Part 1. Following contents is the reflection of my completed academic image processing course in the previous term. So, I am not planning on putting anything into production sphere. Instead, the aim of this article is to try and realize the fundamentals of a few basic image processing techniques. For this reason, I am going to stick to using imageio or numpy mainly to perform most of the manipulations, although I will use other libraries now and then rather than using most wanted tools such as OpenCV :.
Negative transformation, which is the invert of identity transformation. So each value is subtracted by So what happens is that the lighter pixels become dark and the darker picture becomes light.
And it results in image negative. So 1 is added, to make the minimum value at least 1. During log transformation, the dark pixels in an image are expanded as compared to the higher pixel values.
The higher pixel values are kind of compressed in log transformation. This result in the following image enhancement. Gamma correction, or often simply gamma, is a nonlinear operation used to encode and decode luminance or tristimulus values in video or still image systems. Gamma correction is also known as the Power Law Transform. First, our image pixel intensities must be scaled from the range 0, to 0, 1.
From there, we obtain our output gamma corrected image by applying the following equation:. The reason we apply gamma correction is that our eyes perceive color and luminance differently than the sensors in a digital camera. When a sensor on a digital camera picks up twice the amount of photons, the signal is doubled.
However, our eyes do not work like this. Instead, our eyes perceive double the amount of light as only a fraction brighter. Thus, while a digital camera has a linear relationship between brightness our eyes have a non-linear relationship. In order to account for this relationship, we apply gamma correction. Now, Depending on the resolution and size of the image, it will see a 32 x 32 x 3 array of numbers where the 3 refers to RGB values or channels.
The representative array will be x x 3. Each of these numbers is given a value from 0 to which describes the pixel intensity at that point.
Like we mentioned before, the input is a 32 x 32 x 3 array of pixel values. Now, the best way to explain a convolution is to imagine a flashlight that is shining over the top left of the image.
In machine learning terms, this flashlight is called a filter or kernel or sometimes referred to as weights or mask and the region that it is shining over is called the receptive field. Now, this filter is also an array of numbers where the numbers are called weights or parameters.Canny edge detector is the most widely used edge detector in Computer Vision, hence understanding and implementing it will be very important for any CV Engineer.
There are many incomplete implementation are available in GitHub, however we will understand every step and build the complete algorithm. Applying Gaussian Smoothing to an Image using Python from scratch. How to implement Sobel edge detection using Python from scratch. However the returned value will be in radian. This is probably the most important step in Canny Edge Detection Algorithm. We have two different parameters. We want just one line to show the edge rather than having multiple lines for the same edge.
This can be done by the Non-Max Suppression Algorithm. A pixel can have total 4 directions for the gradient shown below since there are total 8 neighboring pixels. Now, consider the following example. Hence we will compare the magnitude of the gradient with both the pixel above 90 Degree and below Degree it. Now in this example white represents higher value and black represents lower value 0. We can see that the bottom pixel has higher value than the one we are comparing with.
Since we started with a black image, the value of this this pixel will remain 0. Here is another example, say the gradient direction of a given pixel is 0 degree.
So we will compare the magnitude of gradient of the right 0 degree and left degree pixel with it. In this example, clearly the magnitude of gradient of the selected pixel is higher than the other two, hence we update our output pixel value by the magnitude of gradient of the selected pixel.
We will repeat this for all the pixel except the boarding pixel of the image.
The output will look like following:. There is one more concept to go through before we jump into the code. In our example we have taken the direction as 90 or 0 degree, where we know we had to compare the top or bottom pixel.
What about 57 Degree or Degree? In order to solve this problem, we will consider a range of degrees to select a neighbor. Look at the below picture. The Gradient Magnitude and Direction. Our output image will start with with a back image with the same width and height of the input image.Here's an interesting article - we'll implement canny edges.
We won't use any prepackaged functions. I assume you know how the algorithm works. If not, read up about the Canny edge detection algorithm! We send in an image, specify the upper and lower threshold and specify the accuracy of the sobel edge detection with the size parameter it is the size of the kernel used in sobel.
For this article, I won't be considering error checking and other unrelated things checking image depth, channels, etc. If you want, you can put a Gaussian blur before you even start any work. Or you could simply clone src. Next, we'll calculate gradient magnitudes and orientations separately. We start with calculating the sobel of the image:. We're calculating for every point. Simple stuff. We need to figure out points that definitely lie on edges - points whose gradient magnitude is greater than upper threshold and are a maxima.
We've initialized all iterators for the magnitude, direction and return images. We store the current pixel in pos. Calculate the gradient direction in terms of degrees and make it positive and store it back. Then, if the pixel's gradient magnitude is not high enough, we simple skip it. Now add this line:. This boolean variables indicates if the current pixel is an edge or not.
At the end, if it passes all tests, it will still be true and we mark the pixel as an edge. Next, we consider each bin of the direction:. If the gradient direction is between This means, the edge is from top left to bottom right. This is done with the sum. The extra if's ensure you don't go out of bounds.
If the magnitude is lower than the neighbours, flag is set to false. After this if-else ladder, we know if the current pixel is a definite edge or not. And based on this, we mark the pixel as white in the return image:. We keep a flag to indicate if the image was changed in the previous iteration or not.
