Alpha-Testing

src/engine.py

@@ -61,8 +61,10 @@ def __init__(self, _configs):
         self.project_name = _configs['project_name']
         self.batch_size = _configs['batch_size']
         self.embedding_dimensionality = _configs['dimensionality']
+        self.base_directory = _configs['base_dir']
         self.source_directory = _configs['source_dir']
         self.output_segmentation_directory = _configs['result_segmentation_dir']
+        self.output_npy_directory = _configs['npy_dir']
         self.output_morphometry_directory = _configs['result_morphometry_dir']
         self.resource_alloc_value = _configs['resource_allocation']
         self.is_stacked = _configs['is_stacked']
@@ -76,8 +78,8 @@ def __init__(self, _configs):
         self.SHARED_MEMORY_SIZE = 20
         self.MIN_PIXELS = 10
         self.BICO_DIR = 'res/bico/'
-        self.TEMP_DIR = self.source_directory + '/temp/'
-        self.LOG_DIR = self.source_directory + '/log/'
+        self.TEMP_DIR = self.base_directory + '/temp/'
+        self.LOG_DIR = self.base_directory + '/log/'
         self.CC_SCALE = 4
 
         self.no_of_tile_processes, self.no_of_cluster_processes = \
@@ -99,6 +101,9 @@ def __init__(self, _configs):
         if not os.path.exists(self.output_segmentation_directory):
             os.mkdir(self.output_segmentation_directory)
 
+        if not os.path.exists(self.output_npy_directory):
+            os.mkdir(self.output_npy_directory)
+
         if not os.path.exists(self.LOG_DIR):
             os.mkdir(self.LOG_DIR)
 
@@ -112,7 +117,8 @@ def __init__(self, _configs):
 
         logging.info(f"Creating the dataset from images.")
         # Creating the dataset
-        self.dataset = PredictionDataset(_source_directory=self.source_directory,
+        self.dataset = PredictionDataset(_configs=self.configs,
+                                         _source_directory=self.source_directory,
                                          _target_resolution=self.TARGET_RESOLUTION,
                                          _sample_dimension=self.SAMPLE_SIZE,
                                          _steps=self.DATASET_STEPS)
@@ -283,7 +289,7 @@ def exec(self):
 
         if self.proceed[0]:
             self.configs['is_segmentation_finished'] = True
-            config_file_path = os.path.join(self.source_directory, "conf.json")
+            config_file_path = os.path.join(self.base_directory, "conf.json")
             with open(config_file_path, 'w+') as file:
                 file.write(json.dumps(self.configs))
 
@@ -381,9 +387,11 @@ def collector_procedure(self, _event_finished):
                     filepath = self.dataset.image_files[image_id]
                     mask_inst = mask_img[0]
                     mask_sem = mask_img[1]
+
+                    npy_out_dir, _ = mkdirs(self.output_npy_directory, filepath)
                     sub_out_dir, fn_short = mkdirs(self.output_segmentation_directory, filepath)
 
-                    np.save(os.path.join(sub_out_dir, "%s_pred.npy" % fn_short[:-4]), mask_img)
+                    np.save(os.path.join(npy_out_dir, "%s_pred.npy" % fn_short[:-4]), mask_img)
 
                     result_file_name = os.path.join(sub_out_dir, "%s_pred.png" % fn_short[:-4])
                     collector_logger.debug(f"Saving the result to: {result_file_name}")

src/morph.py

@@ -29,11 +29,13 @@ def __init__(self, _configs):
         self.configs = _configs
         self.project_id = _configs['project_id']
         self.project_name = _configs['project_name']
+        self.base_directory = _configs['base_dir']
         self.source_directory = _configs['source_dir']
+        self.npy_directory = _configs['npy_dir']
         self.output_segmentation_directory = _configs['result_segmentation_dir']
         self.output_morphometry_directory = _configs['result_morphometry_dir']
 
-        self.LOG_DIR = self.source_directory + '/log/'
+        self.LOG_DIR = self.base_directory + '/log/'
         self.morphometry_logger = self.get_logger("morphometry")
 
         if not os.path.exists(self.output_morphometry_directory):
@@ -51,14 +53,14 @@ def exec(self):
         self.morphometry_logger.info("Morphometry process started")
 
         self.foot_processes_parameter_table(self.source_directory,
-                                            self.output_segmentation_directory,
+                                            self.npy_directory,
                                             self.output_morphometry_directory)
 
         self.combine_FP_SD(self.output_morphometry_directory)
         self.morphometry_logger.info("Morphometry process finished")
 
         self.configs['is_morphometry_finished'] = True
-        config_file_path = os.path.join(self.source_directory, "conf.json")
+        config_file_path = os.path.join(self.base_directory, "conf.json")
         with open(config_file_path, 'w+') as file:
             file.write(json.dumps(self.configs))
 

src/nn/dataset.py

@@ -16,8 +16,9 @@
 
 
 class PredictionDataset(Dataset):
-    def __init__(self, _source_directory, _target_resolution, _sample_dimension, _steps):
+    def __init__(self, _configs, _source_directory, _target_resolution, _sample_dimension, _steps):
 
