364 lines
12 KiB
Python
364 lines
12 KiB
Python
import copy
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import math
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import time
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import cv2
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import numpy as np
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from skelda import utils_pose
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# ==================================================================================================
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core_joints = [
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"shoulder_left",
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"shoulder_right",
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"hip_left",
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"hip_right",
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"elbow_left",
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"elbow_right",
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"knee_left",
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"knee_right",
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"wrist_left",
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"wrist_right",
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"ankle_left",
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"ankle_right",
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]
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# ==================================================================================================
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def undistort_points(points: np.ndarray, caminfo: dict):
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"""Undistorts 2D pixel coordinates"""
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K = np.asarray(caminfo["K"], dtype=np.float32)
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DC = np.asarray(caminfo["DC"][0:5], dtype=np.float32)
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# Undistort camera matrix
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w = caminfo["width"]
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h = caminfo["height"]
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newK, _ = cv2.getOptimalNewCameraMatrix(K, DC, (w, h), 1, (w, h))
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caminfo["K"] = newK
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caminfo["DC"] = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
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# Undistort points
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pshape = points.shape
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points = np.reshape(points, (-1, 1, 2))
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points = cv2.undistortPoints(points, K, DC, P=newK)
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points = points.reshape(pshape)
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return points
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# ==================================================================================================
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def get_camera_P(cam):
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"""Calculate opencv-style projection matrix"""
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R = np.asarray(cam["R"])
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T = np.asarray(cam["T"])
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K = np.asarray(cam["K"])
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Tr = R @ (T * -1)
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P = K @ np.hstack([R, Tr])
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return P
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# ==================================================================================================
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def calc_pair_score(pair, poses_2d, camparams, roomparams, joint_names_2d, use_joints):
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"""Triangulates a pair of persons and scores them based on the reprojection error"""
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cam1 = camparams[pair[0][0]]
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cam2 = camparams[pair[0][1]]
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pose1 = poses_2d[pair[0][0]][pair[0][2]]
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pose2 = poses_2d[pair[0][1]][pair[0][3]]
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# Select core joints
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jids = [joint_names_2d.index(j) for j in use_joints]
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pose1 = pose1[jids]
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pose2 = pose2[jids]
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poses_3d, score = calc_pose_scored(pose1, pose2, cam1, cam2, roomparams)
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return poses_3d, score
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# ==================================================================================================
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def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
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"""Triangulates a pair of persons and scores them based on the reprojection error"""
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# Mask out invisible joints
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min_score = 0.1
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mask1a = pose1[:, 2] >= min_score
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mask2a = pose2[:, 2] >= min_score
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mask = mask1a & mask2a
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# If too few joints are visible return a low score
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if np.sum(mask) < 3:
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pose3d = np.zeros([len(pose1), 4])
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score = 0.0
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return pose3d, score
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# Triangulate points
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points1 = pose1[mask, 0:2].T
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points2 = pose2[mask, 0:2].T
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P1 = get_camera_P(cam1)
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P2 = get_camera_P(cam2)
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points3d = cv2.triangulatePoints(P1, P2, points1, points2)
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points3d = (points3d / points3d[3, :])[0:3, :].T
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pose3d = np.zeros([len(pose1), 4])
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pose3d[mask] = np.concatenate([points3d, np.ones([points3d.shape[0], 1])], axis=-1)
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# If the triangulated points are outside the room drop it
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mean = np.mean(pose3d[mask][:, 0:3], axis=0)
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mins = np.min(pose3d[mask][:, 0:3], axis=0)
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maxs = np.max(pose3d[mask][:, 0:3], axis=0)
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rsize = np.array(roomparams["room_size"]) / 2
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rcent = np.array(roomparams["room_center"])
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wdist = 0.1
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center_outside = np.any((mean > rsize + rcent) | (mean < -rsize + rcent))
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limb_outside = np.any(
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(maxs > rsize + rcent + wdist) | (mins < -rsize + rcent - wdist)
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)
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if center_outside or limb_outside:
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pose3d[:, 3] = 0.001
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score = 0.001
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return pose3d, score
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# Calculate reprojection error
