wip inference

2025-09-15 11:21:30 +02:00
parent e4b298cf06
commit e7624d2786
8 changed files with 1027 additions and 35 deletions
--- a/tools/plot_scripts/results_inference_timeline
+++ b/tools/plot_scripts/results_inference_timeline
@@ -0,0 +1,269 @@
+import json
+import pickle
+import shutil
+from datetime import datetime
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# =========================
+# User-configurable params
+# =========================
+# Single experiment to plot (stem of the .bag file, e.g. "3_smoke_human_walking_2023-01-23")
+EXPERIMENT_NAME = "3_smoke_human_walking_2023-01-23"
+
+# Directory that contains {EXPERIMENT_NAME}_{method}_scores.npy for methods in {"deepsad","ocsvm","isoforest"}
+methods_scores_path = Path(
+    "/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/infer/DeepSAD/test/inference"
+)
+
+# Root data path containing .bag files used to build the cached stats
+all_data_path = Path("/home/fedex/mt/data/subter")
+
+# Output base directory (timestamped subfolder will be created here, then archived and copied to "latest/")
+output_path = Path("/home/fedex/mt/plots/results_inference_timeline_smoothed")
+
+# Cache (stats + labels) directory — same as your original script
+cache_path = output_path
+
+# Assumed LiDAR frame resolution to convert counts -> percent (unchanged from original)
+data_resolution = 32 * 2048
+
+# Frames per second for x-axis time
+FPS = 10.0
+
+# Whether to try to align score sign so that higher = more degraded.
+ALIGN_SCORE_DIRECTION = True
+
+# =========================
+# Smoothing configuration
+# =========================
+# Options: "none", "moving_average", "gaussian", "ema"
+SMOOTHING_METHOD = "ema"
+
+# Moving average window size (in frames). Use odd number for symmetry; <=1 disables.
+MA_WINDOW = 11
+
+# Gaussian sigma (in frames). ~2-3 frames is mild smoothing.
+GAUSSIAN_SIGMA = 2.0
+
+# Exponential moving average factor in (0,1]; smaller = smoother. ~0.2 is a good start.
+EMA_ALPHA = 0.1
+
+# =========================
+# Setup output folders
+# =========================
+datetime_folder_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+latest_folder_path = output_path / "latest"
+archive_folder_path = output_path / "archive"
+output_datetime_path = output_path / datetime_folder_name
+
+output_path.mkdir(exist_ok=True, parents=True)
+output_datetime_path.mkdir(exist_ok=True, parents=True)
+latest_folder_path.mkdir(exist_ok=True, parents=True)
+archive_folder_path.mkdir(exist_ok=True, parents=True)
+
+# =========================
+# Discover experiments
+# =========================
+normal_experiment_paths, anomaly_experiment_paths = [], []
+for bag_file_path in all_data_path.iterdir():
+    if bag_file_path.suffix != ".bag":
+        continue
+    if "smoke" in bag_file_path.name:
+        anomaly_experiment_paths.append(bag_file_path)
+    else:
+        normal_experiment_paths.append(bag_file_path)
+
+normal_experiment_paths = sorted(
+    normal_experiment_paths, key=lambda p: p.stat().st_size
+)
+anomaly_experiment_paths = sorted(
+    anomaly_experiment_paths, key=lambda p: p.stat().st_size
+)
+
+# Find experiment
+exp_path, exp_is_anomaly = None, None
+for p in anomaly_experiment_paths:
+    if p.stem == EXPERIMENT_NAME:
+        exp_path, exp_is_anomaly = p, True
+        break
+if exp_path is None:
+    for p in normal_experiment_paths:
+        if p.stem == EXPERIMENT_NAME:
+            exp_path, exp_is_anomaly = p, False
+            break
+if exp_path is None:
+    raise FileNotFoundError(f"Experiment '{EXPERIMENT_NAME}' not found")
+
+exp_index = (
+    anomaly_experiment_paths.index(exp_path)
+    if exp_is_anomaly
+    else normal_experiment_paths.index(exp_path)
+)
+
+# =========================
+# Load cached statistical data
+# =========================
+with open(cache_path / "missing_points.pkl", "rb") as f:
+    missing_points_normal, missing_points_anomaly = pickle.load(f)
+with open(cache_path / "particles_near_sensor_counts_500.pkl", "rb") as f:
+    near_sensor_normal, near_sensor_anomaly = pickle.load(f)
+
+if exp_is_anomaly:
+    missing_points_series = np.asarray(missing_points_anomaly[exp_index], dtype=float)
+    near_sensor_series = np.asarray(near_sensor_anomaly[exp_index], dtype=float)
+else:
+    missing_points_series = np.asarray(missing_points_normal[exp_index], dtype=float)
+    near_sensor_series = np.asarray(near_sensor_normal[exp_index], dtype=float)
+
+missing_points_pct = (missing_points_series / data_resolution) * 100.0
+near_sensor_pct = (near_sensor_series / data_resolution) * 100.0
+
+# =========================
+# Load manual anomaly frame borders
+# =========================
+manually_labeled_anomaly_frames = {}
