feat: Add z-level, timepoint, and channel selection for registration

gui/app.py

@@ -131,6 +131,9 @@ def __init__(
         downsample_factor,
         flatfield=None,
         darkfield=None,
+        registration_z=None,
+        registration_t=0,
+        registration_channel=0,
     ):
         super().__init__()
         self.tiff_path = tiff_path
@@ -139,6 +142,9 @@ def __init__(
         self.downsample_factor = downsample_factor
         self.flatfield = flatfield
         self.darkfield = darkfield
+        self.registration_z = registration_z
+        self.registration_t = registration_t
+        self.registration_channel = registration_channel
 
     def run(self):
         try:
@@ -153,6 +159,9 @@ def run(self):
                 downsample_factors=(self.downsample_factor, self.downsample_factor),
                 flatfield=self.flatfield,
                 darkfield=self.darkfield,
+                registration_z=self.registration_z,
+                registration_t=self.registration_t,
+                channel_to_use=self.registration_channel,
             )
 
             positions = np.array(tf_full._tile_positions)
@@ -198,7 +207,11 @@ def run(self):
 
             # Create a new TileFusion for the subset
             tf = TileFusion(
-                self.tiff_path, downsample_factors=(self.downsample_factor, self.downsample_factor)
+                self.tiff_path,
+                downsample_factors=(self.downsample_factor, self.downsample_factor),
+                registration_z=self.registration_z,
+                registration_t=self.registration_t,
+                channel_to_use=self.registration_channel,
             )
             tf._tile_positions = selected_positions
             tf.n_tiles = len(selected_indices)
@@ -328,6 +341,9 @@ def __init__(
         fusion_mode="blended",
         flatfield=None,
         darkfield=None,
+        registration_z=None,
+        registration_t=0,
+        registration_channel=0,
     ):
         super().__init__()
         self.tiff_path = tiff_path
@@ -337,6 +353,9 @@ def __init__(
         self.fusion_mode = fusion_mode
         self.flatfield = flatfield
         self.darkfield = darkfield
+        self.registration_z = registration_z
+        self.registration_t = registration_t
+        self.registration_channel = registration_channel
         self.output_path = None
 
     def run(self):
@@ -379,6 +398,9 @@ def run(self):
                 downsample_factors=(self.downsample_factor, self.downsample_factor),
                 flatfield=self.flatfield,
                 darkfield=self.darkfield,
+                registration_z=self.registration_z,
+                registration_t=self.registration_t,
+                channel_to_use=self.registration_channel,
             )
             load_time = time.time() - step_start
             self.progress.emit(f"Loaded {tf.n_tiles} tiles ({tf.Y}x{tf.X} each) [{load_time:.1f}s]")
@@ -702,7 +724,7 @@ class StitcherGUI(QMainWindow):
     def __init__(self):
         super().__init__()
         self.setWindowTitle("Stitcher")
-        self.setMinimumSize(500, 850)
+        self.setMinimumSize(580, 850)
 
         self.worker = None
         self.output_path = None
@@ -714,6 +736,12 @@ def __init__(self):
         self.darkfield = None  # Shape (C, Y, X) or None
         self.flatfield_worker = None
 
+        # Dataset dimension state (for registration z/t selection)
+        self.dataset_n_z = 1
+        self.dataset_n_t = 1
+        self.dataset_n_channels = 1
+        self.dataset_channel_names = []
+
         self.setup_ui()
 
     def setup_ui(self):
@@ -887,6 +915,36 @@ def setup_ui(self):
         downsample_layout.addStretch()
         settings_layout.addWidget(self.downsample_widget)
 
