Small fixes for notebook processing

notebooks/perovstats-multi-process.ipynb

@@ -82,13 +82,15 @@
    "source": [
     "#### Tweak parameters if necessary\n",
     "\n",
-    "##### 1) Disable frequency splitting if necessary with `run_splitting = False`\n",
+    "##### 1) Identify the file type of the scan(s) used.\n",
     "\n",
-    "##### 2) Adjust the cutoff bounds if no cutoff frequency can be found (if one is found its value is logged in the output below)\n",
+    "##### 2) Disable frequency splitting if necessary with `run_splitting = False`\n",
     "\n",
-    "##### 3) Choose the min_rms value (affects isolation of perovskite grains from silicon topology)\n",
+    "##### 3) Adjust the cutoff bounds if no cutoff frequency can be found (if one is found its value is logged in the output below)\n",
     "\n",
-    "##### 4) Select the segmentation method to use\n",
+    "##### 4) Choose the min_rms value (affects isolation of perovskite grains from silicon topology)\n",
+    "\n",
+    "##### 5) Select the segmentation method to use\n",
     "##### As a reminder:\n",
     "- `traditional`:\n",
     "    - hard-coded\n",
@@ -109,6 +111,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# The file extension of the input image(s).\n",
+    "file_ext = \".spm\"\n",
+    "\n",
     "# If you are inputting images without a background material frequency splitting can be disabled here.\n",
     "run_splitting = True # Options: True, False\n",
     "\n",
@@ -121,7 +126,7 @@
     "min_rms = 10\n",
     "\n",
     "\n",
-    "segmentation_method = \"cellpose\" # Options: traditional, cellpose\n",
+    "segmentation_method = \"traditional\" # Options: traditional, cellpose\n",
     "\n",
     "# If 'traditional' segmentation has been chosen:\n",
     "threshold_offset = -0.5\n"
@@ -158,6 +163,7 @@
     "    else:\n",
     "        logger.error(\"Error: custom configuration file could not be found. Please check the set filepath above.\")\n",
     "\n",
+    "config[\"file_ext\"] = file_ext\n",
     "config[\"fourier\"][\"run\"] = run_splitting\n",
     "config[\"fourier\"][\"cutoff_bounds\"] = cutoff_bounds\n",
     "config[\"fourier\"][\"min_rms\"] = min_rms\n",

src/perovstats/cli.py

@@ -32,6 +32,7 @@
 from yaml import safe_load
 
 from .processing import run_process
+from .perovstats_gui import run_gui
 from .config import update_module
 
 
@@ -100,8 +101,15 @@ def create_parser() -> ArgumentParser:
         help="Process AFM images. Additional arguments over-ride defaults or those in the configuration file.",
     )
 
+    gui_parser = subparsers.add_parser(
+        "gui",
+        description="Process AFM images from a GUI.",
+        help="Process AFM images from a GUI.",
+    )
+
     # Run the relevant function with the arguments
     process_parser.set_defaults(func=process)
+    gui_parser.set_defaults(func=run_gui)
 
     return parser
 

src/perovstats/core/io.py

@@ -20,7 +20,7 @@ def save_image(image: np.ndarray, output_dir: Path, filename: str, cmap: str='gr
     filename : str
         The name of the file to be created/ saved to.
     cmap : str
-        The cmap to be used in the imsave() function. By default is 'grey'.
+        The cmap to be used in the imsave() function defined in config. By default is 'grey'.
     """
     output_dir.mkdir(parents=True, exist_ok=True)
     if cmap:

src/perovstats/default_config.yaml

@@ -1,11 +1,12 @@
 base_dir: ./example images/ # Directory in which to search for data files
 output_dir: ./output # Directory to output results to
 file_ext: ".spm" # File extension of the data files.
+colour_scheme: "afmhot"
 loading:
   channel: Height # Channel to pull data from in the data files.
 outliers:
   remove_outliers: true
-  max_z_score: 3.2
+  max_z_score: 3.5
 fourier:
   run: true
   edge_width: 0.03 # Width of the cutoff in frequency when performing the fourier split, as a fraction of the total frequency range. Higher values make the split less sensitive to cutoff choice.
@@ -14,7 +15,7 @@ fourier:
 segmentation:
   segmentation_method: traditional # Options: traditional, cellpose
   traditional:
-    threshold_offset: -1 # Amount to change the calculated threshold by for segmentation. Lower thresholds increase sentitivity.
+    threshold_offset: 1.5 # Amount to change the calculated threshold by for segmentation. Lower thresholds increase sentitivity.
     threshold_block_size: 1000 # Size of each block for local thresholding. Lower values increase segmentation sensitivity.
     smoothing:
       sigma: 4

src/perovstats/fourier.py

@@ -87,23 +87,27 @@ def split_frequencies(
         image_object.low_pass = low_pass
         image_object.file_directory = file_output_dir
 
