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import os
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import sys
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import json
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import glob
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import yaml
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import torch
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import zipfile
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import argparse
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import warnings
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import numpy as np
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import torchvision.transforms as T
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import torchvision.transforms.functional as f
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from tqdm import tqdm
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from PIL import Image
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sys.path.insert(1, os.path.join(sys.path[0], '..'))
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from model.cls_hrnet import get_cls_net
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from model.cls_hrnet_l import get_cls_net as get_cls_net_l
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from utils.utils_keypoints import KeypointsDB
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from utils.utils_lines import LineKeypointsDB
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from utils.utils_heatmap import get_keypoints_from_heatmap_batch_maxpool, get_keypoints_from_heatmap_batch_maxpool_l, \
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coords_to_dict, complete_keypoints
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from utils.utils_calib import FramebyFrameCalib
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warnings.filterwarnings("ignore", category=RuntimeWarning)
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warnings.filterwarnings("ignore", category=np.RankWarning)
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--cfg", type=str, required=True,
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help="Path to the (kp model) configuration file")
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parser.add_argument("--cfg_l", type=str, required=True,
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help="Path to the (line model) configuration file")
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parser.add_argument("--root_dir", type=str, required=True,
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help="Root directory")
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parser.add_argument("--split", type=str, required=True,
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help="Dataset split")
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parser.add_argument("--save_dir", type=str, required=True,
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help="Saving file path")
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parser.add_argument("--weights_kp", type=str, required=True,
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help="Model (keypoints) weigths to use")
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parser.add_argument("--weights_line", type=str, required=True,
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help="Model (lines) weigths to use")
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parser.add_argument("--cuda", type=str, default="cuda:0",
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help="CUDA device index (default: 'cuda:0')")
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parser.add_argument("--kp_th", type=float, default="0.1")
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parser.add_argument("--line_th", type=float, default="0.1")
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parser.add_argument("--max_reproj_err", type=float, default="50")
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parser.add_argument("--main_cam_only", action='store_true')
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parser.add_argument('--use_gt', action='store_true', help='Use ground truth annotations (default: False)')
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args = parser.parse_args()
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return args
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if __name__ == "__main__":
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args = parse_args()
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files = glob.glob(os.path.join(args.root_dir + args.split, "*.jpg"))
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if args.main_cam_only:
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cam_info = json.load(open(args.root_dir + args.split + '/match_info_cam_gt.json'))
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files = [file for file in files if file.split('/')[-1] in cam_info.keys()]
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files = [file for file in files if cam_info[file.split('/')[-1]]['camera'] == 'Main camera center']
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if args.main_cam_only:
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zip_name = args.save_dir + args.split + '_main.zip'
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else:
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zip_name = args.save_dir + args.split + '.zip'
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if args.use_gt:
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if args.main_cam_only:
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zip_name_pred = args.save_dir + args.split + '_main_gt.zip'
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else:
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zip_name_pred = args.save_dir + args.split + '_gt.zip'
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else:
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if args.main_cam_only:
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zip_name_pred = args.save_dir + args.split + '_main_pred.zip'
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else:
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zip_name_pred = args.save_dir + args.split + '_pred.zip'
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print(f"Saving results in {args.save_dir}")
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print(f"file: {zip_name_pred}")
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if args.use_gt:
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transform = T.Resize((540, 960))
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cam = FramebyFrameCalib(960, 540, denormalize=True)
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with zipfile.ZipFile(zip_name_pred, 'w') as zip_file:
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samples, complete = 0., 0.
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for file in tqdm(files, desc="Processing Images"):
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image = Image.open(file)
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file_name = file.split('/')[-1].split('.')[0]
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samples += 1
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json_path = file.split('.')[0] + ".json"
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f = open(json_path)
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data = json.load(f)
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kp_db = KeypointsDB(data, image)
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line_db = LineKeypointsDB(data, image)
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heatmaps, _ = kp_db.get_tensor_w_mask()
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heatmaps = torch.tensor(heatmaps).unsqueeze(0)
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heatmaps_l = line_db.get_tensor()
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heatmaps_l = torch.tensor(heatmaps_l).unsqueeze(0)
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kp_coords = get_keypoints_from_heatmap_batch_maxpool(heatmaps[:, :-1, :, :])
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line_coords = get_keypoints_from_heatmap_batch_maxpool_l(heatmaps_l[:, :-1, :, :])
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kp_dict = coords_to_dict(kp_coords, threshold=0.01)
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lines_dict = coords_to_dict(line_coords, threshold=0.01)
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cam.update(kp_dict, lines_dict)
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final_params_dict = cam.heuristic_voting()
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if final_params_dict:
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complete += 1
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cam_params = final_params_dict['cam_params']
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print("heheheheheheh")
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json_data = json.dumps(cam_params)
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zip_file.writestr(f"camera_{file_name}.json", json_data)
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else:
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device = torch.device(args.cuda if torch.cuda.is_available() else 'cpu')
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cfg = yaml.safe_load(open(args.cfg, 'r'))
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cfg_l = yaml.safe_load(open(args.cfg_l, 'r'))
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loaded_state = torch.load(args.weights_kp, map_location=device)
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model = get_cls_net(cfg)
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model.load_state_dict(loaded_state)
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model.to(device)
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model.eval()
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loaded_state_l = torch.load(args.weights_line, map_location=device)
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model_l = get_cls_net_l(cfg_l)
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model_l.load_state_dict(loaded_state_l)
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model_l.to(device)
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model_l.eval()
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transform = T.Resize((540, 960))
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cam = FramebyFrameCalib(960, 540)
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with zipfile.ZipFile(zip_name_pred, 'w') as zip_file:
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samples, complete = 0., 0.
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for file in tqdm(files, desc="Processing Images"):
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image = Image.open(file)
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file_name = file.split('/')[-1].split('.')[0]
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samples += 1
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with torch.no_grad():
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image = f.to_tensor(image).float().to(device).unsqueeze(0)
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image = image if image.size()[-1] == 960 else transform(image)
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b, c, h, w = image.size()
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heatmaps = model(image)
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heatmaps_l = model_l(image)
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kp_coords = get_keypoints_from_heatmap_batch_maxpool(heatmaps[:, :-1, :, :])
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line_coords = get_keypoints_from_heatmap_batch_maxpool_l(heatmaps_l[:, :-1, :, :])
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kp_dict = coords_to_dict(kp_coords, threshold=args.kp_th)
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lines_dict = coords_to_dict(line_coords, threshold=args.line_th)
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kp_dict, lines_dict = complete_keypoints(kp_dict[0], lines_dict[0], w=w, h=h)
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cam.update(kp_dict, lines_dict)
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final_params_dict = cam.heuristic_voting(refine_lines=True)
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if final_params_dict:
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if final_params_dict['rep_err'] <= args.max_reproj_err:
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complete += 1
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cam_params = final_params_dict['cam_params']
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json_data = json.dumps(cam_params)
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zip_file.writestr(f"camera_{file_name}.json", json_data)
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with zipfile.ZipFile(zip_name, 'w') as zip_file:
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for file in tqdm(files, desc="Processing Images"):
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file_name = file.split('/')[-1].split('.')[0]
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data = json.load(open(file.split('.')[0] + ".json"))
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json_data = json.dumps(data)
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zip_file.writestr(f"{file_name}.json", json_data)
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print(f'Completed {complete} / {samples}')
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