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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,160 @@ | ||
| import json | ||
| import numpy as np | ||
| from tqdm import tqdm | ||
| from scipy.cluster import vq | ||
| import cv2 | ||
| import matplotlib.pyplot as plt | ||
|
|
||
| from datasets.CocoDatasetInfo import CocoDatasetInfo | ||
| from lib.transforms import get_cropalign_matrix, warpAffinePoints | ||
|
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| def draw_skeleton(normed_kpts, h=200, w=200, vis_threshold=0, is_normed=True, returnimg=False): | ||
| origin_connections = [[16,14],[14,12],[17,15],[15,13],[12,13], | ||
| [6,12],[7,13],[6,7],[6,8],[7,9],[8,10], | ||
| [9,11],[2,3],[1,2],[1,3],[2,4],[3,5],[4,6],[5,7]] | ||
| img = np.zeros((int(h), int(w)), dtype=np.float32) | ||
| kptsv = normed_kpts.copy() | ||
| if is_normed: | ||
| kptsv[:, 0] *= w | ||
| kptsv[:, 1] *= h | ||
| kptsv = np.int32(kptsv) | ||
|
|
||
| for kptv in kptsv: | ||
| if kptv[-1] > vis_threshold: | ||
| cv2.circle(img, (kptv[0], kptv[1]), 4, (255, 0, 0), -1) | ||
| idx = 15 | ||
| cv2.circle(img, (kptsv[idx][0], kptsv[idx][1]), 10, (0, 0, 255), -1) | ||
| for conn in origin_connections: | ||
| if kptsv[conn[0] - 1][-1] > vis_threshold and kptsv[conn[1] - 1][-1] > vis_threshold: | ||
| p1, p2 = kptsv[conn[0] - 1], kptsv[conn[1] - 1] | ||
| cv2.line(img, (p1[0], p1[1]), (p2[0], p2[1]), (255, 0, 0), 2) | ||
|
|
||
| if returnimg: | ||
| return img | ||
| else: | ||
| plt.imshow(img) | ||
| plt.show() | ||
|
|
||
| def norm_kpt_by_box(kpts, boxes, keep_ratio=True): | ||
| normed_kpts = np.array(kpts).copy() | ||
| normed_kpts = np.float32(normed_kpts) | ||
|
|
||
| for i, (kpt, box) in enumerate(zip(kpts, boxes)): | ||
| H = get_cropalign_matrix(box, 1.0, 1.0, keep_ratio) | ||
| normed_kpts[i, :, 0:2] = warpAffinePoints(kpt[:, 0:2], H) | ||
|
|
||
| inds = np.where(normed_kpts[:, :, 2] == 0) | ||
| normed_kpts[inds[0], inds[1], :] = 0 | ||
| return normed_kpts | ||
|
|
||
| def cluster_zixi(kpts, cat_num): | ||
| # kpts: center-normalized (N, 17, 3) | ||
| datas = np.array(kpts) | ||
| inds = np.where(datas[:, :, 2] == 0) | ||
| datas[inds[0], inds[1], 0:2] = 0.5 | ||
|
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||
| datas = datas.reshape(len(datas), -1) | ||
| res = vq.kmeans2(datas, cat_num, minit='points', iter=100) | ||
| return res | ||
|
|
||
| def cluster(dataset = 'coco', cat_num = 3, vis_threshold = 0.4, | ||
| minpoints = 8, save_file = './modeling/templates2.json', visualize=False): | ||
| # We try `cat_num` from 1 to 6 multiple times. we want to see | ||
| # what the cluster centers look like when vary the numbers of | ||
| # group. While the kmean method, which is heavily relay on the | ||
| # initial status, gives nearly the same cluster centers when | ||
| # `cat_num` = 3 each time. So we assume the coco dataset accurately | ||
| # have 3 clusters.(a TODO is to visualize this dataset.) And | ||
| # the visualization of the cluster centers seems to reasonable: | ||
| # (1) a full body. (2) a full body without head (3) an upper body. | ||
| # Note that (2) seems representing the backward of a person. | ||
|
|
||
| if dataset == 'coco': | ||
| datainfos = CocoDatasetInfo('./data/coco2017/train2017', | ||