+        self.configs = _configs
         self.source_directory = _source_directory
 
         self.dimension = _sample_dimension
@@ -55,12 +56,21 @@ def n_imgs(self):
 
     def read_file(self, fn):
         img = tifffile.imread(os.path.join(self.source_directory, fn))
-        if len(img.shape) > 3:
-            img = np.max(img, axis=1)
-        if (len(img.shape) == 3) and (img.shape[0] > 2):
-            img = np.max(img, axis=0)
+
+        # Choosing the channel and max projecting if needed
+
+        if self.configs['is_stacked']:
+            if len(img.shape) > 3:
+                if img.shape[0] == 2:
+                    img = np.max(img, axis=1)
+                else:
+                    img = np.max(img, axis=0)
+            if len(img.shape) == 3:
+                img = np.max(img, axis=0)
+
         if len(img.shape) > 2:
-            img = img[0]
+            img = img[self.configs['target_channel']]
+
         img = Image.fromarray(img)
         w, h = img.size
         res = get_resolution(os.path.join(self.source_directory, fn), w)

src/ui/main_window.py

@@ -275,7 +275,7 @@ def check_project_status(self):
                         percentage = 99
                     self.progress_dialog.setValue(percentage)
                 elif self.morphometry is not None:
-                    percentage = (self.morphometry.no_of_processed_images[0] + 1) / self.morphometry.no_of_images[0] * 100
+                    percentage = (self.morphometry.no_of_processed_images[0]) / self.morphometry.no_of_images[0] * 100
                     if percentage > 99:
                         percentage = 99
                     self.progress_dialog.setValue(percentage)
@@ -387,7 +387,7 @@ def add_project_click(self):
             return
 
         # Copying the selected directory and images to the project
-        destination_directory = f"{os.getcwd()}/{PROJECT_DIR}/{selected_path.name}"
+        destination_directory = f"{os.getcwd()}/{PROJECT_DIR}/{selected_path.name}/"
 
         progressDialog = create_progress_dialog(f'Creating project "{selected_path.name}"', 'Please wait', self)
         progressDialog.show()
@@ -407,19 +407,25 @@ def add_project_click(self):
             return
 
         os.mkdir(destination_directory)
+
+        images_directory = os.path.join(destination_directory, 'images/')
+        os.mkdir(images_directory)
+
         for tiff_image in tiff_files:
             # Prevents UI freeze
             QApplication.processEvents()
 
             full_name = os.path.join(selected_directory, tiff_image)
             if os.path.isfile(full_name):
-                shutil.copy(full_name, destination_directory)
+                shutil.copy(full_name, images_directory)
 
         # Saving the configuration file into the project directory (Do not confuse with 'Projects' dir)
         project_configuration = {
             "project_id": f"{uuid.uuid4()}",
             "project_name": f"{selected_path.name}",
-            "source_dir": f"./{PROJECT_DIR}/{selected_path.name}/",
+            "base_dir": f"./{PROJECT_DIR}/{selected_path.name}/",
+            "source_dir": f"./{PROJECT_DIR}/{selected_path.name}/images/",
+            "npy_dir": f"./{PROJECT_DIR}/{selected_path.name}/npy/",
             "result_segmentation_dir": f"./{PROJECT_DIR}/{selected_path.name}/segmentation/",
             "result_morphometry_dir": f"./{PROJECT_DIR}/{selected_path.name}/morphometry/",
             "resource_allocation": 3,

src/utils.py

@@ -181,7 +181,7 @@ def analyze_tiff_files(_path):
             # Based on the reviewed data samples, if number of channels is 2, meaning "tiff_image.shape[1][1] == 2"
             # Only the first channel is usable, and the images are not stacked.
             # If no of channels is bigger than 2, the tiff image contains multiple stacked images
-            if tiff_rank == 4 and tiff_image.shape[1] > 2:
+            if tiff_rank == 4 and (tiff_image.shape[1] > 2 or tiff_image.shape[0] > 2):
                 is_stacked = True
             elif tiff_rank == 3 and tiff_image.shape[0] > 2:
                 is_stacked = True