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| # This script is borrowed from https://github.com/akanazawa/human_dynamics/blob/master/src/util/smooth_bbox.py | |
| # Adhere to their licence to use this script | |
| import numpy as np | |
| import scipy.signal as signal | |
| from scipy.ndimage.filters import gaussian_filter1d | |
| def get_smooth_bbox_params(kps, vis_thresh=2, kernel_size=11, sigma=3): | |
| """ | |
| Computes smooth bounding box parameters from keypoints: | |
| 1. Computes bbox by rescaling the person to be around 150 px. | |
| 2. Linearly interpolates bbox params for missing annotations. | |
| 3. Median filtering | |
| 4. Gaussian filtering. | |
| Recommended thresholds: | |
| * detect-and-track: 0 | |
| * 3DPW: 0.1 | |
| Args: | |
| kps (list): List of kps (Nx3) or None. | |
| vis_thresh (float): Threshold for visibility. | |
| kernel_size (int): Kernel size for median filtering (must be odd). | |
| sigma (float): Sigma for gaussian smoothing. | |
| Returns: | |
| Smooth bbox params [cx, cy, scale], start index, end index | |
| """ | |
| bbox_params, start, end = get_all_bbox_params(kps, vis_thresh) | |
| smoothed = smooth_bbox_params(bbox_params, kernel_size, sigma) | |
| smoothed = np.vstack((np.zeros((start, 3)), smoothed)) | |
| return smoothed, start, end | |
| def kp_to_bbox_param(kp, vis_thresh): | |
| """ | |
| Finds the bounding box parameters from the 2D keypoints. | |
| Args: | |
| kp (Kx3): 2D Keypoints. | |
| vis_thresh (float): Threshold for visibility. | |
| Returns: | |
| [center_x, center_y, scale] | |
| """ | |
| if kp is None: | |
| return | |
| vis = kp[:, 2] > vis_thresh | |
| if not np.any(vis): | |
| return | |
| min_pt = np.min(kp[vis, :2], axis=0) | |
| max_pt = np.max(kp[vis, :2], axis=0) | |
| person_height = np.linalg.norm(max_pt - min_pt) | |
| if person_height < 0.5: | |
| return | |
| center = (min_pt + max_pt) / 2. | |
| scale = 150. / person_height | |
| return np.append(center, scale) | |
| def get_all_bbox_params(kps, vis_thresh=2): | |
| """ | |
| Finds bounding box parameters for all keypoints. | |
| Look for sequences in the middle with no predictions and linearly | |
| interpolate the bbox params for those | |
| Args: | |
| kps (list): List of kps (Kx3) or None. | |
| vis_thresh (float): Threshold for visibility. | |
| Returns: | |
| bbox_params, start_index (incl), end_index (excl) | |
| """ | |
| # keeps track of how many indices in a row with no prediction | |
| num_to_interpolate = 0 | |
| start_index = -1 | |
| bbox_params = np.empty(shape=(0, 3), dtype=np.float32) | |
| for i, kp in enumerate(kps): | |
| bbox_param = kp_to_bbox_param(kp, vis_thresh=vis_thresh) | |
| if bbox_param is None: | |
| num_to_interpolate += 1 | |
| continue | |
| if start_index == -1: | |
| # Found the first index with a prediction! | |
| start_index = i | |
| num_to_interpolate = 0 | |
| if num_to_interpolate > 0: | |
| # Linearly interpolate each param. | |
| previous = bbox_params[-1] | |
| # This will be 3x(n+2) | |
| interpolated = np.array([ | |
| np.linspace(prev, curr, num_to_interpolate + 2) | |
| for prev, curr in zip(previous, bbox_param) | |
| ]) | |
| bbox_params = np.vstack((bbox_params, interpolated.T[1:-1])) | |
| num_to_interpolate = 0 | |
| bbox_params = np.vstack((bbox_params, bbox_param)) | |
| return bbox_params, start_index, i - num_to_interpolate + 1 | |
| def smooth_bbox_params(bbox_params, kernel_size=11, sigma=8): | |
| """ | |
| Applies median filtering and then gaussian filtering to bounding box | |
| parameters. | |
| Args: | |
| bbox_params (Nx3): [cx, cy, scale]. | |
| kernel_size (int): Kernel size for median filtering (must be odd). | |
| sigma (float): Sigma for gaussian smoothing. | |
| Returns: | |
| Smoothed bounding box parameters (Nx3). | |
| """ | |
| smoothed = np.array([signal.medfilt(param, kernel_size) for param in bbox_params.T]).T | |
| return np.array([gaussian_filter1d(traj, sigma) for traj in smoothed.T]).T | |