CODEWITHSUNDEEP
pythoncomputer-visionedge-detectionsobel
KshitijJaju
KshitijJaju

Reputation: 166

How to find basic shapes (brick, cylinder, sphere) in an image using the Sobel operator?

Sample 1- Brick Sample 2- Sphere

I have calculated the Sobel gradient magnitude and direction. But I'm stuck on how to use this further for shape detection.

Image> Grayscaled> Sobel filtered> Sobel gradient and direction calculated> next?

The Sobel kernels used are:

Kx = ([[1, 0, -1],[2, 0, -2],[1, 0, -1]]) 
Ky = ([[1, 2, 1],[0, 0, 0],[-1, -2, -1]])

(I have restriction to only use Numpy and no other library with language Python.)

import numpy as np
def classify(im):

   #Convert to grayscale
   gray = convert_to_grayscale(im/255.)

   #Sobel kernels as numpy arrays

   Kx = np.array([[1, 0, -1],[2, 0, -2],[1, 0, -1]]) 
   Ky = np.array([[1, 2, 1],[0, 0, 0],[-1, -2, -1]])

   Gx = filter_2d(gray, Kx)
   Gy = filter_2d(gray, Ky)

   G = np.sqrt(Gx**2+Gy**2)
   G_direction = np.arctan2(Gy, Gx)

   #labels = ['brick', 'ball', 'cylinder']
   #Let's guess randomly! Maybe we'll get lucky.
   #random_integer = np.random.randint(low = 0, high = 3)

   return labels[random_integer]

def filter_2d(im, kernel):
   '''
   Filter an image by taking the dot product of each 
   image neighborhood with the kernel matrix.
   '''

    M = kernel.shape[0] 
    N = kernel.shape[1]
    H = im.shape[0]
    W = im.shape[1]

    filtered_image = np.zeros((H-M+1, W-N+1), dtype = 'float64')

    for i in range(filtered_image.shape[0]):
        for j in range(filtered_image.shape[1]):
            image_patch = im[i:i+M, j:j+N]
            filtered_image[i, j] = np.sum(np.multiply(image_patch, kernel))

    return filtered_image

def convert_to_grayscale(im):
    '''
    Convert color image to grayscale.
    '''
    return np.mean(im, axis = 2)

Upvotes: 3

Views: 1329

Answers (1)

user1196549
user1196549

Reputation:

You can use the following distinctive characteristics of your shapes:

  • a brick has several straight edges (from four to six, depending on the point of view);

  • a sphere has a single curved edge;

  • a cylindre has two curved edges and to straight edges (though they can be completely hidden).

Use binarization (based on luminance and/or saturation) and extract the outlines. Then find the straight sections, possibly using the Douglas-Peucker simplification algorithm. Finally, analyze the sequences of straight and curved edges.

A possible way to address the final classification task, is to represent the outlines as a string of chunks, either straight or curved, with a rough indication of length (short/medium/long). With imperfect segmentation, every shape will correspond to a set of patterns.

You can work with a training phase to learn a maximum of patterns, then use string matching (where the strings are seen as loops). There will probably be ties to be arbitrated. Another option is approximate string matching.

Upvotes: 1

Related Questions