import gorilla import argparse import os import sys from PIL import Image import os.path as osp import numpy as np import random import importlib import json import torch import torchvision.transforms as transforms import cv2 BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.join(BASE_DIR, '..', 'Pose_Estimation_Model') sys.path.append(os.path.join(ROOT_DIR, 'provider')) sys.path.append(os.path.join(ROOT_DIR, 'utils')) sys.path.append(os.path.join(ROOT_DIR, 'model')) sys.path.append(os.path.join(BASE_DIR, 'model', 'pointnet2')) def get_parser(): parser = argparse.ArgumentParser( description="Pose Estimation") # pem parser.add_argument("--gpus", type=str, default="0", help="path to pretrain model") parser.add_argument("--model", type=str, default="pose_estimation_model", help="path to model file") parser.add_argument("--config", type=str, default="config/base.yaml", help="path to config file, different config.yaml use different config") parser.add_argument("--iter", type=int, default=600000, help="epoch num. for testing") parser.add_argument("--exp_id", type=int, default=0, help="") # input parser.add_argument("--output_dir", nargs="?", help="Path to root directory of the output") parser.add_argument("--cad_path", nargs="?", help="Path to CAD(mm)") parser.add_argument("--rgb_path", nargs="?", help="Path to RGB image") parser.add_argument("--depth_path", nargs="?", help="Path to Depth image(mm)") parser.add_argument("--cam_path", nargs="?", help="Path to camera information") parser.add_argument("--seg_path", nargs="?", help="Path to segmentation information(generated by ISM)") parser.add_argument("--det_score_thresh", default=0.2, help="The score threshold of detection") args_cfg = parser.parse_args() return args_cfg def init(): args = get_parser() exp_name = args.model + '_' + \ osp.splitext(args.config.split("/")[-1])[0] + '_id' + str(args.exp_id) log_dir = osp.join("log", exp_name) cfg = gorilla.Config.fromfile(args.config) cfg.exp_name = exp_name cfg.gpus = args.gpus cfg.model_name = args.model cfg.log_dir = log_dir cfg.test_iter = args.iter cfg.output_dir = args.output_dir cfg.cad_path = args.cad_path cfg.rgb_path = args.rgb_path cfg.depth_path = args.depth_path cfg.cam_path = args.cam_path cfg.seg_path = args.seg_path cfg.det_score_thresh = args.det_score_thresh gorilla.utils.set_cuda_visible_devices(gpu_ids = cfg.gpus) return cfg from data_utils import ( load_im, get_bbox, get_point_cloud_from_depth, get_resize_rgb_choose, ) from draw_utils import draw_detections import pycocotools.mask as cocomask import trimesh rgb_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) def visualize(rgb, pred_rot, pred_trans, model_points, K, save_path): img = draw_detections(rgb, pred_rot, pred_trans, model_points, K, color=(255, 0, 0)) img = Image.fromarray(np.uint8(img)) img.save(save_path) prediction = Image.open(save_path) # concat side by side in PIL rgb = Image.fromarray(np.uint8(rgb)) img = np.array(img) concat = Image.new('RGB', (img.shape[1] + prediction.size[0], img.shape[0])) concat.paste(rgb, (0, 0)) concat.paste(prediction, (img.shape[1], 0)) return concat def _get_template(path, cfg, tem_index=1): rgb_path = os.path.join(path, 'rgb_'+str(tem_index)+'.png') mask_path = os.path.join(path, 'mask_'+str(tem_index)+'.png') xyz_path = os.path.join(path, 'xyz_'+str(tem_index)+'.npy') rgb = load_im(rgb_path).astype(np.uint8) xyz = np.load(xyz_path).astype(np.float32) / 1000.0 mask = load_im(mask_path).astype(np.uint8) == 255 bbox = get_bbox(mask) y1, y2, x1, x2 = bbox mask = mask[y1:y2, x1:x2] rgb = rgb[:,:,::-1][y1:y2, x1:x2, :] if cfg.rgb_mask_flag: rgb = rgb * (mask[:,:,None]>0).astype(np.uint8) rgb = cv2.resize(rgb, (cfg.img_size, cfg.img_size), interpolation=cv2.INTER_LINEAR) rgb = rgb_transform(np.array(rgb)) choose = (mask>0).astype(np.float32).flatten().nonzero()[0] if len(choose) <= cfg.n_sample_template_point: choose_idx = np.random.choice(np.arange(len(choose)), cfg.n_sample_template_point) else: choose_idx = np.random.choice(np.arange(len(choose)), cfg.n_sample_template_point, replace=False) choose = choose[choose_idx] xyz = xyz[y1:y2, x1:x2, :].reshape((-1, 3))[choose, :] rgb_choose = get_resize_rgb_choose(choose, [y1, y2, x1, x2], cfg.img_size) return rgb, rgb_choose, xyz def get_templates(path, cfg): n_template_view = cfg.n_template_view all_tem = [] all_tem_choose = [] all_tem_pts = [] total_nView = 42 for v in range(n_template_view): i = int(total_nView / n_template_view * v) tem, tem_choose, tem_pts = _get_template(path, cfg, i) all_tem.append(torch.FloatTensor(tem).unsqueeze(0).cuda()) all_tem_choose.append(torch.IntTensor(tem_choose).long().unsqueeze(0).cuda()) all_tem_pts.append(torch.FloatTensor(tem_pts).unsqueeze(0).cuda()) return all_tem, all_tem_pts, all_tem_choose def get_test_data(rgb_path, depth_path, cam_path, cad_path, seg_path, det_score_thresh, cfg): dets = [] with open(seg_path) as f: dets_ = json.load(f) # keys: scene_id, image_id, category_id, bbox, score, segmentation for det in dets_: if det['score'] > det_score_thresh: dets.append(det) del dets_ cam_info = json.load(open(cam_path)) K = np.array(cam_info['cam_K']).reshape(3, 3) whole_image = load_im(rgb_path).astype(np.uint8) if len(whole_image.shape)==2: whole_image = np.concatenate([whole_image[:,:,None], whole_image[:,:,None], whole_image[:,:,None]], axis=2) whole_depth = load_im(depth_path).astype(np.float32) * cam_info['depth_scale'] / 1000.0 whole_pts = get_point_cloud_from_depth(whole_depth, K) mesh = trimesh.load_mesh(cad_path) model_points = mesh.sample(cfg.n_sample_model_point).astype(np.float32) / 1000.0 radius = np.max(np.linalg.norm(model_points, axis=1)) all_rgb = [] all_cloud = [] all_rgb_choose = [] all_score = [] all_dets = [] for inst in dets: seg = inst['segmentation'] score = inst['score'] # mask h,w = seg['size'] try: rle = cocomask.frPyObjects(seg, h, w) except: rle = seg mask = cocomask.decode(rle) mask = np.logical_and(mask > 0, whole_depth > 0) if np.sum(mask) > 32: bbox = get_bbox(mask) y1, y2, x1, x2 = bbox else: continue mask = mask[y1:y2, x1:x2] choose = mask.astype(np.float32).flatten().nonzero()[0] # pts cloud = whole_pts.copy()[y1:y2, x1:x2, :].reshape(-1, 3)[choose, :] center = np.mean(cloud, axis=0) tmp_cloud = cloud - center[None, :] flag = np.linalg.norm(tmp_cloud, axis=1) < radius * 1.2 if np.sum(flag) < 4: continue choose = choose[flag] cloud = cloud[flag] if len(choose) <= cfg.n_sample_observed_point: choose_idx = np.random.choice(np.arange(len(choose)), cfg.n_sample_observed_point) else: choose_idx = np.random.choice(np.arange(len(choose)), cfg.n_sample_observed_point, replace=False) choose = choose[choose_idx] cloud = cloud[choose_idx] # rgb rgb = whole_image.copy()[y1:y2, x1:x2, :][:,:,::-1] if cfg.rgb_mask_flag: rgb = rgb * (mask[:,:,None]>0).astype(np.uint8) rgb = cv2.resize(rgb, (cfg.img_size, cfg.img_size), interpolation=cv2.INTER_LINEAR) rgb = rgb_transform(np.array(rgb)) rgb_choose = get_resize_rgb_choose(choose, [y1, y2, x1, x2], cfg.img_size) all_rgb.append(torch.FloatTensor(rgb)) all_cloud.append(torch.FloatTensor(cloud)) all_rgb_choose.append(torch.IntTensor(rgb_choose).long()) all_score.append(score) all_dets.append(inst) ret_dict = {} ret_dict['pts'] = torch.stack(all_cloud).cuda() ret_dict['rgb'] = torch.stack(all_rgb).cuda() ret_dict['rgb_choose'] = torch.stack(all_rgb_choose).cuda() ret_dict['score'] = torch.FloatTensor(all_score).cuda() ninstance = ret_dict['pts'].size(0) ret_dict['model'] = torch.FloatTensor(model_points).unsqueeze(0).repeat(ninstance, 1, 1).cuda() ret_dict['K'] = torch.FloatTensor(K).unsqueeze(0).repeat(ninstance, 1, 1).cuda() return ret_dict, whole_image, whole_pts.reshape(-1, 3), model_points, all_dets if __name__ == "__main__": cfg = init() random.seed(cfg.rd_seed) torch.manual_seed(cfg.rd_seed) # model print("=> creating model ...") MODEL = importlib.import_module(cfg.model_name) model = MODEL.Net(cfg.model) model = model.cuda() model.eval() checkpoint = os.path.join(os.path.dirname((os.path.abspath(__file__))), 'checkpoints', 'sam-6d-pem-base.pth') gorilla.solver.load_checkpoint(model=model, filename=checkpoint) print("=> extracting templates ...") tem_path = os.path.join(cfg.output_dir, 'templates') all_tem, all_tem_pts, all_tem_choose = get_templates(tem_path, cfg.test_dataset) with torch.no_grad(): all_tem_pts, all_tem_feat = model.feature_extraction.get_obj_feats(all_tem, all_tem_pts, all_tem_choose) print("=> loading input data ...") input_data, img, whole_pts, model_points, detections = get_test_data( cfg.rgb_path, cfg.depth_path, cfg.cam_path, cfg.cad_path, cfg.seg_path, cfg.det_score_thresh, cfg.test_dataset ) ninstance = input_data['pts'].size(0) print("=> running model ...") with torch.no_grad(): input_data['dense_po'] = all_tem_pts.repeat(ninstance,1,1) input_data['dense_fo'] = all_tem_feat.repeat(ninstance,1,1) out = model(input_data) if 'pred_pose_score' in out.keys(): pose_scores = out['pred_pose_score'] * out['score'] else: pose_scores = out['score'] pose_scores = pose_scores.detach().cpu().numpy() pred_rot = out['pred_R'].detach().cpu().numpy() pred_trans = out['pred_t'].detach().cpu().numpy() * 1000 print("=> saving results ...") os.makedirs(f"{cfg.output_dir}/sam6d_results", exist_ok=True) for idx, det in enumerate(detections): detections[idx]['score'] = float(pose_scores[idx]) detections[idx]['R'] = list(pred_rot[idx].tolist()) detections[idx]['t'] = list(pred_trans[idx].tolist()) with open(os.path.join(f"{cfg.output_dir}/sam6d_results", 'detection_pem.json'), "w") as f: json.dump(detections, f) print("=> visualizating ...") save_path = os.path.join(f"{cfg.output_dir}/sam6d_results", 'vis_pem.png') valid_masks = pose_scores == pose_scores.max() K = input_data['K'].detach().cpu().numpy()[valid_masks] vis_img = visualize(img, pred_rot[valid_masks], pred_trans[valid_masks], model_points*1000, K, save_path) vis_img.save(save_path)