters of each group relative to the group with the highest number of items but I want to make the code find the optimal k for the kmeans for each group. I am not sure how to determine the best k for each of the groups

Here is the data:

I am trying to remove a background from a video using ffmpeg and a PY library that does that, the PY lib (backgroundremover) just creates a matte.mp4 file as an output, having the background as black and the person as white silhouette.

PY lib: https://github.com/nadermx/backgroundremover#advance-usage-for-video

What I am doing at the moment:

Shrink & convert the video to MP4