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import os
import sys
import glob
import json
import torch
import numpy as np
from torchvision.transforms import v2
from torch.utils.data import Dataset
from PIL import Image
from utils.utils_lines import LineKeypointsDB
from utils.utils_linesWC import LineKeypointsWCDB
class SoccerNetCalibrationDataset(Dataset):
def __init__(self, root_dir, split, transform, main_cam_only=True):
self.root_dir = root_dir
self.split = split
self.transform = transform
#self.match_info = json.load(open(root_dir + split + '/match_info.json'))
self.files = glob.glob(os.path.join(self.root_dir + self.split, "*.jpg"))
if main_cam_only:
self.get_main_camera()
def __len__(self):
return len(self.files)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = self.files[idx]
image = Image.open(img_name)
data = json.load(open(img_name.split('.')[0] + ".json"))
sample = self.transform({'image': image, 'data': data})
img_db = LineKeypointsDB(sample['data'], sample['image'])
target = img_db.get_tensor()
return sample['image'], torch.from_numpy(target).float()
def get_main_camera(self):
self.files = [file for file in self.files if int(self.match_info[file.split('/')[-1]]['ms_time']) == \
int(self.match_info[file.split('/')[-1]]['replay_time'])]
class WorldCup2014Dataset(Dataset):
def __init__(self, root_dir, split, transform):
self.root_dir = root_dir
self.split = split
self.transform = transform
assert self.split in ['train_val', 'test'], f'unknown dataset type {self.split}'
self.files = glob.glob(os.path.join(self.root_dir + self.split, "*.jpg"))
self.homographies = glob.glob(os.path.join(self.root_dir + self.split, "*.homographyMatrix"))
self.num_samples = len(self.files)
self.files.sort()
self.homographies.sort()
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
image = self.get_image_by_index(idx)
homography = self.get_homography_by_index(idx)
img_db = LineKeypointsWCDB(image, homography, (960,540))
target, mask = img_db.get_tensor_w_mask()
sample = self.transform({'image': image, 'target': target, 'mask': mask})
return sample['image'], sample['target'], sample['mask']
def convert_homography_WC14GT_to_SN(self, H):
T = np.eye(3)
#T[0, -1] = -115 / 2
#T[1, -1] = -74 / 2
yard2meter = 0.9144
S = np.eye(3)
S[0, 0] = yard2meter
S[1, 1] = yard2meter
H_SN = S @ (T @ H)
return H_SN
def get_image_by_index(self, index):
img_file = self.files[index]
image = Image.open(img_file)
return image
def get_homography_by_index(self, index):
homography_file = self.homographies[index]
with open(homography_file, 'r') as file:
lines = file.readlines()
matrix_elements = []
for line in lines:
matrix_elements.extend([float(element) for element in line.split()])
homography = np.array(matrix_elements).reshape((3, 3))
homography = self.convert_homography_WC14GT_to_SN(homography)
homography = torch.from_numpy(homography)
homography = homography / homography[2:3, 2:3]
return homography
class TSWorldCupDataset(Dataset):
def __init__(self, root_dir, split, transform):
self.root_dir = root_dir
self.split = split
self.transform = transform
assert self.split in ['train', 'test'], f'unknown dataset type {self.split}'
self.files_txt = self.get_txt()
self.files = self.get_jpg_files()
self.homographies = self.get_homographies()
self.num_samples = len(self.files)
self.files.sort()
self.homographies.sort()
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
image = self.get_image_by_index(idx)
homography = self.get_homography_by_index(idx)
img_db = LineKeypointsWCDB(image, homography, (960,540))
target, mask = img_db.get_tensor_w_mask()
sample = self.transform({'image': image, 'target': target, 'mask': mask})
return sample['image'], sample['target'], sample['mask']
def get_txt(self):
with open(self.root_dir + self.split + '.txt', 'r') as file:
lines = file.readlines()
lines = [line.strip() for line in lines]
return lines
def get_jpg_files(self):
all_jpg_files = []
for dir in self.files_txt:
full_dir = self.root_dir + "Dataset/80_95/" + dir
jpg_files = []
for file in os.listdir(full_dir):
if file.lower().endswith('.jpg') or file.lower().endswith('.jpeg'):
jpg_files.append(os.path.join(full_dir, file))
all_jpg_files.extend(jpg_files)
return all_jpg_files
def get_homographies(self):
all_homographies = []
for dir in self.files_txt:
full_dir = self.root_dir + "Annotations/80_95/" + dir
homographies = []
for file in os.listdir(full_dir):
if file.lower().endswith('.npy'):
homographies.append(os.path.join(full_dir, file))
all_homographies.extend(homographies)
return all_homographies
def convert_homography_WC14GT_to_SN(self, H):
T = np.eye(3)
#T[0, -1] = -115 / 2
#T[1, -1] = -74 / 2
yard2meter = 0.9144
S = np.eye(3)
S[0, 0] = yard2meter
S[1, 1] = yard2meter
H_SN = S @ (T @ H)
return H_SN
def get_image_by_index(self, index):
img_file = self.files[index]
image = Image.open(img_file)
return image
def get_homography_by_index(self, index):
homography_file = self.homographies[index]
homography = np.load(homography_file)
homography = self.convert_homography_WC14GT_to_SN(homography)
homography = torch.from_numpy(homography)
homography = homography / homography[2:3, 2:3]
return homography
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