# -*- coding: utf-8 -*- from .recognition import get_recognizer, get_text from .utils import group_text_box, get_image_list, calculate_md5, get_paragraph,\ download_and_unzip, printProgressBar, diff, reformat_input,\ make_rotated_img_list, set_result_with_confidence,\ reformat_input_batched, merge_to_free from .config import * from bidi import get_display import numpy as np import cv2 import torch import os import sys from PIL import Image from logging import getLogger import yaml import json if sys.version_info[0] == 2: from io import open from six.moves.urllib.request import urlretrieve from pathlib2 import Path else: from urllib.request import urlretrieve from pathlib import Path LOGGER = getLogger(__name__) class Reader(object): def __init__(self, lang_list, gpu=True, model_storage_directory=None, user_network_directory=None, detect_network="craft", recog_network='standard', download_enabled=True, detector=True, recognizer=True, verbose=True, quantize=True, cudnn_benchmark=False): """Create an EasyOCR Reader Parameters: lang_list (list): Language codes (ISO 639) for languages to be recognized during analysis. gpu (bool): Enable GPU support (default) model_storage_directory (string): Path to directory for model data. If not specified, models will be read from a directory as defined by the environment variable EASYOCR_MODULE_PATH (preferred), MODULE_PATH (if defined), or ~/.EasyOCR/. user_network_directory (string): Path to directory for custom network architecture. If not specified, it is as defined by the environment variable EASYOCR_MODULE_PATH (preferred), MODULE_PATH (if defined), or ~/.EasyOCR/. download_enabled (bool): Enabled downloading of model data via HTTP (default). """ self.verbose = verbose self.download_enabled = download_enabled self.model_storage_directory = MODULE_PATH + '/model' if model_storage_directory: self.model_storage_directory = model_storage_directory Path(self.model_storage_directory).mkdir(parents=True, exist_ok=True) self.user_network_directory = MODULE_PATH + '/user_network' if user_network_directory: self.user_network_directory = user_network_directory Path(self.user_network_directory).mkdir(parents=True, exist_ok=True) sys.path.append(self.user_network_directory) if gpu is False: self.device = 'cpu' if verbose: LOGGER.warning('Using CPU. Note: This module is much faster with a GPU.') elif gpu is True: if torch.cuda.is_available(): self.device = 'cuda' elif torch.backends.mps.is_available(): self.device = 'mps' else: self.device = 'cpu' if verbose: LOGGER.warning('Neither CUDA nor MPS are available - defaulting to CPU. Note: This module is much faster with a GPU.') else: self.device = gpu self.detection_models = detection_models self.recognition_models = recognition_models # check and download detection model self.support_detection_network = ['craft', 'dbnet18'] self.quantize=quantize, self.cudnn_benchmark=cudnn_benchmark if detector: detector_path = self.getDetectorPath(detect_network) # recognition model separator_list = {} if recog_network in ['standard'] + [model for model in recognition_models['gen1']] + [model for model in recognition_models['gen2']]: if recog_network in [model for model in recognition_models['gen1']]: model = recognition_models['gen1'][recog_network] recog_network = 'generation1' self.model_lang = model['model_script'] elif recog_network in [model for model in recognition_models['gen2']]: model = recognition_models['gen2'][recog_network] recog_network = 'generation2' self.model_lang = model['model_script'] else: # auto-detect unknown_lang = set(lang_list) - set(all_lang_list) if unknown_lang != set(): raise ValueError(unknown_lang, 'is not supported') # choose recognition model if lang_list == ['en']: self.setModelLanguage('english', lang_list, ['en'], '["en"]') model = recognition_models['gen2']['english_g2'] recog_network = 'generation2' elif 'th' in lang_list: self.setModelLanguage('thai', lang_list, ['th','en'], '["th","en"]') model = recognition_models['gen1']['thai_g1'] recog_network = 'generation1' elif 'ch_tra' in lang_list: self.setModelLanguage('chinese_tra', lang_list, ['ch_tra','en'], '["ch_tra","en"]') model = recognition_models['gen1']['zh_tra_g1'] recog_network = 'generation1' elif 'ch_sim' in lang_list: self.setModelLanguage('chinese_sim', lang_list, ['ch_sim','en'], '["ch_sim","en"]') model = recognition_models['gen2']['zh_sim_g2'] recog_network = 'generation2' elif 'ja' in lang_list: self.setModelLanguage('japanese', lang_list, ['ja','en'], '["ja","en"]') model = recognition_models['gen2']['japanese_g2'] recog_network = 'generation2' elif 'ko' in lang_list: self.setModelLanguage('korean', lang_list, ['ko','en'], '["ko","en"]') model = recognition_models['gen2']['korean_g2'] recog_network = 'generation2' elif 'ta' in lang_list: self.setModelLanguage('tamil', lang_list, ['ta','en'], '["ta","en"]') model = recognition_models['gen1']['tamil_g1'] recog_network = 'generation1' elif 'te' in lang_list: self.setModelLanguage('telugu', lang_list, ['te','en'], '["te","en"]') model = recognition_models['gen2']['telugu_g2'] recog_network = 'generation2' elif 'kn' in lang_list: self.setModelLanguage('kannada', lang_list, ['kn','en'], '["kn","en"]') model = recognition_models['gen2']['kannada_g2'] recog_network = 'generation2' elif set(lang_list) & set(bengali_lang_list): self.setModelLanguage('bengali', lang_list, bengali_lang_list+['en'], '["bn","as","en"]') model = recognition_models['gen1']['bengali_g1'] recog_network = 'generation1' elif set(lang_list) & set(arabic_lang_list): self.setModelLanguage('arabic', lang_list, arabic_lang_list+['en'], '["ar","fa","ur","ug","en"]') model = recognition_models['gen1']['arabic_g1'] recog_network = 'generation1' elif set(lang_list) & set(devanagari_lang_list): self.setModelLanguage('devanagari', lang_list, devanagari_lang_list+['en'], '["hi","mr","ne","en"]') model = recognition_models['gen1']['devanagari_g1'] recog_network = 'generation1' elif set(lang_list) & set(cyrillic_lang_list): self.setModelLanguage('cyrillic', lang_list, cyrillic_lang_list+['en'], '["ru","rs_cyrillic","be","bg","uk","mn","en"]') model = recognition_models['gen2']['cyrillic_g2'] recog_network = 'generation2' else: self.model_lang = 'latin' model = recognition_models['gen2']['latin_g2'] recog_network = 'generation2' self.character = model['characters'] model_path = os.path.join(self.model_storage_directory, model['filename']) # check recognition model file if recognizer: if os.path.isfile(model_path) == False: if not self.download_enabled: raise FileNotFoundError("Missing %s and downloads disabled" % model_path) LOGGER.warning('Downloading recognition model, please wait. ' 'This may take several minutes depending upon your network connection.') download_and_unzip(model['url'], model['filename'], self.model_storage_directory, verbose) assert calculate_md5(model_path) == model['md5sum'], corrupt_msg LOGGER.info('Download complete.') elif calculate_md5(model_path) != model['md5sum']: if not self.download_enabled: raise FileNotFoundError("MD5 mismatch for %s and downloads disabled" % model_path) LOGGER.warning(corrupt_msg) os.remove(model_path) LOGGER.warning('Re-downloading the recognition model, please wait. ' 'This may take several minutes depending upon your network connection.') download_and_unzip(model['url'], model['filename'], self.model_storage_directory, verbose) assert calculate_md5(model_path) == model['md5sum'], corrupt_msg LOGGER.info('Download complete') self.setLanguageList(lang_list, model) else: # user-defined model with