Corner detection python


  • Implementation of corner detection based on OpenCV Harris
  • OpenCV Edge Detection ( cv2.Canny )
  • Detect the Corners of Objects Using Harris Corner Detector
  • Python | Corner Detection with Shi-Tomasi Corner Detection Method using OpenCV
  • Edge Detection Using OpenCV
  • Harris Corner Detector implementation in python
  • Implementation of corner detection based on OpenCV Harris

    Leave a reply In the previous blog, we discussed what are features and how corners are considered as a good feature as compared to edges and flat surfaces. Now it is called the Harris Corner Detector. As discussed in the previous blog, corners are regions in the image with large variations in intensity in all directions.

    For instance, take a look at the below image. If you shift the window by a small amount, then corners will produce a significant change in all directions while edges will output no change if we move the window along the edge direction. And the flat region will output no change in all directions on window movement. So, the authors took this simple idea of finding the difference in intensity for a displacement of u,v in all directions into a mathematical form.

    This is expressed as Here, the window function is either a rectangular window or a Gaussian window which gives weights to pixels underneath. E u,v is the difference in intensities between the original and the moved window. As can be clearly seen, for nearly constant patches the error function will be close to 0 while for distinctive patches this will be larger. Hence, our aim is to find patches where this error function is large.

    In other words, we need to maximize this error function for corner detection. That means we have to maximize the second term. We can do this by applying Taylor Expansion and using some mathematical steps as shown below So, the final equation becomes Then comes the main part. As we have already discussed that corners are the regions in the image with large variations in intensity in all directions.

    So, if you remember that eigenvalues tell us about the variance thus by simply analyzing the eigenvalues of the matrix M we can infer the results. But the authors note that the exact computation of the eigenvalues is computationally expensive, since it requires the computation of a square root, and instead suggests the following score function which determines if a window contains a corner or not. This is shown below Therefore, the algorithm does not have to actually compute the eigenvalue decomposition of the matrix M and instead it is sufficient to evaluate the determinant and trace of matrix M to find the corners.

    Now, depending upon the magnitude of the eigenvalues and the score R , we can decide whether a region is a corner, an edge, or flat. Then we need to do thresholding in order to find the corners. Because we consider only the eigenvalues of the matrix M , we are considering quantities that are invariant also to rotation, which is important because objects that we are tracking might rotate as well as move. So, this makes this algorithm rotation invariant. So, this concludes the Harris Corner Detector.

    I hope you understood this. Below is the syntax for this.

    OpenCV Edge Detection ( cv2.Canny )

    Any gradient value is definitely not an edge, so immediately discard these regions. And any gradient value that falls into the range needs to undergo additional tests: If the particular gradient value is connected to a strong edge i.

    If the gradient pixel is not connected to a strong edge, then discard it. Hysteresis thresholding is actually better explained visually: Figure 9: An example of applying hysteresis to a set of edges. At the top of the graph, we can see that A is a sure edge, since. B is also an edge, even though since it is connected to a sure edge, A.

    C is not an edge since and is not connected to a strong edge. Finally, D is not an edge since and is automatically discarded. Setting these threshold ranges is not always a trivial process. Configuring your development environment To follow this guide, you need to have the OpenCV library installed on your system. Having problems configuring your development environment? Figure Having trouble configuring your dev environment?

    Want access to pre-configured Jupyter Notebooks running on Google Colab? All that said, are you: Short on time? Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?

    Ready to run the code right now on your Windows, macOS, or Linux system? Then join PyImageSearch University today! No installation required. Canny function! ArgumentParser ap. Command line arguments are parsed on Lines A single switch is required, --image, which is the path to the input image we wish to apply edge detection to. GaussianBlur gray, 5, 5 , 0 show the original and blurred images cv2. Lines 17 and 18 then display our original and blurred images on our screen.

    Canny blurred, , show the output Canny edge maps cv2. Canny function to detect edges is performed on Lines The first parameter to cv2. Canny is the image we want to detect edges in — in this case, our grayscale, blurred image. We then supply the and thresholds, respectively. On Line 22, we apply a wide threshold, a mid-range threshold on Line 23, and a tight threshold on Line Note: You can convince yourself that these are wide, mid-range, and tight thresholds by plotting the threshold values on Figure 11 and Figure Finally, Lines display the output edge maps on our screen.

    In the above figure, the top-left image is our input image of coins. We then blur the image slightly to help smooth details and aid in edge detection on the top-right. The wide range, mid-range, and tight range edge maps are then displayed on the bottom, respectively. Using a wide edge map captures the outlines of the coins, but also captures many of the edges of faces and symbols inside the coins.

