def predictions_on_noise_levels(
network,
SNT_levels,
nbr_images_to_evaluate=1000,
particle_center_x_list=lambda: normal(0, 1, 1),
particle_center_y_list=lambda: normal(0, 1, 1),
particle_radius_list=lambda: uniform(1.5, 3, 1),
particle_bessel_orders_list=lambda: [
[
randint(1, 3),
],
],
particle_intensities_list=lambda: [
[
choice([-1, 1]) * uniform(.2, .6, 1),
],
],
image_half_size=lambda: 25,
image_background_level=lambda: uniform(.2, .8),
signal_to_noise_ratio=lambda: uniform(10, 100),
gradient_intensity=lambda: uniform(0, 1),
gradient_direction=lambda: uniform(-pi, pi)):
"""
Function for making predictions on different noise levels
Inputs: network to be evaluated, noise levels to evaluate on and parameters of the image generator(other than noise level)
Number of images to evaluate on
Outputs: predicted positions and target positions.
"""
image_shape = network.get_layer(
index=0).get_config()['batch_input_shape'][1:]
all_predictions = []
all_targets = []
for SNT_level in SNT_levels:
### Create an image generator
image_parameters_function = lambda: deeptrack.get_image_parameters(
particle_center_x_list=particle_center_x_list,
particle_center_y_list=particle_center_y_list,
particle_radius_list=particle_radius_list,
particle_bessel_orders_list=particle_bessel_orders_list,
particle_intensities_list=particle_intensities_list,
image_half_size=image_half_size,
image_background_level=image_background_level,
signal_to_noise_ratio=lambda: SNT_level,
gradient_intensity=gradient_intensity,
gradient_direction=gradient_direction)
image_generator = lambda: deeptrack.get_image_generator(
image_parameters_function)
### Genereate images for evaluation
images, targets = deeptrack.get_images_and_targets(
image_generator, nbr_images_to_evaluate, image_shape=image_shape)
all_targets.append(targets)
predictions = network.predict(images)
all_predictions.append(predictions)
return all_predictions, all_targets
def generate_images_and_targets_from_noise_levels(
network,
SNT_levels,
nbr_images_to_evaluate=1000,
translation_distance=1,
particle_radius_list=lambda: uniform(1.5, 3, 1),
particle_bessel_orders_list=lambda: [
[
randint(1, 3),
],
],
particle_intensities_list=lambda: [
[
choice([-1, 1]) * uniform(.2, .6, 1),
],
],
image_half_size=lambda: 25,
image_background_level=lambda: uniform(.2, .8),
signal_to_noise_ratio=lambda: uniform(10, 100),
gradient_intensity=lambda: uniform(0, 1),
gradient_direction=lambda: uniform(-pi, pi)):
"""
Generates images of different noise levels and corresponding targets
"""
translation_distance = 1
particle_center_x_list = lambda: normal(0, translation_distance,
translation_distance)
particle_center_y_list = lambda: normal(0, translation_distance,
translation_distance)
image_shape = network.get_layer(
index=0).get_config()['batch_input_shape'][1:]
#all_predictions = []
all_targets = []
all_images = []
for SNT_level in SNT_levels:
### Create an image generator
image_parameters_function = lambda: deeptrack.get_image_parameters(
particle_center_x_list=particle_center_x_list,
particle_center_y_list=particle_center_y_list,
particle_radius_list=particle_radius_list,
particle_bessel_orders_list=particle_bessel_orders_list,
particle_intensities_list=particle_intensities_list,
image_half_size=image_half_size,
image_background_level=image_background_level,
signal_to_noise_ratio=lambda: SNT_level,
gradient_intensity=gradient_intensity,
gradient_direction=gradient_direction)
image_generator = lambda: deeptrack.get_image_generator(
image_parameters_function)
### Genereate images for evaluation
images, targets = deeptrack.get_images_and_targets(
image_generator, nbr_images_to_evaluate, image_shape=image_shape)
all_targets.append(targets)
all_images.append(images)
return all_targets, all_images
def evaluate_noise_levels(
network,
SNT_levels,
nbr_images_to_evaluate=1000,
particle_center_x_list = lambda : normal(0, 1, 1),
particle_center_y_list=lambda : normal(0, 1, 1),
particle_radius_list=lambda : uniform(1.5, 3, 1),
particle_bessel_orders_list=lambda : [[randint(1, 3),], ],
particle_intensities_list=lambda : [[choice([-1, 1]) * uniform(.2, .6, 1), ], ],
image_half_size=lambda : 25,
image_background_level=lambda : uniform(.2, .8),
signal_to_noise_ratio=lambda : uniform(10, 100),
gradient_intensity=lambda : uniform(0, 1),
gradient_direction=lambda : uniform(-pi, pi)):
"""
Function for evaluating performance of network for different noise levels
Inputs: network to be evaluated, noise levels to evaluate on and parameters of the image generator(other than noise level)
Number of images to evaluate on
Outputs: Loss,mae and mse for the various noise levels
"""
image_shape = network.get_layer(index=0).get_config()['batch_input_shape'][1:]
losses = []
MSEs = []
MAEs = []
for SNT_level in SNT_levels:
### Create an image generator
image_parameters_function = lambda : deeptrack.get_image_parameters(
particle_center_x_list=particle_center_x_list,
particle_center_y_list=particle_center_y_list,
particle_radius_list=particle_radius_list,
particle_bessel_orders_list=particle_bessel_orders_list,
particle_intensities_list=particle_intensities_list,
image_half_size=image_half_size,
image_background_level=image_background_level,
signal_to_noise_ratio= lambda: SNT_level,
gradient_intensity=gradient_intensity,
gradient_direction= gradient_direction)
image_generator = lambda : deeptrack.get_image_generator(image_parameters_function)
### Genereate images for evaluation
images, targets = deeptrack.get_images_and_targets(image_generator,nbr_images_to_evaluate,image_shape=image_shape)
loss,mse,mae = network.evaluate(images,targets)
### Fix units and append to final results
mse *= image_half_size()**2 #what happens if it is not a function? Silly error?
mae *= image_half_size()
losses.append(loss)
MSEs.append(mse)
MAEs.append(mae)
return losses,MSEs,MAEs