open(os.path.join(self.user_network_directory, recog_network+ '.yaml'), encoding='utf8') as file: recog_config = yaml.load(file, Loader=yaml.FullLoader) global imgH # if custom model, save this variable. (from *.yaml) if recog_config['imgH']: imgH = recog_config['imgH'] available_lang = recog_config['lang_list'] self.setModelLanguage(recog_network, lang_list, available_lang, str(available_lang)) #char_file = os.path.join(self.user_network_directory, recog_network+ '.txt') self.character = recog_config['character_list'] model_file = recog_network+ '.pth' model_path = os.path.join(self.model_storage_directory, model_file) self.setLanguageList(lang_list, recog_config) dict_list = {} for lang in lang_list: dict_list[lang] = os.path.join(BASE_PATH, 'dict', lang + ".txt") if detector: self.detector = self.initDetector(detector_path) if recognizer: if recog_network == 'generation1': network_params = { 'input_channel': 1, 'output_channel': 512, 'hidden_size': 512 } elif recog_network == 'generation2': network_params = { 'input_channel': 1, 'output_channel': 256, 'hidden_size': 256 } else: network_params = recog_config['network_params'] self.recognizer, self.converter = get_recognizer(recog_network, network_params,\ self.character, separator_list,\ dict_list, model_path, device = self.device, quantize=quantize) def getDetectorPath(self, detect_network): if detect_network in self.support_detection_network: self.detect_network = detect_network if self.detect_network == 'craft': from .detection import get_detector, get_textbox elif self.detect_network in ['dbnet18']: from .detection_db import get_detector, get_textbox else: raise RuntimeError("Unsupport detector network. Support networks are craft and dbnet18.") self.get_textbox = get_textbox self.get_detector = get_detector corrupt_msg = 'MD5 hash mismatch, possible file corruption' detector_path = os.path.join(self.model_storage_directory, self.detection_models[self.detect_network]['filename']) if os.path.isfile(detector_path) == False: if not self.download_enabled: raise FileNotFoundError("Missing %s and downloads disabled" % detector_path) LOGGER.warning('Downloading detection model, please wait. ' 'This may take several minutes depending upon your network connection.') download_and_unzip(self.detection_models[self.detect_network]['url'], self.detection_models[self.detect_network]['filename'], self.model_storage_directory, self.verbose) assert calculate_md5(detector_path) == self.detection_models[self.detect_network]['md5sum'], corrupt_msg LOGGER.info('Download complete') elif calculate_md5(detector_path) != self.detection_models[self.detect_network]['md5sum']: if not self.download_enabled: raise FileNotFoundError("MD5 mismatch for %s and downloads disabled" % detector_path) LOGGER.warning(corrupt_msg) os.remove(detector_path) LOGGER.warning('Re-downloading the detection model, please wait. ' 'This may take several minutes depending upon your network connection.') download_and_unzip(self.detection_models[self.detect_network]['url'], self.detection_models[self.detect_network]['filename'], self.model_storage_directory, self.verbose) assert calculate_md5(detector_path) == self.detection_models[self.detect_network]['md5sum'], corrupt_msg else: raise RuntimeError("Unsupport detector network. Support networks are {}.".format(', '.join(self.support_detection_network))) return detector_path def initDetector(self, detector_path): return self.get_detector(detector_path, device = self.device, quantize = self.quantize, cudnn_benchmark = self.cudnn_benchmark ) def setDetector(self, detect_network): detector_path = self.getDetectorPath(detect_network) self.detector = self.initDetector(detector_path) def setModelLanguage(self, language, lang_list, list_lang, list_lang_string): self.model_lang = language if