WIP plotting annotation overlay on reference

examples/plots/atlas_annotations.py

@@ -0,0 +1,172 @@
+"""Create plots showing atlas annotations overlaid on the reference image
+
+This script needs:
+- A local path to where the BrainGlobe atlas is stored
+- A path to a .csv file specifying RGB values for each region in the atlas
+"""
+# %%
+# Imports
+
+import os
+from pathlib import Path
+
+import matplotlib.colors as mcolors
+import numpy as np
+import pandas as pd
+from brainglobe_utils.IO.image import load_any
+from matplotlib import pyplot as plt
+
+from brainglobe_template_builder.plots import save_figure
+
+# %%
+# Specify paths
+
+# Path to the BrainGlobe atlas
+atlas_name = "oldenburg_blackcap_25um_v1.1"
+atlas_dir = Path.home() / ".brainglobe" / atlas_name
+reference_path = atlas_dir / "reference.tiff"
+annotation_path = atlas_dir / "annotation.tiff"
+structures_csv_path = atlas_dir / "structures.csv"
+
+# Path to the csv file containing the RGB values for each region
+# Expected columns are: "acronym", "R", "G", "B"
+# The .csv is in the same folder as this script
+colors_csv_filename = "oldenburg_blackcap_colors.csv"
+currenct_script_dir = Path(os.path.dirname(os.path.abspath(__file__)))
+colors_csv_path = currenct_script_dir / colors_csv_filename
+
+# Path to save the plots
+save_dir = Path.home() / "Downloads"
+
+# %%
+# Load data
+
+reference_img = load_any(reference_path)
+annotation_img = load_any(annotation_path)
+
+# Load both "structures" and "colors" csv files
+structures = pd.read_csv(structures_csv_path)
+colors = pd.read_csv(colors_csv_path, dtype={"R": int, "G": int, "B": int})
+# Merge the two dataframes on the "acronym" column
+# Maintain the order of the "colors" dataframe
+structures = pd.merge(
+    structures, colors, on="acronym", how="right", validate="one_to_one"
+)
+# add an alpha column with all areas being 0.8
+structures.loc[:, "A"] = 1.0
+# Keep only the columns needed for the colormap
+structures = structures[["id", "acronym", "R", "G", "B", "A"]]
+# Divide RGB values by 255 (normalise to 0 - 1)
+for col in ["R", "G", "B"]:
+    structures[col] = structures[col] / 255
+# Add an id=0 (empty areas), with RGBA values (0, 0, 0, 0)
+new_row = pd.DataFrame(
+    {"id": [0], "acronym": "empty", "R": [0], "G": [0], "B": [0], "A": [0]}
+)
+structures = pd.concat([new_row, structures], ignore_index=True)
+# Sort daraframe by id (increasing id values)
+structures = structures.sort_values(by="id")
+# assign new_id in increasing order
+structures.loc[:, "new_id"] = range(len(structures))
+# Save the new dataframe to a csv file here
+structures.to_csv(currenct_script_dir / "colors.csv", index=False)
+
+# Create a dictionary mapping the id to the RGBA values
+id_to_rgba = {
+    row["new_id"]: (row["R"], row["G"], row["B"], row["A"])
+    for _, row in structures.iterrows()
+}
+# Create a colormap using the RGBA values
+annotation_cmap = mcolors.ListedColormap([id_to_rgba[id] for id in id_to_rgba])
+# Create a normalization for the colormap based on the id values
+annotation_cmap_norm = mcolors.BoundaryNorm(
+    list(id_to_rgba.keys()), annotation_cmap.N
+)
+
+# Remap the annotation image to the new ids
+for id in structures["id"].unique():
+    new_id = structures.loc[structures["id"] == id, "new_id"].values[0]
+    annotation_img[annotation_img == id] = new_id
+
+
+# %%
+# Define funciton for plotting
+
+
+def plot_slices(
+    reference: np.ndarray,
+    slices: list[int],
+    annotation=np.ndarray | None,
+    axis: int = 0,
+    vmin_perc: float = 1,
+    vmax_perc: float = 99,
+    save_path: Path | None = None,
+):
+    """Plot slices from a 3D image with optional annotation overlay.
+
+    The slices are shown in a single column.
+
+    Parameters
+    ----------
+    reference : np.ndarray
+        Reference 3D image to plot slices from.
+    slices : list[int]
+        List of slice indices to plot.
+    annotation : np.ndarray, optional
+        Annotation image to overlay on the reference image, by default None.
+        If supplied, must have the same shape as the reference image.
+    axis : int, optional
+        Axis along which to take slices, by default 0.
+    vmin_perc : float, optional
+        Lower percentile for reference image, by default 1.
+    vmax_perc : float, optional
+        Upper percentile for reference image, by default 99.
+    save_path : Path, optional
+        Path to save the plot, by default None (does not save).
+    """
+    n_slices = len(slices)
+    ref_slices = [reference.take(s, axis=axis) for s in slices]
+    height, width = ref_slices[0].shape
+    fig_width = width / 100
+    fig_height = height / 100 * n_slices
+    fig, ax = plt.subplots(n_slices, 1, figsize=(fig_width, fig_height))
+
+    if annotation is not None:
+        ann_slices = [annotation.take(s, axis=axis) for s in slices]
+        # Make the left half of each slice to 0
+        for ann_slice in ann_slices:
+            ann_slice[:, : width // 2] = 0
+
+    for i in range(n_slices):
+        ref_frame = ref_slices[i]
+        ax[i].imshow(
+            ref_frame,
+            cmap="gray",
+            vmin=np.percentile(ref_frame, vmin_perc),
+            vmax=np.percentile(ref_frame, vmax_perc),
+        )
+
+        if annotation is not None:
+            ann_frame = ann_slices[i]
+            ax[i].imshow(
+                ann_frame,
+                cmap=annotation_cmap,
+                norm=annotation_cmap_norm,
+            )
+        ax[i].axis("off")
+
+    fig.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
+    if save_path:
+        save_dir, save_name = save_path.parent, save_path.name.split(".")[0]
+        save_figure(fig, save_dir, save_name)
+
+
+# %%
+# Save plot
+
+plot_slices(
+    reference_img,
+    slices=[120, 260, 356],
+    annotation=annotation_img,
+    save_path=save_dir / "test.png",
+)

