| | """ |
| | Here we define the structuring elements which are used to generate the tasks. |
| | |
| | To make it more "ARC"-like, we have used some well known human understandable |
| | patterns. |
| | |
| | We define two sizes of structuring elements - 3x3 and 5x5. |
| | |
| | List: |
| | Disk, Square(filled), cross, plus, rhombus, square(empty), line-to-right |
| | line-to-left, line-to-top, line-to-bottom. |
| | |
| | """ |
| |
|
| | import numpy as np |
| | from skimage.morphology import disk |
| |
|
| | |
| | SE1_3x3 = np.ones((3, 3), dtype=np.int32) |
| | SE1_5x5 = np.ones((5, 5), dtype=np.int32) |
| |
|
| | |
| | SE2_3x3 = np.array(disk(1), dtype=np.int32) |
| | SE2_5x5 = np.array(disk(2), dtype=np.int32) |
| |
|
| | |
| | SE3_3x3 = np.zeros((3, 3), dtype=np.int32) |
| | SE3_3x3[(0, 1, 2), (0, 1, 2)] = 1 |
| | SE3_3x3[(0, 1, 2), (2, 1, 0)] = 1 |
| | SE3_5x5 = np.zeros((5, 5), dtype=np.int32) |
| | SE3_5x5[(0, 1, 2, 3, 4), (0, 1, 2, 3, 4)] = 1 |
| | SE3_5x5[(0, 1, 2, 3, 4), (4, 3, 2, 1, 0)] = 1 |
| |
|
| | |
| | SE4_3x3 = np.zeros((3, 3), dtype=np.int32) |
| | SE4_3x3[1, :] = 1 |
| | SE4_3x3[:, 1] = 1 |
| | SE4_5x5 = np.zeros((5, 5), dtype=np.int32) |
| | SE4_5x5[2, :] = 1 |
| | SE4_5x5[:, 2] = 1 |
| |
|
| | |
| | SE5_3x3 = np.array(disk(1), dtype=np.int32) |
| | SE5_3x3[1, 1] = 0 |
| | SE5_5x5 = np.array(disk(2), dtype=np.int32) |
| | SE5_5x5[(1, 2, 2, 2, 3), (2, 1, 2, 3, 3)] = 0 |
| |
|
| | |
| | SE7_3x3 = np.ones((3, 3), dtype=np.int32) |
| | SE7_3x3[1, 1] = 0 |
| | SE7_5x5 = np.ones((5, 5), dtype=np.int32) |
| | SE7_5x5[1:4, 1:4] = 0 |
| |
|
| | |
| | SE8_3x3 = np.zeros((3, 3), dtype=np.int32) |
| | SE8_3x3[:, 2] = 1 |
| | SE8_5x5 = np.zeros((5, 5), dtype=np.int32) |
| | SE8_5x5[:, 4] = 1 |
| |
|
| | |
| | SE9_3x3 = np.zeros((3, 3), dtype=np.int32) |
| | SE9_3x3[:, 0] = 1 |
| | SE9_5x5 = np.zeros((5, 5), dtype=np.int32) |
| | SE9_5x5[:, 0] = 1 |
| |
|
| | |
| | SE10_3x3 = np.zeros((3, 3), dtype=np.int32) |
| | SE10_3x3[0, :] = 1 |
| | SE10_5x5 = np.zeros((5, 5), dtype=np.int32) |
| | SE10_5x5[0, :] = 1 |
| |
|
| | |
| | SE11_3x3 = np.zeros((3, 3), dtype=np.int32) |
| | SE11_3x3[-1, :] = 1 |
| | SE11_5x5 = np.zeros((5, 5), dtype=np.int32) |
| | SE11_5x5[-1, :] = 1 |
| |
|
| | list_se_3x3 = [SE3_3x3, SE4_3x3, SE5_3x3, SE7_3x3, SE8_3x3, SE9_3x3, SE10_3x3, SE11_3x3] |
| | list_se_3x3_names = ['SE3_3x3', 'SE4_3x3', 'SE5_3x3', 'SE7_3x3', 'SE8_3x3', 'SE9_3x3', 'SE10_3x3', 'SE11_3x3'] |
| | list_se_5x5 = [SE3_5x5, SE4_5x5, SE5_5x5, SE7_5x5, SE8_5x5, SE9_5x5, SE10_5x5, SE11_5x5] |
| |
|