If it was, we need to check again. If not, we've got our final image. At the beginning of the loop, we set imageChanged to false.
If new edge pixels are found, this will be changed to true. Next, we initialize iterators again:. If we've near the edges, we skip those pixels. A convention we're using for this part is - a pixel with intensity means a 'fresh' edge pixel. It's neighbors have not been checked. After they've been checked, their intensity is changed to Both numbers are arbitrary.
I could have selected instead of 64 as well.Edge detection is an image processing technique for finding the boundaries of objects within images. It mainly works by detecting discontinuities in brightness. One of the most popular and widely used algorithm is Canny edge detector.
Canny edge detector is an edge detection operator that uses multi-stage algorithm to detect a wide range of edges in images. The main stages are:. Learn more here about the theory behind Canny edge detector. Alright, let's implement it in Python using OpenCVinstalling it:. Open up a new Python file and follow along:. Now let's read the image when want to detect its edges:.
I have an example image in my current directory, make sure you do too. Before we pass the image to the Canny edge detector, we need to convert the image to gray scale:. All we need to do now, is to pass this image to cv2. Canny function which finds edges in the input image and marks them in the output map edges using the Canny algorithm:. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges.
Let's see the resulting image:. Interesting, try to fine tune the threshold values and see if you can make it better. If you want to use the live camera, here is the full code for that:. Alright, we are done! The purpose of detecting edges is to capture important events and changes in properties of the world. It is one of the fundamental steps in image processing, image pattern recognition and computer vision techniques.
Detecting and recognizing human faces face detection in Python using OpenCV library that provides us with pre trained haar cascade classifiers.
Hough transform is a popular feature extraction technique to detect any shape within an image.
How to Perform Edge Detection in Python using OpenCV
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Subscribe for our newsletter. Get Python Tutorials.Canny edge detection is a image processing method used to detect edges in an image while suppressing noise. The main steps are as follows:. Convert the image to grayscale. Perform a Gaussian blur on the image. The blur removes some of the noise before further processing the image. A sigma of 1.
An edge occurs when the color of an image changes, hence the intensity of the pixel changes as well. The image magnitude produced results in thick edges. Ideally, the final image should have thin edges. Thus, we must perform non maximum suppression to thin out the edges. Non maximum suppression works by finding the pixel with the maximum value in an edge. In the above image, it occurs when pixel q has an intensity that is larger than both p and r where pixels p and r are the pixels in the gradient direction of q.
If this condition is true, then we keep the pixel, otherwise we set the pixel to zero make it a black pixel. Non maximum suppression without interpolation requires us to divide the 3x3 grid of pixels into 8 sections. I found that using interpolation to give nicer results at the cost of a longer run time.
Both implementations can be found on my github. We notice that the result from non maximum suppression is not perfect, some edges may not actually be edges and there is some noise in the image.
Double thresholding takes care of this. It sets two thresholds, a high and a low threshold. In my algorithm, I normalized all the values such that they will only range from 0 to 1. Pixels with a high value are most likely to be edges.
For example, you might choose the high threshold to be 0. You might also choose a low threshold of 0. The values in between 0. Step 6 will explain how we can determine which weak edge is an actual edge. This threshold is different per image so I had to vary the values. In my implementation I found it helpful to choose a threshold ratio instead of a specific value and multiple that by the max pixel value in the image.
As for the low threshold, I chose a low threshold ratio and multiplied it by the high threshold value:. Doing this allowed me to successfully use approximately the same ratios for other images to successfully detect edges.
Now that we have determined what the strong edges and weak edges are, we need to determine which weak edges are actual edges. To do this, we perform an edge tracking algorithm. Weak edges that are not connected to strong edges will be removed. To speed up this process, my algorithm keeps track of the weak and strong edges that way I can recursively iterate through the strong edges and see if there are connected weak edges instead of having to iterate through every pixel in the image.
Finally, we will iterate through the remaining weak edges and set them to zero resulting in the final processed image:. Introduction Canny edge detection is a image processing method used to detect edges in an image while suppressing noise.
Black and White.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository contains an educational implementation of the Canny Edge Detector in Python 2. I wrote this to generate plots for my Bachelor's thesis about Numerical Edge Detection and not for productive applications.
The algorithm was splitted in it's 5 essential parts. Requirements: You need to have Python 2. You can also use Python Anaconda which already includes all those libraries.
Canny Edge Detection
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Basic Image Processing In Python - Part 2
Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit 5abf00c Nov 6, Usage Requirements: You need to have Python 2.
To generate the Edge Image of lena. You signed in with another tab or window.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. An easy to understand implementation of the Canny Edge Detection Algorithm in python. Jupyter Notebook. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. The value of Sigma to implement Gaussian Blur is image specific, different values can be tested to see which give the best estimate of edges.
The ratio of the thresholds is again another variable, but the ones that I have used in the code give pretty good estimates for any particular image. Non Maxima Suppression with Interpolation although being computationally expensive, gives excellent estimates and is better tha NMS without interpolation.
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Jupyter Notebook Code. Nov 11, Test Image.