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154 ++++++++++++ .../strided_transformer_pose3d/lib/sort.py | 231 ++++++++++++++++++ .../lib/yolo/human_detector.py | 18 ++ .../lib/yolo/preprocess.py | 68 ++++++ .../lib/yolo/util.py | 159 ++++++++++++ .../strided_transformer_pose3d.py | 155 ++++++++++++ 20 files changed, 1626 insertions(+) create mode 100644 pose_estimation_3d/strided_transformer_pose3d/2D_output.png create mode 100644 pose_estimation_3d/strided_transformer_pose3d/3D_output.png create mode 100644 pose_estimation_3d/strided_transformer_pose3d/LICENCE create mode 100644 pose_estimation_3d/strided_transformer_pose3d/README.md create mode 100644 pose_estimation_3d/strided_transformer_pose3d/input.jpg create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/camera.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/experiments/w48_384x288_adam_lr1e-3.yaml create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/gen_kpts.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/__init__.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/default.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/inference.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/transforms.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/utilitys.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/preprocess.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/run.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/sort.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/yolo/human_detector.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/yolo/preprocess.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/yolo/util.py create mode 100644 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[1, h / w] + + +def world_to_camera(X, R, t): + Rt = wrap(qinverse, R) + return wrap(qrot, np.tile(Rt, (*X.shape[:-1], 1)), X - t) + + +def camera_to_world(X, R, t): + return wrap(qrot, np.tile(R, (*X.shape[:-1], 1)), X) + t + + +def wrap(func, *args, unsqueeze=False): + args = list(args) + result = func(*args) + return result + +def qrot(q, v): + assert q.shape[-1] == 4 + assert v.shape[-1] == 3 + assert q.shape[:-1] == v.shape[:-1] + + qvec = q[..., 1:] + uv = np.cross(qvec, v, axis=len(q.shape) - 1) + uuv = np.cross(qvec, uv, axis=len(q.shape) - 1) + return (v + 2 * (q[..., :1] * uv + uuv)) + + +def qinverse(q, inplace=False): + if inplace: + q[..., 1:] *= -1 + return q + else: + w = q[..., :1] + xyz = q[..., 1:] + return torch.cat((w, -xyz), dim=len(q.shape) - 1) + + +def get_uvd2xyz(uvd, gt_3D, cam): + N, T, V,_ = uvd.size() + + dec_out_all = uvd.view(-1, T, V, 3).clone() + root = gt_3D[:, :, 0, :].unsqueeze(-2).repeat(1, 1, V, 1).clone() + enc_in_all = uvd[:, :, :, :2].view(-1, T, V, 2).clone() + + cam_f_all = cam[..., :2].view(-1,1,1,2).repeat(1,T,V,1) + cam_c_all = cam[..., 2:4].view(-1,1,1,2).repeat(1,T,V,1) + + z_global = dec_out_all[:, :, :, 2] + z_global[:, :, 0] = root[:, :, 0, 2] + z_global[:, :, 1:] = dec_out_all[:, :, 1:, 2] + root[:, :, 1:, 2] + z_global = z_global.unsqueeze(-1) + + uv = enc_in_all - cam_c_all + xy = uv * z_global.repeat(1, 1, 1, 2) / cam_f_all + xyz_global = torch.cat((xy, z_global), -1) + xyz_offset = (xyz_global - xyz_global[:, :, 0, :].unsqueeze(-2).repeat(1, 1, V, 1)) + + return xyz_offset + + + \ No newline at end of file diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/experiments/w48_384x288_adam_lr1e-3.yaml b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/experiments/w48_384x288_adam_lr1e-3.yaml new file mode 100644 index 000000000..2844ff613 --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/experiments/w48_384x288_adam_lr1e-3.yaml @@ -0,0 +1,127 @@ +AUTO_RESUME: true +CUDNN: + BENCHMARK: true + DETERMINISTIC: false + ENABLED: true +DATA_DIR: '' +GPUS: (0,1,2,3) +OUTPUT_DIR: 'output' +LOG_DIR: 'log' +WORKERS: 24 +PRINT_FREQ: 100 + +DATASET: + COLOR_RGB: true + DATASET: 'coco' + DATA_FORMAT: jpg + FLIP: true + NUM_JOINTS_HALF_BODY: 8 + PROB_HALF_BODY: 0.3 + ROOT: 'data/coco/' + ROT_FACTOR: 45 + SCALE_FACTOR: 0.35 + TEST_SET: 'val2017' + TRAIN_SET: 'train2017' +MODEL: + INIT_WEIGHTS: true + NAME: pose_hrnet + NUM_JOINTS: 17 + PRETRAINED: 'models/pytorch/imagenet/hrnet_w48-8ef0771d.pth' + TARGET_TYPE: gaussian + IMAGE_SIZE: + - 288 + - 384 + HEATMAP_SIZE: + - 72 + - 96 + SIGMA: 3 + EXTRA: + PRETRAINED_LAYERS: + - 'conv1' + - 'bn1' + - 'conv2' + - 'bn2' + - 'layer1' + - 'transition1' + - 'stage2' + - 'transition2' + - 'stage3' + - 'transition3' + - 'stage4' + FINAL_CONV_KERNEL: 1 + STAGE2: + NUM_MODULES: 1 + NUM_BRANCHES: 2 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + FUSE_METHOD: SUM + STAGE3: + NUM_MODULES: 4 + NUM_BRANCHES: 3 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + - 192 + FUSE_METHOD: SUM + STAGE4: + NUM_MODULES: 3 + NUM_BRANCHES: 4 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + - 192 + - 384 + FUSE_METHOD: SUM +LOSS: + USE_TARGET_WEIGHT: true +TRAIN: + BATCH_SIZE_PER_GPU: 24 + SHUFFLE: true + BEGIN_EPOCH: 0 + END_EPOCH: 210 + OPTIMIZER: adam + LR: 0.001 + LR_FACTOR: 0.1 + LR_STEP: + - 170 + - 200 + WD: 0.0001 + GAMMA1: 0.99 + GAMMA2: 0.0 + MOMENTUM: 0.9 + NESTEROV: false +TEST: + BATCH_SIZE_PER_GPU: 24 + COCO_BBOX_FILE: 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json' + BBOX_THRE: 1.0 + IMAGE_THRE: 0.0 + IN_VIS_THRE: 0.2 + MODEL_FILE: '' + NMS_THRE: 1.0 + OKS_THRE: 0.9 + USE_GT_BBOX: true + FLIP_TEST: true + POST_PROCESS: true + SHIFT_HEATMAP: true +DEBUG: + DEBUG: true + SAVE_BATCH_IMAGES_GT: true + SAVE_BATCH_IMAGES_PRED: true + SAVE_HEATMAPS_GT: true + SAVE_HEATMAPS_PRED: true diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/gen_kpts.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/gen_kpts.py new file mode 100644 index 000000000..1646f930f --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/gen_kpts.py @@ -0,0 +1,95 @@ +import argparse + +from lib.hrnet.lib.utils.utilitys import PreProcess +from lib.hrnet.lib.config import cfg, update_config +from lib.hrnet.lib.utils.transforms import * +from lib.hrnet.lib.utils.inference import get_final_preds + +# Loading human detector model +from lib.yolo.human_detector import yolo_human_det as yolo_det +from lib.sort import Sort + +cfg_dir = 'lib/hrnet/experiments/' +model_dir = 'lib/checkpoint/' + + +def parse_args(): + parser = argparse.ArgumentParser(description='Train keypoints network') + # general + parser.add_argument('--cfg', type=str, default=cfg_dir + 'w48_384x288_adam_lr1e-3.yaml', + help='experiment configure file name') + parser.add_argument('opts', nargs=argparse.REMAINDER, default=None, + help="Modify config options using the command-line") + parser.add_argument('--modelDir', type=str, default=model_dir + 'pose_hrnet_w48_384x288.pth', + help='The model directory') + parser.add_argument('--det-dim', type=int, default=416, + help='The input dimension of the detected image') + parser.add_argument('--thred-score', type=float, default=0.30, + help='The threshold of object Confidence') + parser.add_argument('-a', '--animation', action='store_true', + help='output animation') + parser.add_argument('-np', '--num-person', type=int, default=1, + help='The maximum number of estimated poses') + parser.add_argument("-v", "--video", type=str, default='camera', + help="input video file name") + parser.add_argument('--gpu', type=str, default='0', help='input video') + args = parser.parse_args() + + return args + + +def gen_video_kpts(frame, human_model, pose_model, det_dim=416, num_peroson=1): + # Updating configuration + args = parse_args() + update_config(cfg, args) + + # Loading detector and pose model, initialize sort for track + people_sort = Sort(min_hits=0) + + kpts_result = [] + scores_result = [] + bboxs, scores = yolo_det(frame, human_model, reso=det_dim) + + # Using Sort to track people + people_track = people_sort.update(bboxs) + + # Track the first two people in the video and remove the ID + if people_track.shape[0] == 1: + people_track_ = people_track[-1, :-1].reshape(1, 4) + elif people_track.shape[0] >= 2: + people_track_ = people_track[-num_peroson:, :-1].reshape(num_peroson, 4) + people_track_ = people_track_[::-1] + else: + return None, None + + track_bboxs = [] + for bbox in people_track_: + bbox = [round(i, 2) for i in list(bbox)] + track_bboxs.append(bbox) + # bbox is coordinate location + inputs, origin_img, center, scale = PreProcess(frame, track_bboxs, cfg, num_peroson) + + inputs = inputs[:, [2, 1, 0]] + output = pose_model.run(inputs.astype('float32'))[0] + + # compute coordinate + preds, maxvals = get_final_preds(cfg, output, np.asarray(center), np.asarray(scale)) + + kpts = np.zeros((num_peroson, 17, 2), dtype=np.float32) + scores = np.zeros((num_peroson, 17), dtype=np.float32) + for i, kpt in enumerate(preds): + kpts[i] = kpt + + for i, score in enumerate(maxvals): + scores[i] = score.squeeze() + + kpts_result.append(kpts) + scores_result.append(scores) + + keypoints = np.array(kpts_result) + scores = np.array(scores_result) + + keypoints = keypoints.transpose(1, 0, 2, 3) # (T, M, N, 2) --> (M, T, N, 2) + scores = scores.transpose(1, 0, 2) # (T, M, N) --> (M, T, N) + + return keypoints, scores diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/__init__.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/__init__.py new file mode 100644 index 000000000..15a824755 --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/__init__.py @@ -0,0 +1,8 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from .default import _C as cfg +from .default import update_config diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/default.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/default.py new file mode 100644 index 000000000..050bf4e4e --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/default.py @@ -0,0 +1,141 @@ + +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from yacs.config import CfgNode as CN + + +_C = CN() + +_C.OUTPUT_DIR = '' +_C.LOG_DIR = '' +_C.DATA_DIR = '' +_C.GPUS = (0,) +_C.WORKERS = 4 +_C.PRINT_FREQ = 20 +_C.AUTO_RESUME = False +_C.PIN_MEMORY = True +_C.RANK = 0 + +# Cudnn related params +_C.CUDNN = CN() +_C.CUDNN.BENCHMARK = True +_C.CUDNN.DETERMINISTIC = False +_C.CUDNN.ENABLED = True + +# common params for NETWORK +_C.MODEL = CN() +_C.MODEL.NAME = 'pose_hrnet' +_C.MODEL.INIT_WEIGHTS = True +_C.MODEL.PRETRAINED = '' +_C.MODEL.NUM_JOINTS = 17 +_C.MODEL.TAG_PER_JOINT = True +_C.MODEL.TARGET_TYPE = 'gaussian' +_C.MODEL.IMAGE_SIZE = [256, 256] # width * height, ex: 192 * 256 +_C.MODEL.HEATMAP_SIZE = [64, 64] # width * height, ex: 24 * 32 +_C.MODEL.SIGMA = 2 +_C.MODEL.EXTRA = CN(new_allowed=True) + +_C.LOSS = CN() +_C.LOSS.USE_OHKM = False +_C.LOSS.TOPK = 8 +_C.LOSS.USE_TARGET_WEIGHT = True +_C.LOSS.USE_DIFFERENT_JOINTS_WEIGHT = False + +# DATASET related params +_C.DATASET = CN() +_C.DATASET.ROOT = '' +_C.DATASET.DATASET = 'mpii' +_C.DATASET.TRAIN_SET = 'train' +_C.DATASET.TEST_SET = 'valid' +_C.DATASET.DATA_FORMAT = 'jpg' +_C.DATASET.HYBRID_JOINTS_TYPE = '' +_C.DATASET.SELECT_DATA = False + +# training data augmentation +_C.DATASET.FLIP = True +_C.DATASET.SCALE_FACTOR = 0.25 +_C.DATASET.ROT_FACTOR = 30 +_C.DATASET.PROB_HALF_BODY = 0.0 +_C.DATASET.NUM_JOINTS_HALF_BODY = 8 +_C.DATASET.COLOR_RGB = False + +# train +_C.TRAIN = CN() + +_C.TRAIN.LR_FACTOR = 0.1 +_C.TRAIN.LR_STEP = [90, 110] +_C.TRAIN.LR = 0.001 + +_C.TRAIN.OPTIMIZER = 'adam' +_C.TRAIN.MOMENTUM = 0.9 +_C.TRAIN.WD = 0.0001 +_C.TRAIN.NESTEROV = False +_C.TRAIN.GAMMA1 = 0.99 +_C.TRAIN.GAMMA2 = 0.0 + +_C.TRAIN.BEGIN_EPOCH = 0 +_C.TRAIN.END_EPOCH = 140 + +_C.TRAIN.RESUME = False +_C.TRAIN.CHECKPOINT = '' + +_C.TRAIN.BATCH_SIZE_PER_GPU = 32 +_C.TRAIN.SHUFFLE = True + +# testing +_C.TEST = CN() + +# size of images for each device +_C.TEST.BATCH_SIZE_PER_GPU = 32 +# Test Model Epoch +_C.TEST.FLIP_TEST = False +_C.TEST.POST_PROCESS = False +_C.TEST.SHIFT_HEATMAP = False + +_C.TEST.USE_GT_BBOX = False + +# nms +_C.TEST.IMAGE_THRE = 0.1 +_C.TEST.NMS_THRE = 0.6 +_C.TEST.SOFT_NMS = False +_C.TEST.OKS_THRE = 0.5 +_C.TEST.IN_VIS_THRE = 0.0 +_C.TEST.COCO_BBOX_FILE = '' +_C.TEST.BBOX_THRE = 1.0 +_C.TEST.MODEL_FILE = '' + +# debug +_C.DEBUG = CN() +_C.DEBUG.DEBUG = False +_C.DEBUG.SAVE_BATCH_IMAGES_GT = False +_C.DEBUG.SAVE_BATCH_IMAGES_PRED = False +_C.DEBUG.SAVE_HEATMAPS_GT = False +_C.DEBUG.SAVE_HEATMAPS_PRED = False + + +def update_config(cfg, args): + cfg.defrost() + cfg.merge_from_file(args.cfg) + cfg.merge_from_list(args.opts) + + if args.modelDir: + cfg.OUTPUT_DIR = args.modelDir + + cfg.freeze() + + +if __name__ == '__main__': + import sys + with open(sys.argv[1], 'w') as f: + print(_C, file=f) + diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/inference.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/inference.py new file mode 100644 index 000000000..bd44c0abe --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/inference.py @@ -0,0 +1,67 @@ +import math +import numpy as np + +from lib.hrnet.lib.utils.transforms import transform_preds + +def get_max_preds(batch_heatmaps): + ''' + get predictions from score maps + heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) + ''' + assert isinstance(batch_heatmaps, np.ndarray), \ + 'batch_heatmaps should be numpy.ndarray' + assert batch_heatmaps.ndim == 4, 'batch_images should be 4-ndim' + + batch_size = batch_heatmaps.shape[0] + num_joints = batch_heatmaps.shape[1] + width = batch_heatmaps.shape[3] + heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) + idx = np.argmax(heatmaps_reshaped, 2) + maxvals = np.amax(heatmaps_reshaped, 2) + + maxvals = maxvals.reshape((batch_size, num_joints, 1)) + idx = idx.reshape((batch_size, num_joints, 1)) + + preds = np.tile(idx, (1, 1, 2)).astype(np.float32) + + preds[:, :, 0] = (preds[:, :, 0]) % width + preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) + + pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) + pred_mask = pred_mask.astype(np.float32) + + preds *= pred_mask + return preds, maxvals + + +def get_final_preds(config, batch_heatmaps, center, scale): + coords, maxvals = get_max_preds(batch_heatmaps) + + heatmap_height = batch_heatmaps.shape[2] + heatmap_width = batch_heatmaps.shape[3] + + # post-processing + if config.TEST.POST_PROCESS: + for n in range(coords.shape[0]): + for p in range(coords.shape[1]): + hm = batch_heatmaps[n][p] + px = int(math.floor(coords[n][p][0] + 0.5)) + py = int(math.floor(coords[n][p][1] + 0.5)) + if 1 < px < heatmap_width-1 and 1 < py < heatmap_height-1: + diff = np.array( + [ + hm[py][px+1] - hm[py][px-1], + hm[py+1][px]-hm[py-1][px] + ] + ) + coords[n][p] += np.sign(diff) * .25 + + preds = coords.copy() + + # Transform back + for i in range(coords.shape[0]): + preds[i] = transform_preds( + coords[i], center[i], scale[i], [heatmap_width, heatmap_height] + ) + + return preds, maxvals diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/transforms.