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poses_3d = np.expand_dims(pose3d, axis=0)
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repro1, dists1 = utils_pose.project_poses(poses_3d, cam1, calc_dists=True)
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repro2, dists2 = utils_pose.project_poses(poses_3d, cam2, calc_dists=True)
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error1 = np.linalg.norm(pose1[mask, 0:2] - repro1[0, mask, 0:2], axis=1)
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error2 = np.linalg.norm(pose2[mask, 0:2] - repro2[0, mask, 0:2], axis=1)
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# Set errors of invisible reprojections to a high value
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penalty = (cam1["width"] + cam1["height"]) / 2
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mask1b = (repro1[0, :, 2] < min_score)[mask]
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mask2b = (repro2[0, :, 2] < min_score)[mask]
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error1[mask1b] = penalty
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error2[mask2b] = penalty
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# Convert errors to a score
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# Scale by image size and distance to the camera
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iscale = (cam1["width"] + cam1["height"]) / 2
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error1 = error1.clip(0, iscale / 4) / iscale
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error2 = error2.clip(0, iscale / 4) / iscale
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dscale1 = np.sqrt(np.mean(dists1[0, mask]) / 3.5)
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dscale2 = np.sqrt(np.mean(dists2[0, mask]) / 3.5)
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error1 = error1 * dscale1
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error2 = error2 * dscale2
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error = (error1 + error2) / 2
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scores = 1.0 / (1.0 + error * 10)
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# Drop lowest scores
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drop_k = math.floor(len(pose1) * 0.2)
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score = np.mean(np.sort(scores, axis=-1)[drop_k:])
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# Add score to 3D pose
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full_scores = np.zeros([poses_3d.shape[1], 1])
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full_scores[mask] = np.expand_dims(scores, axis=-1)
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pose3d[:, 3] = full_scores[:, 0]
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return pose3d, score
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# ==================================================================================================
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def calc_grouping(all_pairs, min_score: float):
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"""Groups pairs that share a person"""
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# Calculate the pose center for each pair
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for i in range(len(all_pairs)):
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pair = all_pairs[i]
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pose_3d = pair[2][0]
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mask = pose_3d[:, 3] > min_score
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center = np.mean(pose_3d[mask, 0:3], axis=0)
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all_pairs[i] = all_pairs[i] + [center]
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groups = []
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for i in range(len(all_pairs)):
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pair = all_pairs[i]
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# Create new group if non exists
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if len(groups) == 0:
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groups.append([pair[3], pair[2][0], [pair]])
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continue
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# Check if the pair matches to an existing group
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max_center_dist = 0.9
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max_joint_avg_dist = 0.3
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best_dist = math.inf
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best_group = -1
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for j in range(len(groups)):
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g0 = groups[j]
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center0 = g0[0]
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center1 = pair[3]
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if np.linalg.norm(center0 - center1) < max_center_dist:
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pose0 = g0[1]
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pose1 = pair[2][0]
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# Calculate the distance between the two poses
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mask0 = pose0[:, 3] > min_score
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mask1 = pose1[:, 3] > min_score
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mask = mask0 & mask1
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dists = np.linalg.norm(pose0[mask, 0:3] - pose1[mask, 0:3], axis=1)
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dist = np.mean(dists)
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if dist < max_joint_avg_dist:
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if dist < best_dist:
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best_dist = dist
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best_group = j
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if best_group >= 0:
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# Add pair to existing group and update the mean positions
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group = groups[best_group]
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new_center = (group[0] * len(group[1]) + pair[3]) / (len(group[1]) + 1)
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new_pose = (group[1] * len(group[1]) + pair[2][0]) / (len(group[1]) + 1)
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group[2].append(pair)
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group[0] = new_center
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group[1] = new_pose
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else:
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# Create new group if no match was found
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groups.append([pair[3], pair[2][0], [pair]])
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return groups
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# ==================================================================================================
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def merge_group(poses_3d: np.ndarray, min_score: float):
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"""Merges a group of poses into a single pose"""
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# Merge poses to create initial pose
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# Use only those triangulations with a high score
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imask = poses_3d[:, :, 3:4] > min_score
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sum_poses = np.sum(poses_3d * imask, axis=0)
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sum_mask = np.sum(imask, axis=0)
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initial_pose_3d = np.divide(
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sum_poses, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_poses)
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)
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# Use center as default if the initial pose is empty
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jmask = initial_pose_3d[:, 3] > 0.0
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sum_joints = np.sum(initial_pose_3d[jmask, 0:3], axis=0)
