基于 python easyOCR 使用案例_白发渔礁江渚上……-CSDN博客_easyocr
打包成exe文件,感谢引用文章中的博主。
修改character.py文件的引用为:
from pathlib import Path
import easyocr
import easyocr.model.vgg_model
file_url = r'1KRa316-0.png' # 需识别的图片
split_symbol = ' ' # 默认空格为分隔符
row_space = 15 # 默认字符高度为15px,当识别出来的字符间距超过这个数值时会换行。
def make_reader():
# 将模型加载到内存中。模型文件地址 C:\Users\用户\.EasyOCR\model
reader = easyocr.Reader(['ch_sim', 'en'], gpu=False, model_storage_directory="./easy/model", download_enabled=False, base_directory="./easy") #修改了此处
return reader
修改easyocr 库中的源文件的如下(修改character目录拼接,处理BASE_PATH的逻辑):
# -*- coding: utf-8 -*-
from .detection import get_detector, get_textbox
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
from .config import *
from bidi.algorithm 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
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, recog_network = 'standard',
download_enabled=True, detector=True, recognizer=True,
verbose=True, quantize=True, cudnn_benchmark=False, base_directory=None):
"""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.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.base_directory = BASE_PATH
if base_directory:
self.base_directory = base_directory
Path(self.base_directory).mkdir(parents=True, exist_ok=True)
LOGGER.warning("self.base_directory=%s"%self.base_directory)
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 not torch.cuda.is_available():
self.device = 'cpu'
if verbose:
LOGGER.warning('CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU.')
elif gpu is True:
self.device = 'cuda'
else:
self.device = gpu
self.recognition_models = recognition_models
# check and download detection model
detector_model = 'craft'
corrupt_msg = 'MD5 hash mismatch, possible file corruption'
detector_path = os.path.join(self.model_storage_directory, detection_models[detector_model]['filename'])
if detector:
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(detection_models[detector_model]['url'], detection_models[detector_model]['filename'], self.model_storage_directory, verbose)
assert calculate_md5(detector_path) == detection_models[detector_model]['filesize'], corrupt_msg
LOGGER.info('Download complete')
elif calculate_md5(detector_path) != detection_models[detector_model]['filesize']:
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(detection_models[detector_model]['url'], detection_models[detector_model]['filename'], self.model_storage_directory, verbose)
assert calculate_md5(detector_path) == detection_models[detector_model]['filesize'], corrupt_msg
# 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['gen1']['cyrillic_g1']
recog_network = 'generation1'
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['filesize'], corrupt_msg
LOGGER.info('Download complete.')
elif calculate_md5(model_path) != model['filesize']:
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['filesize'], 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)
imgH = recog_config['imgH']
available_lang = recog_config['lang_list']
self.setModelLanguage(recog_network, lang_list, available_lang, 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, None)
dict_list = {}
for lang in lang_list:
dict_list[lang] = os.path.join(self.base_directory, 'dict', lang + ".txt")
if detector:
self.detector = get_detector(detector_path, self.device, quantize, cudnn_benchmark=cudnn_benchmark)
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 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(self.base_directory, '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(self.base_directory, '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:
symbol = model['symbols']
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):
if reformat:
img, img_cv_grey = reformat_input(img)
text_box_list = get_textbox(self.detector, img, canvas_size, mag_ratio,
text_threshold, link_threshold, low_text,
False, self.device, optimal_num_chars)
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):
result = set_result_with_confidence(result, image_len)
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':
return [ {'boxes':item[0],'text':item[1],'confident':item[2]} for item in result]
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, 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, text_threshold,\
low_text, link_threshold,\
canvas_size, mag_ratio,\
slope_ths, ycenter_ths,\
height_ths,width_ths,\
add_margin, False)
# 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, 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, text_threshold,\
low_text, link_threshold,\
canvas_size, mag_ratio,\
slope_ths, ycenter_ths,\
height_ths,width_ths,\
add_margin, False)
# 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, 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, text_threshold,\
low_text, link_threshold,\
canvas_size, mag_ratio,\
slope_ths, ycenter_ths,\
height_ths, width_ths,\
add_margin, False)
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
使用pyinstaller打包,命令如下:
#使用conda创建干净的环境变量,打出的exe文件会小很多
#安装依赖包
pip install --upgrade pip
pip install easyocr
pip install opencv-python==4.1.2.30
pip install pyinstaller
#打包成exe文件
Pyinstaller -F -i main.ico main.py
把easyocr源码中的character、dict目录放到easy目录下,把easyocr的模型也放到easy目录下,目录结构
编译后把easy目录放到exe同级目录,此种处理比较笨,还未找到其他方法,暂时这样
参考链接:
别再问我Python打包成exe了!(终极版)_简说Python的博客-CSDN博客
python easyOCR 使用案例_白发渔礁江渚上……-CSDN博客_easyocr
试试EasyOCR_罗索