examples/plots/colors.csv

@@ -0,0 +1,26 @@
+id,acronym,R,G,B,A,new_id
+0,empty,0.0,0.0,0.0,0.0,0
+1,P,0.19215686274509805,0.6862745098039216,0.9607843137254902,1.0,1
+2,St,0.9450980392156862,0.796078431372549,0.22745098039215686,1.0,2
+3,Di,0.9411764705882353,0.3568627450980392,0.07058823529411765,1.0,3
+4,Me,0.2784313725490196,0.4823529411764706,0.9490196078431372,1.0,4
+5,Pn,0.8745098039215686,0.8745098039215686,0.21568627450980393,1.0,5
+6,Cb,0.6745098039215687,0.984313725490196,0.2196078431372549,1.0,6
+10,PrVd,0.8862745098039215,0.2627450980392157,0.0392156862745098,1.0,7
+17,PrVv,0.996078431372549,0.6,0.17254901960784313,1.0,8
+18,SpVl,0.23921568627450981,0.20784313725490197,0.5450980392156862,1.0,9
+19,SpVm,0.18823529411764706,0.07058823529411765,0.23137254901960785,1.0,10
+20,Ento,0.13333333333333333,0.9215686274509803,0.6666666666666666,1.0,11
+30,HP,0.47843137254901963,0.01568627450980392,0.011764705882352941,1.0,12
+40,N,0.2196078431372549,0.15294117647058825,0.42745098039215684,1.0,13
+50,H,0.7843137254901961,0.9372549019607843,0.20392156862745098,1.0,14
+60,M,0.5607843137254902,1.0,0.28627450980392155,1.0,15
+70,A,0.12549019607843137,0.7803921568627451,0.8745098039215686,1.0,16
+80,OB,0.24705882352941178,0.9647058823529412,0.5411764705882353,1.0,17
+90,CDL,0.2549019607843137,0.5882352941176471,1.0,1.0,18
+110,N.Rot,0.27450980392156865,0.3803921568627451,0.8392156862745098,1.0,19
+140,Gld,0.2549019607843137,0.27450980392156865,0.6745098039215687,1.0,20
+220,OT,0.1843137254901961,0.9450980392156862,0.6078431372549019,1.0,21
+305,CN-HP,0.9882352941176471,0.7019607843137254,0.21176470588235294,1.0,22
+402,NFT,0.9803921568627451,0.4823529411764706,0.12156862745098039,1.0,23
+505,CN-H,0.8156862745098039,0.1843137254901961,0.0196078431372549,1.0,24

data/oldenburg_blackcap_colors.csv → examples/plots/oldenburg_blackcap_colors.csv

@@ -20,6 +20,6 @@ CN-H,208,47,5
 NFT,250,123,31
 Gld,65,70,172
 PrVd,226,67,10
-PRVv,254,153,44
+PrVv,254,153,44
 SpVl,61,53,139
 SpVm,48,18,59