<jupyter_start><jupyter_text>Mask R-CNN - Training Character Recognition<jupyter_code>from google.colab import drive
drive.mount('/content/drive', force_remount=True)
%tensorflow_version 1.x
!pip install keras==2.1.5
!pip install imagecorruptions
!pip install imgaug==0.4.0
import os
import sys
import itertools
import math
import logging
import json
import re
import random
from collections import OrderedDict
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.lines as lines
from matplotlib.patches import Polygon

os.chdir("/content/drive/My Drive/Artificial Vision/Progetto Finale/Mask RCNN/characters")
# Root directory of the project
ROOT_DIR = os.path.abspath("../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
import mrcnn.model as modellib
from mrcnn.model import log

import characters

%matplotlib inline 
!python characters.py --weights='coco' --logs "model_data/weights_box"<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07453416149068323, 'fraction_numerical': 0.006211180124223602, 'mean_word_length': 5.836363636363636, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '324960', 'license': 'BSD-3-Clause-Attribution', 'n_tokens_mistral': 359, 'n_tokens_neox': 354, 'n_words': 115, 'path': 'Mask RCNN/characters/training.ipynb', 'repo_name': 'Bart94/License_Plate_Detection_and_Recognition', 'size': '1127'}