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prepare_dataset.py
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prepare_dataset.py
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import cv2
import json
import numpy as np
import os
import random
import re
import shutil
import wget
import zipfile
from PIL import Image
from glob import glob
download_links = [
'ftp://smartengines.com/midv-500/dataset/12_deu_drvlic_new.zip',
'ftp://smartengines.com/midv-500/dataset/13_deu_drvlic_old.zip',
'ftp://smartengines.com/midv-500/dataset/14_deu_id_new.zip',
'ftp://smartengines.com/midv-500/dataset/15_deu_id_old.zip',
'ftp://smartengines.com/midv-500/dataset/16_deu_passport_new.zip',
'ftp://smartengines.com/midv-500/dataset/17_deu_passport_old.zip']
'''
download_links = ['ftp://smartengines.com/midv-500/dataset/01_alb_id.zip',
'ftp://smartengines.com/midv-500/dataset/02_aut_drvlic_new.zip',
'ftp://smartengines.com/midv-500/dataset/03_aut_id_old.zip',
'ftp://smartengines.com/midv-500/dataset/04_aut_id.zip',
'ftp://smartengines.com/midv-500/dataset/05_aze_passport.zip',
'ftp://smartengines.com/midv-500/dataset/06_bra_passport.zip',
'ftp://smartengines.com/midv-500/dataset/07_chl_id.zip',
'ftp://smartengines.com/midv-500/dataset/08_chn_homereturn.zip',
'ftp://smartengines.com/midv-500/dataset/09_chn_id.zip',
'ftp://smartengines.com/midv-500/dataset/10_cze_id.zip',
'ftp://smartengines.com/midv-500/dataset/11_cze_passport.zip',
'ftp://smartengines.com/midv-500/dataset/12_deu_drvlic_new.zip',
'ftp://smartengines.com/midv-500/dataset/13_deu_drvlic_old.zip',
'ftp://smartengines.com/midv-500/dataset/14_deu_id_new.zip',
'ftp://smartengines.com/midv-500/dataset/15_deu_id_old.zip',
'ftp://smartengines.com/midv-500/dataset/16_deu_passport_new.zip',
'ftp://smartengines.com/midv-500/dataset/17_deu_passport_old.zip',
'ftp://smartengines.com/midv-500/dataset/18_dza_passport.zip',
'ftp://smartengines.com/midv-500/dataset/19_esp_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/20_esp_id_new.zip',
'ftp://smartengines.com/midv-500/dataset/21_esp_id_old.zip',
'ftp://smartengines.com/midv-500/dataset/22_est_id.zip',
'ftp://smartengines.com/midv-500/dataset/23_fin_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/24_fin_id.zip',
'ftp://smartengines.com/midv-500/dataset/25_grc_passport.zip',
'ftp://smartengines.com/midv-500/dataset/26_hrv_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/27_hrv_passport.zip',
'ftp://smartengines.com/midv-500/dataset/28_hun_passport.zip',
'ftp://smartengines.com/midv-500/dataset/29_irn_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/30_ita_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/31_jpn_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/32_lva_passport.zip',
'ftp://smartengines.com/midv-500/dataset/33_mac_id.zip',
'ftp://smartengines.com/midv-500/dataset/34_mda_passport.zip',
'ftp://smartengines.com/midv-500/dataset/35_nor_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/36_pol_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/37_prt_id.zip',
'ftp://smartengines.com/midv-500/dataset/38_rou_drvlic.zip',
'ftp://smartengines.com/midv-500/dataset/39_rus_internalpassport.zip',
'ftp://smartengines.com/midv-500/dataset/40_srb_id.zip',
'ftp://smartengines.com/midv-500/dataset/41_srb_passport.zip',
'ftp://smartengines.com/midv-500/dataset/42_svk_id.zip',
'ftp://smartengines.com/midv-500/dataset/43_tur_id.zip',
'ftp://smartengines.com/midv-500/dataset/44_ukr_id.zip',
'ftp://smartengines.com/midv-500/dataset/45_ukr_passport.zip',
'ftp://smartengines.com/midv-500/dataset/46_ury_passport.zip',
'ftp://smartengines.com/midv-500/dataset/47_usa_bordercrossing.zip',
'ftp://smartengines.com/midv-500/dataset/48_usa_passportcard.zip',
'ftp://smartengines.com/midv-500/dataset/49_usa_ssn82.zip',
'ftp://smartengines.com/midv-500/dataset/50_xpo_id.zip']
'''
PATH_OFFSET = 40
TARGET_PATH = 'dataset/data/'
TEMP_PATH = 'dataset/temp/'
TEMP_IMAGE_PATH = TEMP_PATH + 'image/'
TEMP_MASK_PATH = TEMP_PATH + 'mask/'
DATA_PATH = 'dataset/train/'
SEED = 230
def read_image(img, label):
image = cv2.imread(img)
mask = np.zeros(image.shape, dtype=np.uint8)
quad = json.load(open(label, 'r'))
coords = np.array(quad['quad'], dtype=np.int32)
cv2.fillPoly(mask, coords.reshape(-1, 4, 2), color=(255, 255, 255))
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
mask = cv2.resize(mask, (mask.shape[1] // 2, mask.shape[0] // 2))
image = cv2.resize(image, (image.shape[1] // 2, image.shape[0] // 2))
mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)[1]
return image, mask
def download_and_unzip():
# Remove Temp Directory and create a new one
if os.path.exists(TEMP_PATH):
shutil.rmtree(TEMP_PATH, ignore_errors=True)
os.mkdir(TEMP_PATH)
os.mkdir(TEMP_IMAGE_PATH)
os.mkdir(TEMP_MASK_PATH)
# Counter for filename
file_idx = 1
for link in download_links:
filename = link[PATH_OFFSET:]
full_filename = TARGET_PATH + filename
directory_name = TARGET_PATH + link[PATH_OFFSET:-4]
print('Collect and prepare datasets...')
