minpt | A path tracer in 300 lines of C | Graphics library
kandi X-RAY | minpt Summary
kandi X-RAY | minpt Summary
Minpt only supports binary .ppm format (P6) as input textures, and .pfm format as environment maps. The rendered images are also generated with .pfm format. To check the rendered images, you want to use tools capable of viewing .pfm files, such as PCG HDRITools or HDRView. Triangle and quad meshes are supported.
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QUESTION
Reposting because i didn't add in my data earlier;
I have been running a DBSCAN algorithm on R for a project where I'm creating clusters of individuals based on the location they are in.
I need clusters of 3 people (k=3), each matched on the location of each individual and my eps value is 1.2
The problem I'm facing with my algorithm is that the cohorts/clusters are of variable size.
This is my output after running the clustering code, and as you can see, there are 5 variables in cluster #2, and I want to split this up into 3 + 2 (so, cluster number 3 will have 3 points and cluster number 4 will have 2 points)
...ANSWER
Answered 2021-Apr-12 at 17:01I'd use integer programming for this.
Set a distance limit. Enumerate all pairs where the distance is under the limit. Extend these to all triples where each of the three pairwise distances is under the limit.
Formulate an integer program with a 0-1 variable for each triple and each pair. The score for a pair/triple is the sum of pairwise distances. The objective is to minimize the sum of the scores of the triples and pairs chosen. For each point, we constrain the sum of triples and pairs that contain it to equal one. We also constrain the number of pairs to be at most two.
For pairs {1, 2}, {1, 3}, {2, 3}, {2, 4}, {3, 4}, {4, 5} and triples {1, 2, 3}, {2, 3, 4}, the program looks like this:
QUESTION
We are using forge viewer(v7) in our web application.
Our requirement is to crop particular room/area from the forge viewer. For example, if we have shown a house model in the forge viewer then if a user select a kitchen(from menu or navbar) then the viewer should show only kitchen area (including all its objects like cabinets, burner, fridge, sink etc.) and all other objects/sections should be hidden. Similarly for bedrooms, baths etc. It will be just for viewing purpose at run time and not for any automation.
We are getting room coordinates(min and max X, Y, Z) with the help of following using forge API(with Revit engine).
...ANSWER
Answered 2021-Jan-06 at 09:50i have another solution. modify model by hand like cut plane, hide, isolate element to retrieve view you want to show. Then use method var data = viewer.getState() and store that data to your database. then use viewer.restoreState(data) to recall your view.
QUESTION
I want to do something like
...ANSWER
Answered 2021-Jan-05 at 23:37Here is one option with list2env. Loop over the 'range' vector with lapply, apply the function, store the output in a list ('lst1'), name the list and use list2env to create those objects in the global env
QUESTION
I'm attempting to run DBSCAN against some grouped coordinates in order to get sub-clusters. I've clustered some spacial data and I'd now like to further divide these regions according to the density of points within them. I think DBSCAN is probably the best way to do this.
My issue is that I can't figure out how to run DBSCAN against each cluster seperately and then output the cluster assignment as a new column. Here's some sample data:
...ANSWER
Answered 2020-Oct-05 at 20:20To be clear, dbscan::dbscan works fine on data.frame objects. You do not need to convert to matrix. It returns an object that includes a vector with the same dimension as the number of records in your input. The issue is that dplyr exposes variables to other functions as individual vectors, rather than as data.frame or matrix objects. You are free to do something like:
QUESTION
I have completed and plotted the DBSCAN cluster in R markdown.
This is my code currently:
...ANSWER
Answered 2020-Sep-28 at 11:07Using an example dataset:
QUESTION
ORB doesn't find keypoints near the edge of an image and I don't understand why. It seems worse that SIFT and SURF and I would expect the opposite.
If I understand correctly then SIFT/SURF use a 16x16 and 20x20 square block respectedly around the test-point so I would expect them not to find keypoints 8 and 10 pixels from an edge. FAST/ORB uses a circle of diameter 7 around the test-point so I expected it to find keypoints even closer to the edge, perhaps as close as 4 pixels (though I think the associated algorithm, BRIEF, to describe keypoints uses a larger window so this would remove some keypoints).
An experiment makes nonsense of my prediction. The minimum distance from the edge in my experiments vary with the size and spacing of the squares but examples are
- SIFT .. 5 pixels
- SURF .. 15 pixels
- ORB .. 39 pixels
Can anyone explain why?
The code I used is below. I drew a grid of squares and applied a Gaussian blur. I expected the algorithms to latch onto the corners but they found the centres of the squares and some artifacts.
...ANSWER
Answered 2020-Aug-31 at 09:26Usually, keypoints at the edge of the image are not useful for most applications. Consider e.g. a moving car or a plane for aerial images. Points at the image border are often not visible in the following frame. When calculating 3D reconstructions of objects most of the time the object of interest lies in the center of the image. Also the fact you mentioned, that most feature detectors work with areas of interest around pixels is important since these regions could give unwanted effects at the image border.
