Add linear velocity calculation and visualization to IMU data processing

- Implemented Euler to rotation matrix conversion for IMU data.
- Added function to compute linear velocity from accelerometer data with gravity compensation.
- Enhanced CSV and PNG saving functionality to automatically create directories.
- Updated statistics printing to include linear acceleration and velocity metrics.
- Expanded plotting functionality to visualize linear acceleration and velocity alongside existing data.
- Added new PNG files for IMU quality visualization.

csv/imu_data.csv

@@ -0,0 +1,501 @@
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visualise.py

@@ -184,21 +184,146 @@ def write_csv(samples, path):
         writer = csv.DictWriter(f, fieldnames=columns)
         writer.writeheader()
         writer.writerows(samples)
+
+def euler_to_rot_matrix(roll_deg, pitch_deg, yaw_deg):
+    """
+    Convert IMU Euler angles to body->world rotation matrix.
+    Assumption:
+        angle_x = roll
+        angle_y = pitch
+        angle_z = yaw
+    Rotation order:
+        R = Rz(yaw) @ Ry(pitch) @ Rx(roll)
+
+    注意：这个顺序要和 IWT603 官方定义确认。
+    如果发现补偿后静止时 linear_acc 不接近 0.需要调整欧拉角顺序或正负号。
+    """
+    roll = np.deg2rad(roll_deg)
+    pitch = np.deg2rad(pitch_deg)
+    yaw = np.deg2rad(yaw_deg)
+
+    cr, sr = np.cos(roll), np.sin(roll)
+    cp, sp = np.cos(pitch), np.sin(pitch)
+    cy, sy = np.cos(yaw), np.sin(yaw)
+
+    Rx = np.array([
+        [1, 0, 0],
+        [0, cr, -sr],
+        [0, sr, cr],
+    ])
+
+    Ry = np.array([
+        [cp, 0, sp],
+        [0, 1, 0],
+        [-sp, 0, cp],
+    ])
+
+    Rz = np.array([
+        [cy, -sy, 0],
+        [sy, cy, 0],
+        [0, 0, 1],
+    ])
+
+    return Rz @ Ry @ Rx
+
+
+def add_linear_velocity(samples, gravity_sign=1.0, acc_deadband=0.15, vel_decay=0.995, bias_seconds=1.0):
+    if not samples:
+        return samples
+
+    # 先计算每一帧的重力补偿线加速度
+    raw_linear_acc_list = []
+
+    for sample in samples:
+        acc_body_g = np.array([
+            sample["acc_x_g"],
+            sample["acc_y_g"],
+            sample["acc_z_g"],
+        ], dtype=float)
+
+        R = euler_to_rot_matrix(
+            sample["angle_x_deg"],
+            sample["angle_y_deg"],
+            sample["angle_z_deg"],
+        )
+
+        acc_world_g = R @ acc_body_g
+        linear_acc_world_g = acc_world_g - np.array([0.0, 0.0, gravity_sign])
+        linear_acc_world = linear_acc_world_g * 9.81
+
+        raw_linear_acc_list.append(linear_acc_world)
+
+    raw_linear_acc_arr = np.array(raw_linear_acc_list)
+
+    # 用前 bias_seconds 秒估计静止零偏
+    t0 = samples[0]["t_s"]
+    bias_indices = [
+        i for i, s in enumerate(samples)
+        if s["t_s"] - t0 <= bias_seconds
+    ]
+
+    if bias_indices:
+        acc_bias = np.mean(raw_linear_acc_arr[bias_indices], axis=0)
+    else:
+        acc_bias = np.zeros(3)
+
+    print("Estimated linear acc bias m/s^2:", acc_bias)
+
+    v = np.zeros(3, dtype=float)
+    last_t = samples[0]["t_s"]
+
+    for i, sample in enumerate(samples):
+        t = sample["t_s"]
+        dt = t - last_t
+        last_t = t
+
+        if dt <= 0 or dt > 0.2:
+            dt = 0.0
+
+        linear_acc_world = raw_linear_acc_arr[i] - acc_bias
+
+        if np.linalg.norm(linear_acc_world) < acc_deadband:
+            linear_acc_world[:] = 0.0
+
+        v = v * vel_decay + linear_acc_world * dt
+
+        sample["lin_acc_x_ms2"] = linear_acc_world[0]
+        sample["lin_acc_y_ms2"] = linear_acc_world[1]
+        sample["lin_acc_z_ms2"] = linear_acc_world[2]
+        sample["vel_x_ms"] = v[0]
+        sample["vel_y_ms"] = v[1]
+        sample["vel_z_ms"] = v[2]
+
+    return samples
 def unique_path(path):
     """
-    If path already exists, append timestamp before extension.
+    Automatically save CSV into ./csv/
-    Example:
+    Automatically save PNG into ./png/
-        imu_quality.png -> imu_quality_20260701_123045.png
+    Create folders if they do not exist.
     """
+
     if not path:
         return path
 