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/transforms.py new file mode 100644 index 000000000..a67d8e580 --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/transforms.py @@ -0,0 +1,112 @@ +import numpy as np +import cv2 + + +def flip_back(output_flipped, matched_parts): + ''' + ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width) + ''' + assert output_flipped.ndim == 4,\ + 'output_flipped should be [batch_size, num_joints, height, width]' + + output_flipped = output_flipped[:, :, :, ::-1] + + # 因为你输入的是翻转后的图像,所以输出的热图他们对应的左右关节也是相反的(训练的时候,输入的是翻转后的图像,target对应的左右关节也是对调过来的)。 + for pair in matched_parts: + tmp = output_flipped[:, pair[0], :, :].copy() + output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] + output_flipped[:, pair[1], :, :] = tmp + + return output_flipped + + +def fliplr_joints(joints, joints_vis, width, matched_parts): + """ + flip coords + """ + # Flip horizontal + joints[:, 0] = width - joints[:, 0] - 1 + + # Change left-right parts + for pair in matched_parts: + joints[pair[0], :], joints[pair[1], :] = \ + joints[pair[1], :], joints[pair[0], :].copy() + joints_vis[pair[0], :], joints_vis[pair[1], :] = \ + joints_vis[pair[1], :], joints_vis[pair[0], :].copy() + + return joints*joints_vis, joints_vis + + +def transform_preds(coords, center, scale, output_size): + target_coords = np.zeros(coords.shape) + trans = get_affine_transform(center, scale, 0, output_size, inv=1) + for p in range(coords.shape[0]): + target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) + return target_coords + + +def get_affine_transform( + center, scale, rot, output_size, + shift=np.array([0, 0], dtype=np.float32), inv=0 +): + if not isinstance(scale, np.ndarray) and not isinstance(scale, list): + print(scale) + scale = np.array([scale, scale]) + + scale_tmp = scale * 200.0 + src_w = scale_tmp[0] + dst_w = output_size[0] + dst_h = output_size[1] + + rot_rad = np.pi * rot / 180 + src_dir = get_dir([0, src_w * -0.5], rot_rad) + dst_dir = np.array([0, dst_w * -0.5], np.float32) + + src = np.zeros((3, 2), dtype=np.float32) + dst = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale_tmp * shift + src[1, :] = center + src_dir + scale_tmp * shift + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + + src[2:, :] = get_3rd_point(src[0, :], src[1, :]) + dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return trans + + +def affine_transform(pt, t): + new_pt = np.array([pt[0], pt[1], 1.]).T + new_pt = np.dot(t, new_pt) + return new_pt[:2] + + +def get_3rd_point(a, b): + direct = a - b + return b + np.array([-direct[1], direct[0]], dtype=np.float32) + + +def get_dir(src_point, rot_rad): + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + + src_result = [0, 0] + src_result[0] = src_point[0] * cs - src_point[1] * sn + src_result[1] = src_point[0] * sn + src_point[1] * cs + + return src_result + + +def crop(img, center, scale, output_size, rot=0): + trans = get_affine_transform(center, scale, rot, output_size) + + dst_img = cv2.warpAffine( + img, trans, (int(output_size[0]), int(output_size[1])), + flags=cv2.INTER_LINEAR + ) + + return dst_img diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/utilitys.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/utilitys.py new file mode 100644 index 000000000..d32e2ff16 --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/utilitys.py @@ -0,0 +1,79 @@ +from lib.hrnet.lib.utils.transforms import * + + +def box_to_center_scale(box, model_image_width, model_image_height): + """convert a box to center,scale information required for pose transformation + Parameters + ---------- + box : (x1, y1, x2, y2) + model_image_width : int + model_image_height : int + + Returns + ------- + (numpy array, numpy array) + Two numpy arrays, coordinates for the center of the box and the scale of the box + """ + center = np.zeros((2), dtype=np.float32) + x1, y1, x2, y2 = box[:4] + box_width, box_height = x2 - x1, y2 - y1 + + center[0] = x1 + box_width * 0.5 + center[1] = y1 + box_height * 0.5 + + aspect_ratio = model_image_width * 1.0 / model_image_height + pixel_std = 200 + + if box_width > aspect_ratio * box_height: + box_height = box_width * 1.0 / aspect_ratio + elif box_width < aspect_ratio * box_height: + box_width = box_height * aspect_ratio + scale = np.array( + [box_width * 1.0 / pixel_std, box_height * 1.0 / pixel_std], + dtype=np.float32) + if center[0] != -1: + scale = scale * 1.25 + + return center, scale + + +# Pre-process +def PreProcess(image, bboxs, cfg, num_pos=2): + def _normalize(x, mean, std): + for i in range(x.shape[0]): + x[i, :, :] = (x[i, :, :] - mean[i]) / std[i] + return x + + if type(image) == str: + data_numpy = cv2.imread(image, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION) + # data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB) + else: + data_numpy = image + + inputs = [] + centers = [] + scales = [] + + for bbox in bboxs[:num_pos]: + c, s = box_to_center_scale(bbox, data_numpy.shape[0], data_numpy.shape[1]) + centers.append(c) + scales.append(s) + r = 0 + + trans = get_affine_transform(c, s, r, cfg.MODEL.IMAGE_SIZE) + input = cv2.warpAffine( + data_numpy, + trans, + (int(cfg.MODEL.IMAGE_SIZE[0]), int(cfg.MODEL.IMAGE_SIZE[1])), + flags=cv2.INTER_LINEAR) + + mean=[0.485, 0.456, 0.406] + std=[0.229, 0.224, 0.225] + input = input / 255.0 + input = input.transpose(2, 0, 1) + input = _normalize(input, mean, std) + input = np.expand_dims(input, 0) + inputs.append(input) + + inputs = np.concatenate(inputs) + return inputs, data_numpy, centers, scales diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/preprocess.py b/pose_estimation_3d/strided_transformer_pose3d/lib/preprocess.py new file mode 100644 index 000000000..289e5d3de --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/preprocess.py @@ -0,0 +1,66 @@ +import json +import numpy as np +import os + +h36m_coco_order = [9, 11, 14, 12, 15, 13, 16, 4, 1, 5, 2, 6, 3] +coco_order = [0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +spple_keypoints = [10, 8, 0, 7] + + +def coco_h36m(keypoints): + temporal = keypoints.shape[0] + keypoints_h36m = np.zeros_like(keypoints, dtype=np.float32) + htps_keypoints = np.zeros((temporal, 4, 2), dtype=np.float32) + + # htps_keypoints: head, thorax, pelvis, spine + htps_keypoints[:, 0, 0] = np.mean(keypoints[:, 1:5, 0], axis=1, dtype=np.float32) + htps_keypoints[:, 0, 1] = np.sum(keypoints[:, 1:3, 1], axis=1, dtype=np.float32) - keypoints[:, 0, 1] + htps_keypoints[:, 1, :] = np.mean(keypoints[:, 5:7, :], axis=1, dtype=np.float32) + htps_keypoints[:, 1, :] += (keypoints[:, 0, :] - htps_keypoints[:, 1, :]) / 3 + + htps_keypoints[:, 2, :] = np.mean(keypoints[:, 11:13, :], axis=1, dtype=np.float32) + htps_keypoints[:, 3, :] = np.mean(keypoints[:, [5, 6, 11, 12], :], axis=1, dtype=np.float32) + + keypoints_h36m[:, spple_keypoints, :] = htps_keypoints + keypoints_h36m[:, h36m_coco_order, :] = keypoints[:, coco_order, :] + + keypoints_h36m[:, 9, :] -= (keypoints_h36m[:, 9, :] - np.mean(keypoints[:, 5:7, :], axis=1, dtype=np.float32)) / 4 + keypoints_h36m[:, 7, 0] += 2*(keypoints_h36m[:, 7, 0] - np.mean(keypoints_h36m[:, [0, 8], 0], axis=1, dtype=np.float32)) + keypoints_h36m[:, 8, 1] -= (np.mean(keypoints[:, 1:3, 1], axis=1, dtype=np.float32) - keypoints[:, 0, 1])*2/3 + + valid_frames = np.where(np.sum(keypoints_h36m.reshape(-1, 34), axis=1) != 0)[0] + + return keypoints_h36m, valid_frames + + +def h36m_coco_format(keypoints, scores): + assert len(keypoints.shape) == 4 and len(scores.shape) == 3 + + h36m_kpts = [] + h36m_scores = [] + valid_frames = [] + + for i in range(keypoints.shape[0]): + kpts = keypoints[i] + score = scores[i] + + new_score = np.zeros_like(score, dtype=np.float32) + + if np.sum(kpts) != 0.: + kpts, valid_frame = coco_h36m(kpts) + h36m_kpts.append(kpts) + valid_frames.append(valid_frame) + + new_score[:, h36m_coco_order] = score[:, coco_order] + new_score[:, 0] = np.mean(score[:, [11, 12]], axis=1, dtype=np.float32) + new_score[:, 8] = np.mean(score[:, [5, 6]], axis=1, dtype=np.float32) + new_score[:, 7] = np.mean(new_score[:, [0, 8]], axis=1, dtype=np.float32) + new_score[:, 10] = np.mean(score[:, [1, 2, 3, 4]], axis=1, dtype=np.float32) + + h36m_scores.append(new_score) + + h36m_kpts = np.asarray(h36m_kpts, dtype=np.float32) + h36m_scores = np.asarray(h36m_scores, dtype=np.float32) + + return h36m_kpts + diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/run.py b/pose_estimation_3d/strided_transformer_pose3d/lib/run.py new file mode 100644 index 000000000..ec8a8cde6 --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/run.py @@ -0,0 +1,154 @@ +import cv2 +import copy +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec + +from lib.preprocess import h36m_coco_format +from lib.hrnet.gen_kpts import gen_video_kpts as hrnet_pose +from lib.camera import * + +def show2Dpose(kps, img): + connections = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], + [5, 6], [0, 7], [7, 8], [8, 9], [9, 10], + [8, 11], [11, 12], [12, 13], [8, 14], [14, 15], [15, 16]] + + LR = np.array([0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], dtype=bool) + + lcolor = (255, 0, 0) + rcolor = (0, 0, 255) + thickness = 5 + + for j,c in enumerate(connections): + start = map(int, kps[c[0]]) + end = map(int, kps[c[1]]) + start = list(start) + end = list(end) + cv2.line(img, (start[0], start[1]), (end[0], end[1]), lcolor if LR[j] else rcolor, thickness) + cv2.circle(img, (start[0], start[1]), thickness=-1, color=(0, 255, 0), radius=5) + cv2.circle(img, (end[0], end[1]), thickness=-1, color=(0, 255, 0), radius=5) + + return img + + +def show3Dpose(vals, ax): + ax.view_init(elev=15., azim=70) + + I = np.array( [0, 0, 1, 4, 2, 5, 0, 7, 8, 8, 14, 15, 11, 12, 8, 9]) + J = np.array( [1, 4, 2, 5, 3, 6, 7, 8, 14, 11, 15, 16, 12, 13, 9, 10]) + + for i in np.arange( len(I) ): + x, y, z = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(3)] + + ax.plot(x, y, z, lw=2) + ax.scatter(x, y, z) + + RADIUS = 0.8 + + ax.set_xlim3d([-RADIUS, RADIUS]) + ax.set_ylim3d([-RADIUS, RADIUS]) + ax.set_aspect('auto') + + white = (1.0, 1.0, 1.0, 0.0) + ax.xaxis.set_pane_color(white) + ax.yaxis.set_pane_color(white) + ax.zaxis.set_pane_color(white) + + ax.tick_params('x', labelbottom = False) + ax.tick_params('y', labelleft = False) + ax.tick_params('z', labelleft = False) + + +def get_pose2D(human_model, pose_model, img): + keypoints, scores = hrnet_pose(img, human_model, pose_model, det_dim=416, num_peroson=1) + if keypoints is not None: + keypoints = h36m_coco_format(keypoints, scores) + return keypoints + else: + return None + + +def showimage(ax, img): + ax.set_xticks([]) + ax.set_yticks([]) + plt.axis('off') + ax.imshow(img) + + +def get_pose3D(model, img, keypoints, fig=None): + i = 0 + frames = 351 + pad = (frames - 1) // 2 + + ## 3D + img_size = img.shape + + ## input frames + start = max(0, i - pad) + end = min(i + pad, len(keypoints[0]) - 1) + + input_2D_no = keypoints[0][start:end + 1] + + left_pad, right_pad = 0, 0 + if input_2D_no.shape[0] != frames: + if i < pad: + left_pad = pad - i + if i > len(keypoints[0]) - pad - 1: + right_pad = i + pad - (len(keypoints[0]) - 1) + + input_2D_no = np.pad(input_2D_no, ((left_pad, right_pad), (0, 0), (0, 0)), 'edge') + + joints_left = [4, 5, 6, 11, 12, 13] + joints_right = [1, 2, 3, 14, 15, 16] + + input_2D = normalize_screen_coordinates(input_2D_no, w=img_size[1], h=img_size[0]) + + input_2D_aug = copy.deepcopy(input_2D) + input_2D_aug[:, :, 0] *= -1 + input_2D_aug[:, joints_left + joints_right] = input_2D_aug[:, joints_right + joints_left] + input_2D = np.concatenate((np.expand_dims(input_2D, axis=0), np.expand_dims(input_2D_aug, axis=0)), 0) + + input_2D = input_2D[np.newaxis, :, :, :, :] + + input_2D = input_2D.astype('float32') + output_3D_non_flip = model.run(input_2D[:, 0])[0] + output_3D_flip = model.run(input_2D[:, 1])[0] + + output_3D_flip[:, :, :, 0] *= -1 + output_3D_flip[:, :, joints_left + joints_right, :] = output_3D_flip[:, :, joints_right + joints_left, :] + + output_3D = (output_3D_non_flip + output_3D_flip) / 2 + + output_3D[:, :, 0, :] = 0 + output_3D[:, :, 0, :] = 0 + post_out = output_3D[0, 0] + + rot = [0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088] + rot = np.array(rot, dtype='float32') + post_out = camera_to_world(post_out, R=rot, t=0) + post_out[:, 2] -= np.min(post_out[:, 2]) + + input_2D_no = input_2D_no[pad] + + ## 2D + image = show2Dpose(input_2D_no, copy.deepcopy(img)) + #cv2.imwrite(str(('%04d' % i)) + '_2D.png', image) + + ## 3D + if fig is None: + fig = plt.figure(figsize=(9.6, 5.4)) + gs = gridspec.GridSpec(1, 1) + gs.update(wspace=-0.00, hspace=0.05) + ax = plt.subplot(gs[0], projection='3d') + show3Dpose(post_out, ax) + return image, ax + + +def detect(img, human_model, pose_model2d, pose_model3d, fig=None): + keypoints = get_pose2D(human_model, pose_model2d, img) + if keypoints is None: + return img, None, False + else: + image_pose2d, fig_pose3d = get_pose3D(pose_model3d, img, keypoints, fig) + return image_pose2d, fig_pose3d, True + + diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py b/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py new file mode 100644 index 000000000..1f12f8fd5 --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py @@ -0,0 +1,231 @@ +""" + https://arxiv.org/abs/1602.00763 +""" +from __future__ import print_function + +from numba import jit +import os.path +import numpy as np +from skimage import io +from scipy.optimize import linear_sum_assignment +import argparse +from filterpy.kalman import KalmanFilter + + +@jit +def iou(bb_test, bb_gt): + """ + Computes IUO between two bboxes in the form [x1,y1,x2,y2] + """ + xx1 = np.maximum(bb_test[0], bb_gt[0]) + yy1 = np.maximum(bb_test[1], bb_gt[1]) + xx2 = np.minimum(bb_test[2], bb_gt[2]) + yy2 = np.minimum(bb_test[3], bb_gt[3]) + w = np.maximum(0., xx2 - xx1) + h = np.maximum(0., yy2 - yy1) + wh = w * h + o = wh / ((bb_test[2] - bb_test[0]) * (bb_test[3] - bb_test[1]) + + (bb_gt[2] - bb_gt[0]) * (bb_gt[3] - bb_gt[1]) - wh) + + return