Python Image_Processing.extraction_image Exemples

Exemple #1

Fichier : Callback.py Projet : zijiwork/Bearcat_captcha

 def calculate_the_best_weight(self):
     if os.listdir(checkpoint_path):
         value = Image_Processing.extraction_image(checkpoint_path)
         extract_num = [
             os.path.splitext(os.path.split(i)[-1])[0] for i in value
         ]
         num = [re.split('-', i) for i in extract_num]
         accs = [float(i[-1]) for i in num]
         losses = [float('-' + str(abs(float(i[-2])))) for i in num]
         index = [acc + loss for acc, loss in zip(accs, losses)]
         model_dict = dict((ind, val) for ind, val in zip(index, value))
         return model_dict.get(max(index))
     else:
         logger.debug('没有可用的检查点')

Exemple #2

from works.project_12306_ocr.settings import train_path
from works.project_12306_ocr.Function_API import Image_Processing

Image_Processing.rename_suffix(Image_Processing.extraction_image(train_path))

Exemple #3

Fichier : app.py Projet : zijiwork/Bearcat_captcha

from works.project_12306_ocr.Callback import CallBack
from works.project_12306_ocr.settings import MODEL
from works.project_12306_ocr.settings import MODEL_NAME
from works.project_12306_ocr.settings import checkpoint_path
from works.project_12306_ocr.settings import App_model_path
from works.project_12306_ocr.settings import MULITI_MODEL_PREDICTION
from works.project_12306_ocr.Function_API import Distinguish_image
from works.project_12306_ocr.Function_API import Image_Processing

gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(device=gpu, enable=True)

app = Flask(__name__)
if os.listdir(App_model_path):
    model_path = Image_Processing.extraction_image(App_model_path)
    logger.debug(f'{model_path}模型加载成功')
else:
    model = operator.methodcaller(MODEL)(Models)
    try:
        model.load_weights(os.path.join(checkpoint_path, CallBack.calculate_the_best_weight()))
    except:
        raise OSError(f'没有任何的权重和模型在{App_model_path}')
    model_save = os.path.join(App_model_path, MODEL_NAME)
    model.save(model_save)
    model_path = [model_save]
    logger.debug(f'{model_path}模型加载成功')


# logger.debug(model_path)

Exemple #4

import random
from works.project_12306_ocr.settings import train_path
from works.project_12306_ocr.Function_API import Image_Processing

train_image = Image_Processing.extraction_image(train_path)
random.shuffle(train_image)
Image_Processing.move_path(train_image)

Exemple #5

Fichier : train_run.py Projet : zijiwork/Bearcat_captcha

from works.project_12306_ocr.settings import DATA_ENHANCEMENT
from works.project_12306_ocr.settings import train_path
from works.project_12306_ocr.settings import train_pack_path
from works.project_12306_ocr.settings import validation_pack_path
from works.project_12306_ocr.settings import test_pack_path
from works.project_12306_ocr.settings import train_enhance_path
from works.project_12306_ocr.Function_API import cheak_path
from works.project_12306_ocr.Function_API import Image_Processing
from works.project_12306_ocr.Function_API import parse_function_ocr

gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(device=gpu, enable=True)

train_dataset = tf.data.TFRecordDataset(
    Image_Processing.extraction_image(train_pack_path)).map(
        parse_function_ocr).batch(BATCH_SIZE)

validation_dataset = tf.data.TFRecordDataset(
    Image_Processing.extraction_image(validation_pack_path)).map(
        parse_function_ocr).batch(BATCH_SIZE)

test_dataset = tf.data.TFRecordDataset(
    Image_Processing.extraction_image(test_pack_path)).map(
        parse_function_ocr).batch(BATCH_SIZE)

logger.debug(train_dataset)

model, c_callback = CallBack.callback(operator.methodcaller(MODEL)(Models))

model.summary()

Exemple #6

from works.project_12306_ocr.settings import validation_path
from works.project_12306_ocr.settings import test_path
from works.project_12306_ocr.settings import IMAGE_HEIGHT
from works.project_12306_ocr.settings import IMAGE_WIDTH
from works.project_12306_ocr.settings import train_enhance_path
from works.project_12306_ocr.settings import DATA_ENHANCEMENT
from works.project_12306_ocr.settings import TFRecord_train_path
from works.project_12306_ocr.settings import TFRecord_validation_path
from works.project_12306_ocr.settings import TFRecord_test_path
from works.project_12306_ocr.Function_API import Image_Processing
from works.project_12306_ocr.Function_API import WriteTFRecord
from concurrent.futures import ThreadPoolExecutor

if DATA_ENHANCEMENT:
    with ThreadPoolExecutor(max_workers=100) as t:
        for i in Image_Processing.extraction_image(train_path):
            task = t.submit(Image_Processing.preprosess_save_images, i,
                            [IMAGE_HEIGHT, IMAGE_WIDTH])
    train_image = Image_Processing.extraction_image(train_enhance_path)
    Image_Processing.save_ocr_class(train_image)
    random.shuffle(train_image)
    train_lable = Image_Processing.extraction_ocr_lable(train_image)
else:
    train_image = Image_Processing.extraction_image(train_path)
    Image_Processing.save_ocr_class(train_image)
    random.shuffle(train_image)
    train_lable = Image_Processing.extraction_ocr_lable(train_image)

validation_image = Image_Processing.extraction_image(validation_path)
validation_lable = Image_Processing.extraction_ocr_lable(validation_image)

Exemple #7

from works.project_12306_ocr.settings import test_path
from works.project_12306_ocr.settings import model_path
from works.project_12306_ocr.settings import BATCH_SIZE
from works.project_12306_ocr.settings import test_pack_path
from works.project_12306_ocr.settings import MODEL_LEAD_NAME
from works.project_12306_ocr.settings import MULITI_MODEL_PREDICTION
from works.project_12306_ocr.Function_API import Image_Processing
from works.project_12306_ocr.Function_API import Distinguish_image
from works.project_12306_ocr.Function_API import parse_function_verification

gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(device=gpu, enable=True)

test_dataset = tf.data.TFRecordDataset(
    Image_Processing.extraction_image(test_pack_path)).map(
        parse_function_verification).batch(BATCH_SIZE)

test_image_list = Image_Processing.extraction_image(test_path)
random.shuffle(test_image_list)

if MULITI_MODEL_PREDICTION:
    model_path = Image_Processing.extraction_image(model_path)
    if not model_path:
        raise OSError(f'{model_path}没有模型')
    for i in test_image_list[:10]:
        Distinguish_image.distinguish_images(model_path, i)
else:
    model_path = os.path.join(model_path, MODEL_LEAD_NAME)
    logger.debug(f'加载模型{model_path}')
    if not os.path.exists(model_path):