def model(img, adjust=False):
"""
@img: 图片
@adjust: 是否调整文字识别结果
"""
cfg_from_file('./ctpn/ctpn/text.yml')
text_recs, img_framed, img = text_detect(img)
text_recs = sort_box(text_recs)
result = charRec(img, text_recs, adjust)
return result, img_framed
def model(img, adjust=False):
"""
@img: 图片
@adjust: 是否调整文字识别结果
"""
cfg_from_file('./ctpn/ctpn/text.yml')
sttime = time.time()
text_recs, img_framed, img = text_detect(img)
print("text_detect时间", time.time() - sttime)
# print("test_rec", text_recs)
# print("img_framed", img_framed)
text_recs = sort_box(text_recs)
result = charRec(img, text_recs, adjust)
return result, img_framed
def model(img, adjust=False):
"""
@img: 图片
@adjust: 是否调整文字识别结果
"""
cfg_from_file('../ctpn/ctpn/text.yml')
text_recs, img_framed, img = text_detect(img) #检测文本框
# text_recs：文本框位置（列表形式），
# img_framed：在原图上标注了文本框的图片，
# img:原图
text_recs = sort_box(text_recs) #排序框
result = charRec(img, text_recs, adjust) #OCR模型，进行字符识别
return result, img_framed
def __init__(self):
cfg_from_file(os.getcwd() + '/ctpn/ctpn/text.yml')
# init session
self.config = tf.ConfigProto(allow_soft_placement=True)
self.sess = tf.Session(config=self.config)
# load network
self.net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state("ctpn/checkpoints/")
# ckpt=tf.train.get_checkpoint_state("output/ctpn_end2end/voc_2007_trainval/")
print('Restoring from {}...'.format(ckpt.model_checkpoint_path), end=' ')
saver.restore(self.sess, ckpt.model_checkpoint_path)
print('done', end=' ')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
print(' done.')
def model(img, adjust=False, detectAngle=False):
"""
@img: 图片
@adjust: 是否调整文字识别结果
"""
angle = 0
if detectAngle:
angle = angle_detect(img=np.copy(img)) ##文字朝向检测
im = Image.fromarray(img)
if angle == 90:
im = im.transpose(Image.ROTATE_90)
elif angle == 180:
im = im.transpose(Image.ROTATE_180)
elif angle == 270:
im = im.transpose(Image.ROTATE_270)
img = np.array(im)
cfg_from_file('./ctpn/ctpn/text.yml')
text_recs, img_framed, img = text_detect(img)
text_recs = sort_box(text_recs)
result = charRec(img, text_recs, adjust)
return result, img_framed
def __init__(self, sess):
super(Detect, self).__init__()
cfg_from_file('ctpn/ctpn/text.yml')
# init session
# config = tf.ConfigProto(allow_soft_placement=True)
# sess = tf.Session(config=config)
# sess = tf.Session()
# load network
net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")))
saver = tf.train.Saver()
ctpn_model_path = 'ctpn/checkpoints/VGGnet_fast_rcnn_iter_50000.ckpt'
print('Restoring from {}...'.format(ctpn_model_path))
saver.restore(sess, ctpn_model_path)
print('Done\n')
self.sess = sess
self.net = net
def __init__(self,first = True):
if not first:
tf.get_variable_scope().reuse_variables()
# cfg_from_file(os.getcwd() + '/ctpn/ctpn/text.yml')
cfg_from_file(os.path.join(os.path.dirname(os.path.abspath(__file__)),'text.yml'))
# init session
self.config = tf.ConfigProto(allow_soft_placement=True)
self.sess = tf.Session(config=self.config)
# load network
self.net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
#ckpt = tf.train.get_checkpoint_state("ctpn/checkpoints/")
ckpt = tf.train.get_checkpoint_state("G:/DeepLearningProjects/Web_SceneRecognition/ScenceRecognition_master/ctpn/checkpoints/")
# ckpt=tf.train.get_checkpoint_state("output/ctpn_end2end/voc_2007_trainval/")
print('Restoring from {}...'.format(ckpt.model_checkpoint_path), end=' ')
saver.restore(self.sess, ckpt.model_checkpoint_path)
print('done', end=' ')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
print(' done.')
