def save_heatmaps(heatmap_malignant, heatmap_benign, short_file_path, view,
horizontal_flip, parameters):
"""
Saves the heatmaps after flipping back to the original direction
"""
image_extension = '.hdf5' if parameters['use_hdf5'] else '.png'
heatmap_malignant = loading.flip_image(heatmap_malignant, view,
horizontal_flip)
heatmap_benign = loading.flip_image(heatmap_benign, view, horizontal_flip)
heatmap_save_path_malignant = os.path.join(
parameters['save_heatmap_path'][0], short_file_path + image_extension)
if heatmap_save_path_malignant.endswith("png"):
saving_images.save_image_as_png(heatmap_malignant,
heatmap_save_path_malignant)
elif heatmap_save_path_malignant.endswith("hdf5"):
saving_images.save_image_as_hdf5(heatmap_malignant,
heatmap_save_path_malignant)
else:
raise RuntimeError()
heatmap_save_path_benign = os.path.join(parameters['save_heatmap_path'][1],
short_file_path + image_extension)
if heatmap_save_path_benign.endswith("png"):
saving_images.save_image_as_png(heatmap_benign,
heatmap_save_path_benign)
elif heatmap_save_path_benign.endswith("hdf5"):
saving_images.save_image_as_hdf5(heatmap_benign,
heatmap_save_path_benign)
else:
raise RuntimeError()
def produce_heatmaps(parameters):
"""
Generate heatmaps for single example
"""
random.seed(parameters['seed'])
image_path = parameters["cropped_mammogram_path"]
model, device = run_producer.load_model(parameters)
metadata = pickling.unpickle_from_file(parameters['metadata_path'])
patches, case = run_producer.sample_patches_single(
image_path=image_path,
view=metadata["view"],
horizontal_flip=metadata['horizontal_flip'],
parameters=parameters,
)
all_prob = run_producer.get_all_prob(
all_patches=patches,
minibatch_size=parameters["minibatch_size"],
model=model,
device=device,
parameters=parameters)
heatmap_malignant, _ = run_producer.probabilities_to_heatmap(
patch_counter=0,
all_prob=all_prob,
image_shape=case[0],
length_stride_list=case[4],
width_stride_list=case[3],
patch_size=parameters['patch_size'],
heatmap_type=parameters['heatmap_type'][0],
)
heatmap_benign, patch_counter = run_producer.probabilities_to_heatmap(
patch_counter=0,
all_prob=all_prob,
image_shape=case[0],
length_stride_list=case[4],
width_stride_list=case[3],
patch_size=parameters['patch_size'],
heatmap_type=parameters['heatmap_type'][1],
)
heatmap_malignant = loading.flip_image(
image=heatmap_malignant,
view=metadata["view"],
horizontal_flip=metadata["horizontal_flip"],
)
heatmap_benign = loading.flip_image(
image=heatmap_benign,
view=metadata["view"],
horizontal_flip=metadata["horizontal_flip"],
)
saving_images.save_image_as_hdf5(
image=heatmap_malignant,
filename=parameters["heatmap_path_malignant"],
)
saving_images.save_image_as_hdf5(
image=heatmap_benign,
filename=parameters["heatmap_path_benign"],
)
def save_heatmaps(heatmap_malignant, heatmap_benign, short_file_path, view,
horizontal_flip, parameters):
"""
Saves the heatmaps after flipping back to the original direction
"""
heatmap_malignant = loading.flip_image(heatmap_malignant, view,
horizontal_flip)
heatmap_benign = loading.flip_image(heatmap_benign, view, horizontal_flip)
heatmap_save_path_malignant = os.path.join(
parameters['save_heatmap_path'][0], short_file_path + '.hdf5')
saving_images.save_image_as_hdf5(heatmap_malignant,
heatmap_save_path_malignant)
heatmap_save_path_benign = os.path.join(parameters['save_heatmap_path'][1],
short_file_path + '.hdf5')
saving_images.save_image_as_hdf5(heatmap_benign, heatmap_save_path_benign)
def extract_center(datum, image):
"""
Compute the optimal center for an image
"""
image = loading.flip_image(image, datum["full_view"],
datum['horizontal_flip'])
if datum["view"] == "MLO":
tl_br_constraint = calc_optimal_centers.get_bottomrightmost_pixel_constraint(
rightmost_x=datum["rightmost_points"][1],
bottommost_y=datum["bottommost_points"][0],
)
elif datum["view"] == "CC":
tl_br_constraint = calc_optimal_centers.get_rightmost_pixel_constraint(
rightmost_x=datum["rightmost_points"][1])
else:
raise RuntimeError(datum["view"])
optimal_center = calc_optimal_centers.get_image_optimal_window_info(
image,
com=np.array(image.shape) // 2,
window_dim=np.array(INPUT_SIZE_DICT[datum["full_view"]]),
tl_br_constraint=tl_br_constraint,
)
return optimal_center["best_center_y"], optimal_center["best_center_x"]
