def error(coords_list):
seeds = fixed_seeds.copy()
print(f'coordinate list : {coords_list}')
print(f'seeds : {seeds}')
for index in range(len(coords_list) // 2):
coordinate = (int(coords_list[2 * index]),
int(coords_list[2 * index + 1]))
seeds.update({coordinate: colour})
segmentation = Segmentation(image_array,
beta,
seeds,
image_name,
reference_path=target_segmentation_path)
segmentation.solve()
segmentation.build_segmentation_image()
return segmentation.compute_error()
def on_solve():
beta_parameter = float(beta_entry.get())
segmentation = Segmentation(image_array, beta_parameter, seeds,
image_name)
segmentation.solve()
if save_segmentation.get():
segmentation.build_segmentation_image()
segmentation.save_segmentation_image()
if draw_contours.get():
segmentation.plot_contours()
segmentation.build_segmentation_image()
segmentation.plot_colours()
for key, value in initial_seeds.items() if value == 'red'
}
initial_coords = list(initial_variable_seeds.keys())
print(f'initial coords = {initial_coords}')
optimal_coords = optimize.minimize(error,
initial_coords,
method='nelder-mead',
constraints=cons,
options={
'xtol': 1e-3,
'disp': True
})
optimal_seeds = fixed_seeds
for index in range(len(optimal_coords) // 2):
coordinate = (int(optimal_coords[2 * index]),
int(optimal_coords[2 * index + 1]))
optimal_seeds.update({coordinate: colour})
# optimal segmentation
segmentation = Segmentation(image_array,
beta,
optimal_seeds,
image_name,
reference_path=target_segmentation_path)
segmentation.solve()
segmentation.build_segmentation_image()
segmentation.compute_error()
segmentation.contours_to_png()