o + + +def convert_bbox_to_z(bbox): + """ + Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form + [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is + the aspect ratio + """ + w = bbox[2] - bbox[0] + h = bbox[3] - bbox[1] + x = bbox[0] + w / 2. + y = bbox[1] + h / 2. + s = w * h # scale is just area + r = w / float(h) + return np.array([x, y, s, r]).reshape((4, 1)) + + +def convert_x_to_bbox(x, score=None): + """ + Takes a bounding box in the centre form [x,y,s,r] and returns it in the form + [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right + """ + w = np.sqrt(x[2] * x[3]) + h = x[2] / w + if (score == None): + return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4)) + else: + return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5)) + + +class KalmanBoxTracker(object): + """ + This class represents the internel state of individual tracked objects observed as bbox. + """ + count = 0 + + def __init__(self, bbox): + """ + Initialises a tracker using initial bounding box. + """ + # define constant velocity model + self.kf = KalmanFilter(dim_x=7, dim_z=4) + self.kf.F = np.array( + [[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]]) + self.kf.H = np.array( + [[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]]) + + self.kf.R[2:, 2:] *= 10. + self.kf.P[4:, 4:] *= 1000. # give high uncertainty to the unobservable initial velocities + self.kf.P *= 10. + self.kf.Q[-1, -1] *= 0.01 + self.kf.Q[4:, 4:] *= 0.01 + + self.kf.x[:4] = convert_bbox_to_z(bbox) + self.time_since_update = 0 + self.id = KalmanBoxTracker.count + KalmanBoxTracker.count += 1 + self.history = [] + self.hits = 0 + self.hit_streak = 0 + self.age = 0 + + def update(self, bbox): + """ + Updates the state vector with observed bbox. + """ + self.time_since_update = 0 + self.history = [] + self.hits += 1 + self.hit_streak += 1 + self.kf.update(convert_bbox_to_z(bbox)) + + def predict(self): + """ + Advances the state vector and returns the predicted bounding box estimate. + """ + if ((self.kf.x[6] + self.kf.x[2]) <= 0): + self.kf.x[6] *= 0.0 + self.kf.predict() + self.age += 1 + if (self.time_since_update > 0): + self.hit_streak = 0 + self.time_since_update += 1 + self.history.append(convert_x_to_bbox(self.kf.x)) + return self.history[-1] + + def get_state(self): + """ + Returns the current bounding box estimate. + """ + return convert_x_to_bbox(self.kf.x) + + +def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3): + """ + Assigns detections to tracked object (both represented as bounding boxes) + + Returns 3 lists of matches, unmatched_detections and unmatched_trackers + """ + if (len(trackers) == 0): + return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int) + iou_matrix = np.zeros((len(detections), len(trackers)), dtype=np.float32) + + for d, det in enumerate(detections): + for t, trk in enumerate(trackers): + iou_matrix[d, t] = iou(det, trk) + matched_indices = linear_sum_assignment(-iou_matrix) + matched_indices = np.asarray(matched_indices) + matched_indices = matched_indices.transpose() + + unmatched_detections = [] + for d, det in enumerate(detections): + if (d not in matched_indices[:, 0]): + unmatched_detections.append(d) + unmatched_trackers = [] + for t, trk in enumerate(trackers): + if (t not in matched_indices[:, 1]): + unmatched_trackers.append(t) + + # filter out matched with low IOU + matches = [] + for m in matched_indices: + if (iou_matrix[m[0], m[1]] < iou_threshold): + unmatched_detections.append(m[0]) + unmatched_trackers.append(m[1]) + else: + matches.append(m.reshape(1, 2)) + if (len(matches) == 0): + matches = np.empty((0, 2), dtype=int) + else: + matches = np.concatenate(matches, axis=0) + + return matches, np.array(unmatched_detections), np.array(unmatched_trackers) + + +class Sort(object): + def __init__(self, max_age=1, min_hits=3): + """ + Sets key parameters for SORT + """ + self.max_age = max_age + self.min_hits = min_hits + self.trackers = [] + self.frame_count = 0 + + def update(self, dets): + """ + Params: + dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...] + Requires: this method must be called once for each frame even with empty detections. + Returns the a similar array, where the last column is the object ID. + + NOTE: The number of objects returned may differ from the number of detections provided. + """ + self.frame_count += 1 + # get predicted locations from existing trackers. + trks = np.zeros((len(self.trackers), 5)) + to_del = [] + ret = [] + for t, trk in enumerate(trks): + pos = self.trackers[t].predict()[0] + trk[:] = [pos[0], pos[1], pos[2], pos[3], 0] + if np.any(np.isnan(pos)): + to_del.append(t) + trks = np.ma.compress_rows(np.ma.masked_invalid(trks)) + for t in reversed(to_del): + self.trackers.pop(t) + matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks) + + # update matched trackers with assigned detections + for t, trk in enumerate(self.trackers): + if t not in unmatched_trks: + d = matched[np.where(matched[:, 1] == t)[0], 0] # d: [n] + trk.update(dets[d, :][0]) + + # create and initialise new trackers for unmatched detections + for i in unmatched_dets: + trk = KalmanBoxTracker(dets[i, :]) + self.trackers.append(trk) + i = len(self.trackers) + for trk in reversed(self.trackers): + d = trk.get_state()[0] + if ((trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits)): + ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive + i -= 1 + # remove dead tracklet + if (trk.time_since_update > self.max_age): + self.trackers.pop(i) + if (len(ret) > 0): + return np.concatenate(ret) + return np.empty((0, 5)) + + +def parse_args(): + """Parse input arguments.""" + parser = argparse.ArgumentParser(description='SORT demo') + parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]', + action='store_true') + args = parser.parse_args() + return args diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/human_detector.py b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/human_detector.py new file mode 100644 index 000000000..09c70b36a --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/human_detector.py @@ -0,0 +1,18 @@ +import os + +from lib.yolo.util import * +from lib.yolo import preprocess + + +def yolo_human_det(img, model=None, reso=416, confidence=0.8): + inp_dim = reso + if type(img) == str: + assert os.path.isfile(img), 'The image path does not exist' + img = cv2.imread(img) + + img, ori_img, img_dim, ratio = preprocess.preproc(img, (inp_dim,inp_dim)) + output = model.run(img.astype('float32')[None, :, :, :])[0] + output = postprocess(output, (416, 416))[0] + bboxs, scores = predictions_to_object(output, ratio, confidence, confidence) + + return np.array(bboxs), np.array(scores) diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/preprocess.py b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/preprocess.py new file mode 100644 index 000000000..e4275ce3d --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/preprocess.py @@ -0,0 +1,68 @@ +import numpy as np +import cv2 +from PIL import Image + + +def letterbox_image(img, inp_dim): + '''resize image with unchanged aspect ratio using padding''' + img_w, img_h = img.shape[1], img.shape[0] + w, h = inp_dim + new_w = int(img_w * min(w/img_w, h/img_h)) + new_h = int(img_h * min(w/img_w, h/img_h)) + resized_image = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_CUBIC) + + canvas = np.full((inp_dim[1], inp_dim[0], 3), 128) + + canvas[(h - new_h) // 2:(h - new_h) // 2 + new_h, (w - new_w) // 2:(w - new_w) // 2 + new_w, :] = resized_image + + return canvas + + +def prep_image(img, inp_dim): + """ + Prepare image for inputting to the neural network. + + Returns a Variable + """ + if type(img) == str: + orig_im = cv2.imread(img) + else: + orig_im = img + dim = orig_im.shape[1], orig_im.shape[0] + img = (letterbox_image(orig_im, (inp_dim, inp_dim))) + img_ = img[:, :, ::-1].transpose((2, 0, 1)).copy() + img_ = np.expand_dims(img_ / 255.0, 0) + return img_, orig_im, dim + +def preproc(img, input_size, swap=(2, 0, 1)): + orig_im = img + dim = orig_im.shape[1], orig_im.shape[0] + if len(img.shape) == 3: + padded_img = np.ones((input_size[0], input_size[1], img.shape[2]), dtype=np.uint8) * 114 + else: + padded_img = np.ones(input_size, dtype=np.uint8) * 114 + + r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) + resized_img = cv2.resize( + img, + (int(img.shape[1] * r), int(img.shape[0] * r)), + interpolation=cv2.INTER_LINEAR, + ).astype(np.uint8) + padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img + + padded_img = padded_img.transpose(swap) + padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) + return padded_img, orig_im, dim, r + + +def inp_to_image(inp): + inp = inp.cpu().squeeze() + inp = inp * 255 + try: + inp = inp.data.numpy() + except RuntimeError: + inp = inp.numpy() + inp = inp.transpose(1, 2, 0) + + inp = inp[:, :, ::-1] + return inp diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/util.py b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/util.py new file mode 100644 index 000000000..a260ce22b --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/util.py @@ -0,0 +1,159 @@ +from __future__ import division + +import numpy as np +import cv2 + + +def preproc(img, input_size, swap=(2, 0, 1)): + if len(img.shape) == 3: + padded_img = np.ones((input_size[0], input_size[1], img.shape[2]), dtype=np.uint8) * 114 + else: + padded_img = np.ones(input_size, dtype=np.uint8) * 114 + + r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) + resized_img = cv2.resize( + img, + (int(img.shape[1] * r), int(img.shape[0] * r)), + interpolation=cv2.INTER_LINEAR, + ).astype(np.uint8) + padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img + + padded_img = padded_img.transpose(swap) + padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) + return padded_img, r + + +def nms(boxes, scores, nms_thr): + """Single class NMS implemented in Numpy.""" + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= nms_thr)[0] + order = order[inds + 1] + + return keep + + +def multiclass_nms(boxes, scores, nms_thr, score_thr, class_agnostic=True): + """Multiclass NMS implemented in Numpy""" + if class_agnostic: + nms_method = multiclass_nms_class_agnostic + else: + nms_method = multiclass_nms_class_aware + return nms_method(boxes, scores, nms_thr, score_thr) + + +def multiclass_nms_class_aware(boxes, scores, nms_thr, score_thr): + """Multiclass NMS implemented in Numpy. Class-aware version.""" + final_dets = [] + num_classes = scores.shape[1] + for cls_ind in range(num_classes): + cls_scores = scores[:, cls_ind] + valid_score_mask = cls_scores > score_thr + if valid_score_mask.sum() == 0: + continue + else: + valid_scores = cls_scores[valid_score_mask] + valid_boxes = boxes[valid_score_mask] + keep = nms(valid_boxes, valid_scores, nms_thr) + if len(keep) > 0: + cls_inds = np.ones((len(keep), 1)) * cls_ind + dets = np.concatenate( + [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 + ) + final_dets.append(dets) + if len(final_dets) == 0: + return None + return np.concatenate(final_dets, 0) + + +def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr): + """Multiclass NMS implemented in Numpy. Class-agnostic version.""" + cls_inds = scores.argmax(1) + cls_scores = scores[np.arange(len(cls_inds)), cls_inds] + + valid_score_mask = cls_scores > score_thr + if valid_score_mask.sum() == 0: + return None + valid_scores = cls_scores[valid_score_mask] + valid_boxes = boxes[valid_score_mask] + valid_cls_inds = cls_inds[valid_score_mask] + keep = nms(valid_boxes, valid_scores, nms_thr) + if keep: + dets = np.concatenate( + [valid_boxes[keep], valid_scores[keep, None], valid_cls_inds[keep, None]], 1 + ) + return dets + + +def postprocess(outputs, img_size, p6=False): + grids = [] + expanded_strides = [] + + if not p6: + strides = [8, 16, 32] + else: + strides = [8, 16, 32, 64] + + hsizes = [img_size[0] // stride for stride in strides] + wsizes = [img_size[1] // stride for stride in strides] + + for hsize, wsize, stride in zip(hsizes, wsizes, strides): + xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) + grid = np.stack((xv, yv), 2).reshape(1, -1, 2) + grids.append(grid) + shape = grid.shape[:2] + expanded_strides.append(np.full((*shape, 1), stride)) + + grids = np.concatenate(grids, 1) + expanded_strides = np.concatenate(expanded_strides, 1) + outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides + outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides + + return outputs + + +def predictions_to_object(predictions,ratio,nms_thr,score_thr): + boxes = predictions[:, :4] + scores = predictions[:, 4:5] * predictions[:, 5:] + + boxes_xyxy = np.ones_like(boxes) + boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2. + boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2. + boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2. + boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2. + boxes_xyxy /= ratio + dets = multiclass_nms(boxes_xyxy, scores, nms_thr, score_thr) + + out_box = [] + out_score = [] + if dets is not None: + final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5] + for i, box in enumerate(final_boxes): + x1, y1, x2, y2 = box + c = int(final_cls_inds[i]) + score = final_scores[i] + if c == 0: + out_box.append([x1, y1, x2, y2]) + out_score.append([score]) + + return out_box, out_score diff --git a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py new file mode 100644 index 000000000..4ab928ccc --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py @@ -0,0 +1,155 @@ +import os +import sys +import numpy as np +import cv2 +import matplotlib +from PIL import Image +import time +import onnxruntime as ort + +import ailia + +matplotlib.use('TkAgg') + +# import original modules +sys.path.append('../../util') +# logger +from logging import getLogger + +import webcamera_utils +from detector_utils import (load_image, plot_results, reverse_letterbox, + write_predictions) +from image_utils import imread # noqa: E402 +from model_utils import check_and_download_models +from utils import get_base_parser, get_savepath, update_parser + +from lib.run import * + +logger = getLogger(__name__) + +os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' + + +# ====================== +# Parameters +# ====================== +REMOTE_PATH_YOLO = 'https://storage.googleapis.com/ailia-models/yolox/' +REMOTE_PATH_POSE = 'https://storage.googleapis.com/ailia-models/strided_transformer_pose3d/' + +# settings +WEIGHT_PATH_YOLO = "yolox_tiny.opt.onnx" +MODEL_PATH_YOLO = "yolox_tiny.opt.onnx.prototxt" + +WEIGHT_PATH_POSE2D = "hrnet_w48-8ef0771d.opt.onnx" +MODEL_PATH_POSE2D = "hrnet_w48-8ef0771d.opt.onnx.prototxt" + +WEIGHT_PATH_POSE3D = "no_refine_4365.opt.onnx" +MODEL_PATH_POSE3D = "no_refine_4365.opt.onnx.prototxt" + +IMAGE_PATH = 'input.jpg' +SAVE_IMAGE_PATH = 'output.png' + +# Default input size +HEIGHT = 416 +WIDTH = 416 + +# ====================== +# Arguemnt Parser Config +# ====================== +parser = get_base_parser('Strided Transformer POSE3D model', IMAGE_PATH, SAVE_IMAGE_PATH) +args = update_parser(parser) + +# ====================== +# Main functions +# ====================== +def recognize_from_image(): + env_id = args.env_id + net_yolo = ailia.Net(MODEL_PATH_YOLO, WEIGHT_PATH_YOLO, env_id=env_id) + net_pose2d = ailia.Net(MODEL_PATH_POSE2D, WEIGHT_PATH_POSE2D, env_id=env_id) + net_pose3d = ailia.Net(MODEL_PATH_POSE3D, WEIGHT_PATH_POSE3D, env_id=env_id) + + # input image loop + for image_path in args.input: + # prepare input data + logger.debug(f'input image: {image_path}') + raw_img = cv2.imread(image_path) + + # inference + logger.info('Start inference...') + image_pose2d, fig_pose3d, _ = detect(raw_img, net_yolo, net_pose2d, net_pose3d) + + # plot result + savepath = get_savepath(args.savepath, image_path) + logger.info(f'saved at : {savepath}') + cv2.imwrite('2D_'+savepath, image_pose2d) + plt.savefig('3D_'+savepath, dpi=200, format='png', bbox_inches='tight') + + logger.info('Script finished successfully.') + +def recognize_from_video(): + env_id = args.env_id + net_yolo = ailia.Net(MODEL_PATH_YOLO, WEIGHT_PATH_YOLO, env_id=env_id) + net_pose2d = ailia.Net(MODEL_PATH_POSE2D, WEIGHT_PATH_POSE2D, env_id=env_id) + net_pose3d = ailia.Net(MODEL_PATH_POSE3D, WEIGHT_PATH_POSE3D, env_id=env_id) + + capture = webcamera_utils.get_capture(args.video) + + # create video writer if savepath is specified as video format + if args.savepath != SAVE_IMAGE_PATH: + logger.warning( + 'currently, video results cannot be output correctly...' + ) + f_h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) + f_w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) + save_h, save_w = f_h, f_w + writer = webcamera_utils.get_writer(args.savepath, save_h, save_w) + else: + writer = None + + frame_shown = False + fig = plt.figure(figsize=(9.6, 5.4)) + while (True): + ret, frame = capture.read() + if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret: + break + if frame_shown and cv2.getWindowProperty('frame', cv2.WND_PROP_VISIBLE) == 0: + break + + frame = cv2.resize(frame, dsize=(960, 640)) + image_pose2d, ax_pose3d, detect_flg = detect(frame, net_yolo, net_pose2d, net_pose3d, fig) + + if detect_flg: + cv2.imshow("Demo", image_pose2d) + plt.pause(.01) + ax_pose3d.clear() + + if not plt.get_fignums(): + break + + # save results + if writer is not None: + writer.write(frame) + + capture.release() + cv2.destroyAllWindows() + if writer is not None: + writer.release() + logger.info('Script finished successfully.') + + +def main(): + # model files check and download + check_and_download_models(WEIGHT_PATH_YOLO, MODEL_PATH_YOLO, REMOTE_PATH_YOLO) + #check_and_download_models(WEIGHT_PATH_POSE2D, MODEL_PATH_POSE2D, REMOTE_PATH_POSE) + #check_and_download_models(WEIGHT_PATH_POSE3D, MODEL_PATH_POSE3D, REMOTE_PATH_POSE) + + if args.video is not None: + # video mode + recognize_from_video() + else: + # image mode + recognize_from_image() + + +if __name__ == '__main__': + main() \ No newline at end of file From fc48ee197d1c78830250540da47cb171297bf91e Mon Sep 17 00:00:00 2001 From: Kazuki Kyakuno Date: Sat, 15 Oct 2022 21:54:48 +0900 Subject: [PATCH 2/7] Remove unused dependency, Fix error using jit --- pose_estimation_3d/strided_transformer_pose3d/lib/sort.py | 2 -- .../strided_transformer_pose3d/strided_transformer_pose3d.py | 5 ++--- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py b/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py index 1f12f8fd5..8072b9d76 100644 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py @@ -3,7 +3,6 @@ """ from __future__ import print_function -from numba import jit import os.path import numpy as np from skimage import io @@ -12,7 +11,6 @@ from filterpy.kalman import KalmanFilter -@jit def iou(bb_test, bb_gt): """ Computes IUO between two bboxes in the form [x1,y1,x2,y2] diff --git a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py index 4ab928ccc..87dd5b1f7 100644 --- a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py +++ b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py @@ -5,7 +5,6 @@ import matplotlib from PIL import Image import time -import onnxruntime as ort import ailia @@ -140,8 +139,8 @@ def recognize_from_video(): def main(): # model files check and download check_and_download_models(WEIGHT_PATH_YOLO, MODEL_PATH_YOLO, REMOTE_PATH_YOLO) - #check_and_download_models(WEIGHT_PATH_POSE2D, MODEL_PATH_POSE2D, REMOTE_PATH_POSE) - #check_and_download_models(WEIGHT_PATH_POSE3D, MODEL_PATH_POSE3D, REMOTE_PATH_POSE) + check_and_download_models(WEIGHT_PATH_POSE2D, MODEL_PATH_POSE2D, REMOTE_PATH_POSE) + check_and_download_models(WEIGHT_PATH_POSE3D, MODEL_PATH_POSE3D, REMOTE_PATH_POSE) if args.video is not None: # video mode From e9aa30db4ef9edd5be70140581c0ac8953e2ceea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=95=A0=E5=B1=B1=E5=89=9B?= Date: Fri, 21 Oct 2022 23:41:31 +0900 Subject: [PATCH 3/7] FIX strided_transformer_pose3d --- .../strided_transformer_pose3d/lib/camera.py | 68 --- .../lib/gen_kpts.py | 498 ++++++++++++++++++ .../experiments/w48_384x288_adam_lr1e-3.yaml | 127 ----- .../lib/hrnet/gen_kpts.py | 95 ---- .../lib/hrnet/lib/config/__init__.py | 8 - .../lib/hrnet/lib/config/default.py | 141 ----- .../lib/hrnet/lib/utils/inference.py | 67 --- .../lib/hrnet/lib/utils/transforms.py | 112 ---- .../lib/hrnet/lib/utils/utilitys.py | 79 --- .../lib/preprocess.py | 66 --- .../strided_transformer_pose3d/lib/run.py | 103 +++- .../strided_transformer_pose3d/lib/sort.py | 231 -------- .../lib/yolo/human_detector.py | 18 - .../lib/yolo/preprocess.py | 68 --- .../lib/yolo/util.py | 159 ------ .../strided_transformer_pose3d.py | 16 +- 16 files changed, 603 insertions(+), 1253 deletions(-) delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/camera.py create mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/experiments/w48_384x288_adam_lr1e-3.yaml delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/gen_kpts.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/__init__.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/default.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/inference.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/transforms.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/utilitys.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/preprocess.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/sort.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/yolo/human_detector.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/yolo/preprocess.py delete mode 100644 pose_estimation_3d/strided_transformer_pose3d/lib/yolo/util.py diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/camera.py b/pose_estimation_3d/strided_transformer_pose3d/lib/camera.py deleted file mode 100644 index 1347b4bbf..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/camera.py +++ /dev/null @@ -1,68 +0,0 @@ -import sys -import numpy as np -import torch - -def normalize_screen_coordinates(X, w, h): - assert X.shape[-1] == 2 - return X / w * 2 - [1, h / w] - - -def world_to_camera(X, R, t): - Rt = wrap(qinverse, R) - return wrap(qrot, np.tile(Rt, (*X.shape[:-1], 1)), X - t) - - -def camera_to_world(X, R, t): - return wrap(qrot, np.tile(R, (*X.shape[:-1], 1)), X) + t - - -def wrap(func, *args, unsqueeze=False): - args = list(args) - result = func(*args) - return result - -def qrot(q, v): - assert q.shape[-1] == 4 - assert v.shape[-1] == 3 - assert q.shape[:-1] == v.shape[:-1] - - qvec = q[..., 1:] - uv = np.cross(qvec, v, axis=len(q.shape) - 1) - uuv = np.cross(qvec, uv, axis=len(q.shape) - 1) - return (v + 2 * (q[..., :1] * uv + uuv)) - - -def qinverse(q, inplace=False): - if inplace: - q[..., 1:] *= -1 - return q - else: - w = q[..., :1] - xyz = q[..., 1:] - return torch.cat((w, -xyz), dim=len(q.shape) - 1) - - -def get_uvd2xyz(uvd, gt_3D, cam): - N, T, V,_ = uvd.size() - - dec_out_all = uvd.view(-1, T, V, 3).clone() - root = gt_3D[:, :, 0, :].unsqueeze(-2).repeat(1, 1, V, 1).clone() - enc_in_all = uvd[:, :, :, :2].view(-1, T, V, 2).clone() - - cam_f_all = cam[..., :2].view(-1,1,1,2).repeat(1,T,V,1) - cam_c_all = cam[..., 2:4].view(-1,1,1,2).repeat(1,T,V,1) - - z_global = dec_out_all[:, :, :, 2] - z_global[:, :, 0] = root[:, :, 0, 2] - z_global[:, :, 1:] = dec_out_all[:, :, 1:, 2] + root[:, :, 1:, 2] - z_global = z_global.unsqueeze(-1) - - uv = enc_in_all - cam_c_all - xy = uv * z_global.repeat(1, 1, 1, 2) / cam_f_all - xyz_global = torch.cat((xy, z_global), -1) - xyz_offset = (xyz_global - xyz_global[:, :, 0, :].unsqueeze(-2).repeat(1, 1, V, 1)) - - return xyz_offset - - - \ No newline at end of file diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py b/pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py new file mode 100644 index 000000000..bbc8d1cb9 --- /dev/null +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py @@ -0,0 +1,498 @@ +import math +import cv2 +import numpy as np +from numba import jit +from skimage import io +from scipy.optimize import linear_sum_assignment +from filterpy.kalman import KalmanFilter + + +def affine_transform(pt, t): + new_pt = np.array([pt[0], pt[1], 1.]).T + new_pt = np.dot(t, new_pt) + return new_pt[:2] + + +def transform_preds(coords, center, scale, output_size): + target_coords = np.zeros(coords.shape) + trans = get_affine_transform(center, scale, 0, output_size, inv=1) + for p in range(coords.shape[0]): + target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) + return target_coords + + +def get_affine_transform( + center, scale, rot, output_size, + shift=np.array([0, 0], dtype=np.float32), inv=0 +): + if not isinstance(scale, np.ndarray) and not isinstance(scale, list): + print(scale) + scale = np.array([scale, scale]) + + scale_tmp = scale * 200.0 + src_w = scale_tmp[0] + dst_w = output_size[0] + dst_h = output_size[1] + + rot_rad = np.pi * rot / 180 + src_dir = get_dir([0, src_w * -0.5], rot_rad) + dst_dir = np.array([0, dst_w * -0.5], np.float32) + + src = np.zeros((3, 2), dtype=np.float32) + dst = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale_tmp * shift + src[1, :] = center + src_dir + scale_tmp * shift + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + + src[2:, :] = get_3rd_point(src[0, :], src[1, :]) + dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return trans + + +def get_dir(src_point, rot_rad): + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + + src_result = [0, 0] + src_result[0] = src_point[0] * cs - src_point[1] * sn + src_result[1] = src_point[0] * sn + src_point[1] * cs + + return src_result + + +def get_3rd_point(a, b): + direct = a - b + return b + np.array([-direct[1], direct[0]], dtype=np.float32) + + +def box_to_center_scale(box, model_image_width, model_image_height): + """convert a box to center,scale information required for pose transformation + Parameters + ---------- + box : (x1, y1, x2, y2) + model_image_width : int + model_image_height : int + + Returns + ------- + (numpy array, numpy array) + Two numpy arrays, coordinates for the center of the box and the scale of the box + """ + center = np.zeros((2), dtype=np.float32) + x1, y1, x2, y2 = box[:4] + box_width, box_height = x2 - x1, y2 - y1 + + center[0] = x1 + box_width * 0.5 + center[1] = y1 + box_height * 0.5 + + aspect_ratio = model_image_width * 1.0 / model_image_height + pixel_std = 200 + + if box_width > aspect_ratio * box_height: + box_height = box_width * 1.0 / aspect_ratio + elif box_width < aspect_ratio * box_height: + box_width = box_height * aspect_ratio + scale = np.array( + [box_width * 1.0 / pixel_std, box_height * 1.0 / pixel_std], + dtype=np.float32) + if center[0] != -1: + scale = scale * 1.25 + + return center, scale + + +# Pre-process +def PreProcess(image, bboxs, num_pos=2): + def _normalize(x, mean, std): + for i in range(x.shape[0]): + x[i, :, :] = (x[i, :, :] - mean[i]) / std[i] + return x + + if type(image) == str: + data_numpy = cv2.imread(image, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION) + # data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB) + else: + data_numpy = image + + inputs = [] + centers = [] + scales = [] + + for bbox in bboxs[:num_pos]: + c, s = box_to_center_scale(bbox, data_numpy.shape[0], data_numpy.shape[1]) + centers.append(c) + scales.append(s) + r = 0 + + trans = get_affine_transform(c, s, r, [288, 384]) + input = cv2.warpAffine( + data_numpy, + trans, + (288, 384), + flags=cv2.INTER_LINEAR) + + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + input = input / 255.0 + input = input.transpose(2, 0, 1) + input = _normalize(input, mean, std) + input = np.expand_dims(input, 0) + inputs.append(input) + + inputs = np.concatenate(inputs) + return inputs, data_numpy, centers, scales + + +def get_max_preds(batch_heatmaps): + ''' + get predictions from score maps + heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) + ''' + assert isinstance(batch_heatmaps, np.ndarray), \ + 'batch_heatmaps should be numpy.ndarray' + assert batch_heatmaps.ndim == 4, 'batch_images should be 4-ndim' + + batch_size = batch_heatmaps.shape[0] + num_joints = batch_heatmaps.shape[1] + width = batch_heatmaps.shape[3] + heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) + idx = np.argmax(heatmaps_reshaped, 2) + maxvals = np.amax(heatmaps_reshaped, 2) + + maxvals = maxvals.reshape((batch_size, num_joints, 1)) + idx = idx.reshape((batch_size, num_joints, 1)) + + preds = np.tile(idx, (1, 1, 2)).astype(np.float32) + + preds[:, :, 0] = (preds[:, :, 0]) % width + preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) + + pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) + pred_mask = pred_mask.astype(np.float32) + + preds *= pred_mask + return preds, maxvals + + +def get_final_preds(batch_heatmaps, center, scale): + coords, maxvals = get_max_preds(batch_heatmaps) + + heatmap_height = batch_heatmaps.shape[2] + heatmap_width = batch_heatmaps.shape[3] + + # post-processing + for n in