def test_openslided_tiler(self):
# openslide_image_model = {
# "type": "slide_image",
# "image_path": "/home/dimathe47/Downloads/CMU-1-Small-Region.svs",
# "name": "CMU-1-Small-Region.svs"
# }
image_path = "/home/dimathe47/Downloads/CMU-1-Small-Region.svs"
image_model = generate_image_model(image_path)
zoom_factor = 1
shape = (224, 224)
step_shape = (224, 224)
tiling_model = generate_tiling_model(image_model, zoom_factor, shape,
step_shape)
n_bins = 128
pilimage_to_matrix_model = generate_pilimage_to_matrix_model(
tiling_model)
histogram_model = generate_histogram_model(pilimage_to_matrix_model,
n_bins,
"temp/histograms.hdf5")
compute_model(histogram_model, verbose=2)
def l2_distance_matrix_computer_factory(computer_func_params):
# X_ds = computer_func_params["base_model"]["output_model"]
# X = ds_utils.read_array(X_ds)
X = computer_utils.compute_model(computer_func_params["base_model"])
# TODO consider all cases
if isinstance(X, np.ndarray):
pass
elif isinstance(X, Iterable):
X = list(X)
def computer_(Q):
Q = np.array(Q, copy=False)
Q = Q.reshape((len(Q), -1))
distances_matrix = l2_distance_matrix(Q, X)
return distances_matrix
return Computer(computer_)
def test_compute(self):
computer_utils.add_factory(
"data_comes_from_some_place_other_than_output_of_another_model",
data_comes_from_some_place_other_than_output_of_another_model,
False)
computer_utils.add_factory("my_sum", my_sum, False)
model = {
"type": "computer",
"computer_func_name": "my_sum",
"input_model": {
"type":
"inmemory_computer",
"computer_func_name":
"data_comes_from_some_place_other_than_output_of_another_model"
}
}
output = computer_utils.compute_model(model)
self.assertEqual(output, 45)
def build_result_slide_tiles_descriptors_models_view_item(
distances: np.ndarray, tiles_descriptors_model: dict):
# normalized_distances = distances/ np.max(distances)
normalized_distances = (distances - np.min(distances)) / (
np.max(distances) - np.min(distances))
# alphas = [128 - 128 * dist for dist in normalized_distances]
# alphas = [128 * 128 ** (-dist) for dist in normalized_distances]
alphas = 128**(1 - normalized_distances)
alphas = alphas.astype(int)
# colors = [(0, 255, 0, int(alpha)) for alpha in alphas]
rect_tiles_model = model_utils.find_rect_tiles_model(
tiles_descriptors_model)
rect_tiles = list(computer_utils.compute_model(rect_tiles_model))
slide_path = find_image_path(tiles_descriptors_model)
slide_view_params = SlideViewParams(slide_path,
grid_rects_0_level=rect_tiles,
grid_color_alphas_0_level=alphas,
grid_visible=True)
item = SlideTilesDescriptorsModelsViewItem([tiles_descriptors_model],
slide_view_params)
return item
def on_search_action(
self, selected_slide_tiles_descriptors_models_view_items: Iterable[
SlideTilesDescriptorsModelsViewItem]):
if not self.query_viewer.slide_view_params.selected_rect_0_level:
QMessageBox.question(self, 'Error', "No query rect selected",
QMessageBox.Ok)
return
slide_tiles_descriptors_models = []
for selected_slide_tiles_descriptors_models_view_item in selected_slide_tiles_descriptors_models_view_items:
for slide_tiles_descriptors_model in selected_slide_tiles_descriptors_models_view_item.slide_tiles_descriptors_models:
slide_tiles_descriptors_models.append(
slide_tiles_descriptors_model)
if not slide_tiles_descriptors_models:
QMessageBox.question(self, 'Error', "No db_models selected",
QMessageBox.Ok)
return
n_selected_images = len(
selected_slide_tiles_descriptors_models_view_items)
chosen_tiles_descriptors_models = self.choose_tiles_descriptors_model(
slide_tiles_descriptors_models, n_selected_images)
if not chosen_tiles_descriptors_models:
QMessageBox.question(self, 'Error',
"No tiles_descriptors_model selected",
QMessageBox.Ok)
return
self.statusBar().showMessage("Searching...")
self.result_items_widget.list_model.update_items([])
selected_rect_0_level_int = [
int(x)
for x in self.query_viewer.slide_view_params.selected_rect_0_level
]
query_tile_descriptor_model = build_query_tiles_descriptors_model(
self.query_viewer.slide_view_params.slide_path,
selected_rect_0_level_int, chosen_tiles_descriptors_models[0])
print("query_tile_descriptor_model", query_tile_descriptor_model)
result_items = []
query_descriptor = computer_utils.compute_model(
query_tile_descriptor_model, force=True)
query_descriptor = list(query_descriptor)[0]
query_descriptor_model = {"type": "list", "list": [query_descriptor]}
for chosen_tiles_descriptors_model in chosen_tiles_descriptors_models:
distance_model = generate_distance_matrix_model(
query_descriptor_model, chosen_tiles_descriptors_model)
distances = computer_utils.compute_model(distance_model,
force=True)
distances = list(distances)
distances = np.array(distances).squeeze()
result_item = build_result_slide_tiles_descriptors_models_view_item(
distances, chosen_tiles_descriptors_model)
result_items.append(result_item)
self.statusBar().showMessage(
"Searching done. Visualization might take several seconds")
self.result_items_widget.list_model.update_items(result_items)
chosen_tiles_descriptors_model_str = tiles_descriptors_model_to_str(
chosen_tiles_descriptors_model)
self.ui.bottom_group_box.setTitle(
"results for tiles_descriptors_model: {}".format(
chosen_tiles_descriptors_model_str))