import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) OUTPUT_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'data', 'output')) INPUT_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'data', 'input')) MASK_RCNN_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'utils', 'model', 'mask_rcnn_model')) MODEL_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'utils', 'model')) MODEL_PATH = os.path.join(MODEL_DIR, 'bg_removal_graph.pb') MIN_RATIO = 0.1 HSV_THRESH = 40 SUB_IMG_COUNTS = 20 SUB_IMG_MARGIN = 5
import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) JOB_TITLE_CSV_PATH = os.path.join(CUR_DIR, 'job_titles.csv') TRAIN_DATA_CSV_PATH = os.path.join(CUR_DIR, 'train.csv') TEST_DATA_CSV_PATH = os.path.join(CUR_DIR, 'test.csv') MODEL_DIR_PATH = make_directory_if_not_exists(os.path.join(CUR_DIR, 'utils', 'model')) MODEL_PATH = os.path.join(MODEL_DIR_PATH, "job_title_model.joblib") CORPUS_PATH = os.path.join(MODEL_DIR_PATH, 'corpus.txt') PREDICT_CSV_PATH = os.path.join(CUR_DIR, 'predict.csv') TEST_RATIO = 0.3 PREDICTION_ONLY = True PREDICTION_ONE_TITLE = True NEW_PREDICTION_PATH = ""
import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) LP_MODEL_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'utils', 'model', 'models', 'lp_detection_model')) ARABIC_LETTER_MODEL_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'utils', 'model', 'models', 'arabic_handwritten_model')) LP_CONFIG_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'utils', 'model', 'cfg')) LP_NAMES_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'utils', 'model', 'names')) ARABIC_LETTER_MODEL_WEIGHTS = os.path.join(ARABIC_LETTER_MODEL_DIR, 'weights.hdf5') ARABIC_LETTER_MODEL = os.path.join(ARABIC_LETTER_MODEL_DIR, 'letter_model.h5') ARABIC_LETTER_MODEL_YAML = os.path.join(ARABIC_LETTER_MODEL_DIR, 'letter_model.yaml') ARABIC_DIGITS_MODEL = os.path.join(ARABIC_LETTER_MODEL_DIR, 'digit_model.h5') ARABIC_DIGITS_YAML = os.path.join(ARABIC_LETTER_MODEL_DIR, 'digit_model.yaml') DETECT_FRAME_PATH = "" HANDWRITTEN_DIGITS_PATH = "/media/mensa/Data/Task/EgyALPR/data/ahdd1/csvTestImages 10k x 784.zip" RESIZE_HANDWRITTEN_DIGITS_PATH = "/media/mensa/Data/Task/EgyALPR/data/ahdd1/csvTestImages 10k x 1024.csv" HANDWRITTEN_DIGITS_TRAINING_IMAGE_PATH = "/media/mensa/Data/Task/EgyALPR/data/ahdd1/csvTrainImages 60k x 1024.zip" HANDWRITTEN_DIGITS_TRAINING_LABEL_PATH = "/media/mensa/Data/Task/EgyALPR/data/ahdd1/csvTrainLabel 60k x 1.zip" HANDWRITTEN_DIGITS_TESTING_IMAGE_PATH = "/media/mensa/Data/Task/EgyALPR/data/ahdd1/csvTestImages 10k x 1024.zip" HANDWRITTEN_DIGITS_TESTING_LABEL_PATH = "/media/mensa/Data/Task/EgyALPR/data/ahdd1/csvTestLabel 10k x 1.zip" HANDWRITTEN_LETTERS_TRAINING_IMAGE_PATH = "/media/mensa/Data/Task/EgyALPR/data/ahcd1/csvTrainImages 13440x1024.zip"
import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) MODEL_DIR = os.path.join(CUR_DIR, 'utils', 'model') DATA_DIR = os.path.join(CUR_DIR, 'data') TRAIN_DATA_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'utils', 'train_data')) FEATURE_CSV_FILE = os.path.join(TRAIN_DATA_DIR, 'feature.csv') LABEL_CSV_FILE = os.path.join(TRAIN_DATA_DIR, 'label.csv') CLASSIFICATION_MODEL = os.path.join(MODEL_DIR, 'classifier.pkl') INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
import os import configparser from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) INPUT_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'input')) OUTPUT_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'output')) PDF_IMAGES_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'pdf_images')) SAMPLE_DIR = os.path.join(CUR_DIR, 'utils', 'sample') SAMPLE_PDF = os.path.join(SAMPLE_DIR, 'sample.pdf') AWS_PDF_STORAGE_BUCKET = 'occupant-pdf' AWS_RESULT_OBJECT = "inbox/ODPS" CONFIG_FILE = os.path.join(CUR_DIR, 'config.cfg') params = configparser.ConfigParser() params.read(CONFIG_FILE) json_file = params.get("DEFAULT", "json_name") VISION_CREDENTIAL_PATH = os.path.join(CUR_DIR, 'utils', 'credential', f'{json_file}') PROCESSED_FILE = os.path.join(CUR_DIR, 'utils', 'processed_files.txt') FONT_SIZE = 0.6 FONT_WIDTH = 2 OCCUPANT_SPACING = 83 MOTORIST_SPACING = 145 REPORT_TEXT_POSITION = { "report_number": [470, 35],
