mahotas | Computer Vision in Python | Computer Vision library

by luispedro Python Version: v1.4.13 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(6)Vulnerabilities Install Support

kandi X-RAY | mahotas Summary

mahotas is a Python library typically used in Artificial Intelligence, Computer Vision, OpenCV, Numpy applications. mahotas has no bugs, it has no vulnerabilities, it has build file available and it has high support. However mahotas has a Non-SPDX License. You can download it from GitHub.

Computer Vision in Python

Support

Quality

Security

License

Reuse

Support

mahotas has a highly active ecosystem.

It has 800 star(s) with 153 fork(s). There are 49 watchers for this library.

It had no major release in the last 12 months.

There are 20 open issues and 63 have been closed. On average issues are closed in 116 days. There are 3 open pull requests and 0 closed requests.

It has a negative sentiment in the developer community.

The latest version of mahotas is v1.4.13

Quality

mahotas has 0 bugs and 0 code smells.

Security

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

mahotas code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

mahotas has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

mahotas releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

mahotas saves you 2838 person hours of effort in developing the same functionality from scratch.

It has 6137 lines of code, 529 functions and 104 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Calculate haralick
Compute the hamalick features
Cooccurence of an image
Verify that A is an integer type
Calculate adog image
Convolve 1 - D array
Gaussian filter
Convolve a matrices
Visualize a scattering function
Convert rgb values to RGB
Slice an array
Interpolate an array
Calculate interest points
Filter labeled labels
Segment a surface of a surface
Creates a dense matrix from f
Deregate a binary image
Deserialize a binary image file
R Calculate the maximum value of a function
Overlay a color image
R Fourier transform
Save image to blob
Compute the laplacian of a 2D array
Soft threshold function
Read an image from a file
Return True if two labels are the same

Get all kandi verified functions for this library.

mahotas Key Features

No Key Features are available at this moment for mahotas.

mahotas Examples and Code Snippets

No Code Snippets are available at this moment for mahotas.

Community Discussions

Trending Discussions on mahotas

Putting list of value into a dataframe as a row

Handling division by 0 error and nan issue in python

Local Binary Patterns with Python & OpenCV

AWS Lambda: "ELF load command address/offset not properly aligned" when deployed via Serverless Framework

AWS Lambda: How to manage deployment packages greater than 250mb

How to join two figures with different sizes in matplotlib?

QUESTION

Putting list of value into a dataframe as a row

Asked 2021-Aug-17 at 09:50

import numpy as np
import cv2
import os
import mahotas
from skimage.io import imread
from skimage.transform import resize
import pandas as pd
from numpy import *


data_path = "/content/drive/My Drive/ADIP/seperate_ricepests6/seperate_ricepests6/"

image_Humoment = pd.DataFrame()

labels = os.listdir(data_path)
for dirname in labels:
    filepath = os.path.join(data_path, dirname)
    print("Extracting ",dirname," ... ")
    for file in os.listdir(filepath):
        filename = os.path.join(filepath, file)       
        image = cv2.imread(filename)
        image_resized = cv2.resize(image, (300,300))
        image_gray = cv2.cvtColor(image_resized, cv2.COLOR_BGR2GRAY)

        df = pd.DataFrame()

        # HUMoments for shape
        image_hu = cv2.HuMoments(cv2.moments(image_gray)).flatten()--------(1)
        df = image_hu.tolist()

    image_Humoment = image_Humoment.append(df, ignore_index=True)

    print(dirname,"ok!")

...

ANSWER

Answered 2021-Aug-17 at 09:50

If you are sure on "getting following results (six features for one image) in image_hu", Then have a empty dataframe having 6 columns and inside for loop append one row(having 6 values) to the end of the Dataframe like below-

Source https://stackoverflow.com/questions/68813769

QUESTION

Handling division by 0 error and nan issue in python

Asked 2021-Aug-16 at 14:06

Hi i am trying to calculate average contrast of an image using this code

...

