Datasets:
File size: 4,919 Bytes
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def load_data (config: dict):
def get_data_paths(config: dict, path_domains: str, paths_data: dict, matching_dict: dict) -> dict:
#### return data paths
def list_items(path, filter):
for path in Path(path).rglob(filter):
yield path.resolve().as_posix()
## data paths dict
data = {'PATH_IMG':[], 'PATH_SP_DATA':[], 'SP_COORDS':[], 'PATH_SP_DATES':[], 'PATH_SP_MASKS':[], 'PATH_LABELS':[], 'MTD_AERIAL':[]}
for domain in path_domains:
aerial = sorted(list(list_items(Path(path_data)/domain, 'IMG*.tif')), key=lambda x: int(x.split('_')[-1][:-4]))
sen2sp = sorted(list(list_items(Path(path_data)/domain, '*data.npy')))
sprods = sorted(list(list_items(Path(path_data)/domain, '*products.txt')))
smasks = sorted(list(list_items(Path(path_data)/domain, '*masks.npy')))
labels = sorted(list(list_items(Path(path_data)/domain, 'MSK*.tif')), key=lambda x: int(x.split('_')[-1][:-4]))
coords = []
for k in aerial:
coords.append(matching_dict[k.split('/')[-1]])
data['PATH_IMG'] += aerial
data['PATH_SP_DATA'] += sen2sp*len(aerial)
data['PATH_SP_DATES'] += sprods*len(aerial)
data['PATH_SP_MASKS'] += smasks*len(aerial)
data['SP_COORDS'] += coords
data['PATH_LABELS'] += labels
if config['aerial_metadata'] == True:
data = adding_encoded_metadata(config['data']['path_metadata_aerial'], data)
return data
###### READING CONFIG AND GETTING DATA PATHS
path_data = config['data']['HF_data_path']
train_domains, val_domains, test_domains = config['data']['domains_train'], config['data']['domains_val'], config['data']['domains_test']
with open(config['data']['path_sp_centroids'], 'r') as file:
matching_dict = json.load(file)
dict_train = get_data_paths(config, train_domains, path_data, matching_dict)
dict_val = get_data_paths(config, val_domains, path_data, matching_dict)
dict_test = get_data_paths(config, test_domains, path_data, matching_dict)
return dict_train, dict_val, dict_test
def adding_encoded_metadata(path_metadata_file: str, dict_paths: dict, loc_enc_size: int = 32):
"""
For every aerial image in the dataset, get metadata, encode and add to data dict.
"""
#### encode metadata
def coordenc_opt(coords, enc_size=32) -> np.array:
d = int(enc_size/2)
d_i = np.arange(0, d / 2)
freq = 1 / (10e7 ** (2 * d_i / d))
x,y = coords[0]/10e7, coords[1]/10e7
enc = np.zeros(d * 2)
enc[0:d:2] = np.sin(x * freq)
enc[1:d:2] = np.cos(x * freq)
enc[d::2] = np.sin(y * freq)
enc[d + 1::2] = np.cos(y * freq)
return list(enc)
def norm_alti(alti: int) -> float:
min_alti = 0
max_alti = 3164.9099121094 ### MAX DATASET
return [(alti-min_alti) / (max_alti-min_alti)]
def format_cam(cam: str) -> np.array:
return [[1,0] if 'UCE' in cam else [0,1]][0]
def cyclical_enc_datetime(date: str, time: str) -> list:
def norm(num: float) -> float:
return (num-(-1))/(1-(-1))
year, month, day = date.split('-')
if year == '2018': enc_y = [1,0,0,0]
elif year == '2019': enc_y = [0,1,0,0]
elif year == '2020': enc_y = [0,0,1,0]
elif year == '2021': enc_y = [0,0,0,1]
sin_month = np.sin(2*np.pi*(int(month)-1/12)) ## months of year
cos_month = np.cos(2*np.pi*(int(month)-1/12))
sin_day = np.sin(2*np.pi*(int(day)/31)) ## max days
cos_day = np.cos(2*np.pi*(int(day)/31))
h,m=time.split('h')
sec_day = int(h) * 3600 + int(m) * 60
sin_time = np.sin(2*np.pi*(sec_day/86400)) ## total sec in day
cos_time = np.cos(2*np.pi*(sec_day/86400))
return enc_y+[norm(sin_month),norm(cos_month),norm(sin_day),norm(cos_day),norm(sin_time),norm(cos_time)]
with open(path_metadata_file, 'r') as f:
metadata_dict = json.load(f)
for img in dict_paths['PATH_IMG']:
curr_img = img.split('/')[-1][:-4]
enc_coords = coordenc_opt([metadata_dict[curr_img]["patch_centroid_x"], metadata_dict[curr_img]["patch_centroid_y"]], enc_size=loc_enc_size)
enc_alti = norm_alti(metadata_dict[curr_img]["patch_centroid_z"])
enc_camera = format_cam(metadata_dict[curr_img]['camera'])
enc_temporal = cyclical_enc_datetime(metadata_dict[curr_img]['date'], metadata_dict[curr_img]['time'])
mtd_enc = enc_coords+enc_alti+enc_camera+enc_temporal
dict_paths['MTD_AERIAL'].append(mtd_enc)
return dict_paths |