range(coords.shape[0]): + for p in range(coords.shape[1]): + hm = batch_heatmaps[n][p] + px = int(math.floor(coords[n][p][0] + 0.5)) + py = int(math.floor(coords[n][p][1] + 0.5)) + if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1: + diff = np.array( + [ + hm[py][px + 1] - hm[py][px - 1], + hm[py + 1][px] - hm[py - 1][px] + ] + ) + coords[n][p] += np.sign(diff) * .25 + + preds = coords.copy() + + # Transform back + for i in range(coords.shape[0]): + preds[i] = transform_preds( + coords[i], center[i], scale[i], [heatmap_width, heatmap_height] + ) + + return preds, maxvals + + +@jit +def iou(bb_test, bb_gt): + """ + Computes IUO between two bboxes in the form [x1,y1,x2,y2] + """ + xx1 = np.maximum(bb_test[0], bb_gt[0]) + yy1 = np.maximum(bb_test[1], bb_gt[1]) + xx2 = np.minimum(bb_test[2], bb_gt[2]) + yy2 = np.minimum(bb_test[3], bb_gt[3]) + w = np.maximum(0., xx2 - xx1) + h = np.maximum(0., yy2 - yy1) + wh = w * h + o = wh / ((bb_test[2] - bb_test[0]) * (bb_test[3] - bb_test[1]) + + (bb_gt[2] - bb_gt[0]) * (bb_gt[3] - bb_gt[1]) - wh) + + return o + + +def convert_bbox_to_z(bbox): + """ + Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form + [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is + the aspect ratio + """ + w = bbox[2] - bbox[0] + h = bbox[3] - bbox[1] + x = bbox[0] + w / 2. + y = bbox[1] + h / 2. + s = w * h # scale is just area + r = w / float(h) + return np.array([x, y, s, r]).reshape((4, 1)) + + +def convert_x_to_bbox(x, score=None): + """ + Takes a bounding box in the centre form [x,y,s,r] and returns it in the form + [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right + """ + w = np.sqrt(x[2] * x[3]) + h = x[2] / w + if (score == None): + return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4)) + else: + return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5)) + + +class KalmanBoxTracker(object): + """ + This class represents the internel state of individual tracked objects observed as bbox. + """ + count = 0 + + def __init__(self, bbox): + """ + Initialises a tracker using initial bounding box. + """ + # define constant velocity model + self.kf = KalmanFilter(dim_x=7, dim_z=4) + self.kf.F = np.array( + [[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0], + [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]]) + self.kf.H = np.array( + [[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]]) + + self.kf.R[2:, 2:] *= 10. + self.kf.P[4:, 4:] *= 1000. # give high uncertainty to the unobservable initial velocities + self.kf.P *= 10. + self.kf.Q[-1, -1] *= 0.01 + self.kf.Q[4:, 4:] *= 0.01 + + self.kf.x[:4] = convert_bbox_to_z(bbox) + self.time_since_update = 0 + self.id = KalmanBoxTracker.count + KalmanBoxTracker.count += 1 + self.history = [] + self.hits = 0 + self.hit_streak = 0 + self.age = 0 + + def update(self, bbox): + """ + Updates the state vector with observed bbox. + """ + self.time_since_update = 0 + self.history = [] + self.hits += 1 + self.hit_streak += 1 + self.kf.update(convert_bbox_to_z(bbox)) + + def predict(self): + """ + Advances the state vector and returns the predicted bounding box estimate. + """ + if ((self.kf.x[6] + self.kf.x[2]) <= 0): + self.kf.x[6] *= 0.0 + self.kf.predict() + self.age += 1 + if (self.time_since_update > 0): + self.hit_streak = 0 + self.time_since_update += 1 + self.history.append(convert_x_to_bbox(self.kf.x)) + return self.history[-1] + + def get_state(self): + """ + Returns the current bounding box estimate. + """ + return convert_x_to_bbox(self.kf.x) + + +def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3): + """ + Assigns detections to tracked object (both represented as bounding boxes) + + Returns 3 lists of matches, unmatched_detections and unmatched_trackers + """ + if (len(trackers) == 0): + return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int) + iou_matrix = np.zeros((len(detections), len(trackers)), dtype=np.float32) + + for d, det in enumerate(detections): + for t, trk in enumerate(trackers): + iou_matrix[d, t] = iou(det, trk) + matched_indices = linear_sum_assignment(-iou_matrix) + matched_indices = np.asarray(matched_indices) + matched_indices = matched_indices.transpose() + + unmatched_detections = [] + for d, det in enumerate(detections): + if (d not in matched_indices[:, 0]): + unmatched_detections.append(d) + unmatched_trackers = [] + for t, trk in enumerate(trackers): + if (t not in matched_indices[:, 1]): + unmatched_trackers.append(t) + + # filter out matched with low IOU + matches = [] + for m in matched_indices: + if (iou_matrix[m[0], m[1]] < iou_threshold): + unmatched_detections.append(m[0]) + unmatched_trackers.append(m[1]) + else: + matches.append(m.reshape(1, 2)) + if (len(matches) == 0): + matches = np.empty((0, 2), dtype=int) + else: + matches = np.concatenate(matches, axis=0) + + return matches, np.array(unmatched_detections), np.array(unmatched_trackers) + + +class Sort(object): + def __init__(self, max_age=1, min_hits=3): + """ + Sets key parameters for SORT + """ + self.max_age = max_age + self.min_hits = min_hits + self.trackers = [] + self.frame_count = 0 + + def update(self, dets): + """ + Params: + dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...] + Requires: this method must be called once for each frame even with empty detections. + Returns the a similar array, where the last column is the object ID. + + NOTE: The number of objects returned may differ from the number of detections provided. + """ + self.frame_count += 1 + # get predicted locations from existing trackers. + trks = np.zeros((len(self.trackers), 5)) + to_del = [] + ret = [] + for t, trk in enumerate(trks): + pos = self.trackers[t].predict()[0] + trk[:] = [pos[0], pos[1], pos[2], pos[3], 0] + if np.any(np.isnan(pos)): + to_del.append(t) + trks = np.ma.compress_rows(np.ma.masked_invalid(trks)) + for t in reversed(to_del): + self.trackers.pop(t) + matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks) + + # update matched trackers with assigned detections + for t, trk in enumerate(self.trackers): + if t not in unmatched_trks: + d = matched[np.where(matched[:, 1] == t)[0], 0] # d: [n] + trk.update(dets[d, :][0]) + + # create and initialise new trackers for unmatched detections + for i in unmatched_dets: + trk = KalmanBoxTracker(dets[i, :]) + self.trackers.append(trk) + i = len(self.trackers) + for trk in reversed(self.trackers): + d = trk.get_state()[0] + if ((trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits)): + ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive + i -= 1 + # remove dead tracklet + if (trk.time_since_update > self.max_age): + self.trackers.pop(i) + if (len(ret) > 0): + return np.concatenate(ret) + return np.empty((0, 5)) + + +def yolo_det(img, detector, threshold, iou): + img_size_h = img.shape[0] + img_size_w = img.shape[1] + + detector.compute(img, threshold, iou) + box_count = detector.get_object_count() + boxes = [] + scores = [] + if box_count < 1: + return np.array([boxes]), np.array([scores]) + else: + for i in range(box_count): + detect_object = detector.get_object(i) + x1 = detect_object.x * img_size_w + y1 = detect_object.y * img_size_h + x2 = detect_object.w * img_size_w + x1 + y2 = detect_object.h * img_size_h + y1 + boxes.append([x1, y1, x2, y2]) + scores.append([detect_object.prob]) + + return np.array(boxes), np.array(scores) + + +def gen_video_kpts(frame, human_model, pose_model, det_dim=416, num_peroson=1): + # Loading detector and pose model, initialize sort for track + people_sort = Sort(min_hits=0) + + kpts_result = [] + scores_result = [] + bboxs, scores = yolo_det(frame, human_model, 0.8, 0.8) + + # Using Sort to track people + people_track = people_sort.update(bboxs) + + # Track the first two people in the video and remove the ID + if people_track.shape[0] == 1: + people_track_ = people_track[-1, :-1].reshape(1, 4) + elif people_track.shape[0] >= 2: + people_track_ = people_track[-num_peroson:, :-1].reshape(num_peroson, 4) + people_track_ = people_track_[::-1] + else: + return None, None + + track_bboxs = [] + for bbox in people_track_: + bbox = [round(i, 2) for i in list(bbox)] + track_bboxs.append(bbox) + # bbox is coordinate location + inputs, origin_img, center, scale = PreProcess(frame, track_bboxs, num_peroson) + + inputs = inputs[:, [2, 1, 0]] + output = pose_model.run(inputs.astype('float32'))[0] + + # compute coordinate + preds, maxvals = get_final_preds(output, np.asarray(center), np.asarray(scale)) + + kpts = np.zeros((num_peroson, 17, 2), dtype=np.float32) + scores = np.zeros((num_peroson, 17), dtype=np.float32) + for i, kpt in enumerate(preds): + kpts[i] = kpt + + for i, score in enumerate(maxvals): + scores[i] = score.squeeze() + + kpts_result.append(kpts) + scores_result.append(scores) + + keypoints = np.array(kpts_result) + scores = np.array(scores_result) + + keypoints = keypoints.transpose(1, 0, 2, 3) # (T, M, N, 2) --> (M, T, N, 2) + scores = scores.transpose(1, 0, 2) # (T, M, N) --> (M, T, N) + + return keypoints, scores diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/experiments/w48_384x288_adam_lr1e-3.yaml b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/experiments/w48_384x288_adam_lr1e-3.yaml deleted file mode 100644 index 2844ff613..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/experiments/w48_384x288_adam_lr1e-3.yaml +++ /dev/null @@ -1,127 +0,0 @@ -AUTO_RESUME: true -CUDNN: - BENCHMARK: true - DETERMINISTIC: false - ENABLED: true -DATA_DIR: '' -GPUS: (0,1,2,3) -OUTPUT_DIR: 'output' -LOG_DIR: 'log' -WORKERS: 24 -PRINT_FREQ: 100 - -DATASET: - COLOR_RGB: true - DATASET: 'coco' - DATA_FORMAT: jpg - FLIP: true - NUM_JOINTS_HALF_BODY: 8 - PROB_HALF_BODY: 0.3 - ROOT: 'data/coco/' - ROT_FACTOR: 45 - SCALE_FACTOR: 0.35 - TEST_SET: 'val2017' - TRAIN_SET: 'train2017' -MODEL: - INIT_WEIGHTS: true - NAME: pose_hrnet - NUM_JOINTS: 17 - PRETRAINED: 'models/pytorch/imagenet/hrnet_w48-8ef0771d.pth' - TARGET_TYPE: gaussian - IMAGE_SIZE: - - 288 - - 384 - HEATMAP_SIZE: - - 72 - - 96 - SIGMA: 3 - EXTRA: - PRETRAINED_LAYERS: - - 'conv1' - - 'bn1' - - 'conv2' - - 'bn2' - - 'layer1' - - 'transition1' - - 'stage2' - - 'transition2' - - 'stage3' - - 'transition3' - - 'stage4' - FINAL_CONV_KERNEL: 1 - STAGE2: - NUM_MODULES: 1 - NUM_BRANCHES: 2 - BLOCK: BASIC - NUM_BLOCKS: - - 4 - - 4 - NUM_CHANNELS: - - 48 - - 96 - FUSE_METHOD: SUM - STAGE3: - NUM_MODULES: 4 - NUM_BRANCHES: 3 - BLOCK: BASIC - NUM_BLOCKS: - - 4 - - 4 - - 4 - NUM_CHANNELS: - - 48 - - 96 - - 192 - FUSE_METHOD: SUM - STAGE4: - NUM_MODULES: 3 - NUM_BRANCHES: 4 - BLOCK: BASIC - NUM_BLOCKS: - - 4 - - 4 - - 4 - - 4 - NUM_CHANNELS: - - 48 - - 96 - - 192 - - 384 - FUSE_METHOD: SUM -LOSS: - USE_TARGET_WEIGHT: true -TRAIN: - BATCH_SIZE_PER_GPU: 24 - SHUFFLE: true - BEGIN_EPOCH: 0 - END_EPOCH: 210 - OPTIMIZER: adam - LR: 0.001 - LR_FACTOR: 0.1 - LR_STEP: - - 170 - - 200 - WD: 0.0001 - GAMMA1: 0.99 - GAMMA2: 0.0 - MOMENTUM: 0.9 - NESTEROV: false -TEST: - BATCH_SIZE_PER_GPU: 24 - COCO_BBOX_FILE: 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json' - BBOX_THRE: 1.0 - IMAGE_THRE: 0.0 - IN_VIS_THRE: 0.2 - MODEL_FILE: '' - NMS_THRE: 1.0 - OKS_THRE: 0.9 - USE_GT_BBOX: true - FLIP_TEST: true - POST_PROCESS: true - SHIFT_HEATMAP: true -DEBUG: - DEBUG: true - SAVE_BATCH_IMAGES_GT: true - SAVE_BATCH_IMAGES_PRED: true - SAVE_HEATMAPS_GT: true - SAVE_HEATMAPS_PRED: true diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/gen_kpts.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/gen_kpts.py deleted file mode 100644 index 1646f930f..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/gen_kpts.py +++ /dev/null @@ -1,95 +0,0 @@ -import argparse - -from lib.hrnet.lib.utils.utilitys import PreProcess -from lib.hrnet.lib.config import cfg, update_config -from lib.hrnet.lib.utils.transforms import * -from lib.hrnet.lib.utils.inference import get_final_preds - -# Loading human detector model -from lib.yolo.human_detector import yolo_human_det as yolo_det -from lib.sort import Sort - -cfg_dir = 'lib/hrnet/experiments/' -model_dir = 'lib/checkpoint/' - - -def parse_args(): - parser = argparse.ArgumentParser(description='Train keypoints network') - # general - parser.add_argument('--cfg', type=str, default=cfg_dir + 'w48_384x288_adam_lr1e-3.yaml', - help='experiment configure file name') - parser.add_argument('opts', nargs=argparse.REMAINDER, default=None, - help="Modify config options using the command-line") - parser.add_argument('--modelDir', type=str, default=model_dir + 'pose_hrnet_w48_384x288.pth', - help='The model directory') - parser.add_argument('--det-dim', type=int, default=416, - help='The input dimension of the detected image') - parser.add_argument('--thred-score', type=float, default=0.30, - help='The threshold of object Confidence') - parser.add_argument('-a', '--animation', action='store_true', - help='output animation') - parser.add_argument('-np', '--num-person', type=int, default=1, - help='The maximum number of estimated poses') - parser.add_argument("-v", "--video", type=str, default='camera', - help="input video file name") - parser.add_argument('--gpu', type=str, default='0', help='input video') - args = parser.parse_args() - - return args - - -def gen_video_kpts(frame, human_model, pose_model, det_dim=416, num_peroson=1): - # Updating configuration - args = parse_args() - update_config(cfg, args) - - # Loading detector and pose model, initialize sort for track - people_sort = Sort(min_hits=0) - - kpts_result = [] - scores_result = [] - bboxs, scores = yolo_det(frame, human_model, reso=det_dim) - - # Using Sort to track people - people_track = people_sort.update(bboxs) - - # Track the first two people in the video and remove the ID - if people_track.shape[0] == 1: - people_track_ = people_track[-1, :-1].reshape(1, 4) - elif people_track.shape[0] >= 2: - people_track_ = people_track[-num_peroson:, :-1].reshape(num_peroson, 4) - people_track_ = people_track_[::-1] - else: - return None, None - - track_bboxs = [] - for bbox in people_track_: - bbox = [round(i, 2) for i in list(bbox)] - track_bboxs.append(bbox) - # bbox is coordinate location - inputs, origin_img, center, scale = PreProcess(frame, track_bboxs, cfg, num_peroson) - - inputs = inputs[:, [2, 1, 0]] - output = pose_model.run(inputs.astype('float32'))[0] - - # compute coordinate - preds, maxvals = get_final_preds(cfg, output, np.asarray(center), np.asarray(scale)) - - kpts = np.zeros((num_peroson, 17, 2), dtype=np.float32) - scores = np.zeros((num_peroson, 17), dtype=np.float32) - for i, kpt in enumerate(preds): - kpts[i] = kpt - - for i, score in enumerate(maxvals): - scores[i] = score.squeeze() - - kpts_result.append(kpts) - scores_result.append(scores) - - keypoints = np.array(kpts_result) - scores = np.array(scores_result) - - keypoints = keypoints.transpose(1, 0, 2, 3) # (T, M, N, 2) --> (M, T, N, 2) - scores = scores.transpose(1, 0, 2) # (T, M, N) --> (M, T, N) - - return keypoints, scores diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/__init__.