| './data/coco2017/annotations/person_keypoints_train2017_pose2seg.json', | ||
| loadimg=False) | ||
|
|
||
| connections = [[16,14],[14,12],[17,15],[15,13],[12,13], | ||
| [6,12],[7,13],[6,7],[6,8],[7,9],[8,10], | ||
| [9,11],[2,3],[1,2],[1,3],[2,4],[3,5],[4,6],[5,7]] | ||
|
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||
| names = ["nose", | ||
| "left_eye","right_eye", | ||
| "left_ear","right_ear", | ||
| "left_shoulder","right_shoulder", | ||
| "left_elbow","right_elbow", | ||
| "left_wrist","right_wrist", | ||
| "left_hip","right_hip", | ||
| "left_knee","right_knee", | ||
| "left_ankle","right_ankle"] | ||
|
|
||
| flip_map = {'left_eye': 'right_eye', | ||
| 'left_ear': 'right_ear', | ||
| 'left_shoulder': 'right_shoulder', | ||
| 'left_elbow': 'right_elbow', | ||
| 'left_wrist': 'right_wrist', | ||
| 'left_hip': 'right_hip', | ||
| 'left_knee': 'right_knee', | ||
| 'left_ankle': 'right_ankle'} | ||
|
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||
| def flip_keypoints(keypoints, keypoint_flip_map, keypoint_coords, width): | ||
| """Left/right flip keypoint_coords. keypoints and keypoint_flip_map are | ||
| accessible from get_keypoints(). | ||
| """ | ||
| flipped_kps = keypoint_coords.copy() | ||
| for lkp, rkp in keypoint_flip_map.items(): | ||
| lid = keypoints.index(lkp) | ||
| rid = keypoints.index(rkp) | ||
| flipped_kps[:, :, lid] = keypoint_coords[:, :, rid] | ||
| flipped_kps[:, :, rid] = keypoint_coords[:, :, lid] | ||
|
|
||
| # Flip x coordinates | ||
| flipped_kps[:, 0, :] = width - flipped_kps[:, 0, :] | ||
| # Maintain COCO convention that if visibility == 0, then x, y = 0 | ||
| inds = np.where(flipped_kps[:, 2, :] == 0) | ||
| flipped_kps[inds[0], 0, inds[1]] = 0 | ||
| return flipped_kps | ||
|
|
||
| all_kpts = [] | ||
| for idx in tqdm(range(len(datainfos))): | ||
| rawdata = datainfos[idx] | ||
| gt_boxes = rawdata['boxes'] | ||
| gt_kpts = rawdata['gt_keypoints'].transpose(0, 2, 1) # (N, 17, 3) | ||
| gt_ignores = rawdata['is_crowd'] | ||
| normed_kpts = norm_kpt_by_box(gt_kpts, gt_boxes) | ||
| normed_kpts_flipped = flip_keypoints(names, flip_map, | ||
| normed_kpts.transpose(0, 2, 1), 1.0).transpose(0, 2, 1) | ||
| normed_kpts = np.vstack((normed_kpts, normed_kpts_flipped)) | ||
| for kpt in normed_kpts: | ||
| if np.sum(kpt)==0: | ||
| continue | ||
| elif np.sum(kpt[:, 2]>0)<minpoints: | ||
| continue | ||
| else: | ||
| all_kpts.append(kpt) | ||
| all_kpts = np.array(all_kpts) | ||
| print ('data to be clustered:', all_kpts.shape) | ||
|
|
||
| res = cluster_zixi(all_kpts, cat_num) | ||
|
|
||
| save_dict = {} | ||
| save_dict['connections'] = connections | ||
| save_dict['names'] = names | ||
| save_dict['flip_map'] = flip_map | ||
| save_dict['vis_threshold'] = vis_threshold | ||
| save_dict['minpoints'] = minpoints | ||
| save_dict['templates'] = [item.tolist() for item in res[0]] | ||
| if save_file is not None: | ||
| with open(save_file, 'w') as result_file: | ||
| json.dump(save_dict, result_file) | ||
|
|
||
| if visualize: | ||
| for center in res[0]: | ||
| center = center.reshape(-1, 3) | ||
| draw_skeleton(center, 200, 200, vis_threshold) | ||
|
|
||
| print ('cluster() done.') | ||
| return res | ||
|
|
||
| else: | ||
| raise NotImplementedError |
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