def compute_box3d_iou()

in lib/utils/fpointnet_utils.py [0:0]

• 47 lines of code
• 4 McCabe index (conditional complexity)

def compute_box3d_iou(center_pred,
                      heading_logits, heading_residuals,
                      size_logits, size_residuals,
                      center_label,
                      heading_class_label, heading_residual_label,
                      size_class_label, size_residual_label):
    ''' Compute 3D bounding box IoU from network output and labels.
    All inputs are numpy arrays.
    Inputs:
        center_pred: (B,3)
        heading_logits: (B,NUM_HEADING_BIN)
        heading_residuals: (B,NUM_HEADING_BIN)
        size_logits: (B,NUM_SIZE_CLUSTER)
        size_residuals: (B,NUM_SIZE_CLUSTER,3)
        center_label: (B,3)
        heading_class_label: (B,)
        heading_residual_label: (B,)
        size_class_label: (B,)
        size_residual_label: (B,3)
    Output:
        iou2ds: (B,) birdeye view oriented 2d box ious
        iou3ds: (B,) 3d box ious
    '''
    batch_size = heading_logits.shape[0]
    heading_class = np.argmax(heading_logits, 1) # B
    heading_residual = np.array([heading_residuals[i,heading_class[i]] \
        for i in range(batch_size)]) # B,
    size_class = np.argmax(size_logits, 1) # B
    size_residual = np.vstack([size_residuals[i,size_class[i],:] \
        for i in range(batch_size)])

    iou2d_list = []
    iou3d_list = []
    for i in range(batch_size):
        heading_angle = class2angle(heading_class[i],
            heading_residual[i], NUM_HEADING_BIN)
        box_size = class2size(size_class[i], size_residual[i])
        corners_3d = get_3d_box(box_size, heading_angle, center_pred[i])

        heading_angle_label = class2angle(heading_class_label[i],
            heading_residual_label[i], NUM_HEADING_BIN)
        box_size_label = class2size(size_class_label[i], size_residual_label[i])
        corners_3d_label = get_3d_box(box_size_label,
            heading_angle_label, center_label[i])

        iou_3d, iou_2d = box3d_iou(corners_3d, corners_3d_label)
        iou3d_list.append(iou_3d)
        iou2d_list.append(iou_2d)
    return np.array(iou2d_list, dtype=np.float32), \
        np.array(iou3d_list, dtype=np.float32)