79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
import numpy as np
|
|
import tensorflow as tf
|
|
from tensorflow import keras
|
|
from tensorflow.keras import layers
|
|
from PIL import Image
|
|
import numpy as np
|
|
import io
|
|
|
|
# copied from value of characters variable in captcha_ocr.ipynb or captcha_ocr_trainAndSaveModel.ipynb
|
|
characters = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'a', 'b', 'c', 'd', 'e', 'f']
|
|
|
|
img_width = 241
|
|
img_height = 62
|
|
|
|
downsample_factor = 4
|
|
|
|
# copied from value of max_length variable in captcha_ocr.ipynb or captcha_ocr_trainAndSaveModel.ipynb
|
|
max_length = 6
|
|
|
|
char_to_num = layers.StringLookup(
|
|
vocabulary=list(characters),
|
|
mask_token=None)
|
|
|
|
num_to_char = layers.StringLookup(
|
|
vocabulary=char_to_num.get_vocabulary(),
|
|
mask_token=None, invert=True)
|
|
|
|
def encode_single_sample(img_path):
|
|
# 1. Read image
|
|
img = tf.io.read_file(img_path)
|
|
# 2. Decode and convert to grayscale
|
|
img = tf.io.decode_png(img, channels=1)
|
|
# 3. Convert to float32 in [0, 1] range
|
|
img = tf.image.convert_image_dtype(img, tf.float32)
|
|
# 4. Resize to the desired size
|
|
img = tf.image.resize(img, [img_height, img_width])
|
|
# 5. Transpose the image because we want the time
|
|
# dimension to correspond to the width of the image.
|
|
img = tf.transpose(img, perm=[1, 0, 2])
|
|
# 7. Return a dict as our model is expecting two inputs
|
|
return asSingleSampleBatch(img)
|
|
|
|
def asSingleSampleBatch(img):
|
|
array = keras.utils.img_to_array(img)
|
|
array = np.expand_dims(array, axis=0)
|
|
return array
|
|
|
|
def decode_batch_predictions(pred):
|
|
input_len = np.ones(pred.shape[0]) * pred.shape[1]
|
|
# Use greedy search. For complex tasks, you can use beam search
|
|
results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][:, :max_length]
|
|
# Iterate over the results and get back the text
|
|
output_text = []
|
|
for res in results:
|
|
res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8")
|
|
output_text.append(res)
|
|
return output_text
|
|
|
|
model = None
|
|
def _getModel():
|
|
global model
|
|
if model is None:
|
|
print("loading model...")
|
|
model = load_model()
|
|
model.summary()
|
|
return model
|
|
|
|
def load_model():
|
|
_model = keras.models.load_model('model')
|
|
__model = keras.models.Model(
|
|
_model.get_layer(name="image").input,
|
|
_model.get_layer(name="dense2").output)
|
|
return __model
|
|
|
|
def getTextInCaptchaImage(captchaImageFile):
|
|
batchImages = encode_single_sample(captchaImageFile)
|
|
preds = _getModel().predict(batchImages)
|
|
return decode_batch_predictions(preds)[0]
|