101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
|
from pathlib import Path
|
||
|
|
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
# Path to your .npy file with anomaly scores
|
||
|
|
scores_path = Path(
|
||
|
|
"/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/infer/"
|
||
|
|
"DeepSAD/subter_test/inference/3_smoke_human_walking_2023-01-23.npy"
|
||
|
|
)
|
||
|
|
|
||
|
|
# Frames per second
|
||
|
|
fps = 10
|
||
|
|
|
||
|
|
# Load all scores
|
||
|
|
all_scores = np.load(scores_path)
|
||
|
|
|
||
|
|
# (Optional) If your scores are a 1D array, remove or adjust this reshape
|
||
|
|
# The final shape is expected to be (1, total_frames) or (N,) if you want only one line.
|
||
|
|
all_scores = all_scores.reshape(1, -1) # shape becomes (1, total_frames)
|
||
|
|
|
||
|
|
# We only want frames [0..299] => 300 frames
|
||
|
|
max_frames = 300
|
||
|
|
scores = all_scores[0, :max_frames] # Take the first row's first 300 frames
|
||
|
|
|
||
|
|
# Convert frames to time in seconds: time[i] = i / fps
|
||
|
|
time_values = np.arange(len(scores)) / fps # shape is (max_frames,)
|
||
|
|
|
||
|
|
# Recalculate y-limit based on the selected slice
|
||
|
|
y_limit = 1.1 * np.max(scores[np.isfinite(scores)])
|
||
|
|
# If you want to ensure y_limit >= 10, uncomment below:
|
||
|
|
# y_limit = max(y_limit, 10)
|
||
|
|
|
||
|
|
print("Selected Scores Shape:", scores.shape)
|
||
|
|
print("Time Range:", time_values[0], "->", time_values[-1])
|
||
|
|
print("Y-limit:", y_limit)
|
||
|
|
|
||
|
|
# --------------------------
|
||
|
|
# Improve default font sizes
|
||
|
|
# --------------------------
|
||
|
|
plt.rcParams.update(
|
||
|
|
{
|
||
|
|
"figure.dpi": 150,
|
||
|
|
"font.size": 14,
|
||
|
|
"axes.labelsize": 16,
|
||
|
|
"axes.titlesize": 16,
|
||
|
|
"xtick.labelsize": 14,
|
||
|
|
"ytick.labelsize": 14,
|
||
|
|
"legend.fontsize": 14,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
# --------------------------------------------------
|
||
|
|
# Create the figure (half the previous width = 3.84)
|
||
|
|
# --------------------------------------------------
|
||
|
|
fig, ax = plt.subplots(figsize=(3.84, 7))
|
||
|
|
|
||
|
|
# Plot the scores vs. time
|
||
|
|
ax.plot(time_values, scores, label="Anomaly Score", color="blue")
|
||
|
|
|
||
|
|
# Set axis labels
|
||
|
|
ax.set_xlabel("Time [s]", fontsize=16)
|
||
|
|
ax.set_ylabel("Score", fontsize=16)
|
||
|
|
|
||
|
|
# Restrict X-axis from 0 to last time value (or 30 s if exactly 300 frames)
|
||
|
|
ax.set_xlim([0, time_values[-1]])
|
||
|
|
ax.set_ylim([0, y_limit])
|
||
|
|
|
||
|
|
# -------------------------
|
||
|
|
# Customize the ticks
|
||
|
|
# -------------------------
|
||
|
|
# Example: only 4 ticks on the x-axis (0, 10, 20, 30) if 300 frames => 30 s
|
||
|
|
ax.set_xticks([0, 10, 20, 30])
|
||
|
|
|
||
|
|
# Guarantee at least one tick at y=10.
|
||
|
|
# Adjust other ticks as you like; here we use 0 and the y_limit.
|
||
|
|
# If y_limit <= 10, you might want to override it.
|
||
|
|
if y_limit > 10:
|
||
|
|
ax.set_yticks([0, 10, round(y_limit, 1)]) # round for cleaner display
|
||
|
|
else:
|
||
|
|
# If your data doesn't go that high, just keep 0 and y_limit
|
||
|
|
ax.set_yticks([0, round(y_limit, 1)])
|
||
|
|
|
||
|
|
# Optionally add a legend
|
||
|
|
ax.legend(loc="upper right")
|
||
|
|
|
||
|
|
plt.axvline(x=22, color="r", label="shown image")
|
||
|
|
|
||
|
|
# Tight layout for small figure
|
||
|
|
plt.tight_layout()
|
||
|
|
|
||
|
|
# ----------------------
|
||
|
|
# Save to disk, no show
|
||
|
|
# ----------------------
|
||
|
|
output_filename = f"{scores_path.stem}_static_time_plot.png"
|
||
|
|
plt.savefig(output_filename, dpi=150)
|
||
|
|
|
||
|
|
# Clear the figure if you continue using matplotlib
|
||
|
|
plt.clf()
|