v2 working

3ddisplay_replay_smoothed.py

@@ -0,0 +1,58 @@
+from scipy.signal import savgol_filter
+import numpy as np
+import pickle
+from matplotlib import pyplot as plt
+
+with open("replay_xyz.pkl", "rb") as f:
+    points3DList = pickle.load(f)
+
+skeleton = [
+    [0, 1], [0, 2],
+    [1, 3], [2, 4],
+    [5, 7], [7, 9],
+    [6, 8], [8, 10],
+    [5, 6],
+    [5, 11], [6, 12],
+    [11, 12],
+    [11, 13], [13, 15],
+    [12, 14], [14, 16]
+]
+
+keys_sorted = sorted(points3DList.keys())
+points_sequence = np.array([points3DList[k] for k in keys_sorted])  # (frames, points, 3)
+
+# --- Filtr Savitzky-Golaya ---
+window_length = 7  # musi być nieparzyste
+polyorder = 2
+smoothed_sequence = savgol_filter(points_sequence, window_length=window_length,
+                                  polyorder=polyorder, axis=0, mode='nearest')
+
+plt.ion()
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+
+points_plot = ax.scatter([], [], [], c='r', marker='o', s=50)
+lines_plot = [ax.plot([0,0],[0,0],[0,0], c='b')[0] for _ in skeleton]
+
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+ax.set_xlim(-0.6, 0.4)
+ax.set_ylim(1.2, 2.2)
+ax.set_zlim(-0.5, 1.1)
+ax.view_init(elev=20, azim=-60)
+
+for frame_points in smoothed_sequence:
+    X = frame_points[:,0] - 0.25
+    Z = -frame_points[:,1] + 0.5
+    Y = frame_points[:,2]
+
+    points_plot._offsets3d = (X, Y, Z)
+
+    for idx, (i, j) in enumerate(skeleton):
+        lines_plot[idx].set_data([X[i], X[j]], [Y[i], Y[j]])
+        lines_plot[idx].set_3d_properties([Z[i], Z[j]])
+
+    fig.canvas.draw()
+    fig.canvas.flush_events()
+    plt.pause(0.001)