ANSWER

Answered 2021-Aug-16 at 13:42

You could try to raise an exception in case of specific error.

Source https://stackoverflow.com/questions/68803564

QUESTION

Local Binary Patterns with Python & OpenCV

Asked 2021-Apr-08 at 11:39

Everyone I made a machine learning project using binary patterns to detect plant diseases using haralick textures from images, I trained it with 5 different set of data and it predicting in 60% accuracy. Now i got a situation that to print 3 probable diseases on one image. Example i uploaded one image and predicted it got ‘Mites’ and also want to check is there any other 3 probable diseases in image of plant.

how to achieve the 3 probable in python using local binary patterns?

Full code am trying

...

ANSWER

Answered 2021-Apr-08 at 11:39

LinearSVC does not provide predict_proba (which would give you the top 3 predicted classes), but it provides the decision_function which gives the signed distance from the hyperplane. (see related question)

So, change this part:

Source https://stackoverflow.com/questions/66982280

QUESTION

AWS Lambda: "ELF load command address/offset not properly aligned" when deployed via Serverless Framework

Asked 2020-Dec-15 at 08:11

I have a Lambda function in python3.6 that uses the following packages:

...

ANSWER

Answered 2020-Jun-24 at 21:14

I couldn't fix this with serverless. So I decided to sls deploy without pymongo and once serverless generated the .requirements.zip file, I copied that file elsewhere and once again ran sls deploy but this time with only pymongo (and pymongo[srv]) in requirements.txt. That generated .requirements.zip containing pymongo and its dependencies. I merged files from this .requirments.zip and the one requirements.zip generated from the first sls deploy. This way I got all other dependencies (opencv2, numpy, joblib etc) and pymongo in one .requirements.zip file.

After that I zipped the source code plus the merged .requirements.zip file and manually uploaded the zip to s3. It came down to 128MB zipped. Pointed my lambda function to use this deployment package from S3 and it worked. I got pymongo along with opencv2 and other dependencies.

But, a drawback is that you have to upload to S3 and update the function yourself. Until this is problem is fixed, I am going to have to use this "hack".

Source https://stackoverflow.com/questions/62560559

QUESTION

AWS Lambda: How to manage deployment packages greater than 250mb

Asked 2020-Jun-24 at 01:09

I have some image processing code in Python3.6 that I am trying to deploy to AWS Lambda using AWS SAM. The total size of all dependencies (pip packages) is greater than 250mb. I've already broken down the function into smaller parts but they all end up using the same packages, so that did not help in reducing the size.

I am using the following packages:

...

ANSWER

Answered 2020-Jun-24 at 01:08

AWS very recently released EFS support for Lambda. You should be able to put your dependencies there. There are some examples in this blog post: Using Amazon EFS for AWS Lambda in your serverless applications.

Source https://stackoverflow.com/questions/62546019

QUESTION

How to join two figures with different sizes in matplotlib?

Asked 2020-Feb-20 at 13:08

I have two different figures from matplotlib. The first one shows cell sizes through time :

...

ANSWER

Answered 2020-Feb-20 at 10:48

You can use the add_axes method of a Matplotlib's Figure:

Here I've just a Q&D example that you definitely have to tweak (a lot) to get exactly what you want

Source https://stackoverflow.com/questions/60317239

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

Vulnerabilities

No vulnerabilities reported

Install mahotas

If you are using conda, you can install mahotas from conda-forge using the following commands:.

Support

Issue Tracker: github mahotas issues. Mailing List: Use the pythonvision mailing list for questions, bug submissions, etc. Or ask on stackoverflow (tag mahotas). Main Author & Maintainer: Luis Pedro Coelho (follow on twitter or github). Mahotas also includes code by Zachary Pincus [from scikits.image], Peter J. Verveer [from scipy.ndimage], and Davis King [from dlib], Christoph Gohlke, as well as others. For more general discussion of computer vision in Python, the pythonvision mailing list is a much better venue and generates a public discussion log for others in the future. You can use it for mahotas or general computer vision in Python questions.

Find more information at: