vectorize | Convert raster image to vectorized contours or polygons | Computer Vision library

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kandi X-RAY | vectorize Summary

vectorize is a Python library typically used in Artificial Intelligence, Computer Vision applications. vectorize has no bugs, it has no vulnerabilities and it has low support. However vectorize build file is not available. You can download it from GitHub.

convert raster image to vectorized contours or polygons.

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vectorize has a low active ecosystem.

It has 2 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

vectorize has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of vectorize is current.

Quality

vectorize has 0 bugs and 0 code smells.

Security

vectorize has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

vectorize code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

vectorize does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

vectorize releases are not available. You will need to build from source code and install.

vectorize has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed vectorize and discovered the below as its top functions. This is intended to give you an instant insight into vectorize implemented functionality, and help decide if they suit your requirements.

Compute the contours of a continuous image .
Vectorize data .
Read an image .
Define a numpy array .
Write geojson data to file .

Get all kandi verified functions for this library.

vectorize Key Features

No Key Features are available at this moment for vectorize.

vectorize Examples and Code Snippets

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Community Discussions

Trending Discussions on vectorize

Lookup Values by Corresponding Column Header in Pandas 1.2.0 or newer

Getting optimal vocab size and embedding dimensionality using GridSearchCV

NumPy maxima of groups defined by a label array

Is the timing of MATLAB reliable? If yes, can we reproduce the performance with julia, fortran, etc.?

How to use fn_map to map each row in an array C to its coresponding one in the array B

R how to speed up pattern matching using vectors

Julia: Vectorize function along a specific axis of a matrix

How to vectorize the following, the append is probably the bottleneck

How to vectorise int8 multiplcation in C (AVX2)

Numpy: How to get the sums of integer array slices based on indexes from another array in a vectorized manner?

QUESTION

Lookup Values by Corresponding Column Header in Pandas 1.2.0 or newer

Asked 2022-Feb-13 at 20:11

The operation pandas.DataFrame.lookup is "Deprecated since version 1.2.0", and has since invalidated a lot of previous answers.

This post attempts to function as a canonical resource for looking up corresponding row col pairs in pandas versions 1.2.0 and newer.

Some previous answers to this type of question (now deprecated):

Some Current Answers to this Question:

Standard LookUp Values With Default Range Index

Given the following DataFrame:

...

ANSWER

Answered 2021-Nov-18 at 21:34

Standard LookUp Values With Any Index

The documentation on Looking up values by index/column labels recommends using NumPy indexing via factorize and reindex as the replacement for the deprecated DataFrame.lookup.

Source https://stackoverflow.com/questions/69352472

QUESTION

Getting optimal vocab size and embedding dimensionality using GridSearchCV

Asked 2022-Feb-06 at 09:13

I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.

...

ANSWER

Answered 2022-Feb-02 at 08:53

I tried with scikeras but I got errors because it doesn't accept not-numerical inputs (in our case the input is in str format). So I came back to the standard keras wrapper.

The focal point here is that the model is not built correctly. The TextVectorization must be put inside the Sequential model like shown in the official documentation.

So the build_model function becomes:

Source https://stackoverflow.com/questions/70884608

QUESTION

NumPy maxima of groups defined by a label array

Asked 2022-Feb-03 at 04:49

I have two arrays, one is a list of values and one is a list of IDs corresponding to each value. Some IDs have multiple values. I want to create a new array that contains the maximum value recorded for each id, which will have a length equal to the number of unique ids.

Example using a for loop:

...

ANSWER

Answered 2022-Feb-02 at 23:19

Here's a solution, which, although not 100% vectorized, (per my benchmarks) takes about half the time as your does (using your sample data). The performance improvement probably becomes more drastic with more data:

Source https://stackoverflow.com/questions/70964001

QUESTION

Is the timing of MATLAB reliable? If yes, can we reproduce the performance with julia, fortran, etc.?

Asked 2022-Jan-05 at 08:26

Originally this is a problem coming up in mathematica.SE, but since multiple programming languages have involved in the discussion, I think it's better to rephrase it a bit and post it here.

In short, michalkvasnicka found that in the following MATLAB sample

...

ANSWER

Answered 2021-Dec-30 at 12:23

tic/toc should be fine, but it looks like the timing is being skewed by memory pre-allocation.

I can reproduce similar timings to your MATLAB example, however

On first run (clear workspace)
- Loop approach takes 2.08 sec
- Vectorised approach takes 1.04 sec
- Vectorisation saves 50% execution time
On second run (workspace not cleared)
- Loop approach takes 2.55 sec
- Vectorised approach takes 0.065 sec
- Vectorisation "saves" 97.5% execution time

My guess would be that since the loop approach explicitly creates a new matrix via zeros, the memory is reallocated from scratch on every run and you don't see the speed improvement on subsequent runs.

However, when HH remains in memory and the HH=___ line outputs a matrix of the same size, I suspect MATLAB is doing some clever memory allocation to speed up the operation.

