test.py

def extract_features(landmarks):
    distances = calculate_pairwise_distances(landmarks)
    angles = []
    num_points = len(landmarks) // 2
    for i in range(num_points):
        for j in range(num_points):
            for k in range(num_points):
                if i != j and i != k and j != k:
                    angle = calculate_angle([landmarks[2*i], landmarks[2*i + 1]],
                                            [landmarks[2*j], landmarks[2*j + 1]],
                                            [landmarks[2*k], landmarks[2*k + 1]])
                    angles.append(angle)

    keypoints = [(landmarks[i], landmarks[i + 1]) for i in range(0, len(landmarks), 2)]
    # Bounding Box
    min_x = min([kp[0] for kp in keypoints])
    max_x = max([kp[0] for kp in keypoints])
    min_y = min([kp[1] for kp in keypoints])
    max_y = max([kp[1] for kp in keypoints])
    bounding_box_area = (max_x - min_x) * (max_y - min_y)

    # Complexity of Pose
    hull = ConvexHull(keypoints)
    convex_hull_area = hull.volume

    # Normalization using torso length
    torso_length = np.sqrt((keypoints[MOVENET_LANDMARK_NAMES["NOSE"]][0] -
                            (keypoints[MOVENET_LANDMARK_NAMES["LEFT_HIP"]][0] +
                             keypoints[MOVENET_LANDMARK_NAMES["RIGHT_HIP"]][0]) / 2) ** 2 +
                           (keypoints[MOVENET_LANDMARK_NAMES["NOSE"]][1] -
                            (keypoints[MOVENET_LANDMARK_NAMES["LEFT_HIP"]][1] +
                             keypoints[MOVENET_LANDMARK_NAMES["RIGHT_HIP"]][1]) / 2) ** 2)
    normalized_distances = [d / torso_length for d in distances]

    symmetry_metrics = compute_symmetry(keypoints)

    return distances + angles + [bounding_box_area, convex_hull_area] + normalized_distances + symmetry_metrics

def extract_additional_features(keypoints):
    # Convert keypoints to pairs for simplicity
    keypoints = [(keypoints[i], keypoints[i + 1]) for i in range(0, len(keypoints), 2)]

    # Areas of triangles
    triangle_areas = [triangle_area(kp1[0], kp1[1], kp2[0], kp2[1], kp3[0], kp3[1])
                      for kp1 in keypoints for kp2 in keypoints for kp3 in keypoints]

    # Center of Gravity
    centroid = compute_centroid(keypoints)
    distances_to_centroid = [np.sqrt((kp[0] - centroid[0]) ** 2 + (kp[1] - centroid[1]) ** 2) for kp in keypoints]

    # Angles
    angles = [angle_between_three_points(kp1, kp2, kp3)
              for kp1 in keypoints for kp2 in keypoints for kp3 in keypoints if
              kp1 != kp2 and kp2 != kp3 and kp1 != kp3]

    return triangle_areas + distances_to_centroid + angles