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import pandas as pd
from glob import glob
from torch.utils.data import Dataset
import os
from PIL import Image
import numpy as np
import cv2
def get_IDS(IMG_DIR='output/images_preprocessed', era=False, CATALOGUE_FN='output/cdli_catalogue_data.csv'):
img_fns = glob(os.path.join(IMG_DIR, '*.png'))
IDS = [os.path.basename(fn).rstrip('.png') for fn in img_fns]
if era:
IDS = list(set(IDS) & set(pd.read_csv(
CATALOGUE_FN, usecols=['id_text', 'era'], dtype={'id_text': object}
).dropna(subset=['era']).set_index('id_text').to_dict()['era'].keys()))
return IDS
def pad_zeros(x):
x_new = str(x)
return (6-len(x_new))*'0'+x_new
class TabletEraDataset(Dataset):
ERA_INDICES = {
'early_bronze': 0,
'mid_late_bronze': 1,
'iron': 2
}
def __init__(self, CATALOGUE_FN='output/cdli_catalogue_data.csv', IMG_DIR='output/images_preprocessed', IDS=None):
self.id2era = pd.read_csv(
CATALOGUE_FN, usecols=['id_text', 'era'], dtype={'id_text': object}
).dropna(subset=['era']).set_index('id_text').to_dict()['era']
self.img_fns = glob(os.path.join(IMG_DIR, '*.png'))
self.IDS = [os.path.basename(fn).rstrip('.png') for fn in self.img_fns]
if IDS is not None:
print(f'Filtering {len(self.IDS)} IDS down to provided {len(IDS)}...')
IDS_set = set(IDS)
indices = [i for i, ID in enumerate(self.IDS) if ID in IDS_set]
self.img_fns = [self.img_fns[i] for i in indices]
self.IDS = [self.IDS[i] for i in indices]
def __len__(self):
return len(self.IDS)
def __getitem__(self, idx):
fn = self.img_fns[idx]
ID = self.IDS[idx]
era = self.id2era[ID]
img = np.asarray(Image.open(fn))
return img.astype(np.float32) / 255, self.ERA_INDICES[era]
class TabletPeriodDataset(Dataset):
# based on (normed) periods with at least 100 photos:
PERIOD_INDICES = {
'other': 0,
'Ur III': 1,
'Neo-Assyrian': 2,
'Old Babylonian': 3,
'Middle Babylonian': 4,
'Neo-Babylonian': 5,
'Old Akkadian': 6,
'Achaemenid': 7,
'Early Old Babylonian': 8,
'ED IIIb': 9,
'Middle Assyrian': 10,
'Old Assyrian': 11,
'Uruk III': 12,
'Proto-Elamite': 13,
'Lagash II': 14,
'Ebla': 15,
'ED IIIa': 16,
'Hellenistic': 17,
'ED I-II': 18,
'Middle Elamite': 19,
'Hittite': 20,
'Uruk IV': 21
}
PROVENIENCE_INDICES = {
'Nineveh': 1,
'Nippur': 2,
'unknown': 3,
'Umma': 4,
'Puzris-Dagan': 5,
'Girsu': 6,
'Ur': 7,
'Uruk': 8,
'Kanesh': 9,
'Assur': 10,
'Adab': 11,
'Garsana': 12,
'Gasur/Nuzi': 13,
'Susa': 14,
'Sippar-Yahrurum': 15,
'Larsa': 16,
'Nerebtum': 17,
'mod. Babylonia': 18,
'Parsa': 19,
'Kish': 20,
'Kalhu': 21,
'Tuttul': 22,
'Suruppak': 23,
'Babili': 24,
'Ebla': 25,
'mod. Beydar': 26,
'Akhetaten': 27,
'Esnunna': 28,
'Borsippa': 29,
'Kar-Tukulti-Ninurta': 30,
'mod. Jemdet Nasr': 31,
'mod. northern Babylonia': 32,
'Alalakh': 33,
'Hattusa': 34,
'Isin': 35,
'Elbonia': 36,
'Sibaniba': 37,
'Tutub': 38,
'Pi-Kasi': 39,
'Irisagrig': 40,
'Ansan': 41,
'Dilbat': 42,
'Zabalam': 43,
'mod. Mugdan/ Umm al-Jir': 44,
'Marad': 45,
'Eridu': 46,
'Seleucia': 47,
'mod. Abu Halawa': 48,
'Dur-Untas': 49,
'Nagar': 50,
'Lagaba': 51,
'Asnakkum': 52,
'Dur-Kurigalzu': 53,
'mod. Tell Sabaa': 54,
'mod. Abu Jawan': 55,
'mod. Tell Fakhariyah': 56,
'Dur-Abi-esuh': 57,
'Ugarit': 58,
'mod. Diqdiqqah': 59,
'Tarbisu': 60,
'Lagash': 61,
'Kisurra': 62,
'Elammu': 63,
'Du-Enlila': 64,
'Kutha': 65,
'mod. Umm el-Hafriyat': 66,
'Dur-Sarrukin': 67,
'Bad-Tibira': 68,
'Bit-zerija': 69,
'Kilizu': 70,
'mod. Pasargadae': 71,
'Abdju': 72,
'Surmes': 73,
'mod. Qatibat': 74,
'Tigunanum': 75,
'mod. Tell al-Lahm': 76,
'mod. Mesopotamia': 77,
'Subat-Enlil': 78,
'mod. Konar Sandal': 79,
'Gissi': 80,
'Agamatanu': 81,
'Aqa': 82,
'Kapri-sa-naqidati': 83,
'Esura': 84,