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/__init__.py deleted file mode 100644 index 15a824755..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# ------------------------------------------------------------------------------ -# Copyright (c) Microsoft -# Licensed under the MIT License. -# Written by Bin Xiao (Bin.Xiao@microsoft.com) -# ------------------------------------------------------------------------------ - -from .default import _C as cfg -from .default import update_config diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/default.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/default.py deleted file mode 100644 index 050bf4e4e..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/config/default.py +++ /dev/null @@ -1,141 +0,0 @@ - -# ------------------------------------------------------------------------------ -# Copyright (c) Microsoft -# Licensed under the MIT License. -# Written by Bin Xiao (Bin.Xiao@microsoft.com) -# ------------------------------------------------------------------------------ - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os - -from yacs.config import CfgNode as CN - - -_C = CN() - -_C.OUTPUT_DIR = '' -_C.LOG_DIR = '' -_C.DATA_DIR = '' -_C.GPUS = (0,) -_C.WORKERS = 4 -_C.PRINT_FREQ = 20 -_C.AUTO_RESUME = False -_C.PIN_MEMORY = True -_C.RANK = 0 - -# Cudnn related params -_C.CUDNN = CN() -_C.CUDNN.BENCHMARK = True -_C.CUDNN.DETERMINISTIC = False -_C.CUDNN.ENABLED = True - -# common params for NETWORK -_C.MODEL = CN() -_C.MODEL.NAME = 'pose_hrnet' -_C.MODEL.INIT_WEIGHTS = True -_C.MODEL.PRETRAINED = '' -_C.MODEL.NUM_JOINTS = 17 -_C.MODEL.TAG_PER_JOINT = True -_C.MODEL.TARGET_TYPE = 'gaussian' -_C.MODEL.IMAGE_SIZE = [256, 256] # width * height, ex: 192 * 256 -_C.MODEL.HEATMAP_SIZE = [64, 64] # width * height, ex: 24 * 32 -_C.MODEL.SIGMA = 2 -_C.MODEL.EXTRA = CN(new_allowed=True) - -_C.LOSS = CN() -_C.LOSS.USE_OHKM = False -_C.LOSS.TOPK = 8 -_C.LOSS.USE_TARGET_WEIGHT = True -_C.LOSS.USE_DIFFERENT_JOINTS_WEIGHT = False - -# DATASET related params -_C.DATASET = CN() -_C.DATASET.ROOT = '' -_C.DATASET.DATASET = 'mpii' -_C.DATASET.TRAIN_SET = 'train' -_C.DATASET.TEST_SET = 'valid' -_C.DATASET.DATA_FORMAT = 'jpg' -_C.DATASET.HYBRID_JOINTS_TYPE = '' -_C.DATASET.SELECT_DATA = False - -# training data augmentation -_C.DATASET.FLIP = True -_C.DATASET.SCALE_FACTOR = 0.25 -_C.DATASET.ROT_FACTOR = 30 -_C.DATASET.PROB_HALF_BODY = 0.0 -_C.DATASET.NUM_JOINTS_HALF_BODY = 8 -_C.DATASET.COLOR_RGB = False - -# train -_C.TRAIN = CN() - -_C.TRAIN.LR_FACTOR = 0.1 -_C.TRAIN.LR_STEP = [90, 110] -_C.TRAIN.LR = 0.001 - -_C.TRAIN.OPTIMIZER = 'adam' -_C.TRAIN.MOMENTUM = 0.9 -_C.TRAIN.WD = 0.0001 -_C.TRAIN.NESTEROV = False -_C.TRAIN.GAMMA1 = 0.99 -_C.TRAIN.GAMMA2 = 0.0 - -_C.TRAIN.BEGIN_EPOCH = 0 -_C.TRAIN.END_EPOCH = 140 - -_C.TRAIN.RESUME = False -_C.TRAIN.CHECKPOINT = '' - -_C.TRAIN.BATCH_SIZE_PER_GPU = 32 -_C.TRAIN.SHUFFLE = True - -# testing -_C.TEST = CN() - -# size of images for each device -_C.TEST.BATCH_SIZE_PER_GPU = 32 -# Test Model Epoch -_C.TEST.FLIP_TEST = False -_C.TEST.POST_PROCESS = False -_C.TEST.SHIFT_HEATMAP = False - -_C.TEST.USE_GT_BBOX = False - -# nms -_C.TEST.IMAGE_THRE = 0.1 -_C.TEST.NMS_THRE = 0.6 -_C.TEST.SOFT_NMS = False -_C.TEST.OKS_THRE = 0.5 -_C.TEST.IN_VIS_THRE = 0.0 -_C.TEST.COCO_BBOX_FILE = '' -_C.TEST.BBOX_THRE = 1.0 -_C.TEST.MODEL_FILE = '' - -# debug -_C.DEBUG = CN() -_C.DEBUG.DEBUG = False -_C.DEBUG.SAVE_BATCH_IMAGES_GT = False -_C.DEBUG.SAVE_BATCH_IMAGES_PRED = False -_C.DEBUG.SAVE_HEATMAPS_GT = False -_C.DEBUG.SAVE_HEATMAPS_PRED = False - - -def update_config(cfg, args): - cfg.defrost() - cfg.merge_from_file(args.cfg) - cfg.merge_from_list(args.opts) - - if args.modelDir: - cfg.OUTPUT_DIR = args.modelDir - - cfg.freeze() - - -if __name__ == '__main__': - import sys - with open(sys.argv[1], 'w') as f: - print(_C, file=f) - diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/inference.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/inference.py deleted file mode 100644 index bd44c0abe..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/inference.py +++ /dev/null @@ -1,67 +0,0 @@ -import math -import numpy as np - -from lib.hrnet.lib.utils.transforms import transform_preds - -def get_max_preds(batch_heatmaps): - ''' - get predictions from score maps - heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) - ''' - assert isinstance(batch_heatmaps, np.ndarray), \ - 'batch_heatmaps should be numpy.ndarray' - assert batch_heatmaps.ndim == 4, 'batch_images should be 4-ndim' - - batch_size = batch_heatmaps.shape[0] - num_joints = batch_heatmaps.shape[1] - width = batch_heatmaps.shape[3] - heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) - idx = np.argmax(heatmaps_reshaped, 2) - maxvals = np.amax(heatmaps_reshaped, 2) - - maxvals = maxvals.reshape((batch_size, num_joints, 1)) - idx = idx.reshape((batch_size, num_joints, 1)) - - preds = np.tile(idx, (1, 1, 2)).astype(np.float32) - - preds[:, :, 0] = (preds[:, :, 0]) % width - preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) - - pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) - pred_mask = pred_mask.astype(np.float32) - - preds *= pred_mask - return preds, maxvals - - -def get_final_preds(config, batch_heatmaps, center, scale): - coords, maxvals = get_max_preds(batch_heatmaps) - - heatmap_height = batch_heatmaps.shape[2] - heatmap_width = batch_heatmaps.shape[3] - - # post-processing - if config.TEST.POST_PROCESS: - for n in range(coords.shape[0]): - for p in range(coords.shape[1]): - hm = batch_heatmaps[n][p] - px = int(math.floor(coords[n][p][0] + 0.5)) - py = int(math.floor(coords[n][p][1] + 0.5)) - if 1 < px < heatmap_width-1 and 1 < py < heatmap_height-1: - diff = np.array( - [ - hm[py][px+1] - hm[py][px-1], - hm[py+1][px]-hm[py-1][px] - ] - ) - coords[n][p] += np.sign(diff) * .25 - - preds = coords.copy() - - # Transform back - for i in range(coords.shape[0]): - preds[i] = transform_preds( - coords[i], center[i], scale[i], [heatmap_width, heatmap_height] - ) - - return preds, maxvals diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/transforms.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/transforms.py deleted file mode 100644 index a67d8e580..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/transforms.py +++ /dev/null @@ -1,112 +0,0 @@ -import numpy as np -import cv2 - - -def flip_back(output_flipped, matched_parts): - ''' - ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width) - ''' - assert output_flipped.ndim == 4,\ - 'output_flipped should be [batch_size, num_joints, height, width]' - - output_flipped = output_flipped[:, :, :, ::-1] - - # 因为你输入的是翻转后的图像,所以输出的热图他们对应的左右关节也是相反的(训练的时候,输入的是翻转后的图像,target对应的左右关节也是对调过来的)。 - for pair in matched_parts: - tmp = output_flipped[:, pair[0], :, :].copy() - output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] - output_flipped[:, pair[1], :, :] = tmp - - return output_flipped - - -def fliplr_joints(joints, joints_vis, width, matched_parts): - """ - flip coords - """ - # Flip horizontal - joints[:, 0] = width - joints[:, 0] - 1 - - # Change left-right parts - for pair in matched_parts: - joints[pair[0], :], joints[pair[1], :] = \ - joints[pair[1], :], joints[pair[0], :].copy() - joints_vis[pair[0], :], joints_vis[pair[1], :] = \ - joints_vis[pair[1], :], joints_vis[pair[0], :].copy() - - return joints*joints_vis, joints_vis - - -def transform_preds(coords, center, scale, output_size): - target_coords = np.zeros(coords.shape) - trans = get_affine_transform(center, scale, 0, output_size, inv=1) - for p in range(coords.shape[0]): - target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) - return target_coords - - -def get_affine_transform( - center, scale, rot, output_size, - shift=np.array([0, 0], dtype=np.float32), inv=0 -): - if not isinstance(scale, np.ndarray) and not isinstance(scale, list): - print(scale) - scale = np.array([scale, scale]) - - scale_tmp = scale * 200.0 - src_w = scale_tmp[0] - dst_w = output_size[0] - dst_h = output_size[1] - - rot_rad = np.pi * rot / 180 - src_dir = get_dir([0, src_w * -0.5], rot_rad) - dst_dir = np.array([0, dst_w * -0.5], np.float32) - - src = np.zeros((3, 2), dtype=np.float32) - dst = np.zeros((3, 2), dtype=np.float32) - src[0, :] = center + scale_tmp * shift - src[1, :] = center + src_dir + scale_tmp * shift - dst[0, :] = [dst_w * 0.5, dst_h * 0.5] - dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir - - src[2:, :] = get_3rd_point(src[0, :], src[1, :]) - dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) - - if inv: - trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) - else: - trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) - - return trans - - -def affine_transform(pt, t): - new_pt = np.array([pt[0], pt[1], 1.]).T - new_pt = np.dot(t, new_pt) - return new_pt[:2] - - -def get_3rd_point(a, b): - direct = a - b - return b + np.array([-direct[1], direct[0]], dtype=np.float32) - - -def get_dir(src_point, rot_rad): - sn, cs = np.sin(rot_rad), np.cos(rot_rad) - - src_result = [0, 0] - src_result[0] = src_point[0] * cs - src_point[1] * sn - src_result[1] = src_point[0] * sn + src_point[1] * cs - - return src_result - - -def crop(img, center, scale, output_size, rot=0): - trans = get_affine_transform(center, scale, rot, output_size) - - dst_img = cv2.warpAffine( - img, trans, (int(output_size[0]), int(output_size[1])), - flags=cv2.INTER_LINEAR - ) - - return dst_img diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/utilitys.py b/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/utilitys.py deleted file mode 100644 index d32e2ff16..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/hrnet/lib/utils/utilitys.py +++ /dev/null @@ -1,79 +0,0 @@ -from lib.hrnet.lib.utils.transforms import * - - -def box_to_center_scale(box, model_image_width, model_image_height): - """convert a box to center,scale information required for pose transformation - Parameters - ---------- - box : (x1, y1, x2, y2) - model_image_width : int - model_image_height : int - - Returns - ------- - (numpy array, numpy array) - Two numpy arrays, coordinates for the center of the box and the scale of the box - """ - center = np.zeros((2), dtype=np.float32) - x1, y1, x2, y2 = box[:4] - box_width, box_height = x2 - x1, y2 - y1 - - center[0] = x1 + box_width * 0.5 - center[1] = y1 + box_height * 0.5 - - aspect_ratio = model_image_width * 1.0 / model_image_height - pixel_std = 200 - - if box_width > aspect_ratio * box_height: - box_height = box_width * 1.0 / aspect_ratio - elif box_width < aspect_ratio * box_height: - box_width = box_height * aspect_ratio - scale = np.array( - [box_width * 1.0 / pixel_std, box_height * 1.0 / pixel_std], - dtype=np.float32) - if center[0] != -1: - scale = scale * 1.25 - - return center, scale - - -# Pre-process -def PreProcess(image, bboxs, cfg, num_pos=2): - def _normalize(x, mean, std): - for i in range(x.shape[0]): - x[i, :, :] = (x[i, :, :] - mean[i]) / std[i] - return x - - if type(image) == str: - data_numpy = cv2.imread(image, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION) - # data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB) - else: - data_numpy = image - - inputs = [] - centers = [] - scales = [] - - for bbox in bboxs[:num_pos]: - c, s = box_to_center_scale(bbox, data_numpy.shape[0], data_numpy.shape[1]) - centers.append(c) - scales.append(s) - r = 0 - - trans = get_affine_transform(c, s, r, cfg.MODEL.IMAGE_SIZE) - input = cv2.warpAffine( - data_numpy, - trans, - (int(cfg.MODEL.IMAGE_SIZE[0]), int(cfg.MODEL.IMAGE_SIZE[1])), - flags=cv2.INTER_LINEAR) - - mean=[0.485, 0.456, 0.406] - std=[0.229, 0.224, 0.225] - input = input / 255.0 - input = input.transpose(2, 0, 1) - input = _normalize(input, mean, std) - input = np.expand_dims(input, 0) - inputs.append(input) - - inputs = np.concatenate(inputs) - return inputs, data_numpy, centers, scales diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/preprocess.py b/pose_estimation_3d/strided_transformer_pose3d/lib/preprocess.py deleted file mode 100644 index 289e5d3de..