Going into the source code of OpenCV ORB (848-849) uses a function with an edgeThreshold that can be defined using cv::ORB::create() and is set to a default value of 31 pixels. "This is size of the border where the features are not detected. It should roughly match the patchSize parameter."
QUESTION
Question: The best way to find out the Eps and MinPts parameters for DBSCAN algorithm?
Problem: The goal is to find the locations (clusters) based on coordinates (input data). The algorithm calculates the most visited areas and retrieves these clusters.
Approach:
I defined the epsilon (EPS) parameter as 1.5 km - converted to radians to be used by the DBSCAN algorithm: epsilon = 1.5 / 6371.0088 (ref to this 1.5 km: https://geoffboeing.com/2014/08/clustering-to-reduce-spatial-data-set-size/).
If I define the MinPts to a low value (e.g. MinPts = 5, it will produce 2000 clusters), the DBSCAN will produce too many clusters and I want to limit the relevance/size of the clusters to an acceptable value. I use the haversine metric and ball tree algorithm to calculate great-circle distances between points.
Suggestions:
- knn approach to find EPS;
- domain knowledge and to decide the best values for EPS and MinPts.
Data: I'm using 160k coordinates but the program should be capable to handle different data inputs.
...ANSWER
Answered 2020-Aug-13 at 11:59As you may know, setting MinPts high will not only prevent small clusters from forming, but will also change the shape of larger clusters as its outskirts will be considered outliers.
Consider instead a third way to reduce the number of clusters; simply sort by descending size (number of coordinates) and limit that to 4 or 5. This way, you won't be shown all the small clusters if you're not interested in them, but you can instead treat all those points as noise.
You're essentially using DBSCAN for something it's not meant for, namely to find the n largest clusters, but that's fine - you just need to "tweak the algorithm" to fit your use case.
Update
If you know the entire dataset and it will not change in the future, I would just tweak minPts manually, based on your knowledge.
In scientific environments and with varying data sets, you consider the data as "generated from a stochastic process". However, that would mean that there is a chance - no matter how small - that there are minPts dogs in a remote forest somewhere at the same time, or minPts - 1 dogs in Central Park, where it's normally overcrowded.
What I mean by that is that if you go down the scientific road, you need to find a balance between the deterministic value of minPts and the probabilistic distribution of the points in your data set.
In my experience, it all comes down to whether or not you trust your knowledge, or would like to defer responsibility. In some government/scientific/large corporate positions, it's a safer choice to pin something on an algorithm than on gut feeling. In other situations, it's safe to use gut feeling.
QUESTION
I am trying to perform hyperparameter tuning for Spatio-Temporal K-Means clustering by using it in a pipeline with a Decision Tree classifier. The idea is to use K-Means clustering algorithm to generate cluster-distance space matrix and clustered labels which will be then passed to Decision Tree classifier. For hyperparameter tuning, just use parameters for K-Means algorithm.
I am using Python 3.8 and sklearn 0.22.
The data I am interested is having 3 columns/attributes: 'time', 'x' and 'y' (x and y are spatial coordinates).
The code is:
...ANSWER
Answered 2020-May-25 at 13:36250 and 251 are respectively the shapes of your train and validation in GridSearchCV
look at your custom estimator...
QUESTION
I am trying to perform hyperparameter tuning for Spatio-Temporal K-Means clustering by using it in a pipeline with a Decision Tree classifier. The idea is to use K-Means clustering algorithm to generate cluster-distance space matrix and clustered labels which will be then passed to Decision Tree classifier. For hyperparameter tuning, just use parameters for K-Means algorithm.
I am using Python 3.8 and sklearn 0.22.
The data I am interested is having 3 columns/attributes: 'time', 'x' and 'y' (x and y are spatial coordinates).
The code is:
...ANSWER
Answered 2020-May-25 at 11:59Your error message says it all: All intermediate steps should be transformers and implement fit and transform. In your case, your class ST_KMeans() has to implement a transform function as well to be used in a pipeline. Besides, best-practice is usually to inherit from the classes BaseEstimator and TransformerMixin from the module sklearn.base:
QUESTION
I am confused, about the OPTICS algorithm. A set of points can be considered as a cluster, if they are density-connected. A point p is density-connected to a point q if there is an object o such that both p and q are density-reachable from o wrt epsilon and MinPts.
In my case (epsilon=5, minPts=2, L1-norm=Manhattan distance) H is a core point, since it is has more than 2 points in its epsilon distance. H is density-reachable from G and to G, because they share E. The same is true with H and S, because they share T. After all, E, T, S and G are within the epsilon range of H. In my opinion E, G, H, S, T are in the same cluster.
If I run it with sklearn.optics it gives me the result of the picture, where H is a noise point.
Why is E, G, H, S and T not in the same cluster?
...ANSWER
Answered 2020-May-22 at 11:52The final clustering result is based on the xi-step method (Figure 19 in the OPTICS paper) and not on the definition of reachability, which is actually the definition of the final clustering in DBSCAN.
In the xi-step method the algorithm detects the valleys or bumps from the reachability plot as clusters. In the reachability plot
the reachability distance between H and S is relatively high, this is why H is called an outlier.
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