quantize | Color quantization in JavaScript | Compression library

by need12648430 JavaScript Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | quantize Summary

quantize is a JavaScript library typically used in Utilities, Compression applications. quantize has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Quantize.js is a tiny (10.1 kB minified) color quantization library. It extracts approximate color palletes from an input image using a variety of different methods. It's similar to Color Thief, but twice as fast in Chrome with multiple quantization methods supported.

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quantize has a low active ecosystem.

It has 4 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of quantize is current.

Quality

quantize has no bugs reported.

Security

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

License

quantize 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.

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

Installation instructions are not available. Examples and code snippets are available.

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quantize Key Features

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quantize Examples and Code Snippets

Quantize a model .

python

Lines of Code : 145

License : Non-SPDX (Apache License 2.0)

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def _static_range_quantize(
    saved_model_path: str,
    signature_keys: Sequence[str],
    tags: Collection[str],
    output_directory: str,
    representative_dataset: Optional[
        repr_dataset.RepresentativeDatasetOrMapping] = None
) ->.

Quantize and dequantize an array .

python

Lines of Code : 83

License : Non-SPDX (Apache License 2.0)

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def quantize_and_dequantize_v2(
    input,  # pylint: disable=redefined-builtin
    input_min,
    input_max,
    signed_input=True,
    num_bits=8,
    range_given=False,
    round_mode="HALF_TO_EVEN",
    name=None,
    narrow_range=False,
    axis

Quantize a saved model .

python

Lines of Code : 74

License : Non-SPDX (Apache License 2.0)

Copy

def quantize(
    saved_model_path: str,
    signature_keys: Optional[Sequence[str]] = None,
    tags: Optional[Collection[str]] = None,
    output_directory: Optional[str] = None,
    quantization_options: Optional[quant_opts_pb2.QuantizationOptions

Community Discussions

Trending Discussions on quantize

Tflite detext error: cv2.error: OpenCV(4.5.2) :-1: error: (-5:Bad argument) in function 'rectangle'

Truncating all values in a file to 6 digits after the decimal in Python

unsupported operand type(s) for *: 'NoneType' and 'decimal.Decimal'

Are these normal speed of Bert Pretrained Model Inference in PyTorch

Converting SSD object detection model to TFLite and quantize it from float to uint8 for EdgeTPU

k-mean clustering - inertia only gets larger

Draw or resize plotted quantized image with nearest neighbour scaling

How to remove "infinite" while loop to improve MATLAB code?

tflite converter error operation not supported

ValueError: Cannot set tensor: Dimension mismatch. Got 3 but expected 4 for input 0

QUESTION

Tflite detext error: cv2.error: OpenCV(4.5.2) :-1: error: (-5:Bad argument) in function 'rectangle'

Asked 2021-Jun-10 at 13:39

As I detect my tflite file, the problem happened.

The command I wrote.

...

ANSWER

Answered 2021-Jun-10 at 12:41

The problem is that you are passing tuples with floats into the function's parameters as the points. Here is the error reproduced:

Source https://stackoverflow.com/questions/67921192

QUESTION

Truncating all values in a file to 6 digits after the decimal in Python

Asked 2021-Jun-07 at 14:37

I have a file that has thousands of values in scientific notation up to 12 digits after the decimal. I am trying to use Python to truncate all values in this file to 6 digits after the decimal and overwrite the existing file. Can I just use the decimal package to do this?

...

ANSWER

Answered 2021-Jun-07 at 14:37

I found an answer to your question:

Source https://stackoverflow.com/questions/67873421

QUESTION

unsupported operand type(s) for *: 'NoneType' and 'decimal.Decimal'

Asked 2021-May-30 at 20:01

I am trying to calculate the subtotal, VAT and Total for creating an invoice. I`ve got an error

...

ANSWER

Answered 2021-May-30 at 20:01

The error says you are using a * operation where the first number is actually not a number, but None.

Since the last one is just a suggestion I assume the error will be on the first time you use it, when you calculate the subtotal.

Source https://stackoverflow.com/questions/67765263

QUESTION

Are these normal speed of Bert Pretrained Model Inference in PyTorch

Asked 2021-May-26 at 23:43

I am testing Bert base and Bert distilled model in Huggingface with 4 scenarios of speeds, batch_size = 1:

...

ANSWER

Answered 2021-May-26 at 20:38

No, you can speed it up.

First, why are you testing it with batch size 1?

Both tokenizer and model accept batched inputs. Basically, you can pass a 2D array/list that contains a single sample at each row. See the documentation for tokenizer: https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.PreTrainedTokenizer.__call__ The same applies for the models.

Also, your for loop is sequential even if you use batch size larger than 1. You can create a test data and then use Trainer class with trainer.predict()

Also see this discussion of mine at the HF forums: https://discuss.huggingface.co/t/urgent-trainer-predict-and-model-generate-creates-totally-different-predictions/3426

Source https://stackoverflow.com/questions/67699354

QUESTION

Converting SSD object detection model to TFLite and quantize it from float to uint8 for EdgeTPU

Asked 2021-May-21 at 10:11

I am having problems converting a SSD object detection model into a uint8 TFLite for the EdgeTPU.