print('Dataset available... ', directory_name)
if not os.path.exists(directory_name):
if not os.path.isfile(full_filename):
# file not found, execute wget download
print ('Downloading:', link)
wget.download(link, TARGET_PATH)
# Unzip archives
with zipfile.ZipFile(full_filename, 'r') as zip_ref:
zip_ref.extractall(TARGET_PATH)
print('Prepare dataset... ', directory_name)
img_dir_path = './' + directory_name + '/images/'
gt_dir_path = './' + directory_name + '/ground_truth/'
# Remove unessesary files
if os.path.isfile(img_dir_path + filename + '.tif'):
os.remove(img_dir_path + filename.replace('.zip', '.tif'))
if os.path.isfile(gt_dir_path + filename + '.json'):
os.remove(gt_dir_path + filename.replace('.zip', '.json'))
# Load Images and Groundtruth and store as numpy array
for images, ground_truth in zip(sorted(os.listdir(img_dir_path)), sorted(os.listdir(gt_dir_path))):
img_list = sorted(glob(img_dir_path + images + '/*.tif'))
label_list = sorted(glob(gt_dir_path + ground_truth + '/*.json'))
for img, label in zip(img_list, label_list):
image, mask = read_image(img, label)
cv2.imwrite(TEMP_IMAGE_PATH + 'image' + str(file_idx) + '.png', image)
cv2.imwrite(TEMP_MASK_PATH + 'image' + str(file_idx) + '.png', mask)
file_idx += 1
print('----------------------------------------------------------------------')
def train_validation_split():
# Remove Temp Directory and create a new one
if os.path.exists(DATA_PATH):
shutil.rmtree(DATA_PATH, ignore_errors=True)
# Create folders to hold images and masks
folders = ['train_frames/image', 'train_masks/image', 'val_frames/image', 'val_masks/image', 'test_frames/image',
'test_masks/image']
for folder in folders:
os.makedirs(DATA_PATH + folder)
# Get all frames and masks, sort them, shuffle them to generate data sets.
all_frames = os.listdir(TEMP_IMAGE_PATH)
all_masks = os.listdir(TEMP_MASK_PATH)
all_frames.sort(key=lambda var: [int(x) if x.isdigit() else x
for x in re.findall(r'[^0-9]|[0-9]+', var)])
all_masks.sort(key=lambda var: [int(x) if x.isdigit() else x
for x in re.findall(r'[^0-9]|[0-9]+', var)])
random.seed(SEED)
random.shuffle(all_frames)
# Generate train, val, and test sets for frames
train_split = int(0.7 * len(all_frames))
val_split = int(0.9 * len(all_frames))
train_frames = all_frames[:train_split]
val_frames = all_frames[train_split:val_split]
test_frames = all_frames[val_split:]
# Generate corresponding mask lists for masks
train_masks = [f for f in all_masks if f in train_frames]
val_masks = [f for f in all_masks if f in val_frames]
test_masks = [f for f in all_masks if f in test_frames]
# Add train, val, test frames and masks to relevant folders
def add_frames(dir_name, image):
img = Image.open(TEMP_IMAGE_PATH + image)
img.save(DATA_PATH + '/{}'.format(dir_name) + '/' + image)
def add_masks(dir_name, image):
img = Image.open(TEMP_MASK_PATH + image)
img.save(DATA_PATH + '/{}'.format(dir_name) + '/' + image)
frame_folders = [(train_frames, 'train_frames/image'), (val_frames, 'val_frames/image'),
(test_frames, 'test_frames/image')]
mask_folders = [(train_masks, 'train_masks/image'), (val_masks, 'val_masks/image'),
(test_masks, 'test_masks/image')]
print('Split images into train, test and validation...')
# Add frames
for folder in frame_folders:
array = folder[0]
name = [folder[1]] * len(array)
list(map(add_frames, name, array))
# Add masks
for folder in mask_folders:
array = folder[0]
name = [folder[1]] * len(array)
list(map(add_masks, name, array))
def main():
download_and_unzip()
train_validation_split()
if __name__ == '__main__':
main()