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sbb_binarization/sbb_binarize/sbb_binarize.py

256 lines
10 KiB
Python

5 years ago
#! /usr/bin/env python3
__version__= '1.0'
import argparse
import sys
import os
import numpy as np
import warnings
import cv2
from keras.models import load_model
import tensorflow as tf
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with warnings.catch_warnings():
warnings.simplefilter("ignore")
__doc__=\
"""
Tool to load model and binarize a given image.
"""
class sbb_binarize:
def __init__(self,image,model, patches='false',save=None ):
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self.image=image
self.patches=patches
self.save=save
self.model_dir=model
def resize_image(self,img_in,input_height,input_width):
return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST)
def start_new_session_and_model(self):
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
self.session =tf.Session(config=config)# tf.InteractiveSession()
def load_model(self,model_name):
self.model = load_model(self.model_dir+'/'+model_name , compile=False)
self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1]
self.img_width=self.model.layers[len(self.model.layers)-1].output_shape[2]
self.n_classes=self.model.layers[len(self.model.layers)-1].output_shape[3]
def end_session(self):
self.session.close()
del self.model
del self.session
def predict(self,model_name):
self.load_model(model_name)
img=cv2.imread(self.image)
img_width_model=self.img_width
img_height_model=self.img_height
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if self.patches=='true' or self.patches=='True':
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margin = int(0.1 * img_width_model)
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width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
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img = img / float(255.0)
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img_h = img.shape[0]
img_w = img.shape[1]
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prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
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if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
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if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
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for i in range(nxf):
for j in range(nyf):
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if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
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if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
elif j > 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
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if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = self.model.predict(
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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if i==0 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
:] = seg_color
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elif i==nxf-1 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
:] = seg_color
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elif i==0 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
:] = seg_color
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elif i==nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
:] = seg_color
elif i==0 and j!=0 and j!=nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
:] = seg_color
elif i==nxf-1 and j!=0 and j!=nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
:] = seg_color
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else:
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
:] = seg_color
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prediction_true = prediction_true.astype(np.uint8)
else:
img_h_page=img.shape[0]
img_w_page=img.shape[1]
img = img /float( 255.0)
img = self.resize_image(img, img_height_model, img_width_model)
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label_p_pred = self.model.predict(
img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
seg_color =np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = self.resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8)
return prediction_true[:,:,0]
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def run(self):
self.start_new_session_and_model()
models_n=os.listdir(self.model_dir)
img_last=0
for model_in in models_n:
res=self.predict(model_in)
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img_fin=np.zeros((res.shape[0],res.shape[1],3) )
res[:,:][res[:,:]==0]=2
res=res-1
res=res*255
img_fin[:,:,0]=res
img_fin[:,:,1]=res
img_fin[:,:,2]=res
img_fin=img_fin.astype(np.uint8)
img_fin=(res[:,:]==0)*255
img_last=img_last+img_fin
kernel = np.ones((5,5),np.uint8)
img_last[:,:][img_last[:,:]>0]=255
img_last=(img_last[:,:]==0)*255
if self.save is not None:
cv2.imwrite(self.save,img_last)
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def main():
parser=argparse.ArgumentParser()
parser.add_argument('-i','--image', dest='inp1', default=None, help='image.')
parser.add_argument('-p','--patches', dest='inp3', default=False, help='by setting this parameter to true you let the model to see the image in patches.')
parser.add_argument('-s','--save', dest='inp4', default=False, help='save prediction with a given name here. The name and format should be given (outputname.tif).')
parser.add_argument('-m','--model', dest='inp2', default=None, help='models directory.')
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options=parser.parse_args()
possibles=globals()
possibles.update(locals())
x=sbb_binarize(options.inp1,options.inp2,options.inp3,options.inp4)
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x.run()
if __name__=="__main__":
main()