As far as I know, I have been searching in different forums, stack overflow threads and github issues and I think I am following the right steps. Something must be wrong on my jupyter notebook since I can't achive my proposal.

I am sharing with you my steps explained on a Jupyter Notebook. I think it will be more clear.

...

ANSWER

Answered 2021-May-04 at 08:17

The process, as @JaesungChung answered is well done.

My problem was on the application which was running the .tflite model. I quantized my model output to uint8, so I had to reescale my obtained values to get the right results.

I.e. I had 10 objects because I was requesting all the detected objects with an score above 0.5. My results were no scaled, so the detected objects scores could be perfectly 104. I had to reescale that number dividing by 255.

The same happened when graphing my results. So I had to divide that number and multiplicate by the height and width.

Source https://stackoverflow.com/questions/67330342

QUESTION

k-mean clustering - inertia only gets larger

Asked 2021-May-20 at 16:46

I am trying to use the KMeans clustering from faiss on a human pose dataset of body joints. I have 16 body parts so a dimension of 32. The joints are scaled in a range between 0 and 1. My dataset consists of ~ 900.000 instances. As mentioned by faiss (faiss_FAQ):

As a rule of thumb there is no consistent improvement of the k-means quantizer beyond 20 iterations and 1000 * k training points

Applying this to my problem I randomly select 50000 instances for training. As I want to check for a number of clusters k between 1 and 30.

Now to my "problem":

The inertia is increasing directly as the number of cluster increases (n_cluster on the x-axis):

I tried varying the number of iterations, the number of redos, verbose and spherical, but the results stay the same or get worse. I do not think that it is a problem of my implementation; I tested it on a small example with 2D data and very clear clusters and it worked.

Is it that the data is just bad clustered or is there another problem/mistake I have missed? Maybe the scaling of the values between 0 and 1? Should I try another approach?

...

ANSWER

Answered 2021-May-20 at 16:46

I found my mistake. I had to increase the parameter max_points_per_centroid. As I have so many data points it sampled a sub-batch for the fit. For a larger number of clusters this sub-batch is larger. See FAQ of faiss:

max_points_per_centroid * k: there are too many points, making k-means unnecessarily slow. Then the training set is sampled

The larger subbatch of course has a larger inertia as there are more points in total.

Source https://stackoverflow.com/questions/67550997

QUESTION

Draw or resize plotted quantized image with nearest neighbour scaling

Asked 2021-May-18 at 18:38

Following this example of K means clustering I want to recreate the same - only I'm very keen for the final image to contain just the quantized colours (+ white background). As it is, the colour bars get smooshed together to create a pixel line of blended colours.

Whilst they look very similar, the image (top half) is what I've got from CV2 it contains 38 colours total. The lower image only has 10 colours and is what I'm after.

Let's look at a bit of that with 6 times magnification:

I've tried :

...

ANSWER

Answered 2021-May-18 at 16:27

I recommend you to show the image using cv2.imshow, instead of using matplotlib.

cv2.imshow shows the image "pixel to pixel" by default, while matplotlib.pyplot matches the image dimensions to the size of the axes.

Source https://stackoverflow.com/questions/67589929

QUESTION

How to remove "infinite" while loop to improve MATLAB code?

Asked 2021-Apr-28 at 17:39

I am implementing a logarithmic quantizer and what I would like to do is to optimize the code as much as possible. The precise point where I would like to make a change is the last else statement where the equation to be implemented is:

q(u) = u_i if u_i/(1+step) < u <= u_i/(1-step)
u_i = p^(1-i)u_o for i=1,2,...

The parameters p, step, u_o are some constants to be chosen.

More information regarding the quantizer can be found at this paper: Adaptive Backstepping Control of Uncertain Nonlinear Systems with Input Quantization.

In order to code a function to implement it in MATLAB, I wrote the following piece of code:

...

ANSWER

Answered 2021-Apr-28 at 17:39

Assuming u_min>0 and 0

<1, you can simplify (u > u_i/(1+step)) && (u <= u_i/(1-step)) to:

Source https://stackoverflow.com/questions/66075396

QUESTION

tflite converter error operation not supported

Asked 2021-Apr-25 at 10:05

I was trying to convert .pb model of albert to tflite

I made .pb model using https://github.com/google-research/albert in tf 1.15

And I used tconverter = tf.compat.v1.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory to make tflite file(in tf 2.4.1)

but

...

ANSWER

Answered 2021-Apr-25 at 10:05

Please consider using the Select TF option in order to fall back to the TF ops when TFLite builtin op coverage does not fit your case.

For the conversion procedure, you can enable the Select TF option as follows:

Source https://stackoverflow.com/questions/67251401

QUESTION

ValueError: Cannot set tensor: Dimension mismatch. Got 3 but expected 4 for input 0

Asked 2021-Apr-13 at 11:19

I am new to TF and Keras. I have model trained and saved using following code

...

ANSWER

Answered 2021-Apr-13 at 11:19

Instead of removing batch size in the graph, you can expand the dimension by using expand_dims:

Source https://stackoverflow.com/questions/67068742

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install quantize

You can download it from GitHub.

Support

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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