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/preprocess.py +++ /dev/null @@ -1,66 +0,0 @@ -import json -import numpy as np -import os - -h36m_coco_order = [9, 11, 14, 12, 15, 13, 16, 4, 1, 5, 2, 6, 3] -coco_order = [0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] -spple_keypoints = [10, 8, 0, 7] - - -def coco_h36m(keypoints): - temporal = keypoints.shape[0] - keypoints_h36m = np.zeros_like(keypoints, dtype=np.float32) - htps_keypoints = np.zeros((temporal, 4, 2), dtype=np.float32) - - # htps_keypoints: head, thorax, pelvis, spine - htps_keypoints[:, 0, 0] = np.mean(keypoints[:, 1:5, 0], axis=1, dtype=np.float32) - htps_keypoints[:, 0, 1] = np.sum(keypoints[:, 1:3, 1], axis=1, dtype=np.float32) - keypoints[:, 0, 1] - htps_keypoints[:, 1, :] = np.mean(keypoints[:, 5:7, :], axis=1, dtype=np.float32) - htps_keypoints[:, 1, :] += (keypoints[:, 0, :] - htps_keypoints[:, 1, :]) / 3 - - htps_keypoints[:, 2, :] = np.mean(keypoints[:, 11:13, :], axis=1, dtype=np.float32) - htps_keypoints[:, 3, :] = np.mean(keypoints[:, [5, 6, 11, 12], :], axis=1, dtype=np.float32) - - keypoints_h36m[:, spple_keypoints, :] = htps_keypoints - keypoints_h36m[:, h36m_coco_order, :] = keypoints[:, coco_order, :] - - keypoints_h36m[:, 9, :] -= (keypoints_h36m[:, 9, :] - np.mean(keypoints[:, 5:7, :], axis=1, dtype=np.float32)) / 4 - keypoints_h36m[:, 7, 0] += 2*(keypoints_h36m[:, 7, 0] - np.mean(keypoints_h36m[:, [0, 8], 0], axis=1, dtype=np.float32)) - keypoints_h36m[:, 8, 1] -= (np.mean(keypoints[:, 1:3, 1], axis=1, dtype=np.float32) - keypoints[:, 0, 1])*2/3 - - valid_frames = np.where(np.sum(keypoints_h36m.reshape(-1, 34), axis=1) != 0)[0] - - return keypoints_h36m, valid_frames - - -def h36m_coco_format(keypoints, scores): - assert len(keypoints.shape) == 4 and len(scores.shape) == 3 - - h36m_kpts = [] - h36m_scores = [] - valid_frames = [] - - for i in range(keypoints.shape[0]): - kpts = keypoints[i] - score = scores[i] - - new_score = np.zeros_like(score, dtype=np.float32) - - if np.sum(kpts) != 0.: - kpts, valid_frame = coco_h36m(kpts) - h36m_kpts.append(kpts) - valid_frames.append(valid_frame) - - new_score[:, h36m_coco_order] = score[:, coco_order] - new_score[:, 0] = np.mean(score[:, [11, 12]], axis=1, dtype=np.float32) - new_score[:, 8] = np.mean(score[:, [5, 6]], axis=1, dtype=np.float32) - new_score[:, 7] = np.mean(new_score[:, [0, 8]], axis=1, dtype=np.float32) - new_score[:, 10] = np.mean(score[:, [1, 2, 3, 4]], axis=1, dtype=np.float32) - - h36m_scores.append(new_score) - - h36m_kpts = np.asarray(h36m_kpts, dtype=np.float32) - h36m_scores = np.asarray(h36m_scores, dtype=np.float32) - - return h36m_kpts - diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/run.py b/pose_estimation_3d/strided_transformer_pose3d/lib/run.py index ec8a8cde6..784e84c06 100644 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/run.py +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/run.py @@ -1,11 +1,75 @@ import cv2 import copy +import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec -from lib.preprocess import h36m_coco_format -from lib.hrnet.gen_kpts import gen_video_kpts as hrnet_pose -from lib.camera import * +from lib.gen_kpts import gen_video_kpts as hrnet_pose + + +h36m_coco_order = [9, 11, 14, 12, 15, 13, 16, 4, 1, 5, 2, 6, 3] +coco_order = [0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] +spple_keypoints = [10, 8, 0, 7] + + +def coco_h36m(keypoints): + temporal = keypoints.shape[0] + keypoints_h36m = np.zeros_like(keypoints, dtype=np.float32) + htps_keypoints = np.zeros((temporal, 4, 2), dtype=np.float32) + + # htps_keypoints: head, thorax, pelvis, spine + htps_keypoints[:, 0, 0] = np.mean(keypoints[:, 1:5, 0], axis=1, dtype=np.float32) + htps_keypoints[:, 0, 1] = np.sum(keypoints[:, 1:3, 1], axis=1, dtype=np.float32) - keypoints[:, 0, 1] + htps_keypoints[:, 1, :] = np.mean(keypoints[:, 5:7, :], axis=1, dtype=np.float32) + htps_keypoints[:, 1, :] += (keypoints[:, 0, :] - htps_keypoints[:, 1, :]) / 3 + + htps_keypoints[:, 2, :] = np.mean(keypoints[:, 11:13, :], axis=1, dtype=np.float32) + htps_keypoints[:, 3, :] = np.mean(keypoints[:, [5, 6, 11, 12], :], axis=1, dtype=np.float32) + + keypoints_h36m[:, spple_keypoints, :] = htps_keypoints + keypoints_h36m[:, h36m_coco_order, :] = keypoints[:, coco_order, :] + + keypoints_h36m[:, 9, :] -= (keypoints_h36m[:, 9, :] - np.mean(keypoints[:, 5:7, :], axis=1, dtype=np.float32)) / 4 + keypoints_h36m[:, 7, 0] += 2 * ( + keypoints_h36m[:, 7, 0] - np.mean(keypoints_h36m[:, [0, 8], 0], axis=1, dtype=np.float32)) + keypoints_h36m[:, 8, 1] -= (np.mean(keypoints[:, 1:3, 1], axis=1, dtype=np.float32) - keypoints[:, 0, 1]) * 2 / 3 + + valid_frames = np.where(np.sum(keypoints_h36m.reshape(-1, 34), axis=1) != 0)[0] + + return keypoints_h36m, valid_frames + + +def h36m_coco_format(keypoints, scores): + assert len(keypoints.shape) == 4 and len(scores.shape) == 3 + + h36m_kpts = [] + h36m_scores = [] + valid_frames = [] + + for i in range(keypoints.shape[0]): + kpts = keypoints[i] + score = scores[i] + + new_score = np.zeros_like(score, dtype=np.float32) + + if np.sum(kpts) != 0.: + kpts, valid_frame = coco_h36m(kpts) + h36m_kpts.append(kpts) + valid_frames.append(valid_frame) + + new_score[:, h36m_coco_order] = score[:, coco_order] + new_score[:, 0] = np.mean(score[:, [11, 12]], axis=1, dtype=np.float32) + new_score[:, 8] = np.mean(score[:, [5, 6]], axis=1, dtype=np.float32) + new_score[:, 7] = np.mean(new_score[:, [0, 8]], axis=1, dtype=np.float32) + new_score[:, 10] = np.mean(score[:, [1, 2, 3, 4]], axis=1, dtype=np.float32) + + h36m_scores.append(new_score) + + h36m_kpts = np.asarray(h36m_kpts, dtype=np.float32) + h36m_scores = np.asarray(h36m_scores, dtype=np.float32) + + return h36m_kpts + def show2Dpose(kps, img): connections = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], @@ -67,13 +131,6 @@ def get_pose2D(human_model, pose_model, img): return None -def showimage(ax, img): - ax.set_xticks([]) - ax.set_yticks([]) - plt.axis('off') - ax.imshow(img) - - def get_pose3D(model, img, keypoints, fig=None): i = 0 frames = 351 @@ -152,3 +209,29 @@ def detect(img, human_model, pose_model2d, pose_model3d, fig=None): return image_pose2d, fig_pose3d, True +def normalize_screen_coordinates(X, w, h): + assert X.shape[-1] == 2 + return X / w * 2 - [1, h / w] + + +def camera_to_world(X, R, t): + return wrap(qrot, np.tile(R, (*X.shape[:-1], 1)), X) + t + + +def wrap(func, *args, unsqueeze=False): + args = list(args) + result = func(*args) + return result + + +def qrot(q, v): + assert q.shape[-1] == 4 + assert v.shape[-1] == 3 + assert q.shape[:-1] == v.shape[:-1] + + qvec = q[..., 1:] + uv = np.cross(qvec, v, axis=len(q.shape) - 1) + uuv = np.cross(qvec, uv, axis=len(q.shape) - 1) + return (v + 2 * (q[..., :1] * uv + uuv)) + + diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py b/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py deleted file mode 100644 index 1f12f8fd5..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/sort.py +++ /dev/null @@ -1,231 +0,0 @@ -""" - https://arxiv.org/abs/1602.00763 -""" -from __future__ import print_function - -from numba import jit -import os.path -import numpy as np -from skimage import io -from scipy.optimize import linear_sum_assignment -import argparse -from filterpy.kalman import KalmanFilter - - -@jit -def iou(bb_test, bb_gt): - """ - Computes IUO between two bboxes in the form [x1,y1,x2,y2] - """ - xx1 = np.maximum(bb_test[0], bb_gt[0]) - yy1 = np.maximum(bb_test[1], bb_gt[1]) - xx2 = np.minimum(bb_test[2], bb_gt[2]) - yy2 = np.minimum(bb_test[3], bb_gt[3]) - w = np.maximum(0., xx2 - xx1) - h = np.maximum(0., yy2 - yy1) - wh = w * h - o = wh / ((bb_test[2] - bb_test[0]) * (bb_test[3] - bb_test[1]) - + (bb_gt[2] - bb_gt[0]) * (bb_gt[3] - bb_gt[1]) - wh) - - return o - - -def convert_bbox_to_z(bbox): - """ - Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form - [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is - the aspect ratio - """ - w = bbox[2] - bbox[0] - h = bbox[3] - bbox[1] - x = bbox[0] + w / 2. - y = bbox[1] + h / 2. - s = w * h # scale is just area - r = w / float(h) - return np.array([x, y, s, r]).reshape((4, 1)) - - -def convert_x_to_bbox(x, score=None): - """ - Takes a bounding box in the centre form [x,y,s,r] and returns it in the form - [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right - """ - w = np.sqrt(x[2] * x[3]) - h = x[2] / w - if (score == None): - return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4)) - else: - return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5)) - - -class KalmanBoxTracker(object): - """ - This class represents the internel state of individual tracked objects observed as bbox. - """ - count = 0 - - def __init__(self, bbox): - """ - Initialises a tracker using initial bounding box. - """ - # define constant velocity model - self.kf = KalmanFilter(dim_x=7, dim_z=4) - self.kf.F = np.array( - [[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0], - [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]]) - self.kf.H = np.array( - [[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]]) - - self.kf.R[2:, 2:] *= 10. - self.kf.P[4:, 4:] *= 1000. # give high uncertainty to the unobservable initial velocities - self.kf.P *= 10. - self.kf.Q[-1, -1] *= 0.01 - self.kf.Q[4:, 4:] *= 0.01 - - self.kf.x[:4] = convert_bbox_to_z(bbox) - self.time_since_update = 0 - self.id = KalmanBoxTracker.count - KalmanBoxTracker.count += 1 - self.history = [] - self.hits = 0 - self.hit_streak = 0 - self.age = 0 - - def update(self, bbox): - """ - Updates the state vector with observed bbox. - """ - self.time_since_update = 0 - self.history = [] - self.hits += 1 - self.hit_streak += 1 - self.kf.update(convert_bbox_to_z(bbox)) - - def predict(self): - """ - Advances the state vector and returns the predicted bounding box estimate. - """ - if ((self.kf.x[6] + self.kf.x[2]) <= 0): - self.kf.x[6] *= 0.0 - self.kf.predict() - self.age += 1 - if (self.time_since_update > 0): - self.hit_streak = 0 - self.time_since_update += 1 - self.history.append(convert_x_to_bbox(self.kf.x)) - return self.history[-1] - - def get_state(self): - """ - Returns the current bounding box estimate. - """ - return convert_x_to_bbox(self.kf.x) - - -def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3): - """ - Assigns detections to tracked object (both represented as bounding boxes) - - Returns 3 lists of matches, unmatched_detections and unmatched_trackers - """ - if (len(trackers) == 0): - return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int) - iou_matrix = np.zeros((len(detections), len(trackers)), dtype=np.float32) - - for d, det in enumerate(detections): - for t, trk in enumerate(trackers): - iou_matrix[d, t] = iou(det, trk) - matched_indices = linear_sum_assignment(-iou_matrix) - matched_indices = np.asarray(matched_indices) - matched_indices = matched_indices.transpose() - - unmatched_detections = [] - for d, det in enumerate(detections): - if (d not in matched_indices[:, 0]): - unmatched_detections.append(d) - unmatched_trackers = [] - for t, trk in enumerate(trackers): - if (t not in matched_indices[:, 1]): - unmatched_trackers.append(t) - - # filter out matched with low IOU - matches = [] - for m in matched_indices: - if (iou_matrix[m[0], m[1]] < iou_threshold): - unmatched_detections.append(m[0]) - unmatched_trackers.append(m[1]) - else: - matches.append(m.reshape(1, 2)) - if (len(matches) == 0): - matches = np.empty((0, 2), dtype=int) - else: - matches = np.concatenate(matches, axis=0) - - return matches, np.array(unmatched_detections), np.array(unmatched_trackers) - - -class Sort(object): - def __init__(self, max_age=1, min_hits=3): - """ - Sets key parameters for SORT - """ - self.max_age = max_age - self.min_hits = min_hits - self.trackers = [] - self.frame_count = 0 - - def update(self, dets): - """ - Params: - dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...] - Requires: this method must be called once for each frame even with empty detections. - Returns the a similar array, where the last column is the object ID. - - NOTE: The number of objects returned may differ from the number of detections provided. - """ - self.frame_count += 1 - # get predicted locations from existing trackers. - trks = np.zeros((len(self.trackers), 5)) - to_del = [] - ret = [] - for t, trk in enumerate(trks): - pos = self.trackers[t].predict()[0] - trk[:] = [pos[0], pos[1], pos[2], pos[3], 0] - if np.any(np.isnan(pos)): - to_del.append(t) - trks = np.ma.compress_rows(np.ma.masked_invalid(trks)) - for t in reversed(to_del): - self.trackers.pop(t) - matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks) - - # update matched trackers with assigned detections - for t, trk in enumerate(self.trackers): - if t not in unmatched_trks: - d = matched[np.where(matched[:, 1] == t)[0], 0] # d: [n] - trk.update(dets[d, :][0]) - - # create and initialise new trackers for unmatched detections - for i in unmatched_dets: - trk = KalmanBoxTracker(dets[i, :]) - self.trackers.append(trk) - i = len(self.trackers) - for trk in reversed(self.trackers): - d = trk.get_state()[0] - if ((trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits)): - ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive - i -= 1 - # remove dead tracklet - if (trk.time_since_update > self.max_age): - self.trackers.pop(i) - if (len(ret) > 0): - return np.concatenate(ret) - return np.empty((0, 5)) - - -def parse_args(): - """Parse input arguments.""" - parser = argparse.ArgumentParser(description='SORT demo') - parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]', - action='store_true') - args = parser.parse_args() - return args diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/human_detector.py b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/human_detector.py deleted file mode 100644 index 09c70b36a..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/human_detector.py +++ /dev/null @@ -1,18 +0,0 @@ -import os - -from lib.yolo.util import * -from lib.yolo import preprocess - - -def yolo_human_det(img, model=None, reso=416, confidence=0.8): - inp_dim = reso - if type(img) == str: - assert os.path.isfile(img), 'The image path does not exist' - img = cv2.imread(img) - - img, ori_img, img_dim, ratio = preprocess.preproc(img, (inp_dim,inp_dim)) - output = model.run(img.astype('float32')[None, :, :, :])[0] - output = postprocess(output, (416, 416))[0] - bboxs, scores = predictions_to_object(output, ratio, confidence, confidence) - - return np.array(bboxs), np.array(scores) diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/preprocess.py b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/preprocess.py deleted file mode 100644 index e4275ce3d..