+        # Registration z/t selection (shown when registration enabled AND multi-z/t dataset)
+        self.reg_zt_widget = QWidget()
+        self.reg_zt_widget.setVisible(False)
+        reg_zt_layout = QHBoxLayout(self.reg_zt_widget)
+        reg_zt_layout.setContentsMargins(20, 0, 0, 0)
+        self.reg_z_label = QLabel("Z-level:")
+        reg_zt_layout.addWidget(self.reg_z_label)
+        self.reg_z_spin = QSpinBox()
+        self.reg_z_spin.setRange(0, 0)
+        self.reg_z_spin.setValue(0)
+        self.reg_z_spin.setToolTip("Z-level to use for registration")
+        self.reg_z_spin.setFixedWidth(60)
+        reg_zt_layout.addWidget(self.reg_z_spin)
+        self.reg_t_label = QLabel("Timepoint:")
+        reg_zt_layout.addWidget(self.reg_t_label)
+        self.reg_t_spin = QSpinBox()
+        self.reg_t_spin.setRange(0, 0)
+        self.reg_t_spin.setValue(0)
+        self.reg_t_spin.setToolTip("Timepoint to use for registration")
+        self.reg_t_spin.setFixedWidth(60)
+        reg_zt_layout.addWidget(self.reg_t_spin)
+        self.reg_channel_label = QLabel("Channel:")
+        reg_zt_layout.addWidget(self.reg_channel_label)
+        self.reg_channel_combo = QComboBox()
+        self.reg_channel_combo.setToolTip("Channel to use for registration")
+        self.reg_channel_combo.setMinimumWidth(120)
+        reg_zt_layout.addWidget(self.reg_channel_combo)
+        reg_zt_layout.addStretch()
+        settings_layout.addWidget(self.reg_zt_widget)
+
         self.blend_checkbox = QCheckBox("Enable blending")
         self.blend_checkbox.setChecked(False)
         self.blend_checkbox.toggled.connect(self.on_blend_toggled)
@@ -978,6 +1036,31 @@ def on_file_dropped(self, file_path):
         self.clear_flatfield_button.setEnabled(False)
         self.save_flatfield_button.setEnabled(False)
 
+        # Load dataset dimensions for registration z/t selection
+        try:
+            from tilefusion import TileFusion
+
+            tf_temp = TileFusion(file_path)
+            self.dataset_n_z = tf_temp.n_z
+            self.dataset_n_t = tf_temp.n_t
+            self.dataset_n_channels = tf_temp.channels
+            if "channel_names" in tf_temp._metadata:
+                self.dataset_channel_names = tf_temp._metadata["channel_names"]
+            else:
+                self.dataset_channel_names = [
+                    f"Channel {i}" for i in range(self.dataset_n_channels)
+                ]
+            tf_temp.close()
+            if self.dataset_n_z > 1 or self.dataset_n_t > 1:
+                self.log(f"Dataset: {self.dataset_n_z} z-levels, {self.dataset_n_t} timepoints")
+            self._update_reg_zt_controls()
+        except Exception:
+            self.dataset_n_z = 1
+            self.dataset_n_t = 1
+            self.dataset_n_channels = 1
+            self.dataset_channel_names = []
+            self._update_reg_zt_controls()
+
         # Auto-load existing flatfield if present, otherwise disable correction
         # For directories (SQUID folders), also check inside the directory
         if path.is_dir():
@@ -997,6 +1080,41 @@ def on_file_dropped(self, file_path):
 
     def on_registration_toggled(self, checked):
         self.downsample_widget.setVisible(checked)
+        self._update_reg_zt_controls()
+
+    def _update_reg_zt_controls(self):
+        """Update visibility and ranges of registration z/t controls."""
+        registration_enabled = self.registration_checkbox.isChecked()
+        has_multi_z = self.dataset_n_z > 1
+        has_multi_t = self.dataset_n_t > 1
+        has_multi_channel = self.dataset_n_channels > 1
+
+        # Show z/t widget only when registration is enabled AND dataset has multi-z or multi-t or multi-channel
+        show_zt = registration_enabled and (has_multi_z or has_multi_t or has_multi_channel)
+        self.reg_zt_widget.setVisible(show_zt)
+
+        if show_zt:
+            # Update z spinbox
+            self.reg_z_label.setVisible(has_multi_z)
+            self.reg_z_spin.setVisible(has_multi_z)
+            if has_multi_z:
+                self.reg_z_spin.setRange(0, self.dataset_n_z - 1)
+                self.reg_z_spin.setValue(self.dataset_n_z // 2)  # Default to middle
+
+            # Update t spinbox
+            self.reg_t_label.setVisible(has_multi_t)
+            self.reg_t_spin.setVisible(has_multi_t)
+            if has_multi_t:
+                self.reg_t_spin.setRange(0, self.dataset_n_t - 1)
+                self.reg_t_spin.setValue(0)  # Default to first timepoint
+
+            # Update channel combo
+            self.reg_channel_label.setVisible(has_multi_channel)
+            self.reg_channel_combo.setVisible(has_multi_channel)
+            if has_multi_channel:
+                self.reg_channel_combo.clear()
+                self.reg_channel_combo.addItems(self.dataset_channel_names)
+                self.reg_channel_combo.setCurrentIndex(0)
 
     def on_blend_toggled(self, checked):
         self.blend_value_widget.setVisible(checked)
@@ -1228,6 +1346,13 @@ def run_stitching(self):
         flatfield = self.flatfield if self.flatfield_checkbox.isChecked() else None
         darkfield = self.darkfield if self.flatfield_checkbox.isChecked() else None
 
