def getPredict(hps, mode, gasmeter_filename, save_file_name):
xp = tf.placeholder(
tf.float32,
[None, captchaBoxHeight * captchaBoxWidth * gen.ImageDepth])
yp = tf.placeholder(tf.float32,
[None, captchaCharacterLength * CHAR_SET_LEN])
model = ResNetModel.ResNetModel(hps, xp, yp, mode, captchaBoxHeight,
captchaBoxWidth, gen.ImageDepth)
model.create_graph(captchaCharacterLength)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, save_file_name)
# image = Image.open(gasmeter_filename)
# image = image.resize((captchaBoxWidth,captchaBoxHeight), Image.BICUBIC)
image = cv2.imread(gasmeter_filename)
image = ImageTool.imageResize(image, captchaBoxWidth, captchaBoxHeight)
if gen.ImageDepth == 1:
image = ImageTool.convertImgBGR2Gray(image)
# image = ImageTool.convertImgRGB2Gray(image)
ImageTool.showImagePIL(image)
images = ImageTool.repeatImage2Tensor(image, hps.batch_nums)
feed_dict = {xp: images, model.is_training_ph: False}
outputs = sess.run([model.outputs], feed_dict=feed_dict)
text = get_predict_text(outputs)
return text
def getPredict(hps, mode, gasmeter_filename, save_file_name):
xp = tf.placeholder(
tf.float32,
[None, captchaBoxHeight * captchaBoxWidth * gen.ImageDepth])
yp = tf.placeholder(tf.float32,
[None, captchaCharacterLength * CHAR_SET_LEN])
model = ResNetModel.ResNetModel(hps, xp, yp, mode, captchaBoxHeight,
captchaBoxWidth, gen.ImageDepth)
model.create_graph(captchaCharacterLength)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, save_file_name)
# images = gen.get_batch_gasmeter_digit_area_from_filename(gasmeter_filename, hps.batch_nums)
image = cv2.imread(gasmeter_filename)
# gasmeter = GasmeterStyle0(captchaBoxWidth,captchaBoxHeight,desImageDepth=gen.ImageDepth)
gasmeter = GasmeterStyle1(captchaBoxWidth,
captchaBoxHeight,
desImageDepth=gen.ImageDepth)
gasmeter.setImage(image)
image = gasmeter.getRollerBlackArea()
ImageTool.showImagePIL(image)
images = ImageTool.repeatImage2Tensor(image, hps.batch_nums)
feed_dict = {xp: images, model.is_training_ph: False}
outputs = sess.run([model.outputs], feed_dict=feed_dict)
text = get_predict_text(outputs)
return text
def getPredict(hps, mode, save_file_name, gen):
xp = tf.placeholder(
tf.float32,
[None, captchaBoxHeight * captchaBoxWidth * gen.ImageDepth])
yp = tf.placeholder(tf.float32,
[None, captchaCharacterLength * CHAR_SET_LEN])
model = ResNetModel.ResNetModel(hps, xp, yp, mode, captchaBoxHeight,
captchaBoxWidth, gen.ImageDepth)
model.create_graph(captchaCharacterLength)
gen1 = GenImageGasMeterStyle1m1(captchaCharacterLength,
captchaBoxWidth,
captchaBoxHeight,
imageDepth=1)
gen2 = GenImageGasMeterStyle1m2(captchaCharacterLength,
captchaBoxWidth,
captchaBoxHeight,
imageDepth=1)
gen3 = GenImageGasMeterStyle1m3(captchaCharacterLength,
captchaBoxWidth,
captchaBoxHeight,
imageDepth=1)
gens = [gen1, gen2, gen3]
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
saver = tf.train.Saver()
saver.restore(sess, save_file_name)
while True:
gen = random.choice(gens)
oriText, image = gen.get_text_and_image()
images = ImageTool.repeatImage2Tensor(image, hps.batch_nums)
feed_dict = {xp: images, model.is_training_ph: False}
outputs = sess.run([model.outputs], feed_dict=feed_dict)
predictText = get_predict_text(outputs)
title = "text:%s, predict:%s" % (oriText, predictText)
ImageTool.showImagePIL(image, title)
print(title)
