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tbpp_model.py
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tbpp_model.py
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"""Keras implementation of TextBoxes++."""
from keras.layers import Activation
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import GlobalAveragePooling2D
from keras.layers import Input
from keras.layers import MaxPooling2D
from keras.layers import concatenate
from keras.layers import Reshape
from keras.layers import ZeroPadding2D
from keras.models import Model
from utils.layers import Normalize
from ssd_model import ssd512_body
from ssd_model_dense import dsod512_body
def multibox_head(source_layers, num_priors, normalizations=None, softmax=True):
num_classes = 2
class_activation = 'softmax' if softmax else 'sigmoid'
mbox_conf = []
mbox_loc = []
mbox_quad = []
mbox_rbox = []
for i in range(len(source_layers)):
x = source_layers[i]
name = x.name.split('/')[0]
# normalize
if normalizations is not None and normalizations[i] > 0:
name = name + '_norm'
x = Normalize(normalizations[i], name=name)(x)
# confidence
name1 = name + '_mbox_conf'
x1 = Conv2D(num_priors[i] * num_classes, (3, 5), padding='same', name=name1)(x)
x1 = Flatten(name=name1+'_flat')(x1)
mbox_conf.append(x1)
# location, Delta(x,y,w,h)
name2 = name + '_mbox_loc'
x2 = Conv2D(num_priors[i] * 4, (3, 5), padding='same', name=name2)(x)
x2 = Flatten(name=name2+'_flat')(x2)
mbox_loc.append(x2)
# quadrilateral, Delta(x1,y1,x2,y2,x3,y3,x4,y4)
name3 = name + '_mbox_quad'
x3 = Conv2D(num_priors[i] * 8, (3, 5), padding='same', name=name3)(x)
x3 = Flatten(name=name3+'_flat')(x3)
mbox_quad.append(x3)
# rotated rectangle, Delta(x1,y1,x2,y2,h)
name4 = name + '_mbox_rbox'
x4 = Conv2D(num_priors[i] * 5, (3, 5), padding='same', name=name4)(x)
x4 = Flatten(name=name4+'_flat')(x4)
mbox_rbox.append(x4)
mbox_conf = concatenate(mbox_conf, axis=1, name='mbox_conf')
mbox_conf = Reshape((-1, num_classes), name='mbox_conf_logits')(mbox_conf)
mbox_conf = Activation(class_activation, name='mbox_conf_final')(mbox_conf)
mbox_loc = concatenate(mbox_loc, axis=1, name='mbox_loc')
mbox_loc = Reshape((-1, 4), name='mbox_loc_final')(mbox_loc)
mbox_quad = concatenate(mbox_quad, axis=1, name='mbox_quad')
mbox_quad = Reshape((-1, 8), name='mbox_quad_final')(mbox_quad)
mbox_rbox = concatenate(mbox_rbox, axis=1, name='mbox_rbox')
mbox_rbox = Reshape((-1, 5), name='mbox_rbox_final')(mbox_rbox)
predictions = concatenate([mbox_loc, mbox_quad, mbox_rbox, mbox_conf], axis=2, name='predictions')
return predictions
def TBPP512(input_shape=(512, 512, 3), softmax=True):
"""TextBoxes++512 architecture.
# Arguments
input_shape: Shape of the input image.
# References
- [TextBoxes++: A Single-Shot Oriented Scene Text Detector](https://arxiv.org/abs/1801.02765)
"""
# SSD body
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_body(x)
num_maps = len(source_layers)
# Add multibox head for classification and regression
num_priors = [14] * num_maps
normalizations = [1] * num_maps
output_tensor = multibox_head(source_layers, num_priors, normalizations, softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
model.aspect_ratios = [[1,2,3,5,1/2,1/3,1/5] * 2] * num_maps
#model.shifts = [[(0.0, 0.0)] * 7 + [(0.0, 1.0)] * 7] * num_maps
model.shifts = [[(0.0, -0.5)] * 7 + [(0.0, 0.5)] * 7] * num_maps
model.special_ssd_boxes = False
model.scale = 0.5
return model
def TBPP512_dense(input_shape=(512, 512, 3), softmax=True):
"""DenseNet based Architecture for TextBoxes++512.
"""
# DSOD body
x = input_tensor = Input(shape=input_shape)
source_layers = dsod512_body(x)
num_maps = len(source_layers)
# Add multibox head for classification and regression
num_priors = [14] * num_maps
normalizations = [1] * num_maps
output_tensor = multibox_head(source_layers, num_priors, normalizations, softmax)
model = Model(input_tensor, output_tensor)
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
model.aspect_ratios = [[1,2,3,5,1/2,1/3,1/5] * 2] * num_maps
#model.shifts = [[(0.0, 0.0)] * 7 + [(0.0, 1.0)] * 7] * num_maps
model.shifts = [[(0.0, -0.5)] * 7 + [(0.0, 0.5)] * 7] * num_maps
model.special_ssd_boxes = False
model.scale = 0.5
return model