Google Maps Distance

You can make a distance map by eroding a binary image (with values 0 and 1) iteratively and summing the results of every iteration. Take a 8-connected neighborhood into account results in a nicely illustrated distance transform as shown on Wikipedia:

Source: :Distance_Transformation.gif by Soyweiser CC-BY SA 3.0

Erosion and dilation are two basic morphological filters and have been widely used in both academic and industrial fields. When they are used in industry, such as automated visual inspection, their implementation cost especially for large masks is a challenging issue. In this paper, we propose a FDT (form distance transform) method for implementing erosion and dilation for some regular shapes. In this proposed method, the implementation of erosion and dilation is first converted into the computation of its FDT. Then a propagation technique is used to compute the FDT. The computational cost of the new method is independent of mask sizes. In contrast of the direct implementation, if the pixel number in a morphological mask is N, the proposed method reduces the implementation cost from O(N) to O(1).

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Is it really faster to do many dilation passes (roughly as many as half the largest distance in the image, which can be the image size) than just four passes of the algorithm in ImageJ, even if a GPU is very fast on a single pass?

This sounds like an O(N) algorithm to me, so it will be slow on large images.

You can use this distance calculator to find out the distance between two or more points anywhere on the earth. In other words, the distance between A and B. Click once on the map to place the first marker and then click again to position the second marker. The distance between the points will then be displayed. You can also build up a series of locations to find a total distance.

An important feature of this distance calculator tool is that it is "as the crow flies", so traveling in real life will normally involve larger distances, but this may also help those who need to measure off-road distances.

Clearly you, the user inputs two 'points' that are used to calculate the distance. Calculating the "As the Crow Flies" distance is a matter of using Great Circle formula. Then the main problem is converting the Google Map unit to miles and kilometers.

Map showing the distance between Belfast and Dublin. As far as I can see this is an accurate estimate (if there is such a thing). This trip would be approximately 100 miles by road so the proverbial crow would have less distance to travel.

In addition to this tool we also offer a couple other tools that can help find the distance on a map. You can use the mileage calculator to compare the difference between driving or flying between 2 cities. If on the other hand you want to click multiple points on the map in order to find the distance of the entire line you can do that with the distance calculator. We are always trying to find better ways to provide you with the information you need. If you have a suggestion please let us know.

Get the travel distance and time for a matrix of origins and destinations. Get Started Start building with the Distance Matrix API. explore Get started with Google Maps Platform Create an account, generate an API key, and start building. add_road Make a distance matrix request Get the travel distance and journey duration for a matrix of origins and destinations. code Client libraries Use Java, Python, Go, or Node.js client libraries to work with Google Maps Services on your server. code OpenAPI Specification Get the OpenAPI specification for the Distance Matrix API, also available as a Postman collection. Features Learn about core features of the Distance Matrix API. Get traffic information Make a distance matrix request that calculates travel time based on current traffic conditions. Specify side of road Specify whether a calculated route should pass through a particular side of the road. Example app Run live code samples on your local machine and favorite code playgrounds with the Maps JavaScript API. code Distance Matrix Service Use the DistanceMatrixService object to fetch the distances between a set of locations. Help & support Get help. Give help. Join the community. Stack Overflow Get help. Give Help. Build Maps karma.

RR Distance Maps creates a distance map, a generalization of a protein contact mapin which residue-residue distances are shown with color gradations.In a protein contact map,a pair of residues is simply marked as contacting or not contactingbased on some criterion such as a cutoff distance.RR Distance Maps generates a color-coded map of the C-C distances within an individual protein chain or a combined map for two or more related chains. In a combined map, the average (intrachain) distances and/or their standard deviations can be shown.Both quantities can be shown on the same map with different dimensionsof color. For two chains, a map of the distance differences can also be shown.See also:Ramachandran Plot,Find Clashes/Contacts,Morph Conformations, distance, andRRDistMaps: a UCSF Chimera tool for viewing and comparing protein distance maps.Chen JE, Huang CC, Ferrin TE.Bioinformatics. 2015 May 1;31(9):1484-6.There are several ways to startRR Distance Maps, a tool in the Structure Comparison category.

Individual chains or blocks of chainscan be chosen from the Chains list with the left mouse button. Ctrl-click toggles the status of an individual chain.Only protein chains containing -carbons will be handled.If a single chain is chosen, an individual distance map will be generated. If multiple chains are chosen, they must have sequences similar enoughto be aligned. The sequences will be aligned to obtain residue equivalences,and distance differences (if two chains), means, and standard deviationswill be calculated for equivalenced residue pairs.

Choices for map Display:Distance - C-C distances within a single chain or average C-C distances among equivalenced residue pairs in multiple chainsStd Dev (multiple chains only)- standard deviations of distances among equivalenced residue pairsBoth (multiple chains only)- both the average distances and standard deviations among equivalenced residue pairsDifference (two chains only) - differences in distance for equivalenced pairs:the distance in the first structure minus that in the secondThe colors can be changed in the options,and values outside of the range of interest can bemasked by adjusting the green outlinewithin the color key.

Clicking Export brings up a dialog for saving the matrix data(distances, and if multiple chains, standard deviations, and if two chains,differences) to a text file as tab-separated or comma-separated values.Close exits from RR Distance Maps, andHelp opens this manual page in a browser window.

Note: a simple binary contact-map-like appearance can be obtained bymasking low (contact) distances after settingthe excluded color to black and all other colors to white,or using some other similar dark-light scheme.

Combined (multiple-chain) maps only include residues from fully populated alignment columns.Only sequence alignment columns containing residues from all chosen chainsare used to define the residue equivalences in a combined map.

Combined (multiple-chain) maps omit terminal residues.Related to the preceding point,the N-terminal two positions and C-terminal two positions are omitted from a combined map, because it was found that large standard deviations arising from these positions made it difficult to see variations on a smaller, more generally useful scale.

Yes, when you go to the setup of the running datafields of your watch, scroll to the map then select edit. You may select how much fields you want to have and which datafield. So might even setup distance and avg. pace at the same time.

Following the deep learning revolution in the prediction of intra-chain residue-residue distances and tertiary structures, recently some deep learning methods were developed to predict the inter-chain residue-residue contact map of homodimers and/or heterodimers, such as ComplexContact18, DeepHomo19, DRcon20, and GLINTER21 that predicts the contact map for both homodimers and heterodimers using as input a graph representation of protein monomer structure and the row attention maps generated from multiple sequence alignments (MSAs) by the MSA transformer22. The attention map calculated by the MSA transformer is a kind of residue-residue co-evolutionary feature extracted from MSAs. It has been automatically trained on millions of MSAs to capture the co-evolutionary information across many diverse protein families during its unsupervised pretraining. Despite the significant progress, the accuracy of inter-chain contact prediction is still much lower than that of intra-chain contact/distance prediction, which calls for the development of more methods to tackle this problem.

In this work, we develop a protein complex distance prediction method (CDPred) based on a deep learning architecture combining the strengths of the deep residual network23, a channel-wise attention mechanism, and a spatial-wise attention mechanism to predict the inter-chain distance maps of both homodimers and heterodimers. As in GLINTER, the attention map of the MSA generated by the MSA transformer is used as one input for CDPred. The predicted distance map for monomers in dimers is used as another input feature. Different from the existing deep learning methods, CDPred predicts inter-chain distances rather than binary inter-chain contacts (contact or no contact) that the current methods, such as DeepHomo and GLINTER predict. We test the CDPred rigorously on two homodimer test datasets and two heterodimer test datasets. For these datasets, CDPred yields much higher accuracy than DeepHomo and GLINTER. 17dc91bb1f