Edge detection algorithm python


  • Implement Canny edge detector using Python from scratch
  • How to Perform Edge Detection in Python using OpenCV
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  • Edge Detection Using OpenCV
  • Edge Detection in Images using Python
  • Canny Edge Detection OpenCV Python
  • Python – Edge Detection using Pillow
  • Implement Canny edge detector using Python from scratch

    However the returned value will be in radian. Finally we will return both the magnitude and direction of gradient. As you have seen already we can plot the Gradient Magnitude. 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.

    We will start with a black image where all pixel values are zero. 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.

    How to Perform Edge Detection in Python using OpenCV

    Canny in Usually, in Matlab and OpenCV we use the canny edge detection for many popular tasks in edge detection such as lane detection, sketching, border removal, now we will learn the internal working and implementation of this algorithm from scratch.

    The basic steps involved in this algorithm are: Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course Noise reduction using Gaussian filter Gradient calculation along the horizontal and vertical axis Non-Maximum suppression of false edges Double thresholding for segregating strong and weak edges Now let us understand these concepts in detail: 1.

    Noise reduction using Gaussian filter This step is of utmost importance in the Canny edge detection. It uses a Gaussian filter for the removal of noise from the image, it is because this noise can be assumed as edges due to sudden intensity change by the edge detector. The sum of the elements in the Gaussian kernel is 1, so, the kernel should be normalized before applying as convolution to the image. The equation for Gaussian filter kernel is 2. Gradient calculation When the image is smoothed, the derivatives Ix and Iy are calculated w.

    It can be implemented by using the Sobel-Feldman kernels convolution with image as given: Sobel Kernels after applying these kernel we can use the gradient magnitudes and the angle to further process this step. The magnitude and angle can be calculated as 3.

    Non-Maximum Suppression This step aims at reducing the duplicate merging pixels along the edges to make them uneven. If the magnitude of the current pixel is greater than the magnitude of the neighbors, nothing changes, otherwise, the magnitude of the current pixel is set to zero. Double Thresholding The gradient magnitudes are compared with two specified threshold values, the first one is lower than the second. The gradients that are smaller than the low threshold value are suppressed, the gradients higher than the high threshold value are marked as strong ones and the corresponding pixels are included in the final edge map.

    All the rest gradients are marked as weak ones and pixels corresponding to these gradients are considered in the next step. Edge Tracking using Hysteresis Since a weak edge pixel caused by true edges will be connected to a strong edge pixel, pixel W with weak gradient is marked as edge and included in the final edge map if and only if it is involved in the same connected component as some pixel S with strong gradient.

    In other words, there should be a chain of neighbor weak pixels connecting W and S the neighbors are 8 pixels around the considered one.

    We will make up and implement an algorithm that finds all the connected components of the gradient map considering each pixel only once. After that, you can decide which pixels will be included in the final edge map. Below is the implementation.

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    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.

    Edge Detection Using OpenCV

    GaussianBlur gray, 5, 50 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.

    Edge Detection in Images using Python

    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.

    Canny Edge Detection OpenCV Python

    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. This makes it a four-stage process, which includes: Noise Reduction Calculating Intensity Gradient of the Image Suppression of False Edges Hysteresis Thresholding Noise Reduction Raw image pixels can often lead to noisy edges, so it is important to reduce noise before computing edges In Canny Edge Detection, a Gaussian blur filter is used to essentially remove or minimize unnecessary detail that could lead to undesirable edges.

    Have a look at the tiger in the two images below, Gaussian blur has been applied to the image to the right. As you can see, it appears slightly blurred, but still retains a significant amount of detail from which edges can be computed. To learn more about blurring, please click here. Comparison of Original and Blurred image Calculating Intensity Gradient of the Image Once the image has been smoothed blurredit is filtered with a Sobel kernel, both horizontally and vertically.

    The results from these filtering operations are then used to calculate both the intensity gradient magnitudeand the direction for each pixel, as shown below. The figure below right shows the result of this combined processing step. Comparison of Original and Sobel Filter image Suppression of False Edges After reducing noise and calculating the intensity gradient, the algorithm in this step uses a technique called non-maximum suppression of edges to filter out unwanted pixels which may not actually constitute an edge.

    To accomplish this, each pixel is compared to its neighboring pixels, in the positive and negative gradient direction. If the gradient magnitude of the current pixel is greater than its neighbouring pixels, it is left unchanged. Otherwise, the magnitude of the current pixel is set to zero.

    The following image illustrates an example.

    Python – Edge Detection using Pillow

    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. We will start with a black image where all pixel values are zero. 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.


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