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sum_mask = np.sum(jmask)
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center = np.divide(
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sum_joints, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_joints)
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)
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initial_pose_3d[~jmask, 0:3] = center
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# Drop joints with low scores
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offset = 0.1
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mask = poses_3d[:, :, 3:4] > (min_score - offset)
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# Drop outliers that are far away from the other proposals
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max_dist = 1.2
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distances = np.linalg.norm(
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poses_3d[:, :, :3] - initial_pose_3d[np.newaxis, :, :3], axis=2
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)
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dmask = distances <= max_dist
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mask = mask & np.expand_dims(dmask, axis=-1)
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# Select the best-k proposals for each joint that are closest to the initial pose
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keep_best = 3
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sorted_indices = np.argsort(distances, axis=0)
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best_k_mask = np.zeros_like(mask, dtype=bool)
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num_joints = poses_3d.shape[1]
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for i in range(num_joints):
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valid_indices = sorted_indices[:, i][mask[sorted_indices[:, i], i, 0]]
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best_k_mask[valid_indices[:keep_best], i, 0] = True
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mask = mask & best_k_mask
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# Final pose computation with combined masks
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sum_poses = np.sum(poses_3d * mask, axis=0)
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sum_mask = np.sum(mask, axis=0)
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final_pose_3d = np.divide(
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sum_poses, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_poses)
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)
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return final_pose_3d
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# ==================================================================================================
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def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d, min_score=0.95):
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"""Triangulates 3D poses from 2D poses of multiple views"""
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# Convert poses and camparams to numpy arrays
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camparams = copy.deepcopy(camparams)
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for i in range(len(camparams)):
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poses_2d[i] = np.asarray(poses_2d[i])
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camparams[i]["K"] = np.array(camparams[i]["K"])
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camparams[i]["R"] = np.array(camparams[i]["R"])
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camparams[i]["T"] = np.array(camparams[i]["T"])
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camparams[i]["DC"] = np.array(camparams[i]["DC"][0:5])
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# Undistort 2D points
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for i in range(len(camparams)):
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poses = poses_2d[i]
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cam = camparams[i]
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poses[:, :, 0:2] = undistort_points(poses[:, :, 0:2], cam)
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poses_2d[i] = poses
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# Create pairs of persons
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num_persons = [len(p) for p in poses_2d]
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all_pairs = []
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for i in range(len(poses_2d)):
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poses = poses_2d[i]
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for j in range(i + 1, len(poses_2d)):
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poses2 = poses_2d[j]
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for k in range(len(poses)):
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for l in range(len(poses2)):
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pid1 = sum(num_persons[:i]) + k
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pid2 = sum(num_persons[:j]) + l
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all_pairs.append([(i, j, k, l), (pid1, pid2)])
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# Calculate pair scores
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for i in range(len(all_pairs)):
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pair = all_pairs[i]
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pose_3d, score = calc_pair_score(
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pair, poses_2d, camparams, roomparams, joint_names_2d, core_joints
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)
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all_pairs[i].append((pose_3d, score))
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# import draw_utils
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# poses3D = np.array([pose_3d])
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# _ = draw_utils.utils_view.show_poses3d(
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# poses3D, core_joints, {}, camparams
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# )
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# draw_utils.utils_view.show_plots()
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# Drop pairs with low scores
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all_pairs = [p for p in all_pairs if p[2][1] > min_score]
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# Group pairs that share a person
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groups = calc_grouping(all_pairs, min_score)
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# Calculate full 3D poses
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poses_3d = []
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for pair in all_pairs:
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cam1 = camparams[pair[0][0]]
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cam2 = camparams[pair[0][1]]
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pose1 = poses_2d[pair[0][0]][pair[0][2]]
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pose2 = poses_2d[pair[0][1]][pair[0][3]]
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pose_3d, _ = calc_pose_scored(pose1, pose2, cam1, cam2, roomparams)
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pair.append(pose_3d)
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# Merge groups
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poses_3d = []
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for group in groups:
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poses = np.array([p[4] for p in group[2]])
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pose_3d = merge_group(poses, min_score)
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poses_3d.append(pose_3d)
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if len(poses_3d) > 0:
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poses3D = np.array(poses_3d)
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else:
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poses3D = np.zeros([1, len(joint_names_2d), 4])
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return poses3D
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