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