'Nahalla': 85,
'Bit-Sahtu': 86,
'mod. Sepphoris': 87,
'Dusabar': 88,
'mod. Tell Sifr': 89,
'Nasir': 90,
'Kumu': 91,
'Kazallu': 92,
'Kapru': 93,
'Hurruba': 94,
'mod. Deh-e-no, Iran': 95,
"mod. Za'aleh": 96,
'mod. Tepe Farukhabad': 97,
'Hursagkalama': 98,
'Carchemish': 99,
'mod. Ben Shemen, Israel': 100,
'Kutalla': 101,
'Der': 102,
'Imgur-Enlil': 103,
'mod. Hillah': 104,
'mod. Uhudu': 105,
'mod. Mahmudiyah': 106,
'Terqa': 107,
'Arrapha': 108,
'mod. Tell en-Nasbeh': 109,
'mod. Kalah Shergat': 110,
'Kar-Nabu': 111,
'Harran': 112,
'mod. Til-Buri': 113,
'Shuruppak': 114,
'mod. Abu Salabikh': 115,
"Ma'allanate": 116,
'Kar-Mullissu': 117,
'mod. Naqs-i-Rustam': 118
}
GENRE_INDICES = {
'Administrative': 1,
'Letter': 2,
'Legal': 3,
'Royal/Monumental': 4,
'Literary': 5,
'Lexical': 6,
'Omen': 7,
'uncertain': 8,
'Administrative ?': 1,
'School': 9,
'Mathematical': 10,
'Prayer/Incantation': 11,
'Lexical ?': 6,
'Scientific': 12,
'Ritual': 13,
'Letter ?': 2,
'Literary ?': 5,
'fake (modern)': 14,
'Lexical; Literary': 6,
'Legal ?': 3,
'Literary; Mathematical': 5,
'Astronomical': 15,
'Lexical; Mathematical': 6,
'School ?': 9,
'Mathematical ?': 10,
'Royal/Monumental ?': 4,
'Private/Votive': 16,
'fake (modern) ?': 14,
'Other (see subgenre)': 8,
'Historical': 2,
'Literary; Lexical': 5,
'Lexical; Literary; Mathematical': 6,
'Literary; Administrative': 5,
'Literary; Letter': 5,
'Scientific ?': 12,
'Royal/Monumental; Literary': 4,
'Private/Votive ?': 16,
'School; Literary': 9,
'Prayer/Incantation ?': 11,
'Ritual ?': 13,
'Lexical; School': 6
}
def __init__(self, CATALOGUE_FN='output/cdli_catalogue_data.csv', IMG_DIR='output/images', IDS=None, mask=False):
df = pd.read_csv(
CATALOGUE_FN, usecols=['id_text', 'era', 'period_normed', 'provenience_normed', 'genre'], dtype={'id_text': object}
).dropna(subset=['era'])
df["id_text"] = df.id_text.apply(lambda x: pad_zeros(x))
df = df[df['period_normed'].isin(TabletPeriodDataset.PERIOD_INDICES.keys())]
self.id2period = df.set_index('id_text').to_dict()['period_normed']
self.id2provenience = df.set_index('id_text').to_dict()['provenience_normed']
self.id2genre = df.set_index('id_text').to_dict()['genre']
self.genre = df.set_index('id_text').to_dict()['genre']
self.img_fns = glob(os.path.join(IMG_DIR, '*.png'))
self.IDS = [os.path.basename(fn).rstrip('.png') for fn in self.img_fns]
if IDS is not None:
print(f'Filtering {len(self.IDS)} IDS down to provided {len(IDS)}...')
IDS_set = set(IDS)
indices = [i for i, ID in enumerate(self.IDS) if ID in IDS_set]
self.img_fns = [self.img_fns[i] for i in indices]
self.IDS = [self.IDS[i] for i in indices]
self.mask = mask
def __len__(self):
return len(self.IDS)
def __getitem__(self, idx):
fn = self.img_fns[idx]
ID = self.IDS[idx]
try:
period = self.id2period[ID]
except KeyError as ke:
#print('Key Not Found in Period Dictionary:', ke)
period = 0
try:
genre = self.id2genre[ID]
except KeyError as ke:
#print('Key Not Found in Period Dictionary:', ke)
genre = 8 # other/uncertain
try:
provenience = self.id2provenience[ID]
except KeyError as ke:
#print('Key Not Found in Period Dictionary:', ke)
provenience = 3 # unknown
img = np.asarray(Image.open(fn))
alpha = 3 # Contrast control (1.0-3.0)
beta = 0 # Brightness control (0-100)
adjusted = cv2.convertScaleAbs(img, alpha=alpha, beta=beta)
img = img.astype(np.float32) / 255
img = cv2.GaussianBlur(img, (11,11), 0)
if self.mask:
img = (img > 0.125).astype(np.float32) ### 0.25 was great for most besides the really dark ones
return ID, img, self.PERIOD_INDICES.get(period, 0), self.GENRE_INDICES.get(genre, 8), self.PROVENIENCE_INDICES.get(provenience, 3) # 0: other |