data loading and plotting for results wip

tools/plot_scripts/data_spherical_projection_as_trained.py

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+import argparse
+import shutil
+from datetime import datetime
+from pathlib import Path
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.colors import LinearSegmentedColormap, ListedColormap
+from PIL import Image
+
+# --- Setup output folders ---
+output_path = Path("/home/fedex/mt/plots/data_2d_projections_training")
+datetime_folder_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+output_datetime_path = output_path / datetime_folder_name
+latest_folder_path = output_path / "latest"
+archive_folder_path = output_path / "archive"
+
+for folder in (
+    output_path,
+    output_datetime_path,
+    latest_folder_path,
+    archive_folder_path,
+):
+    folder.mkdir(exist_ok=True, parents=True)
+
+# --- Parse command-line arguments ---
+parser = argparse.ArgumentParser(
+    description="Plot two 2D projections as used in training (unstretched, grayscale)"
+)
+parser.add_argument(
+    "--input1",
+    type=Path,
+    default=Path(
+        "/home/fedex/mt/data/subter/new_projection/1_loop_closure_illuminated_2023-01-23.npy"
+    ),
+    help="Path to first .npy file containing 2D projection data",
+)
+parser.add_argument(
+    "--input2",
+    type=Path,
+    default=Path(
+        "/home/fedex/mt/data/subter/new_projection/3_smoke_human_walking_2023-01-23.npy"
+    ),
+    help="Path to second .npy file containing 2D projection data",
+)
+parser.add_argument(
+    "--frame1",
+    type=int,
+    default=955,
+    help="Frame index to plot from first file (0-indexed)",
+)
+parser.add_argument(
+    "--frame2",
+    type=int,
+    default=242,
+    help="Frame index to plot from second file (0-indexed)",
+)
+
+args = parser.parse_args()
+
+# --- Load the numpy projection data ---
+proj_data1 = np.load(args.input1)
+proj_data2 = np.load(args.input2)
+
+# Choose the desired frames
+try:
+    frame1 = proj_data1[args.frame1]
+    frame2 = proj_data2[args.frame2]
+except IndexError as e:
+    raise ValueError(f"Frame index out of range: {e}")
+
+# Debug info: Print the percentage of missing data in each frame
+print(f"Frame 1 missing data percentage: {np.isnan(frame1).mean() * 100:.2f}%")
+print(f"Frame 2 missing data percentage: {np.isnan(frame2).mean() * 100:.2f}%")
+
+# --- Create a figure with 2 vertical subplots ---
+fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(10, 5))
+
+# Create custom colormap for missing data visualization
+missing_color = [1, 0, 0, 1]  # Red with full alpha
+cmap_missing = ListedColormap([missing_color])
+
+# Replace the plotting section
+for ax, frame, title in zip(
+    (ax1, ax2),
+    (frame1, frame2),
+    (
+        "Normal LiDAR Frame",
+        "Degraded LiDAR Frame (Smoke)",
+    ),
+):
+    # Create mask for missing data (directly from NaN values)
+    missing_mask = np.isnan(frame)
+
+    # Plot the valid data in grayscale
+    frame_valid = np.copy(frame)
+    frame_valid[missing_mask] = 0  # Set missing values to black in base image
+    im = ax.imshow(frame_valid, cmap="gray", aspect="equal", vmin=0, vmax=0.8)
+
+    # Overlay missing data in red with reduced alpha
+    ax.imshow(
+        missing_mask,
+        cmap=ListedColormap([[1, 0, 0, 1]]),  # Pure red
+        alpha=0.3,  # Reduced alpha for better visibility
+    )
+
+    ax.set_title(title)
+    ax.axis("off")
+
+# Adjust layout
+plt.tight_layout()
+
+# Create a more informative legend
+legend_elements = [
+    mpatches.Patch(facecolor="red", alpha=0.7, label="Missing Data"),
+    mpatches.Patch(facecolor="white", label="Close Distance (0m)"),
+    mpatches.Patch(facecolor="gray", label="Mid Distance"),
+    mpatches.Patch(facecolor="black", label="Far Distance (70m)"),
+]
+
+# Add legend with better positioning and formatting
+fig.legend(
+    handles=legend_elements,
+    loc="center right",
+    bbox_to_anchor=(0.98, 0.5),
+    title="Distance Information",
+    framealpha=1.0,
+)
+
+# Save the plot
+output_file = output_datetime_path / "data_2d_projections_training.png"
+plt.savefig(output_file, dpi=300, bbox_inches="tight", pad_inches=0.1)
+plt.close()
+
+print(f"Plot saved to: {output_file}")
+
+# --- Create grayscale training images ---
+for degradation_status, frame_number, frame in (
+    ("normal", args.frame1, frame1),
+    ("smoke", args.frame2, frame2),
+):
+    frame_gray = np.nan_to_num(frame, nan=0).astype(np.float32)
+    gray_image = Image.fromarray(frame_gray, mode="F")
+    gray_output_file = (
+        output_datetime_path
+        / f"frame_{frame_number}_training_{degradation_status}.tiff"
+    )
+    gray_image.save(gray_output_file)
+    print(f"Training image saved to: {gray_output_file}")
+
+# --- Handle folder structure ---
+shutil.rmtree(latest_folder_path, ignore_errors=True)
+latest_folder_path.mkdir(exist_ok=True, parents=True)
+
+for file in output_datetime_path.iterdir():
+    shutil.copy2(file, latest_folder_path)
+
+script_path = Path(__file__)
+shutil.copy2(script_path, output_datetime_path)
+shutil.copy2(script_path, latest_folder_path)
+
+shutil.move(output_datetime_path, archive_folder_path)
+print(f"Output archived to: {archive_folder_path}")