    The mid-range edge map also performs similarly. Finally, the tight range edge map is able to capture just the outline of the coins while discarding the rest. Unlike Figure 11, the Canny thresholds for Figure 12 give us nearly reversed results.

    Using the wide range edge map, we are able to find the outlines of the pills. The mid-range edge map also gives us the outlines of the pills, but also some of the digits imprinted on the pill. Finally, the tight edge map does not help us at all — the outline of the pills is nearly completely lost. How do we choose optimal Canny edge detection parameters? You might be wondering, is there a way to reliably tune these parameters without simply guessing, checking, and viewing the results?

    The answer is yes! What's next? I recommend PyImageSearch University. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science?

    All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. My mission is to change education and how complex Artificial Intelligence topics are taught.

    If you're serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today. Join me in computer vision mastery. Click here to join PyImageSearch University Summary In this lesson, we learned how to use image gradients, one of the most fundamental building blocks of computer vision and image processing, to create an edge detector.

    Specifically, we focused on the Canny edge detector, the most well known and most used edge detector in the computer vision community. Canny function to detect edges in images. However, one of the biggest drawbacks of the Canny edge detector is tuning the upper and lower thresholds for the hysteresis step. If our threshold was too wide, we would get too many edges.

    And if our threshold was too tight, we would not detect many edges at all! To download the source code to this post and be notified when future tutorials are published here on PyImageSearch , simply enter your email address in the form below!

    Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! Download the code! All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV. I created this website to show you what I believe is the best possible way to get your start.

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    Detect the Corners of Objects Using Harris Corner Detector

    We also normalize the window to have zero mean and unit variance, in order to obtain illumination invariance. In order to obtain rotational invariance MOPS descriptor, by taking care of the orientation that is not discussed in this article for the time being. Feature matching Now that the features in the image are detected and described, the next step is to write code to match them, i. The simplest approach is the following: write a procedure that compares two features and outputs a distance between them.

    Python | Corner Detection with Shi-Tomasi Corner Detection Method using OpenCV

    For example, we simply sum the absolute value of differences between the descriptor elements. We then use this distance to compute the best match between a feature in one image and the set of features in another image by finding the one with the smallest distance.

    The distance used here is the Manhattan distance. The following figure shows the structure of the python code to implement the algorithm. As can be seen from the following animations, lesser and lesser corner features are detected when the threshold is increased.

    Input Image Harris Corner Features Detected for different threshold values log10 Input Image The following figure shows the result of thresholding on the Harris corner strength R values and the minimum eigenvalue for the Harris matrix respectively, for each pixel, before applying non-maximum suppression computing the local maximum.

    The next animation shows the features detected after applying non-maximum suppression, with different threshold values. This is expected to work well when the images being compared are related by a translation. Matching Images with Detected Features: Results on a few images First the Harris corner features and the simple descriptors are computed for each of the images to be compared.

    Edge Detection Using OpenCV

    Next, distance between each pair of corner feature descriptors is computed, by simply computing the sum the absolute value of differences between the descriptor elements. This distance is then used to compute the best match between a feature in one image and the set of features in another image by finding the one with the smallest distance.

    The following examples show how the matching works with simple feature descriptors around the Harris corners for images obtained using mutual translations. E u,v is the difference in intensities between the original and the moved window.

    Harris Corner Detector implementation in python

    As can be clearly seen, for nearly constant patches the error function will be close to 0 while for distinctive patches this will be larger. Hence, our aim is to find patches where this error function is large. In other words, we need to maximize this error function for corner detection. That means we have to maximize the second term. We can do this by applying Taylor Expansion and using some mathematical steps as shown below So, the final equation becomes Then comes the main part.

    As we have already discussed that corners are the regions in the image with large variations in intensity in all directions. So, if you remember that eigenvalues tell us about the variance thus by simply analyzing the eigenvalues of the matrix M we can infer the results. But the authors note that the exact computation of the eigenvalues is computationally expensive, since it requires the computation of a square root, and instead suggests the following score function which determines if a window contains a corner or not.

    This is shown below Therefore, the algorithm does not have to actually compute the eigenvalue decomposition of the matrix M and instead it is sufficient to evaluate the determinant and trace of matrix M to find the corners. Now, depending upon the magnitude of the eigenvalues and the score Rwe can decide whether a region is a corner, an edge, or flat.

    Then we need to do thresholding in order to find the corners.


    Corner detection python