Scripts to perform edge detection with Sobel filter

[Namyalg]

This script performs edge detection using the Sobel filter, (no inbuilt functions from the openCV library are used)

Issue relates to #93

[disha2sinha]

put space after comma

[disha2sinha]

Space after comma

[disha2sinha]

insert space before comma

[disha2sinha]

Give space after comma and remove space before comma

[disha2sinha]

insert space around -

[disha2sinha]

Space around ==

[Namyalg]

Okay will make the changes immediately

…

On Sat, Sep 5, 2020, 9:06 PM Disha Sinha ***@***.***> wrote: ***@***.**** approved this pull request. ------------------------------ In Image-Processing/Sobel-edge-detection/sobel-edge-detect.py <#230 (comment)> : > @@ -0,0 +1,76 @@ +import numpy as np +import cv2 +from google.colab.patches import cv2_imshow +from math import sqrt, atan + +#This script can be used for edge-detection without the use of any inbuilt openCV libraries +#Here the sobel filter is applied after blurring the image. + +kernelblur = np.array([1/16,1/8,1/16,1/8,1/4,1/8,1/16,1/8,1/16]).reshape(3,3) put space after comma ------------------------------ In Image-Processing/Sobel-edge-detection/sobel-edge-detect.py <#230 (comment)> : > @@ -0,0 +1,76 @@ +import numpy as np +import cv2 +from google.colab.patches import cv2_imshow +from math import sqrt, atan + +#This script can be used for edge-detection without the use of any inbuilt openCV libraries +#Here the sobel filter is applied after blurring the image. + +kernelblur = np.array([1/16,1/8,1/16,1/8,1/4,1/8,1/16,1/8,1/16]).reshape(3,3) +kernelsobelX = np.array([-1,-2,-1,0,0,0,1,2,1]).reshape(3,3) Space after comma ------------------------------ In Image-Processing/Sobel-edge-detection/sobel-edge-detect.py <#230 (comment)> : > + + #Convolution is applied here + apply_filter = [] + for i in range(1, height): + for j in range(1, width): + temp = pixels[i:i+3, j:j+3] + product = np.multiply(temp, kernel) + apply_filter.append(sum(sum(product))) + + #The pixels are converted back to the image + apply_filter = np.array(apply_filter).reshape(height-1, width-1) + return(apply_filter) + +def sobel(img): + height = img.shape[0] + width= img.shape[1] insert space before comma ------------------------------ In Image-Processing/Sobel-edge-detection/sobel-edge-detect.py <#230 (comment)> : > + #The image is blurred, so the noise is removed + blur = convolute(img, kernelblur, height, width) + + height = height - 1 + width = width - 1 + + #The sobel filter is applied in the X and Y direction separately + convoluted_Y = convolute(blur, kernelsobelX, height, width) + convoluted_X = convolute(blur, kernelsobelY, height, width) + + #Every pixel in convoluted_X can be called gx, in convoluted_Y can be called gy, + #The resultant will be sqrt(pow(gx**2) + pow(gy**2)) + resultant = [] + for i in range(height-1): + for j in range(width-1): + in_x = pow(convoluted_X[i,j] ,2) Give space after comma and remove space before comma ------------------------------ In Image-Processing/Sobel-edge-detection/sobel-edge-detect.py <#230 (comment)> : > + + #The image is blurred, so the noise is removed + blur = convolute(img, kernelblur, height, width) + + height = height - 1 + width = width - 1 + + #The sobel filter is applied in the X and Y direction separately + convoluted_Y = convolute(blur, kernelsobelX, height, width) + convoluted_X = convolute(blur, kernelsobelY, height, width) + + #Every pixel in convoluted_X can be called gx, in convoluted_Y can be called gy, + #The resultant will be sqrt(pow(gx**2) + pow(gy**2)) + resultant = [] + for i in range(height-1): + for j in range(width-1): insert space around - ------------------------------ In Image-Processing/Sobel-edge-detection/sobel-edge-detect.py <#230 (comment)> : > + convoluted_Y = convolute(blur, kernelsobelX, height, width) + convoluted_X = convolute(blur, kernelsobelY, height, width) + + #Every pixel in convoluted_X can be called gx, in convoluted_Y can be called gy, + #The resultant will be sqrt(pow(gx**2) + pow(gy**2)) + resultant = [] + for i in range(height-1): + for j in range(width-1): + in_x = pow(convoluted_X[i,j] ,2) + in_y = pow(convoluted_Y[i,j] , 2) + gradient = sqrt(in_x + in_y) + reusultant.append(grad) + resultant = np.array(resultant).reshape(height-1, width-1) + cv2_imshow(resultant) + +if __name__ == "__main__": Space around == — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#230 (review)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AM3BEYLXDJIW6P3ZB3ZPFC3SEJLHPANCNFSM4QPPWHSA> .

[disha2sinha]

Make the changes stated below and you are good to go

[Namyalg]

Yep, all done