canny | Canny edge detection on grayscale and RGB color images | Computer Vision library

by mubastan Python Version: Current License: No License

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kandi X-RAY | canny Summary

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

Canny edge detection on grayscale and RGB color images, in Python.

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canny has a low active ecosystem.

It has 24 star(s) with 15 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 696 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of canny is current.

Quality

canny has 0 bugs and 0 code smells.

Security

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

canny code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

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

Gradient of an image
Compute the gradient of each channel
Calculates the maximum gradient of an image
Calculate the gray gradient of the image
Compute the gradient of an image

Get all kandi verified functions for this library.

canny Key Features

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canny Examples and Code Snippets

No Code Snippets are available at this moment for canny.

Community Discussions

Trending Discussions on canny

How to import Skimage to segment an image with watershed?

Houghlines not reaching edge of image

How to reduce the width of lines in black and white images and make them thinner and smoother

Need to identify the "tray is empty or not" using opencv

How to find contours in dotted text captcha image

How to compare two point of two objects

Finding straight lines from tightly coupled lines and noise curvy lines

How do I remove background from an image like this?

opencv: how to merge near contours to get the one big outest contour?

Detect wall corner with openCV - Hard

QUESTION

How to import Skimage to segment an image with watershed?

Asked 2022-Mar-14 at 01:01

I'm trying to use Skimage to segment an image with watershed, but I always get this error. Do you have a solution please?

AttributeError: module 'skimage.morphology' has no attribute 'watershed'

Source code : https://scikit-image.org/docs/0.12.x/auto_examples/xx_applications/plot_coins_segmentation.html

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ANSWER

Answered 2022-Mar-14 at 01:01

You are for some reason looking at the old documentation for scikit-image, version 0.12. (See the 0.12.x in the URL that you shared.) You can look at the examples for the latest released version at:

https://scikit-image.org/docs/stable/auto_examples/

Concretely for your code, you need to update the import to from skimage.segmentation import watershed.

Source https://stackoverflow.com/questions/71461134

QUESTION

Houghlines not reaching edge of image

Asked 2022-Mar-10 at 08:41

I am trying to detect all the lines in the following image:

Using the following code I am able to detect almost all lines:

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ANSWER

Answered 2022-Mar-10 at 08:41

The issue stems from the houghpeaks properties being to restrictive to find the smaller lines segments. The approach I used is to:

use your current code to obtain the angle that the detected lines are at (I just did [lines.theta] and found that theta will be equal to -84)
use again your code but only with that angle included, while relaxing the houghpeaks properties to allow many more lines to be detected.
you'll get many more lines detected that overlap, and we'll discard those by setting a similarity threshold based on their coefficients.

here's the implementation

Source https://stackoverflow.com/questions/71368930

QUESTION

How to reduce the width of lines in black and white images and make them thinner and smoother

Asked 2022-Mar-09 at 05:29

I have a set of black and white images as shown below like Figure 1 and I want to have them with much smaller lines like Figure 2, even though no lines are missing as much as possible. I also tried morphological methods and algorithms like Canny with Python, but I did not get the result I wanted.

Figure 1

Figure 2

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ANSWER

Answered 2022-Mar-09 at 05:29

I think you are looking for skeletonization, see skimage.skeletonize, example here :

Source https://stackoverflow.com/questions/71404307

QUESTION

Need to identify the "tray is empty or not" using opencv

Asked 2022-Mar-01 at 20:33

I want to recognize the tray is empty or not in the given image using OpenCV in python.

below is what I have tried

detect the biggest rectangle and cropped by using the below code

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ANSWER

Answered 2022-Mar-01 at 20:33

I recommend you to:

do camera calibration to make the lines in your image straight, and
align your camera well to your production line so you can simplify the image processing and make it more robust, and
use better illumination conditions if possible.

Given the images above, here is a brute force solution:

you can first extract the region of interest (ROI) through: HSV segmentation followed by contour detection, then you can extract the rectangular shape of the tray using this SO answer.

Source https://stackoverflow.com/questions/71261812

QUESTION

How to find contours in dotted text captcha image

Asked 2022-Feb-28 at 09:23

I am newbie to OpenCV. I'm trying to find the contours of the captcha image. It does not work only when my captcha image contains the dotted text.

I have done following code for that:

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ANSWER

Answered 2022-Feb-28 at 09:23

Here is my code and output

Source https://stackoverflow.com/questions/71261999

QUESTION

How to compare two point of two objects

Asked 2022-Feb-02 at 12:05

My purpose is to check whether these cubes are in one column or not. Having problem with detecting objects properly. not sure why numbers are placed there like that. My main questions:

How to compare two point of two objects to check if they are fit well or not?
How can I improve detecting objects?

Thanks in advance.

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ANSWER

Answered 2022-Feb-02 at 12:05

Since the boxes are touching each other, watershed algorithm works well in this cases to extract the box contours. Then the x coordinates position of the rectangle contours can be used to say whether they are aligned or not.