We can prove this theory with the following test:

Source https://stackoverflow.com/questions/70531385

QUESTION

How to use fn_map to map each row in an array C to its coresponding one in the array B

Asked 2022-Jan-03 at 18:53

Since I am working with TensorFlow, I would like to know how to map my rows from a tensor C to the index of its corresponding row in matrix B.

Here is the code I wrote:

...

ANSWER

Answered 2022-Jan-03 at 18:53

You do not have to use tf.map_fn. Maybe try something like this:

Source https://stackoverflow.com/questions/70559051

QUESTION

R how to speed up pattern matching using vectors

Asked 2021-Dec-11 at 12:50

I have a column in one dataframe with city and state names in it:

ac <- c("san francisco ca", "pittsburgh pa", "philadelphia pa", "washington dc", "new york ny", "aliquippa pa", "gainesville fl", "manhattan ks")

ac <- as.data.frame(ac)

I would like to search for the values in ac$ac in another data frame column, d$description and return the value of column id if there is a match.

...

ANSWER

Answered 2021-Dec-07 at 19:46

Try this sapply with grep.

Source https://stackoverflow.com/questions/70236614

QUESTION

Julia: Vectorize function along a specific axis of a matrix

Asked 2021-Dec-02 at 13:34

What is the best way in Julia to vectorize a function along a specific axis? For example sum up all the rows of a matrix. Is it possible with the dot notation?

...

ANSWER

Answered 2021-Dec-02 at 13:27

Try using the dims argument on a lot of functions that deal with sets of values.

Source https://stackoverflow.com/questions/70199016

QUESTION

How to vectorize the following, the append is probably the bottleneck

Asked 2021-Nov-27 at 18:06

I have the following dataframe with two columns c1 and c2, I want to add a new column c3 based on the following logic, what I have works but is slow, can anyone suggest a way to vectorize this?

Must be grouped based on c1 and c2, then for each group, the new column c3 must be populated sequentially from values where the key is the value of c1 and each "sub group" will have subsequent values, IOW values[value_of_c1][idx], where idx is the "sub group", example below
The first group (1, 'a'), here c1 is 1, the "sub group" "a" index is 0 (first sub group of 1) so c3 for all rows in this group is values[1][0]
The second group (1, 'b') here c1 is still 1 but "sub group" is "b" so index 1 (second sub group of 1) so for all rows in this group c3 is values[1][1]
The third group (2, 'y') here c1 is now 2, "sub group" is "a" and the index is 0 (first sub group of 2), so for all rows in this group c3 is values[2][0]
And so on
values will have the necessary elements to satisfy this logic.

Code

...

ANSWER

Answered 2021-Nov-27 at 17:43

In [203]: a = pd.DataFrame([[k, value, idx] for k,v in values.items() for idx,value in enumerate(v)], columns=['c1', 'c3', 'gr'])
     ...: b = df.assign(gr=df.groupby(['c1']).transform(lambda x: (x.ne(x.shift()).cumsum())- 1))
     ...: print(b)
     ...: b.merge(a).drop(columns='gr')
     ...:
# b
    c1 c2  gr
0    1  a   0
1    1  a   0
2    1  a   0
3    1  b   1
4    1  b   1
5    1  b   1
6    2  y   0
7    2  y   0
8    2  y   0
9    2  z   1
10   2  z   1
11   2  z   1
Out[203]:
    c1 c2  c3
0    1  a  a1
1    1  a  a1
2    1  a  a1
3    1  b  a2
4    1  b  a2
5    1  b  a2
6    2  y  b1
7    2  y  b1
8    2  y  b1
9    2  z  b2
10   2  z  b2
11   2  z  b2

Source https://stackoverflow.com/questions/70136823

QUESTION

How to vectorise int8 multiplcation in C (AVX2)

Asked 2021-Nov-05 at 03:42

How do I vectorize this C function with AVX2?

...

ANSWER

Answered 2021-Nov-04 at 23:23

You need to add restrict qualifier to mark c that it cannot alias with b.

The issue is that int8_t is likely signed char which can alias with any other type according to strict aliasing rule. Therefore the compiler cannot be sure that setting c[i] will not modify b[i]. The forces the compiler to fetch data on every iteration.

Presence of const does not mean anything because it only limit programmer from modifying data via pointer b.

After replacing the prototype to:

Source https://stackoverflow.com/questions/69846842

QUESTION

Numpy: How to get the sums of integer array slices based on indexes from another array in a vectorized manner?

Asked 2021-Oct-28 at 23:14

Given two integer arrays a and b, where the elements in b represent indices of a...

...

ANSWER

Answered 2021-Oct-28 at 23:09

Is that what you are looking for?

Source https://stackoverflow.com/questions/69761783

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Vulnerabilities

No vulnerabilities reported

Install vectorize

You can download it from GitHub.
You can use vectorize like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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