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import copy
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import os
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import sys
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import glob
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import json
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import torch
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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 torchvision.transforms import v2
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from torch.utils.data import Dataset
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from PIL import Image
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from utils.utils_keypoints import KeypointsDB
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from utils.utils_keypointsWC import KeypointsWCDB
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class SoccerNetCalibrationDataset(Dataset):
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def __init__(self, root_dir, split, transform, main_cam_only=True):
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self.root_dir = root_dir
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self.split = split
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self.transform = transform
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self.match_info = json.load(open(root_dir + split + '/match_info.json'))
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self.files = self.get_image_files(rate=1)
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if main_cam_only:
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self.get_main_camera()
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def get_image_files(self, rate=3):
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files = glob.glob(os.path.join(self.root_dir + self.split, "*.jpg"))
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files.sort()
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if rate > 1:
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files = files[::rate]
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return files
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def __len__(self):
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return len(self.files)
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def __getitem__(self, idx):
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if torch.is_tensor(idx):
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idx = idx.tolist()
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img_name = self.files[idx]
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image = Image.open(img_name)
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data = json.load(open(img_name.split('.')[0] + ".json"))
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data = self.correct_labels(data)
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sample = self.transform({'image': image, 'data': data})
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img_db = KeypointsDB(sample['data'], sample['image'])
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target, mask = img_db.get_tensor_w_mask()
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image = sample['image']
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return image, torch.from_numpy(target).float(), torch.from_numpy(mask).float()
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def get_main_camera(self):
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self.files = [file for file in self.files if int(self.match_info[file.split('/')[-1]]['ms_time']) == \
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int(self.match_info[file.split('/')[-1]]['replay_time'])]
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def correct_labels(self, data):
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if 'Goal left post left' in data.keys():
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data['Goal left post left '] = copy.deepcopy(data['Goal left post left'])
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del data['Goal left post left']
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return data
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class WorldCup2014Dataset(Dataset):
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def __init__(self, root_dir, split, transform):
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self.root_dir = root_dir
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self.split = split
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self.transform = transform
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assert self.split in ['train_val', 'test'], f'unknown dataset type {self.split}'
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self.files = glob.glob(os.path.join(self.root_dir + self.split, "*.jpg"))
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self.homographies = glob.glob(os.path.join(self.root_dir + self.split, "*.homographyMatrix"))
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self.num_samples = len(self.files)
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self.files.sort()
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self.homographies.sort()
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def __len__(self):
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return self.num_samples
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def __getitem__(self, idx):
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image = self.get_image_by_index(idx)
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homography = self.get_homography_by_index(idx)
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img_db = KeypointsWCDB(image, homography, (960,540))
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target, mask = img_db.get_tensor_w_mask()
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sample = self.transform({'image': image, 'target': target, 'mask': mask})
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return sample['image'], sample['target'], sample['mask']
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def convert_homography_WC14GT_to_SN(self, H):
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T = np.eye(3)
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yard2meter = 0.9144
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S = np.eye(3)
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S[0, 0] = yard2meter
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S[1, 1] = yard2meter
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H_SN = S @ (T @ H)
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return H_SN
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def get_image_by_index(self, index):
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img_file = self.files[index]
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image = Image.open(img_file)
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return image
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def get_homography_by_index(self, index):
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homography_file = self.homographies[index]
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with open(homography_file, 'r') as file:
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lines = file.readlines()
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matrix_elements = []
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for line in lines:
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matrix_elements.extend([float(element) for element in line.split()])
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homography = np.array(matrix_elements).reshape((3, 3))
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homography = self.convert_homography_WC14GT_to_SN(homography)
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homography = torch.from_numpy(homography)
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homography = homography / homography[2:3, 2:3]
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return homography
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class TSWorldCupDataset(Dataset):
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def __init__(self, root_dir, split, transform):
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self.root_dir = root_dir
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self.split = split
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self.transform = transform
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assert self.split in ['train', 'test'], f'unknown dataset type {self.split}'
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self.files_txt = self.get_txt()
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self.files = self.get_jpg_files()
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self.homographies = self.get_homographies()
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self.num_samples = len(self.files)
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self.files.sort()
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self.homographies.sort()
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def __len__(self):
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return self.num_samples
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def __getitem__(self, idx):
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image = self.get_image_by_index(idx)
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homography = self.get_homography_by_index(idx)
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img_db = KeypointsWCDB(image, homography, (960,540))
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target, mask = img_db.get_tensor_w_mask()
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sample = self.transform({'image': image, 'target': target, 'mask': mask})
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return sample['image'], sample['target'], sample['mask']
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def get_txt(self):
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with open(self.root_dir + self.split + '.txt', 'r') as file:
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lines = file.readlines()
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lines = [line.strip() for line in lines]
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return lines
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def get_jpg_files(self):
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all_jpg_files = []
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for dir in self.files_txt:
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full_dir = self.root_dir + "Dataset/80_95/" + dir
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jpg_files = []
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for file in os.listdir(full_dir):
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if file.lower().endswith('.jpg') or file.lower().endswith('.jpeg'):
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jpg_files.append(os.path.join(full_dir, file))
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all_jpg_files.extend(jpg_files)
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return all_jpg_files
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def get_homographies(self):
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all_homographies = []
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for dir in self.files_txt:
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full_dir = self.root_dir + "Annotations/80_95/" + dir
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homographies = []
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for file in os.listdir(full_dir):
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if file.lower().endswith('.npy'):
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homographies.append(os.path.join(full_dir, file))
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all_homographies.extend(homographies)
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return all_homographies
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def convert_homography_WC14GT_to_SN(self, H):
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T = np.eye(3)
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yard2meter = 0.9144
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S = np.eye(3)
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S[0, 0] = yard2meter
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S[1, 1] = yard2meter
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H_SN = S @ (T @ H)
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return H_SN
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def get_image_by_index(self, index):
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img_file = self.files[index]
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image = Image.open(img_file)
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return image
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def get_homography_by_index(self, index):
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homography_file = self.homographies[index]
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homography = np.load(homography_file)
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homography = self.convert_homography_WC14GT_to_SN(homography)
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homography = torch.from_numpy(homography)
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homography = homography / homography[2:3, 2:3]
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return homography
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