Steps:

Watershed algorithm to get markers.
Filter out boxes(rectangles)
To see if its aligned or not using standard deviation of all the boxes x coordinates. (the threshold can be set accordingly)

Code:

Source https://stackoverflow.com/questions/70929010

QUESTION

Finding straight lines from tightly coupled lines and noise curvy lines

Asked 2022-Jan-17 at 20:48

I have this image for a treeline crop. I need to find the general direction in which the crop is aligned. I'm trying to get the Hough lines of the image, and then find the mode of distribution of angles.

I've been following this tutorialon crop lines, however in that one, the crop lines are sparse. Here they are densely pack, and after grayscaling, blurring, and using canny edge detection, this is what i get

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ANSWER

Answered 2022-Jan-02 at 14:10

You can use a 2D FFT to find the general direction in which the crop is aligned (as proposed by mozway in the comments). The idea is that the general direction can be easily extracted from centred beaming rays appearing in the magnitude spectrum when the input contains many lines in the same direction. You can find more information about how it works in this previous post. It works directly with the input image, but it is better to apply the Gaussian + Canny filters.

Here is the interesting part of the magnitude spectrum of the filtered gray image:

The main beaming ray can be easily seen. You can extract its angle by iterating over many lines with an increasing angle and sum the magnitude values on each line as in the following figure:

Here is the magnitude sum of each line plotted against the angle (in radian) of the line:

Based on that, you just need to find the angle that maximize the computed sum.

Here is the resulting code:

Source https://stackoverflow.com/questions/70545797

QUESTION

How do I remove background from an image like this?

Asked 2022-Jan-12 at 17:44

I want to remove the background, and draw the outline of the box shown in the image(there are multiple such images with a similar background) . I tried multiple methods in OpenCV, however I am unable to determine the combination of features which can help remove background for this image. Some of the approaches tried out were:

Edge Detection - Since the background itself has edges of its own, using edge detection on its own(such as Canny and Sobel) didnt seem to give good results.
Channel Filtering / Thresholding - Both the background and foreground have a similar white color, so I was unable to find a correct threshold to filter the foreground.
Contour Detection - Since the background itself has a lot of contours, just using the largest contour area, as is often used for background removal, also didnt work.

I would be open to tools in Computer Vision or of Deep Learning (in Python) to solve this particular problem.

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ANSWER

Answered 2022-Jan-07 at 01:57

The Concept

This is one of the cases where it is really useful to fine-tune the kernels of which you are using to dilate and erode the canny edges detected from the images. Here is an example, where the dilation kernel is np.ones((4, 2)) and the erosion kernel is np.ones((13, 7)):

The Code

Source https://stackoverflow.com/questions/70556110

QUESTION

opencv: how to merge near contours to get the one big outest contour?

Asked 2021-Dec-04 at 02:11

I am trying to digitize the kid's drawing into SVG or transparent png file format so that they can be used in Scratch. The white paper should be replaced by transparent background and all the drawing part should be preserved.

My plan is to get the outest contour of the drawing and generate a mask, then use the mask to get the drawing part without paper background.

The problem is the drawing may not consecutive which means there may have some small holes leading to break the entire drawing contour to many many small contours.

Now I want to concatenate the near outest contours to form a big outest contour for masking.

The original drawing and the processed result is attached.

Code:

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ANSWER

Answered 2021-Dec-04 at 02:08

import cv2, numpy as np

# Read Image
img = cv2.imread('/home/stephen/Desktop/test_img.png')
img  =cv2.resize(img, (750,1000))

Source https://stackoverflow.com/questions/70222415

QUESTION

Detect wall corner with openCV - Hard

Asked 2021-Oct-15 at 05:09

I have this image (sorry for the size):

Edit: this is just a simple wall corner of my room, nothing special...

I'm trying to detect the exactly corner, but so far failed to do so. I try to use gray and bilateral, mask and canny and of course combinations of some openCV corner algorithms like Harris, EigenValsAndVecs, MinEigenVal and more... So far, I found a lot of points, or none.
Any idea please?

Thanks

...

ANSWER

Answered 2021-Oct-15 at 05:09

This seems rather straightforward. First you compute the gradient magnitude, which will find the lines in between the three mostly uniform regions (b in the image below), and then you apply something like the Harris corner detector (or any other variety will work too) to find the intersection point between the straight lines (c in the image below). I used a sigma of 4 for both the Gaussian gradient magnitude and the smoothing in the Harris detector.

I did this test quickly in MATLAB, because it was open, but you have access to the same DIPlib algorithms I used (disclosure: I'm an author of DIPlib). It would look something like this:

Source https://stackoverflow.com/questions/69568028

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

Vulnerabilities

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Install canny

You can download it from GitHub.
You can use canny 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.

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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