Initial commit

p-mpjpe.py

+def p_mpjpe(predicted, target):
+    """
+    Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
+    often referred to as "Protocol #2" in many papers.
+    """
+    assert predicted.shape == target.shape
+
+    muX = np.mean(target, axis=1, keepdims=True)
+    # print(muX.shape)
+    muY = np.mean(predicted, axis=1, keepdims=True)
+    X0 = target - muX
+    Y0 = predicted - muY
+
+    normX = np.sqrt(np.sum(X0 ** 2, axis=(1, 2), keepdims=True))
+    normY = np.sqrt(np.sum(Y0 ** 2, axis=(1, 2), keepdims=True))
+
+    X0 /= normX
+    Y0 /= normY
+
+    H = np.matmul(X0.transpose(0, 2, 1), Y0)
+    U, s, Vt = np.linalg.svd(H)
+    V = Vt.transpose(0, 2, 1)
+    R = np.matmul(V, U.transpose(0, 2, 1))
+
+    # Avoid improper rotations (reflections), i.e. rotations with det(R) = -1
+    sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
+    V[:, :, -1] *= sign_detR
+    s[:, -1] *= sign_detR.flatten()
+    R = np.matmul(V, U.transpose(0, 2, 1))  # Rotation
+
+    tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)
+
+    a = tr * normX / normY  # Scale
+    t = muX - a * np.matmul(muY, R)  # Translation
+
+    # Perform rigid transformation on the input
+    predicted_aligned = a * np.matmul(predicted, R) + t
+    # print('11111'+ str(np.shape(predicted_aligned)))
+    mean_17 = np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape) - 1),axis=0)
+    all = np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape) - 1))
+    # Return MPJPE
+    return all,mean_17
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