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/preprocess.py +++ /dev/null @@ -1,68 +0,0 @@ -import numpy as np -import cv2 -from PIL import Image - - -def letterbox_image(img, inp_dim): - '''resize image with unchanged aspect ratio using padding''' - img_w, img_h = img.shape[1], img.shape[0] - w, h = inp_dim - new_w = int(img_w * min(w/img_w, h/img_h)) - new_h = int(img_h * min(w/img_w, h/img_h)) - resized_image = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_CUBIC) - - canvas = np.full((inp_dim[1], inp_dim[0], 3), 128) - - canvas[(h - new_h) // 2:(h - new_h) // 2 + new_h, (w - new_w) // 2:(w - new_w) // 2 + new_w, :] = resized_image - - return canvas - - -def prep_image(img, inp_dim): - """ - Prepare image for inputting to the neural network. - - Returns a Variable - """ - if type(img) == str: - orig_im = cv2.imread(img) - else: - orig_im = img - dim = orig_im.shape[1], orig_im.shape[0] - img = (letterbox_image(orig_im, (inp_dim, inp_dim))) - img_ = img[:, :, ::-1].transpose((2, 0, 1)).copy() - img_ = np.expand_dims(img_ / 255.0, 0) - return img_, orig_im, dim - -def preproc(img, input_size, swap=(2, 0, 1)): - orig_im = img - dim = orig_im.shape[1], orig_im.shape[0] - if len(img.shape) == 3: - padded_img = np.ones((input_size[0], input_size[1], img.shape[2]), dtype=np.uint8) * 114 - else: - padded_img = np.ones(input_size, dtype=np.uint8) * 114 - - r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) - resized_img = cv2.resize( - img, - (int(img.shape[1] * r), int(img.shape[0] * r)), - interpolation=cv2.INTER_LINEAR, - ).astype(np.uint8) - padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img - - padded_img = padded_img.transpose(swap) - padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) - return padded_img, orig_im, dim, r - - -def inp_to_image(inp): - inp = inp.cpu().squeeze() - inp = inp * 255 - try: - inp = inp.data.numpy() - except RuntimeError: - inp = inp.numpy() - inp = inp.transpose(1, 2, 0) - - inp = inp[:, :, ::-1] - return inp diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/util.py b/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/util.py deleted file mode 100644 index a260ce22b..000000000 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/yolo/util.py +++ /dev/null @@ -1,159 +0,0 @@ -from __future__ import division - -import numpy as np -import cv2 - - -def preproc(img, input_size, swap=(2, 0, 1)): - if len(img.shape) == 3: - padded_img = np.ones((input_size[0], input_size[1], img.shape[2]), dtype=np.uint8) * 114 - else: - padded_img = np.ones(input_size, dtype=np.uint8) * 114 - - r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) - resized_img = cv2.resize( - img, - (int(img.shape[1] * r), int(img.shape[0] * r)), - interpolation=cv2.INTER_LINEAR, - ).astype(np.uint8) - padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img - - padded_img = padded_img.transpose(swap) - padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) - return padded_img, r - - -def nms(boxes, scores, nms_thr): - """Single class NMS implemented in Numpy.""" - x1 = boxes[:, 0] - y1 = boxes[:, 1] - x2 = boxes[:, 2] - y2 = boxes[:, 3] - - areas = (x2 - x1 + 1) * (y2 - y1 + 1) - order = scores.argsort()[::-1] - - keep = [] - while order.size > 0: - i = order[0] - keep.append(i) - xx1 = np.maximum(x1[i], x1[order[1:]]) - yy1 = np.maximum(y1[i], y1[order[1:]]) - xx2 = np.minimum(x2[i], x2[order[1:]]) - yy2 = np.minimum(y2[i], y2[order[1:]]) - - w = np.maximum(0.0, xx2 - xx1 + 1) - h = np.maximum(0.0, yy2 - yy1 + 1) - inter = w * h - ovr = inter / (areas[i] + areas[order[1:]] - inter) - - inds = np.where(ovr <= nms_thr)[0] - order = order[inds + 1] - - return keep - - -def multiclass_nms(boxes, scores, nms_thr, score_thr, class_agnostic=True): - """Multiclass NMS implemented in Numpy""" - if class_agnostic: - nms_method = multiclass_nms_class_agnostic - else: - nms_method = multiclass_nms_class_aware - return nms_method(boxes, scores, nms_thr, score_thr) - - -def multiclass_nms_class_aware(boxes, scores, nms_thr, score_thr): - """Multiclass NMS implemented in Numpy. Class-aware version.""" - final_dets = [] - num_classes = scores.shape[1] - for cls_ind in range(num_classes): - cls_scores = scores[:, cls_ind] - valid_score_mask = cls_scores > score_thr - if valid_score_mask.sum() == 0: - continue - else: - valid_scores = cls_scores[valid_score_mask] - valid_boxes = boxes[valid_score_mask] - keep = nms(valid_boxes, valid_scores, nms_thr) - if len(keep) > 0: - cls_inds = np.ones((len(keep), 1)) * cls_ind - dets = np.concatenate( - [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 - ) - final_dets.append(dets) - if len(final_dets) == 0: - return None - return np.concatenate(final_dets, 0) - - -def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr): - """Multiclass NMS implemented in Numpy. Class-agnostic version.""" - cls_inds = scores.argmax(1) - cls_scores = scores[np.arange(len(cls_inds)), cls_inds] - - valid_score_mask = cls_scores > score_thr - if valid_score_mask.sum() == 0: - return None - valid_scores = cls_scores[valid_score_mask] - valid_boxes = boxes[valid_score_mask] - valid_cls_inds = cls_inds[valid_score_mask] - keep = nms(valid_boxes, valid_scores, nms_thr) - if keep: - dets = np.concatenate( - [valid_boxes[keep], valid_scores[keep, None], valid_cls_inds[keep, None]], 1 - ) - return dets - - -def postprocess(outputs, img_size, p6=False): - grids = [] - expanded_strides = [] - - if not p6: - strides = [8, 16, 32] - else: - strides = [8, 16, 32, 64] - - hsizes = [img_size[0] // stride for stride in strides] - wsizes = [img_size[1] // stride for stride in strides] - - for hsize, wsize, stride in zip(hsizes, wsizes, strides): - xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) - grid = np.stack((xv, yv), 2).reshape(1, -1, 2) - grids.append(grid) - shape = grid.shape[:2] - expanded_strides.append(np.full((*shape, 1), stride)) - - grids = np.concatenate(grids, 1) - expanded_strides = np.concatenate(expanded_strides, 1) - outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides - outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides - - return outputs - - -def predictions_to_object(predictions,ratio,nms_thr,score_thr): - boxes = predictions[:, :4] - scores = predictions[:, 4:5] * predictions[:, 5:] - - boxes_xyxy = np.ones_like(boxes) - boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2. - boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2. - boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2. - boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2. - boxes_xyxy /= ratio - dets = multiclass_nms(boxes_xyxy, scores, nms_thr, score_thr) - - out_box = [] - out_score = [] - if dets is not None: - final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5] - for i, box in enumerate(final_boxes): - x1, y1, x2, y2 = box - c = int(final_cls_inds[i]) - score = final_scores[i] - if c == 0: - out_box.append([x1, y1, x2, y2]) - out_score.append([score]) - - return out_box, out_score diff --git a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py index 4ab928ccc..6d6cccb5f 100644 --- a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py +++ b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py @@ -5,7 +5,6 @@ import matplotlib from PIL import Image import time -import onnxruntime as ort import ailia @@ -64,7 +63,16 @@ # ====================== def recognize_from_image(): env_id = args.env_id - net_yolo = ailia.Net(MODEL_PATH_YOLO, WEIGHT_PATH_YOLO, env_id=env_id) + net_yolo = ailia.Detector( + MODEL_PATH_YOLO, + WEIGHT_PATH_YOLO, + 80, + format=ailia.NETWORK_IMAGE_FORMAT_BGR, + channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST, + range=ailia.NETWORK_IMAGE_RANGE_U_INT8, + algorithm=ailia.DETECTOR_ALGORITHM_YOLOX, + env_id=env_id) + net_pose2d = ailia.Net(MODEL_PATH_POSE2D, WEIGHT_PATH_POSE2D, env_id=env_id) net_pose3d = ailia.Net(MODEL_PATH_POSE3D, WEIGHT_PATH_POSE3D, env_id=env_id) @@ -140,8 +148,8 @@ def recognize_from_video(): def main(): # model files check and download check_and_download_models(WEIGHT_PATH_YOLO, MODEL_PATH_YOLO, REMOTE_PATH_YOLO) - #check_and_download_models(WEIGHT_PATH_POSE2D, MODEL_PATH_POSE2D, REMOTE_PATH_POSE) - #check_and_download_models(WEIGHT_PATH_POSE3D, MODEL_PATH_POSE3D, REMOTE_PATH_POSE) + check_and_download_models(WEIGHT_PATH_POSE2D, MODEL_PATH_POSE2D, REMOTE_PATH_POSE) + check_and_download_models(WEIGHT_PATH_POSE3D, MODEL_PATH_POSE3D, REMOTE_PATH_POSE) if args.video is not None: # video mode From 88e8e6edbf541eaa0ca61648cbedd0380bf4e679 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=95=A0=E5=B1=B1=E5=89=9B?= Date: Fri, 21 Oct 2022 23:44:20 +0900 Subject: [PATCH 4/7] remove jit strided_transformer_pose3d --- pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py b/pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py index bbc8d1cb9..46451dc89 100644 --- a/pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py +++ b/pose_estimation_3d/strided_transformer_pose3d/lib/gen_kpts.py @@ -1,7 +1,6 @@ import math import cv2 import numpy as np -from numba import jit from skimage import io from scipy.optimize import linear_sum_assignment from filterpy.kalman import KalmanFilter @@ -212,7 +211,6 @@ def get_final_preds(batch_heatmaps, center, scale): return preds, maxvals -@jit def iou(bb_test, bb_gt): """ Computes IUO between two bboxes in the form [x1,y1,x2,y2] From ee7fac133e00efef5b22af5f6525b6215a4b76a3 Mon Sep 17 00:00:00 2001 From: Kazuki Kyakuno Date: Tue, 5 Mar 2024 11:53:33 +0900 Subject: [PATCH 5/7] Add model to list --- README.md | 3 +++ .../strided_transformer_pose3d/strided_transformer_pose3d.py | 4 +--- scripts/download_all_models.sh | 1 + 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 67285b346..10af56573 100644 --- a/README.md +++ b/README.md @@ -472,6 +472,9 @@ The collection of pre-trained, state-of-the-art AI models. | [](pose_estimation_3d/3dmppe_posenet/) |[3dmppe_posenet](/pose_estimation_3d/3dmppe_posenet/) | [PoseNet of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image"](https://github.com/mks0601/3DMPPE_POSENET_RELEASE) | Pytorch | 1.2.6 and later | | | [](pose_estimation_3d/gast/) |[gast](/pose_estimation_3d/gast/) | [A Graph Attention Spatio-temporal Convolutional Networks for 3D Human Pose Estimation in Video (GAST-Net)](https://github.com/fabro66/GAST-Net-3DPoseEstimation) | Pytorch | 1.2.7 and later | [EN](https://medium.com/axinc-ai/gast-a-machine-learning-model-that-predicts-a-3d-skeleton-from-a-2d-skeleton-44449d1ff78d) [JP](https://medium.com/axinc/gast-2d%E3%81%AE%E9%AA%A8%E6%A0%BC%E3%81%8B%E3%82%893d%E3%81%AE%E9%AA%A8%E6%A0%BC%E3%82%92%E4%BA%88%E6%B8%AC%E3%81%99%E3%82%8B%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92%E3%83%A2%E3%83%87%E3%83%AB-6c70fc4b3b3a) | | [](pose_estimation_3d/mediapipe_pose_world_landmarks/) |[mediapipe_pose_world_landmarks](/pose_estimation_3d/mediapipe_pose_world_landmarks/) | [MediaPipe Pose real-world 3D coordinates](https://google.github.io/mediapipe/solutions/pose.html#pose_world_landmarks) | TensorFlow Lite | 1.2.10 and later | | +| [](pose_estimation_3d/strided_transformer_pose3d/) |[strided_transformer_pose3d](/pose_estimation_3d/strided_transformer_pose3d/) | [Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation](https://github.com/Vegetebird/StridedTransformer-Pose3D) | Pytorch | 1.2.10 and later | | + + ## Road detection diff --git a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py index 6d6cccb5f..e1e07adf8 100644 --- a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py +++ b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py @@ -3,8 +3,6 @@ import numpy as np import cv2 import matplotlib -from PIL import Image -import time import ailia @@ -20,7 +18,7 @@ write_predictions) from image_utils import imread # noqa: E402 from model_utils import check_and_download_models -from utils import get_base_parser, get_savepath, update_parser +from arg_utils import get_base_parser, get_savepath, update_parser from lib.run import * diff --git a/scripts/download_all_models.sh b/scripts/download_all_models.sh index cb72c15f8..d14e9573d 100755 --- a/scripts/download_all_models.sh +++ b/scripts/download_all_models.sh @@ -280,6 +280,7 @@ cd ../../pose_estimation_3d/3dmppe_posenet; python3 3dmppe_posenet.py ${OPTION} cd ../../pose_estimation_3d/pose-hg-3d; python3 pose-hg-3d.py ${OPTION} cd ../../pose_estimation_3d/gast; python3 gast.py ${OPTION} cd ../../pose_estimation_3d/mediapipe_pose_world_landmarks; python3 mediapipe_pose_world_landmarks.py ${OPTION} +cd ../../pose_estimation_3d/strided_transformer_pose3d; python3 strided_transformer_pose3d.py ${OPTION} cd ../../road_detection/codes-for-lane-detection; python3 codes-for-lane-detection.py ${OPTION} cd ../../road_detection/roneld; python3 roneld.py ${OPTION} cd ../../road_detection/road-segmentation-adas; python3 road-segmentation-adas.py ${OPTION} From 693606a742d61dc4b46c90fc24484de05ddf4dcc Mon Sep 17 00:00:00 2001 From: Kazuki Kyakuno Date: Tue, 5 Mar 2024 11:57:01 +0900 Subject: [PATCH 6/7] Update output image path --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 10af56573..edf0907b2 100644 --- a/README.md +++ b/README.md @@ -472,7 +472,7 @@ The collection of pre-trained, state-of-the-art AI models. | [](pose_estimation_3d/3dmppe_posenet/) |[3dmppe_posenet](/pose_estimation_3d/3dmppe_posenet/) | [PoseNet of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image"](https://github.com/mks0601/3DMPPE_POSENET_RELEASE) | Pytorch | 1.2.6 and later | | | [](pose_estimation_3d/gast/) |[gast](/pose_estimation_3d/gast/) | [A Graph Attention Spatio-temporal Convolutional Networks for 3D Human Pose Estimation in Video (GAST-Net)](https://github.com/fabro66/GAST-Net-3DPoseEstimation) | Pytorch | 1.2.7 and later | [EN](https://medium.com/axinc-ai/gast-a-machine-learning-model-that-predicts-a-3d-skeleton-from-a-2d-skeleton-44449d1ff78d) [JP](https://medium.com/axinc/gast-2d%E3%81%AE%E9%AA%A8%E6%A0%BC%E3%81%8B%E3%82%893d%E3%81%AE%E9%AA%A8%E6%A0%BC%E3%82%92%E4%BA%88%E6%B8%AC%E3%81%99%E3%82%8B%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92%E3%83%A2%E3%83%87%E3%83%AB-6c70fc4b3b3a) | | [](pose_estimation_3d/mediapipe_pose_world_landmarks/) |[mediapipe_pose_world_landmarks](/pose_estimation_3d/mediapipe_pose_world_landmarks/) | [MediaPipe Pose real-world 3D coordinates](https://google.github.io/mediapipe/solutions/pose.html#pose_world_landmarks) | TensorFlow Lite | 1.2.10 and later | | -| [](pose_estimation_3d/strided_transformer_pose3d/) |[strided_transformer_pose3d](/pose_estimation_3d/strided_transformer_pose3d/) | [Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation](https://github.com/Vegetebird/StridedTransformer-Pose3D) | Pytorch | 1.2.10 and later | | +| [](pose_estimation_3d/strided_transformer_pose3d/) |[strided_transformer_pose3d](/pose_estimation_3d/strided_transformer_pose3d/) | [Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation](https://github.com/Vegetebird/StridedTransformer-Pose3D) | Pytorch | 1.2.10 and later | | From cb2ee1856c8ae3a7dd085f9189d111ec866f5b43 Mon Sep 17 00:00:00 2001 From: Kazuki Kyakuno Date: Tue, 5 Mar 2024 12:00:11 +0900 Subject: [PATCH 7/7] Fix video mode --- .../strided_transformer_pose3d.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py index e1e07adf8..198f15cde 100644 --- a/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py +++ b/pose_estimation_3d/strided_transformer_pose3d/strided_transformer_pose3d.py @@ -94,7 +94,15 @@ def recognize_from_image(): def recognize_from_video(): env_id = args.env_id - net_yolo = ailia.Net(MODEL_PATH_YOLO, WEIGHT_PATH_YOLO, env_id=env_id) + net_yolo = ailia.Detector( + MODEL_PATH_YOLO, + WEIGHT_PATH_YOLO, + 80, + format=ailia.NETWORK_IMAGE_FORMAT_BGR, + channel=ailia.NETWORK_IMAGE_CHANNEL_FIRST, + range=ailia.NETWORK_IMAGE_RANGE_U_INT8, + algorithm=ailia.DETECTOR_ALGORITHM_YOLOX, + env_id=env_id) net_pose2d = ailia.Net(MODEL_PATH_POSE2D, WEIGHT_PATH_POSE2D, env_id=env_id) net_pose3d = ailia.Net(MODEL_PATH_POSE3D, WEIGHT_PATH_POSE3D, env_id=env_id)