+        cmap = config["colour_scheme"]
+
         # Convert high-pass and low-pass to image format
         arr = high_pass
         img_dir = Path(file_output_dir) / "images"
-        save_image(arr, img_dir, f"{filename}_highpass.png", cmap="grey")
+        save_image(arr, img_dir, f"{filename}_highpass.png", cmap=cmap)
 
         arr = low_pass
-        save_image(arr, img_dir, f"{filename}_lowpass.png", cmap="grey")
+        save_image(arr, img_dir, f"{filename}_lowpass.png", cmap=cmap)
     else:
         logger.info(f"[{image_object.filename}] : Frequency splitting is disabled by config, the original image will be used.")
         if image_object.image_flattened is not None:
             image_object.high_pass = image_object.image_flattened
+            image_object.low_pass = np.zeros_like(image_object.high_pass)
         else:
             image_object.high_pass = image_object.image_original
+            image_object.low_pass = np.zeros_like(image_object.high_pass)
 
     arr = image_object.image_original
     arr = normalise_array(arr)
-    save_image(arr, file_output_dir / "images", f"{filename}_original.png", cmap="grey")
+    save_image(arr, file_output_dir / "images", f"{filename}_original.png", cmap=cmap)
 
 
 def perform_fourier(

src/perovstats/grains.py

@@ -10,6 +10,7 @@
 from skimage import morphology
 from scipy.ndimage import binary_fill_holes
 from scipy import stats
+import matplotlib.cm as cm
 
 from .core.classes import Grain, ImageData
 from .core.image_processing import normalise_array
@@ -210,6 +211,9 @@ def _create_grain_objects(
     return circularity_data
 
 def _save_mask_images(config, image_object, mask_data, filename, mask_details):
+    cmap = config["colour_scheme"]
+    get_cmap = cm.get_cmap(cmap)
+
     # Remove mask outlines of edge grains and smear grains from the mask
     mask_rgb = mask_data["mask_rgb"]
     mask_rgb[image_object.indent_mask > 0] = [0, 0, 0]
@@ -224,30 +228,32 @@ def _save_mask_images(config, image_object, mask_data, filename, mask_details):
     image_object.cleaned_mask = new_mask
 
     # Save the cleaned mask
-    save_image(new_mask, save_dir, f"{filename}_mask.png")
+    save_image(new_mask, save_dir, f"{filename}_mask.png", cmap=cmap)
 
     # Save high-pass with mask overlay
     high_pass = image_object.high_pass
-    rgb_highpass = np.stack((high_pass,)*3, axis=-1)
-    rgb_highpass = normalise_array(rgb_highpass)
-    rgb_highpass[new_mask > 0] = [1, 0, 0]
-    save_image(rgb_highpass, save_dir, f"{filename}_highpass_mask_overlay.png")
+    norm_highpass = normalise_array(high_pass)
+    rgba_highpass = get_cmap(norm_highpass)
+    rgb_highpass = rgba_highpass[..., :3]
+    rgb_highpass[new_mask > 0] = [0, 0, 1]
+    save_image(rgb_highpass, save_dir, f"{filename}_highpass_mask_overlay.png", cmap=cmap)
 
     # Save original image with mask overlay
     original = image_object.image_original
-    rgb_original = np.stack((original,)*3, axis=-1)
-    rgb_original = normalise_array(rgb_original)
-    rgb_original[new_mask > 0] = [1, 0, 0]
-    save_image(rgb_original, save_dir, f"{filename}_original_mask_overlay.png")
+    norm_original = normalise_array(original)
+    rgba_original = get_cmap(norm_original)
+    rgb_original = rgba_original[..., :3]
+    rgb_original[new_mask > 0] = [0, 0, 1]
+    save_image(rgb_original, save_dir, f"{filename}_original_mask_overlay.png", cmap=cmap)
 
-    # Save the high-pass image with solid grains and red sections identifying smear areas
+    # Save the high-pass image with solid grains and pink sections identifying smear areas
     save_image(mask_rgb, save_dir, f"{filename}_rgb_grains.png", cmap=None)
     smear_overlay = np.stack((image_object.high_pass,)*3, axis=-1)
     smear_overlay = normalise_array(smear_overlay)
     mask_2d = np.all(mask_rgb == 0, axis=2)
     smear_overlay[mask_2d == 0] = [1, 1, 1]
-    smear_overlay[image_object.smears == 1] = [1, 0, 0]
-    save_image(smear_overlay, save_dir, f"{filename}_smears.png")
+    smear_overlay[image_object.smears == 1] = [1, 0, 1]
+    save_image(smear_overlay, save_dir, f"{filename}_smears.png", cmap=cmap)
 
