Fixed case where skeleton is 1 pixel or less

src/microglia_analyzer/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.0.0"
+__version__ = "1.0.1"
 
 import re
 

src/microglia_analyzer/_widget.py

@@ -40,6 +40,7 @@ def __init__(self):
 
         self.sources_folder = None
         self.active_worker = False
+        self.n_images = 0
 
     # -------- UI: ----------------------------------
 
@@ -228,8 +229,8 @@ def measures_panel(self):
         self.export_measures_button.clicked.connect(self.export_measures)
         layout.addWidget(self.export_measures_button)
 
-        self.run_batch_button = QPushButton("☑️ Run batch")
-        self.run_batch_button.setFont(self.font)
+        self.run_batch_button = QPushButton("▶ Run batch")
+        # self.run_batch_button.setFont(self.font)
         self.run_batch_button.clicked.connect(self.run_batch)
         layout.addWidget(self.run_batch_button)
 
@@ -330,9 +331,10 @@ def export_measures(self):
         self.thread.start()
 
     def run_batch(self):
-        # self.total = len(self.get_all_tiff_files(self.sources_folder))
+        self.n_images = len(self.get_all_tiff_files(self.sources_folder))
         self.pbr = progress()
         self.pbr.set_description("Running on folder...")
+        self.run_batch_button.setText(f"▶ Run batch ({str(1).zfill(2)}/{self.n_images})")
         self.set_active_ui(False)
         self.thread = QThread()
 
@@ -360,6 +362,8 @@ def run_batch(self):
     def end_batch(self):
         self.pbr.close()
         self.set_active_ui(True)
+        self.n_images = 0
+        self.run_batch_button.setText("▶ Run batch")
         show_info("Batch completed.")
 
     def write_measures(self):
@@ -452,10 +456,12 @@ def end_worker(self):
 
     def update_pbr(self, text, current, total):
         self.pbr.set_description(text)
-        if (total != self.total):
-            self.pbr.reset(total=total)
-            self.total = total
-        self.pbr.update(current)
+        # if (total != self.total):
+        #     self.pbr.reset(total=total)
+        #     self.total = total
+        if (self.n_images > 0):
+            self.run_batch_button.setText(f"▶ Run batch ({str(current+1).zfill(2)}/{self.n_images})")
+        # self.pbr.update(current)
 
     def clear_attributes(self):
         self.sources_folder = None

src/microglia_analyzer/ma_worker.py

@@ -309,21 +309,6 @@ def classification_postprocessing(self):
             used.add(i)
         self.classifications = clean_boxes
 
-    def _bind_classifications(self):
-        labeled = self.mask
-        regions = regionprops(labeled)
-        bindings = {int(l): (None, 0.0, None) for l in np.unique(labeled) if l != 0} # label: (class, IoU)
-        for region in regions:
-            seg_bbox = list(map(int, region.bbox))
-            bindings[region.label] = (0, 0.0, seg_bbox)
-            for box, cls in zip(self.classifications['boxes'], self.classifications['classes']):
-                x1, y1, x2, y2 = list(map(int, box))
-                detect_bbox = [y1, x1, y2, x2]
-                iou = calculate_iou(seg_bbox, detect_bbox)
-                if iou > bindings[region.label][1]: # iou > 0.2 and
-                    bindings[region.label] = (cls, iou, seg_bbox)
-        self.bindings = bindings
-
     def bind_classifications(self):
         labeled = self.mask
         regions = regionprops(labeled)
@@ -381,20 +366,12 @@ def analyze_as_graph(self):
             if label == 0:
                 continue
             mask = (self.mask == label).astype(np.uint8)
+            if np.sum(mask) < 3:
+                continue
             results[label], skeleton = self.analyze_skeleton(mask)
             skeletons = np.maximum(skeletons, skeleton)
         self.graph_metrics = results
         self.skeleton = skeletons
-
-    def sorted_by_class(self, bindings, common_labels):
-        sorted_bindings = {}
-        for label, (cls, iou, seg_bbox) in bindings.items():
-            if label not in common_labels:
-                continue
-            if cls not in sorted_bindings:
-                sorted_bindings[cls] = []
-            sorted_bindings[cls].append((label, iou, seg_bbox))
-        return sorted_bindings
 
     def sort_labels_by_class(self, data, valid_labels):
         filtered = {label: value for label, value in data.items() if label in valid_labels}