[classifier] cleanup and preparations

fixtracks/widgets/classifier.py

@@ -102,18 +102,8 @@ class ClassifierWidget(QTabWidget):
         return self._size_classifier
 
 
-def main():
-    import pickle
-    from fixtracks.info import PACKAGE_ROOT
-    from PySide6.QtWidgets import QApplication
-
-    datafile = PACKAGE_ROOT / "data/merged_small.pkl"
-    print(datafile)
-    with open(datafile, "rb") as f:
-        df = pickle.load(f)
-
-    coords = np.stack(df.keypoints.values,).astype(np.float32)[:,:,:]
 
+def test_sizeClassifier(coords):
     app = QApplication([])
     window = QWidget()
     window.setMinimumSize(200, 200)
@@ -121,14 +111,38 @@ def main():
     win = SizeClassifier()
     win.setCoordinates(coords)
 
-    btn = QPushButton("get bounds")
-    btn.clicked.connect(lambda: win.selections())
+    layout.addWidget(win)
+    window.setLayout(layout)
+    window.show()
+    app.exec()
+
 
+def test_neighborhoodClassifier(coords):
+    app = QApplication([])
+    window = QWidget()
+    window.setMinimumSize(200, 200)
+    layout = QVBoxLayout()
+    win = SizeClassifier()
+    win.setCoordinates(coords)
     layout.addWidget(win)
-    layout.addWidget(btn)
     window.setLayout(layout)
     window.show()
     app.exec()
 
+
+def main():
+    import pickle
+    from fixtracks.info import PACKAGE_ROOT
+    from PySide6.QtWidgets import QApplication
+    datafile = PACKAGE_ROOT / "data/merged_small.pkl"
+    print(datafile)
+    with open(datafile, "rb") as f:
+        df = pickle.load(f)
+
+    coords = np.stack(df.keypoints.values,).astype(np.float32)
+    frames = df.frame.values
+    test_sizeClassifier(coords)
+
+
 if __name__ == "__main__":
     main()