def predict():
clearData()
cfg_from_file(r'./ctpn/ctpn/text.yml')
# init session
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = 0.4
sess = tf.Session(config=config)
with gfile.FastGFile(r'.\ctpn\ctpn\data\ctpn.pb', 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
sess.run(tf.global_variables_initializer())
input_img = sess.graph.get_tensor_by_name('Placeholder:0')
output_cls_prob = sess.graph.get_tensor_by_name('Reshape_2:0')
output_box_pred = sess.graph.get_tensor_by_name(
'rpn_bbox_pred/Reshape_1:0')
im_names = os.listdir('./demo/test_images')
index = 0
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print(('finding card number for {:s}'.format(im_name)))
img = cv2.imread('./demo/test_images' + '//' + im_name)
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
blobs, im_scales = _get_blobs(img, None)
if cfg.TEST.HAS_RPN:
im_blob = blobs['data']
blobs['im_info'] = np.array(
[[im_blob.shape[1], im_blob.shape[2], im_scales[0]]],
dtype=np.float32)
cls_prob, box_pred = sess.run([output_cls_prob, output_box_pred],
feed_dict={input_img: blobs['data']})
rois, _ = proposal_layer(cls_prob,
box_pred,
blobs['im_info'],
'TEST',
anchor_scales=cfg.ANCHOR_SCALES)
scores = rois[:, 0]
boxes = rois[:, 1:5] / im_scales[0]
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis],
img.shape[:2])
draw_boxes(img, im_name, boxes, scale, index)
index += 1
print('recognizing card number:')
crnn = CRNN(image_shape=crnn_config.image_shape,
min_len=crnn_config.min_len,
max_len=crnn_config.max_len,
lstm_hidden=crnn_config.lstm_hidden,
pool_size=crnn_config.pool_size,
learning_decay_rate=crnn_config.learning_decay_rate,
learning_rate=crnn_config.learning_rate,
learning_decay_steps=crnn_config.learning_decay_steps,
mode=crnn_config.mode,
dict=crnn_config.dict,
is_training=True,
train_label_path=crnn_config.predict_label_path,
train_images_path=crnn_config.predict_images_path,
charset_path=crnn_config.charset_path)
result = crnn.predict(epoch=crnn_config.epoch,
batch_size=crnn_config.batch_size,
train_images_path=crnn_config.cardNum_path,
train_label_path=crnn_config.predict_label_path,
restore=True,
fonts=crnn_config.fonts,
logs_path=crnn_config.logs_path,
models_path=crnn_config.models_path)
return result
import pprint
import sys
import os.path
sys.path.append(os.getcwd())
this_dir = os.path.dirname(__file__)
from ctpn.lib.fast_rcnn.train import get_training_roidb, train_net
from ctpn.lib.fast_rcnn.config import cfg_from_file, get_output_dir, get_log_dir
from ctpn.lib.datasets.factory import get_imdb
from ctpn.lib.networks.factory import get_network
from ctpn.lib.fast_rcnn.config import cfg
if __name__ == '__main__':
cfg_from_file('text.yml')
print('Using config:')
pprint.pprint(cfg)
imdb = get_imdb('voc_2007_trainval')
print('Loaded dataset `{:s}` for training'.format(imdb.name))
roidb = get_training_roidb(imdb)
output_dir = get_output_dir(imdb, None)
log_dir = get_log_dir(imdb)
print('Output will be saved to `{:s}`'.format(output_dir))
print('Logs will be saved to `{:s}`'.format(log_dir))
device_name = '/gpu:0'
print(device_name)
network = get_network('VGGnet_train')
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
draw_boxes(img, image_name, boxes, scale)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
if __name__ == '__main__':
if os.path.exists("data/results/"):
shutil.rmtree("data/results/")
os.makedirs("data/results/")
cfg_from_file('ctpn/text.yml')
# init session
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# load network
net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state(cfg.TEST.checkpoints_path)
print('Restoring from {}...'.format(ckpt.model_checkpoint_path),
end=' ')
saver.restore(sess, ckpt.model_checkpoint_path)
def lineModel(img):
cfg_from_file('./ctpn/ctpn/text.yml')
result = lineRec(img)
return result
import sys
sys.path.append('/'.join(os.getcwd().split('/')[:-2]))
print sys.path
os.system('echo $PYTHONPATH')
from ctpn.lib.fast_rcnn.train import get_training_roidb, train_net
from ctpn.lib.fast_rcnn.config import cfg_from_file, get_output_dir, get_log_dir
from ctpn.lib.datasets.factory import get_imdb
from ctpn.lib.networks.factory import get_network
from ctpn.lib.fast_rcnn.config import cfg
reload(sys)
sys.setdefaultencoding('utf8')
if __name__ == '__main__':
cfg_from_file('./text.yml')
print('Using config:')
pprint.pprint(cfg)
imdb = get_imdb('voc_2007_train')
print('Loaded dataset `{:s}` for training'.format(imdb.name))
roidb = get_training_roidb(imdb)
output_dir = get_output_dir(imdb, None)
log_dir = get_log_dir(imdb)
print('Output will be saved to `{:s}`'.format(output_dir))
print('Logs will be saved to `{:s}`'.format(log_dir))
device_name = '/gpu:0'
print(device_name)
network = get_network('VGGnet_train')