+labels_json_path = cache_path / "manually_labeled_anomaly_frames.json"
+if labels_json_path.exists():
+    with open(labels_json_path, "r") as f:
+        labeled_json = json.load(f)
+        for file in labeled_json.get("files", []):
+            manually_labeled_anomaly_frames[file["filename"]] = (
+                file.get("semi_target_begin_frame"),
+                file.get("semi_target_end_frame"),
+            )
+exp_npy_filename = exp_path.with_suffix(".npy").name
+anomaly_window = manually_labeled_anomaly_frames.get(exp_npy_filename, (None, None))
+
+
+# =========================
+# Load method scores and normalize
+# =========================
+def zscore_1d(x, eps=1e-12):
+    mu, sigma = np.mean(x), np.std(x, ddof=0)
+    return np.zeros_like(x) if sigma < eps else (x - mu) / sigma
+
+
+def maybe_align_direction(z, window):
+    start, end = window
+    if start is None or end is None:
+        return z
+    inside_mean = np.mean(z[start:end]) if end > start else 0
+    outside = np.concatenate([z[:start], z[end:]]) if start > 0 or end < len(z) else []
+    outside_mean = np.mean(outside) if len(outside) else inside_mean
+    return z if inside_mean >= outside_mean else -z
+
+
+methods = ["deepsad", "ocsvm", "isoforest"]
+method_zscores = {}
+for m in methods:
+    s = np.load(methods_scores_path / f"{EXPERIMENT_NAME}_{m}_scores.npy")
+    s = np.asarray(s, dtype=float).ravel()
+    n = min(len(s), len(missing_points_pct))
+    s, missing_points_pct, near_sensor_pct = (
+        s[:n],
+        missing_points_pct[:n],
+        near_sensor_pct[:n],
+    )
+    z = zscore_1d(s)
+    if ALIGN_SCORE_DIRECTION:
+        z = maybe_align_direction(z, anomaly_window)
+    method_zscores[m] = z
+
+
+# =========================
+# Smoothing
+# =========================
+def moving_average(x, window):
+    if window <= 1:
+        return x
+    if window % 2 == 0:
+        window += 1
+    return np.convolve(x, np.ones(window) / window, mode="same")
+
+
+def gaussian_smooth(x, sigma):
+    from scipy.ndimage import gaussian_filter1d
+
+    return gaussian_filter1d(x, sigma=sigma, mode="nearest") if sigma > 0 else x
+
+
+def ema(x, alpha):
+    y = np.empty_like(x)
+    y[0] = x[0]
+    for i in range(1, len(x)):
+        y[i] = alpha * x[i] + (1 - alpha) * y[i - 1]
+    return y
+
+
+def apply_smoothing(x):
+    m = SMOOTHING_METHOD.lower()
+    if m == "none":
+        return x
+    if m == "moving_average":
+        return moving_average(x, MA_WINDOW)
+    if m == "gaussian":
+        return gaussian_smooth(x, GAUSSIAN_SIGMA)
+    if m == "ema":
+        return ema(x, EMA_ALPHA)
+    raise ValueError(f"Unknown SMOOTHING_METHOD: {SMOOTHING_METHOD}")
+
+
+smoothed_z = {k: apply_smoothing(v) for k, v in method_zscores.items()}
+smoothed_missing = apply_smoothing(missing_points_pct)
+smoothed_near = apply_smoothing(near_sensor_pct)
+
+# =========================
+# Plot
+# =========================
+t = np.arange(len(missing_points_pct)) / FPS
+
+
+def plot_series(y2, ylabel, fname, title_suffix):
+    fig, axz = plt.subplots(figsize=(14, 6), constrained_layout=True)
+    axy = axz.twinx()
+    for m in methods:
+        axz.plot(t, smoothed_z[m], label=f"{m} (z)")
+    axy.plot(t, y2, linestyle="--", label=ylabel)
+    start, end = anomaly_window
+    if start and end:
+        axz.axvline(start / FPS, linestyle=":", alpha=0.6)
+        axz.axvline(end / FPS, linestyle=":", alpha=0.6)
+    axz.set_xlabel("Time (s)")
+    axz.set_ylabel("Anomaly score (z)")
+    axy.set_ylabel(ylabel)
+    axz.set_title(f"{EXPERIMENT_NAME}\n{title_suffix}\nSmoothing: {SMOOTHING_METHOD}")
+    lines1, labels1 = axz.get_legend_handles_labels()
+    lines2, labels2 = axy.get_legend_handles_labels()
+    axz.legend(lines1 + lines2, labels1 + labels2, loc="upper right")
+    axz.grid(True, alpha=0.3)
+    fig.savefig(output_datetime_path / fname, dpi=150)
+    plt.close(fig)
+
+
+plot_series(
+    smoothed_missing,
+    "Missing points (%)",
+    f"{EXPERIMENT_NAME}_zscores_vs_missing.png",
+    "Degradation vs Missing Points",
+)
+plot_series(
+    smoothed_near,
+    "Near-sensor points (%)",
+    f"{EXPERIMENT_NAME}_zscores_vs_near.png",
+    "Degradation vs Near-Sensor Points (<0.5m)",
+)
+
+# =========================
+# Save & archive
+# =========================
+shutil.rmtree(latest_folder_path, ignore_errors=True)
+latest_folder_path.mkdir(exist_ok=True, parents=True)
+for f in output_datetime_path.iterdir():
+    shutil.copy2(f, latest_folder_path)
+shutil.copy2(__file__, output_datetime_path)
+shutil.copy2(__file__, latest_folder_path)
+shutil.move(output_datetime_path, archive_folder_path)
+print("Done. Plots saved and archived.")