Merge pull request #1043 from annie-xd-wang/update-features-optimize-volume-search3D

Update features: optimize volume search 3d

Author: AdvancedImagingUTSW

src/navigate/model/analysis/boundary_detect.py

@@ -524,9 +524,11 @@ def map_labels(
         The maximum number of z steps
     positions : array
         Array of positions
+    target_labels : array
+        Array of target label index.
     """
     if target_pixel_size >= current_pixel_size:
-        return 1, [position]
+        return 1, [position], [0]
     if overlap < 0:
         overlap = 0
     if x_direction not in ["x", "-x", "y", "-y"]:
@@ -555,6 +557,12 @@ def map_labels(
     regionprops = measure.regionprops(labeled_image)
     z_range = 1
 
+    target_labels_index = []
+    start = -1
+    end = -1
+    start_weight = current_image_width + current_image_height
+    end_weight = 0
+
     for i in range(target_num):
         min_z, min_y, min_x, max_z, max_y, max_x = regionprops[i].bbox
 
@@ -565,6 +573,53 @@ def map_labels(
         ) < filter_pixel_number:
             continue
 
+        target_labels_index.append(i)
+        # find the start and end position
+        centroid_z, centroid_y, centroid_x = regionprops[i].centroid 
+        if start < 0:
+            start = len(target_labels_index) - 1
+            start_weight = centroid_x + centroid_y
+        elif centroid_x + centroid_y < start_weight:
+            if end_weight < start_weight:
+                end = start
+                end_weight = start_weight
+            start = len(target_labels_index) - 1
+            start_weight = centroid_x + centroid_y
+        elif centroid_x + centroid_y > end_weight:
+            end = len(target_labels_index) - 1
+            end_weight = centroid_x + centroid_y
+
+    # build a graph
+    weight_graph = [[0 for i in range(len(target_labels_index))] for _ in range(len(target_labels_index))]
+    for i in range(len(target_labels_index)):
+        centroid_z_i, centroid_y_i, centroid_x_i = regionprops[target_labels_index[i]].centroid
+        for j in range(len(target_labels_index)):
+            if i == j:
+                weight_graph[j][i] = weight_graph[i][j] = current_image_width + current_image_height
+                continue
+            centroid_z_j, centroid_y_j, centroid_x_j = regionprops[target_labels_index[j]].centroid
+            weight_graph[i][j] = abs(centroid_x_i - centroid_x_j) + abs(centroid_y_i - centroid_y_j)
+            weight_graph[j][i] = weight_graph[i][j]
+    # find the appoximation shortest path in (x, y).
+    visited_num = 1
+    target_labels = [target_labels_index[start]]
+    pre = start
+    visited = [False] * len(target_labels_index)
+    visited[start] = True
+    while visited_num < len(target_labels_index):
+        while True:
+            idx = weight_graph[pre].index(min(weight_graph[pre]))
+            weight_graph[pre][idx] = weight_graph[idx][pre] = current_image_width + current_image_height
+            if not visited[idx]:
+                break
+        target_labels.append(target_labels_index[idx])
+        pre = idx
+        visited[idx] = True
+        visited_num += 1
+    
+    for i in target_labels:
+        min_z, min_y, min_x, max_z, max_y, max_x = regionprops[i].bbox
+
         num_x = math.ceil((max_x - min_x) / (x_pixel * (1 - overlap)))
         shift_x = (num_x * x_pixel - (max_x - min_x)) // 2
 
@@ -615,4 +670,4 @@ def map_labels(
                 for x_pos, y_pos in product(x_positions, y_positions)
             ]
 
-    return z_range, position_table
+    return z_range, position_table, target_labels

src/navigate/model/features/volume_search.py

@@ -31,16 +31,20 @@
 #
 
 from queue import Queue
+import numpy as np
+from tifffile import imwrite
+from os import path
+
 from navigate.model.analysis.boundary_detect import (
     find_tissue_boundary_2d,
     binary_detect,
     map_boundary,
     find_cell_boundary_3d,
     map_labels,
 )
-import numpy as np
-from tifffile import imwrite
-from os import path
+from navigate.tools.multipos_table_tools import(
+    write_to_csv_file
+)
 
 
 def draw_box(img, xl, yl, xu, yu, fill=65535):
@@ -579,7 +583,7 @@ def data_func(self, frame_ids):
         z_end = microscope_state_config["end_position"]
         z_step = microscope_state_config["step_size"]
 
-        if microscope_state_config["multiposition_count"] == 0:
+        if len(self.model.configuration["multi_positions"]) == 0:
             pos_dict = self.model.get_stage_position()
             position = [
                 pos_dict[f"{axis}_pos"] for axis in ["x", "y", "z", "theta", "f"]
@@ -648,7 +652,7 @@ def data_func(self, frame_ids):
             "CameraParameters"
         ][self.target_resolution]["img_y_pixels"]
 
-        z_range, positions = map_labels(
+        z_range, positions, target_labels = map_labels(
             labeled_image,
             position,
             z_start,
@@ -665,6 +669,20 @@ def data_func(self, frame_ids):
             filter_pixel_number=self.filter_pixel_number,
         )
 
+        # save positions
+        write_to_csv_file(positions, path.join(
+            self.model.configuration["experiment"]["Saving"]["save_directory"],
+            "positions.csv",
+        ),)
+        # save target label index sequences
+        target_labels_file = path.join(
+            self.model.configuration["experiment"]["Saving"]["save_directory"],
+            "target_labels.txt"
+        )
+        with open(target_labels_file, "w") as f:
+            for idx in target_labels:
+                f.write(f"{idx}\n")
+
         self.model.event_queue.put(("multiposition", positions))
         self.model.configuration["multi_positions"] = positions
         if len(positions) > 0:

src/navigate/tools/multipos_table_tools.py

@@ -31,6 +31,7 @@
 
 # Standard library imports
 from math import ceil
+import csv
 
 # Third party imports
 import numpy as np
@@ -245,3 +246,28 @@ def update_table(table, pos, append=False):
     table.resetColors()
     table.redraw()
     table.tableChanged()
+
+def write_to_csv_file(positions, file_path):
+    """Write positions to a csv file.
+    
+    Parameters
+    ----------
+    pos: list or np.array
+        A list of positions.
+    file_path: str
+        The target csv file path.
+    Returns
+    -------
+    result: bool
+        Whether positions are saved successfully.
+    """
+    try:
+        with open(file_path, "w", newline="") as f:
+            writer = csv.writer(f)
+            writer.writerow(["X", "Y", "Z", "R", "F"])
+
+            for p in positions:
+                writer.writerow(p)
+        return True
+    except FileNotFoundError:
+        return False