     # Save area and circularity data for all grains and export a histogram of them each
     image_object.mask_areas = mask_details['areas']

src/perovstats/perovstats_gui.py

@@ -0,0 +1,119 @@
+import tkinter as tk
+from tkinter import ttk
+import numpy as np
+
+from .processing import run_process
+
+WINDOW_SIZE = (600,400)
+WINDOW_RESIZE = False
+
+class PerovStatsGUI():
+    def __init__(self, root):
+        self.root = root
+        self.root.title("PerovStats GUI")
+        self.root.geometry("1000x600")
+
+        # Configure the main window grid weights
+        # Column 0 (Left), Column 2 (Right) weight = 0 (stays small)
+        # Column 1 (Center) weight = 1 (expands to fill space)
+        self.root.columnconfigure(0, weight=0)
+        self.root.columnconfigure(1, weight=1)
+        self.root.columnconfigure(2, weight=0)
+        self.root.rowconfigure(0, weight=1) # Make the row fill the height
+
+        # --- 1. Left Sidebar ---
+        self.left_bar = tk.Frame(self.root, width=200, pady=20, padx=10)
+        self.left_bar.grid(row=0, column=0, sticky="nsew")
+
+        self.upload_btn = tk.Button(self.left_bar, text="Upload scan(s)")
+        self.upload_btn.grid(row=0, column=0, padx=25, pady=5, sticky="ew")
+        ttk.Separator(self.left_bar, orient="horizontal").grid(row=1, column=0, pady=20, sticky="ew")
+
+        self.analysis_mode = tk.StringVar(value="auto")
+
+        tk.Label(self.left_bar, text="Segmentation method:").grid(row=2, column=0, sticky="w")
+
+        # 3. First Radio Button
+        self.radio_auto = tk.Radiobutton(
+            self.left_bar,
+            text="Traditional",
+            variable=self.analysis_mode,
+            value="traditional"
+        )
+        self.radio_auto.grid(row=3, column=0, columnspan=2, sticky="w")
+
+        # 4. Second Radio Button
+        self.radio_manual = tk.Radiobutton(
+            self.left_bar,
+            text="Cellpose ML",
+            variable=self.analysis_mode,
+            value="cellpose"
+        )
+        self.radio_manual.grid(row=4, column=0, columnspan=2, sticky="w")
+
+
+        ttk.Separator(self.left_bar, orient="horizontal").grid(row=5, column=0, pady=20, sticky="ew")
+
+        self.run_btn = tk.Button(self.left_bar, text="Frequency Splitting")
+        self.run_btn.grid(row=6, column=0, padx=25, pady=5, sticky="ew")
+
+        self.run_btn = tk.Button(self.left_bar, text="Image Segmentation")
+        self.run_btn.grid(row=7, column=0, padx=25, pady=5, sticky="ew")
+
+        self.run_btn = tk.Button(self.left_bar, text="Grain Processing")
+        self.run_btn.grid(row=8, column=0, padx=25, pady=5, sticky="ew")
+
+        # --- 2. Central Area ---
+        self.center_area = tk.Frame(self.root, bg="white", borderwidth=2, relief="sunken")
+        self.center_area.grid(row=0, column=1, sticky="nsew")
+
+        # Centering a label inside the center area
+        self.center_area.columnconfigure(0, weight=1)
+        self.center_area.rowconfigure(0, weight=1)
+        tk.Label(self.center_area, text="Upload a scan and configure/ run the steps on the left to generate data.", bg="white").grid(row=0, column=0)
+
+        # --- 3. Right Sidebar (Settings) ---
+        self.right_bar = tk.Frame(self.root, width=250, bg="#ecf0f1", padx=10, pady=10)
+        self.right_bar.grid(row=0, column=2, sticky="nsew")
+
+        tk.Label(self.right_bar, text="Settings", bg="#ecf0f1", font=('Helvetica', 10, 'bold')).grid(row=0, column=0, pady=10)
+
+        # Example of why Grid is better for settings:
+        tk.Label(self.right_bar, text="Threshold:", bg="#ecf0f1").grid(row=1, column=0, sticky="w")
+        tk.Entry(self.right_bar, width=10).grid(row=1, column=1, padx=5, pady=5)
+
+
+    def frequency_settings(self):
+        pass
+
+    def segmentation_settings(self):
+        pass
+
+    def grain_settings(self):
+        pass
+
+
+    def upload_files(self):
+        pass
+
+
+    def run_splitting(self):
+        pass
+
+
+    def run_segmentation(self):
+        pass
+
+
+    def run_smears(self):
+        pass
+
+
+    def run_grains(self):
+        pass
+
+
+def run_gui(args):
+    root = tk.Tk()
+    app = PerovStatsGUI(root)
+    root.mainloop()