Image marker recognition, threshold, opening, connected components and centroid calculation

detect_marker_still.py

"""
Detect markers in image and display results
"""
import time
import numpy as np

import matplotlib.pyplot as plt
from scipy import ndimage
from skimage import io
from skimage import morphology


def do_threshold(array, value):
    return (array >= value) * array


def difference_ms(prev_t):
    return round((time.time() - prev_t) * 1000, 1)


def main(path):
    original = io.imread(path)

    threshold = 160
    opening_size = 5

    t0 = time.time()
    thresholded = do_threshold(original, threshold)  # very slow
    print(f'Threshold:\t{difference_ms(t0)}ms')

    t1 = time.time()
    structure = morphology.disk(opening_size)
    opened = ndimage.binary_opening(thresholded, structure)  # quite slow
    structure = np.ones((3, 3), dtype=np.int)
    print(f'Opening:\t\t{difference_ms(t1)}ms')

    t2 = time.time()
    labels, ncomponents = ndimage.label(opened)  # fast
    print(f'Connect:\t\t{difference_ms(t2)}ms')

    t3 = time.time()
    centers = ndimage.measurements.center_of_mass(opened, labels, range(1, ncomponents + 1))  # fast
    # Note, performing moment calculation on masked image may be faster (parallelizable).
    print(f'Center:\t\t\t{difference_ms(t3)}ms')

    total_ms = difference_ms(t0)
    print(f'Total:\t\t\t{total_ms}ms')

    print(f'ncomponents: {ncomponents}')
    print(f'centers: {centers}')

    #
    # Plot
    #
    fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True)

    axis = axes[0, 0]
    axis.imshow(original, cmap=plt.cm.gray)
    axis.set_title('original')
    axis.axis('off')

    axis = axes[0, 1]
    axis.imshow(thresholded, cmap=plt.cm.gray)
    axis.set_title(f'threshold: {threshold}')
    axis.axis('off')

    axis = axes[1, 0]
    axis.imshow(opened, cmap=plt.cm.gray)
    axis.set_title(f'opening; disk r={opening_size}')
    axis.axis('off')

    axis = axes[1, 1]
    axis.imshow(original, cmap=plt.cm.gray)
    axis.set_title(f'{ncomponents} center(s)')
    axis.scatter(np.array(centers)[:, 1], np.array(centers)[:, 0], s=50, c='red', marker='+')
    axis.axis('off')

    # fig.suptitle(f'Total {total_ms}ms', fontsize=12)
    # fig.tight_layout(rect=[0, 0.03, 1, 0.95])

    plt.savefig('plot.png', bbox_inches='tight')
    plt.show()

    fig, axis = plt.subplots(1, 1, figsize=(8, 8), sharex=True, sharey=True)
    axis.imshow(original, cmap=plt.cm.gray)
    axis.scatter(np.array(centers)[:, 1], np.array(centers)[:, 0], s=50, c='red', marker='+')
    axis.axis('off')
    plt.savefig('result.png', bbox_inches='tight')


if __name__ == "__main__":
    path = 'luminance_raw_with_IR_light.jpg'
    path = 'more-marker-raw_IR_light.jpg'
    main(path)