import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) IMAGE_RESULT_DIR = make_directory_if_not_exists(os.path.join( CUR_DIR, 'result')) WEIGHTS_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'utils', 'weights')) TEST_DATA_DIR = os.path.join(CUR_DIR, 'test_data') POSITIVE_DIR = os.path.join(TEST_DATA_DIR, 'positives') NEGATIVE_DIR = os.path.join(TEST_DATA_DIR, 'HealthyAll') SUB_NEGATIVE_DIR = os.path.join(CUR_DIR, 'sub_healthy') ROC_CURVE_PATH = os.path.join(IMAGE_RESULT_DIR, 'roc_curve.png') NOISY_VS_TISSUE_MODEL_PATH = os.path.join(CUR_DIR, 'utils', 'NoisyVSTissueModel.h5') GENERATOR_WEIGHT = os.path.join(WEIGHTS_DIR, 'generator_127.h5') DISCRIMINATOR_WEIGHT = os.path.join(WEIGHTS_DIR, 'discriminator_127.h5') OPT_THRESH_PATH = os.path.join(IMAGE_RESULT_DIR, 'opt_threshold.txt') OPT_RESULT_CSV = os.path.join(IMAGE_RESULT_DIR, 'result.csv') PROCESS_CSV = os.path.join(IMAGE_RESULT_DIR, 'process_thresh_fpr_tpr.csv') MAX_GAN_LOSS_MSE = 11 MIN_GAN_LOSS_MSE = 2 OPT_THRESH = 0.0005 LOCAL = True if LOCAL: TEMP_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'temp'))
import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) PDF_IMAGES_DIR = make_directory_if_not_exists(os.path.join('/tmp', 'pdf_images')) CONFIG_FILE = os.path.join(CUR_DIR, 'config.cfg') PDF_RET = False
import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) ORIGIN_IMG_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'data', 'origin_images')) TEST_IMG_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'data', 'test_images')) MODEL_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'data', 'model')) MYSQL_CREDENTIAL_PATH = os.path.join(CUR_DIR, 'utils', 'mysql_credential.json') INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' # IMG_SITE_DIR = os.path.join(CUR_DIR, 'images', 'original') IMG_SITE_DIR = os.path.join('/opt', 'lampp', 'htdocs', 'server', 'images', 'original') FLANN_INDEX_KDTREE = 0 STANDARD_WIDTH = 800 STANDARD_HEIGHT = 600 SM_THRESH = 0.8
import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) MODEL_DIR = os.path.join(CUR_DIR, 'utils', 'model') INPUT_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'input')) VIDEO_INPUT_DIR = make_directory_if_not_exists( os.path.join(CUR_DIR, 'video_input')) UPLOAD_FOLDER = make_directory_if_not_exists( os.path.join(CUR_DIR, 'static', 'uploads')) CAFFEMODEL_PATH = os.path.join(MODEL_DIR, 'SSD_MobileNet.caffemodel') PROTOTXT_PATH = os.path.join(MODEL_DIR, 'SSD_MobileNet_prototxt.txt') YOLO_WEIGHT_PATH = os.path.join(MODEL_DIR, 'yolov3.weights') YOLO_CONFIG_PATH = os.path.join(MODEL_DIR, 'yolov3.cfg') YOLO_COCO_PATH = os.path.join(MODEL_DIR, 'coco.names') PB_MODEL_PATH = os.path.join(MODEL_DIR, 'frcnn_inception_v2.pb') PB_TEXT_PATH = os.path.join(MODEL_DIR, 'frcnn_inception_v2_graph.pbtxt') DETECT_CONFIDENCE = 0.3 OVERLAP_THRESH = 0.5 SAFE_DISTANCE = 200 FOCUS_LENGTH = 615 SERVER_HOST = "0.0.0.0" SERVER_PORT = 5000 GPU = True LOCAL = True
import os from utils.folder_file_manager import make_directory_if_not_exists CUR_DIR = os.path.dirname(os.path.abspath(__file__)) PDF_IMAGES_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'pdf_images')) PDF_UPLOAD_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'static', 'upload')) OUTPUT_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'output')) ARCHIVE_DIR = make_directory_if_not_exists(os.path.join(CUR_DIR, 'archive_dir')) INVOICE_DIR = os.path.join(CUR_DIR, 'invoice_dir') VISION_CREDENTIAL_PATH = os.path.join(CUR_DIR, 'utils', 'credential', 'tonal-studio-295208-5eedce5679d0.json') ROTATION_Y_THREAD = 200 Y_BIND_THREAD = 10 FRAME_THRESH_EMPHASIZE = 51 FRAME_THRESH_NOISE = 6 THREADING_ITEM = 2 LOCAL = True SERVER_HOST = "0.0.0.0" SERVER_PORT = 5000 DB_USERNAME = "******" DB_PASSWORD = "******" DB_NAME = "invoice_db" DB_HOST = "localhost"