set(lang_list) - set(list_lang) != set(): if language == 'ch_tra' or language == 'ch_sim': language = 'chinese' raise ValueError(language.capitalize() + ' is only compatible with English, try lang_list=' + list_lang_string) def getChar(self, fileName): char_file = os.path.join(BASE_PATH, 'character', fileName) with open(char_file, "r", encoding="utf-8-sig") as input_file: list = input_file.read().splitlines() char = ''.join(list) return char def setLanguageList(self, lang_list, model): self.lang_char = [] for lang in lang_list: char_file = os.path.join(BASE_PATH, 'character', lang + "_char.txt") with open(char_file, "r", encoding = "utf-8-sig") as input_file: char_list = input_file.read().splitlines() self.lang_char += char_list if model.get('symbols'): symbol = model['symbols'] elif model.get('character_list'): symbol = model['character_list'] else: symbol = '0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ ' self.lang_char = set(self.lang_char).union(set(symbol)) self.lang_char = ''.join(self.lang_char) def detect(self, img, min_size = 20, text_threshold = 0.7, low_text = 0.4,\ link_threshold = 0.4,canvas_size = 2560, mag_ratio = 1.,\ slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5,\ width_ths = 0.5, add_margin = 0.1, reformat=True, optimal_num_chars=None, threshold = 0.2, bbox_min_score = 0.2, bbox_min_size = 3, max_candidates = 0, ): if reformat: img, img_cv_grey = reformat_input(img) text_box_list = self.get_textbox(self.detector, img, canvas_size = canvas_size, mag_ratio = mag_ratio, text_threshold = text_threshold, link_threshold = link_threshold, low_text = low_text, poly = False, device = self.device, optimal_num_chars = optimal_num_chars, threshold = threshold, bbox_min_score = bbox_min_score, bbox_min_size = bbox_min_size, max_candidates = max_candidates, ) horizontal_list_agg, free_list_agg = [], [] for text_box in text_box_list: horizontal_list, free_list = group_text_box(text_box, slope_ths, ycenter_ths, height_ths, width_ths, add_margin, (optimal_num_chars is None)) if min_size: horizontal_list = [i for i in horizontal_list if max( i[1] - i[0], i[3] - i[2]) > min_size] free_list = [i for i in free_list if max( diff([c[0] for c in i]), diff([c[1] for c in i])) > min_size] horizontal_list_agg.append(horizontal_list) free_list_agg.append(free_list) return horizontal_list_agg, free_list_agg def recognize(self, img_cv_grey, horizontal_list=None, free_list=None,\ decoder = 'greedy', beamWidth= 5, batch_size = 1,\ workers = 0, allowlist = None, blocklist = None, detail = 1,\ rotation_info = None,paragraph = False,\ contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\ y_ths = 0.5, x_ths = 1.0, reformat=True, output_format='standard'): if reformat: img, img_cv_grey = reformat_input(img_cv_grey) if allowlist: ignore_char = ''.join(set(self.character)-set(allowlist)) elif blocklist: ignore_char = ''.join(set(blocklist)) else: ignore_char = ''.join(set(self.character)-set(self.lang_char)) if self.model_lang in ['chinese_tra','chinese_sim']: decoder = 'greedy' if (horizontal_list==None) and (free_list==None): y_max, x_max = img_cv_grey.shape horizontal_list = [[0, x_max, 0, y_max]] free_list = [] # without gpu/parallelization, it is faster to process image one by one if ((batch_size == 1) or (self.device == 'cpu')) and not rotation_info: result = [] for bbox in horizontal_list: h_list = [bbox] f_list = [] image_list, max_width = get_image_list(h_list, f_list, img_cv_grey, model_height = imgH) result0 = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list,\ ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths,\ workers, self.device) result += result0 for bbox in free_list: h_list = [] f_list = [bbox] image_list, max_width = get_image_list(h_list, f_list, img_cv_grey, model_height = imgH) result0 = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list,\ ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths,\ workers, self.device) result += result0 # default mode will try to process multiple boxes at the same time else: image_list, max_width = get_image_list(horizontal_list, free_list, img_cv_grey, model_height = imgH) image_len = len(image_list) if rotation_info and image_list: image_list = make_rotated_img_list(rotation_info, image_list) max_width = max(max_width, imgH) result = get_text(self.character, imgH, int(max_width), self.recognizer, self.converter, image_list,\ ignore_char, decoder, beamWidth, batch_size, contrast_ths, adjust_contrast, filter_ths,\ workers, self.device) if rotation_info and (horizontal_list+free_list): # Reshape result to be a list of lists, each row being for # one of the rotations (first row being no rotation) result = set_result_with_confidence( [result[image_len*i:image_len*(i+1)] for i in range(len(rotation_info) + 1)]) if self.model_lang == 'arabic': direction_mode = 'rtl' result = [list(item) for item in result] for item in result: item[1] = get_display(item[1]) else: direction_mode = 'ltr' if paragraph: result = get_paragraph(result, x_ths=x_ths, y_ths=y_ths, mode = direction_mode) if detail == 0: return [item[1] for item in result] elif output_format == 'dict': if paragraph: return [ {'boxes':item[0],'text':item[1]} for item in result] return [ {'boxes':item[0],'text':item[1],'confident':item[2]} for item in result] elif output_format == 'json': if paragraph: return [json.dumps({'boxes':[list(map(int, lst)) for lst in item[0]],'text':item[1]}, ensure_ascii=False) for item in result] return [json.dumps({'boxes':[list(map(int, lst)) for lst in item[0]],'text':item[1],'confident':item[2]}, ensure_ascii=False) for item in result] elif output_format == 'free_merge': return merge_to_free(result, free_list) else: return result def readtext(self, image, decoder = 'greedy', beamWidth= 5, batch_size = 1,\ workers = 0, allowlist = None, blocklist = None, detail = 1,\ rotation_info = None, paragraph = False, min_size = 20,\ contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\ text_threshold = 0.7, low_text = 0.4, link_threshold = 0.4,\ canvas_size = 2560, mag_ratio = 1.,\ slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5,\ width_ths = 0.5, y_ths = 0.5, x_ths = 1.0, add_margin = 0.1, threshold = 0.2, bbox_min_score = 0.2, bbox_min_size = 3, max_candidates = 0, output_format='standard'): ''' Parameters: image: file path or numpy-array or a byte stream object ''' img, img_cv_grey = reformat_input(image) horizontal_list, free_list = self.detect(img, min_size = min_size, text_threshold = text_threshold,\ low_text = low_text, link_threshold = link_threshold,\ canvas_size = canvas_size, mag_ratio = mag_ratio,\ slope_ths = slope_ths, ycenter_ths = ycenter_ths,\ height_ths = height_ths, width_ths= width_ths,\ add_margin = add_margin, reformat = False,\ threshold = threshold, bbox_min_score = bbox_min_score,\ bbox_min_size = bbox_min_size, max_candidates = max_candidates ) # get the 1st result from hor & free list as self.detect returns a list of depth 3 horizontal_list, free_list = horizontal_list[0], free_list[0] result = self.recognize(img_cv_grey, horizontal_list, free_list,\ decoder, beamWidth, batch_size,\ workers, allowlist, blocklist, detail, rotation_info,\ paragraph, contrast_ths, adjust_contrast,\ filter_ths, y_ths, x_ths, False, output_format) return result def readtextlang(self, image, decoder = 'greedy', beamWidth= 5, batch_size = 1,\ workers = 0, allowlist = None, blocklist = None, detail = 1,\ rotation_info = None, paragraph = False, min_size = 20,\ contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\ text_threshold = 0.7, low_text = 0.4, link_threshold = 0.4,\ canvas_size = 2560, mag_ratio = 1.,\ slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5,\ width_ths = 0.5, y_ths = 0.5, x_ths = 1.0, add_margin = 0.1, threshold = 0.2, bbox_min_score = 0.2, bbox_min_size = 3, max_candidates = 0, output_format='standard'): ''' Parameters: image: file path or numpy-array or a byte stream object ''' img, img_cv_grey = reformat_input(image) horizontal_list, free_list = self.detect(img, min_size = min_size, text_threshold = text_threshold,\ low_text = low_text, link_threshold = link_threshold,\ canvas_size = canvas_size, mag_ratio = mag_ratio,\ slope_ths = slope_ths, ycenter_ths = ycenter_ths,\ height_ths = height_ths, width_ths= width_ths,\ add_margin = add_margin, reformat = False,\ threshold = threshold, bbox_min_score = bbox_min_score,\ bbox_min_size = bbox_min_size, max_candidates = max_candidates ) # get the 1st result from hor & free list as self.detect returns a list of depth 3 horizontal_list, free_list = horizontal_list[0], free_list[0] result = self.recognize(img_cv_grey, horizontal_list, free_list,\ decoder, beamWidth, batch_size,\ workers, allowlist, blocklist, detail, rotation_info,\ paragraph, contrast_ths, adjust_contrast,\ filter_ths, y_ths, x_ths, False, output_format) char = [] directory = 'characters/' for i in range(len(result)): char.append(result[i][1]) def search(arr,x): g = False for i in range(len(arr)): if arr[i]==x: g = True return 1 if g == False: return -1 def tupleadd(i): a = result[i] b = a + (filename[0:2],) return b for filename in os.listdir(directory): if filename.endswith(".txt"): with open ('characters/'+ filename,'rt',encoding="utf8") as myfile: chartrs = str(myfile.read().splitlines()).replace('\n','') for i in range(len(char)): res = search(chartrs,char[i]) if res != -1: if filename[0:2]=="en" or filename[0:2]=="ch": print(tupleadd(i)) def readtext_batched(self, image, n_width=None, n_height=None,\ decoder = 'greedy', beamWidth= 5, batch_size = 1,\ workers = 0, allowlist = None, blocklist = None, detail = 1,\ rotation_info = None, paragraph = False, min_size = 20,\ contrast_ths = 0.1,adjust_contrast = 0.5, filter_ths = 0.003,\ text_threshold = 0.7, low_text = 0.4, link_threshold = 0.4,\ canvas_size = 2560, mag_ratio = 1.,\ slope_ths = 0.1, ycenter_ths = 0.5, height_ths = 0.5,\ width_ths = 0.5, y_ths = 0.5, x_ths = 1.0, add_margin = 0.1, threshold = 0.2, bbox_min_score = 0.2, bbox_min_size = 3, max_candidates = 0, output_format='standard'): ''' Parameters: image: file path or numpy-array or a byte stream object When sending a list of images, they all must of the same size, the following parameters will automatically resize if they are not None n_width: int, new width n_height: int, new height ''' img, img_cv_grey = reformat_input_batched(image, n_width, n_height) horizontal_list_agg, free_list_agg = self.detect(img, min_size = min_size, text_threshold = text_threshold,\ low_text = low_text, link_threshold = link_threshold,\ canvas_size = canvas_size, mag_ratio = mag_ratio,\ slope_ths = slope_ths, ycenter_ths = ycenter_ths,\ height_ths = height_ths, width_ths= width_ths,\ add_margin = add_margin, reformat = False,\ threshold = threshold, bbox_min_score = bbox_min_score,\ bbox_min_size = bbox_min_size, max_candidates = max_candidates ) result_agg = [] # put img_cv_grey in a list if its a single img img_cv_grey = [img_cv_grey] if len(img_cv_grey.shape) == 2 else img_cv_grey for grey_img, horizontal_list, free_list in zip(img_cv_grey, horizontal_list_agg, free_list_agg): result_agg.append(self.recognize(grey_img, horizontal_list, free_list,\ decoder, beamWidth, batch_size,\ workers, allowlist, blocklist, detail, rotation_info,\ paragraph, contrast_ths, adjust_contrast,\ filter_ths, y_ths, x_ths, False, output_format)) return result_agg