+        # Get registration z/t values (None means use default middle z)
+        registration_z = self.reg_z_spin.value() if self.dataset_n_z > 1 else None
+        registration_t = self.reg_t_spin.value() if self.dataset_n_t > 1 else 0
+        registration_channel = (
+            self.reg_channel_combo.currentIndex() if self.dataset_n_channels > 1 else 0
+        )
+
         self.worker = FusionWorker(
             self.drop_area.file_path,
             self.registration_checkbox.isChecked(),
@@ -1236,6 +1361,9 @@ def run_stitching(self):
             fusion_mode,
             flatfield=flatfield,
             darkfield=darkfield,
+            registration_z=registration_z,
+            registration_t=registration_t,
+            registration_channel=registration_channel,
         )
         self.worker.progress.connect(self.log)
         self.worker.finished.connect(self.on_fusion_finished)
@@ -1294,13 +1422,23 @@ def run_preview(self):
         flatfield = self.flatfield if self.flatfield_checkbox.isChecked() else None
         darkfield = self.darkfield if self.flatfield_checkbox.isChecked() else None
 
+        # Get registration z/t values (None means use default middle z)
+        registration_z = self.reg_z_spin.value() if self.dataset_n_z > 1 else None
+        registration_t = self.reg_t_spin.value() if self.dataset_n_t > 1 else 0
+        registration_channel = (
+            self.reg_channel_combo.currentIndex() if self.dataset_n_channels > 1 else 0
+        )
+
         self.preview_worker = PreviewWorker(
             self.drop_area.file_path,
             self.preview_cols_spin.value(),
             self.preview_rows_spin.value(),
             self.downsample_spin.value(),
             flatfield=flatfield,
             darkfield=darkfield,
+            registration_z=registration_z,
+            registration_t=registration_t,
+            registration_channel=registration_channel,
         )
         self.preview_worker.progress.connect(self.log)
         self.preview_worker.finished.connect(self.on_preview_finished)

src/tilefusion/core.py

@@ -110,6 +110,8 @@ def __init__(
         region: Optional[str] = None,
         flatfield: Optional[np.ndarray] = None,
         darkfield: Optional[np.ndarray] = None,
+        registration_z: Optional[int] = None,
+        registration_t: int = 0,
     ):
         self.tiff_path = Path(tiff_path)
         if not self.tiff_path.exists():
@@ -194,6 +196,18 @@ def __init__(
         self._time_folders = self._metadata.get("time_folders", None)
         self._middle_z = self.n_z // 2  # Use middle z-level for registration
 
+        # Registration z/t selection (validate after n_z/n_t are known)
+        if registration_z is None:
+            self._registration_z = self._middle_z
+        else:
+            if registration_z < 0 or registration_z >= self.n_z:
+                raise ValueError(f"registration_z={registration_z} out of range [0, {self.n_z})")
+            self._registration_z = registration_z
+
+        if registration_t < 0 or registration_t >= self.n_t:
+            raise ValueError(f"registration_t={registration_t} out of range [0, {self.n_t})")
+        self._registration_t = registration_t
+
         # Configuration
         self.downsample_factors = tuple(downsample_factors)
         self.ssim_window = int(ssim_window)
@@ -447,10 +461,12 @@ def _update_profiles(self) -> None:
     # I/O methods (delegate to format-specific loaders)
     # -------------------------------------------------------------------------
 
-    def _read_tile(self, tile_idx: int, z_level: int = None, time_idx: int = 0) -> np.ndarray:
+    def _read_tile(self, tile_idx: int, z_level: int = None, time_idx: int = None) -> np.ndarray:
         """Read a single tile from the input data (all channels)."""
         if z_level is None:
-            z_level = self._middle_z  # Default to middle z for registration
+            z_level = self._registration_z  # Default to registration z-level
+        if time_idx is None:
+            time_idx = self._registration_t  # Default to registration timepoint
 
         if self._is_zarr_format:
             zarr_ts = self._metadata["tensorstore"]
@@ -493,11 +509,13 @@ def _read_tile_region(
         y_slice: slice,
         x_slice: slice,
         z_level: int = None,
-        time_idx: int = 0,
+        time_idx: int = None,
     ) -> np.ndarray:
         """Read a region of a tile from the input data."""
         if z_level is None:
-            z_level = self._middle_z  # Default to middle z for registration
+            z_level = self._registration_z  # Default to registration z-level
+        if time_idx is None:
+            time_idx = self._registration_t  # Default to registration timepoint
 
         if self._is_zarr_format:
             zarr_ts = self._metadata["tensorstore"]