-    if not os.path.exists(path):
+    root, ext = os.path.splitext(os.path.basename(path))
-        return path
+    ext = ext.lower()
+
+    if ext == ".csv":
+        folder = "csv"
+    elif ext == ".png":
+        folder = "png"
+    else:
+        folder = "output"
+
+    os.makedirs(folder, exist_ok=True)
+
+    new_path = os.path.join(folder, root + ext)
+
+    if not os.path.exists(new_path):
+        return new_path
 
-    root, ext = os.path.splitext(path)
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    return "{}_{}{}".format(root, timestamp, ext)
+    return os.path.join(folder, "{}_{}{}".format(root, timestamp, ext))
 
 def print_stats(samples):
     if not samples:
@@ -238,6 +363,35 @@ def print_stats(samples):
                 float(np.max(freq)),
             )
         )
+    if "lin_acc_x_ms2" in samples[0]:
+        print("\nLINEAR ACC m/s^2")
+        for field in ("lin_acc_x_ms2", "lin_acc_y_ms2", "lin_acc_z_ms2"):
+            values = np.array([sample[field] for sample in samples], dtype=float)
+            diffs = np.diff(values)
+            diff_std = float(np.std(diffs)) if len(diffs) else 0.0
+            print(
+                "  {:16s} mean={: .6f} std={:.6f} ptp={:.6f} diff_std={:.6f}".format(
+                    field,
+                    float(np.mean(values)),
+                    float(np.std(values)),
+                    float(np.ptp(values)),
+                    diff_std,
+                )
+            )
+
+    if "vel_x_ms" in samples[0]:
+        print("\nVELOCITY m/s")
+        for field in ("vel_x_ms", "vel_y_ms", "vel_z_ms"):
+            values = np.array([sample[field] for sample in samples], dtype=float)
+            print(
+                "  {:16s} mean={: .6f} std={:.6f} ptp={:.6f} final={: .6f}".format(
+                    field,
+                    float(np.mean(values)),
+                    float(np.std(values)),
+                    float(np.ptp(values)),
+                    float(values[-1]),
+                )
+            )
 
 
 def plot_samples(samples, output=None, show=True):
@@ -245,24 +399,42 @@ def plot_samples(samples, output=None, show=True):
         raise ValueError("No samples to plot.")
 
     t = np.array([sample["t_s"] for sample in samples], dtype=float)
-    fig, axes = plt.subplots(4, 1, sharex=True, figsize=(13, 9))
+    fig, axes = plt.subplots(6, 1, sharex=True, figsize=(13, 13))
 
     plot_group(
         axes[0],
         t,
         samples,
         ("acc_x_g", "acc_y_g", "acc_z_g"),
-        "Acceleration (g)",
+        "Raw Acc (g)",
     )
+
     plot_group(
         axes[1],
         t,
         samples,
+        ("lin_acc_x_ms2", "lin_acc_y_ms2", "lin_acc_z_ms2"),
+        "Linear Acc (m/s²)",
+    )
+
+    plot_group(
+        axes[2],
+        t,
+        samples,
+        ("vel_x_ms", "vel_y_ms", "vel_z_ms"),
+        "Velocity (m/s)",
+    )
+
+    plot_group(
+        axes[3],
+        t,
+        samples,
         ("gyro_x_dps", "gyro_y_dps", "gyro_z_dps"),
         "Gyro (deg/s)",
     )
+
     plot_group(
-        axes[2],
+        axes[4],
         t,
         samples,
         ("angle_x_deg", "angle_y_deg", "angle_z_deg"),
@@ -270,11 +442,11 @@ def plot_samples(samples, output=None, show=True):
     )
 
     freq = np.array([sample["freq_hz"] for sample in samples], dtype=float)
-    axes[3].plot(t, freq, label="freq_hz", color="tab:purple", linewidth=1.0)
+    axes[5].plot(t, freq, label="freq_hz", linewidth=1.0)
-    axes[3].set_ylabel("Hz")
+    axes[5].set_ylabel("Hz")
-    axes[3].set_xlabel("Time (s)")
+    axes[5].set_xlabel("Time (s)")
-    axes[3].grid(True, alpha=0.3)
+    axes[5].grid(True, alpha=0.3)
-    axes[3].legend(loc="upper right")
+    axes[5].legend(loc="upper right")
 
     fig.suptitle("IMU Data Quality Overview")
     fig.tight_layout()
@@ -328,6 +500,12 @@ def main():
             args.max_gap_ms,
         )
 
+    # 关键：没有样本就直接退出，避免后面 CSV/plot 误报或报错
+    if not samples:
+        print("No samples parsed. Check input mode, serial port, baudrate, or log file format.")
+        return
+
+    samples = add_linear_velocity(samples, gravity_sign=1.0)
     print_stats(samples)
 
     if args.csv: