def target_generator(height,
width,
x_target,
y_target,
gamma,
amplitude,
print_attribute='no_print'):
radius = gamma * 3
matrix = np.zeros((height, width))
psf_frame = detection_psf_frame(x_target, y_target, radius)
for i in range(psf_frame['y_target_minus_r'],
psf_frame['y_target_plus_r']):
for j in range(psf_frame['x_target_minus_r'],
psf_frame['x_target_plus_r']):
x = np.array([((j - x_target) / (gamma * math.sqrt(2))),
(((j + 1) - x_target) / (gamma * math.sqrt(2)))])
y = np.array([((i - y_target) / (gamma * math.sqrt(2))),
(((i + 1) - y_target) / (gamma * math.sqrt(2)))])
erf_x = erf(x)
erf_y = erf(y)
integral = amplitude * ((erf_x[1] - erf_x[0]) *
(erf_y[1] - erf_y[0])) / (8 * (gamma**2))
matrix[i, j] = integral
if (print_attribute == 'print'):
print_matrix(matrix, 'target')
return matrix
def frame_generator(height, width, x_target, y_target, gamma, amplitude, print_attribute='no_print'):
noise = noise_generator(height, width)
target = target_generator(height, width, x_target, y_target, gamma, amplitude)
matrix = noise + target
# matrix = target
if (print_attribute == 'print'):
print_matrix(matrix, 'frame')
return matrix
def noise_generator(height, width, print_attribute='no_print'):
matrix = np.zeros((height, width))
row, col = matrix.shape
mean = 0
sigma = 1
gauss = np.random.normal(mean, sigma, (row, col))
gauss = gauss.reshape(row, col)
noise = matrix + gauss
# noise = matrix_module(height, width, noise)
if (print_attribute == 'print'):
print_matrix(noise, 'noise')
return noise
# noise_generator('print')
noise = noise_generator(height, width)
result_matrix += noise
return result_matrix
#main
amplitude_range = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
result_amplitude = []
for amplitude_iterator in range(0, len(amplitude_range)):
experiment_pre_result = []
for experiment_iterator in range(0, experiment_iterations):
targets = target_creator()
matrix = frame_generator(targets, amplitude_range[amplitude_iterator])
print_matrix(matrix, 'checkRobastFrame', width, height)
pre_result_targets = true_robust_algorithm(matrix, gamma, h)
robust_targets = search_true_target(pre_result_targets, gamma)
result = 0
for i in range(0, len(targets)):
for j in range(0, len(robust_targets)):
if (targets[i]['x'] == robust_targets[j]['x']) and (
targets[i]['y'] == robust_targets[j]['y']):
result += 1
break
experiment_pre_result.append(result)
result_amplitude.append(experiment_pre_result)
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# for amplitude_iterator in range(0, len(amplitude_range)):
# y = np.zeros(11)