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🎨 clean up code

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Konstantin Baierer 2020-10-15 15:33:30 +02:00
parent 150f03154f
commit 71d44408b3
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sbb_binarize/sbb_binarize.py
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@ -1,60 +1,57 @@
#! /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
with warnings.catch_warnings():
warnings.simplefilter("ignore")
__doc__=\
""" """
Tool to load model and binarize a given image. Tool to load model and binarize a given image.
""" """
from argparse import ArgumentParser
from os import listdir
from os.path import join
from warnings import catch_warnings, simplefilter
import numpy as np
import cv2
from keras.models import load_model
import tensorflow as tf
# XXX better to set env var before tensorflow import to suppress those specific warnings
with catch_warnings():
simplefilter("ignore")
class sbb_binarize: class sbb_binarize:
def __init__(self,image,model, patches='false',save=None ):
self.image=image # TODO use True/False for patches
self.patches=patches def __init__(self, image, model, patches='false', save=None):
self.save=save self.image = image
self.model_dir=model self.patches = patches
self.save = save
self.model_dir = model
def resize_image(self,img_in,input_height,input_width): def resize_image(self,img_in,input_height,input_width):
return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST) return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def start_new_session_and_model(self): def start_new_session_and_model(self):
config = tf.ConfigProto() config = tf.ConfigProto()
config.gpu_options.allow_growth=True config.gpu_options.allow_growth = True
self.session =tf.Session(config=config)# tf.InteractiveSession() self.session = tf.Session(config=config) # tf.InteractiveSession()
def load_model(self,model_name): def load_model(self,model_name):
self.model = load_model(self.model_dir+'/'+model_name , compile=False)
self.model = load_model(join(self.model_dir, model_name), compile=False)
self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1] 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.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] self.n_classes = self.model.layers[len(self.model.layers)-1].output_shape[3]
def end_session(self): def end_session(self):
self.session.close() self.session.close()
del self.model del self.model
del self.session del self.session
def predict(self,model_name): def predict(self,model_name):
self.load_model(model_name) self.load_model(model_name)
img=cv2.imread(self.image) img = cv2.imread(self.image)
img_width_model=self.img_width img_width_model = self.img_width
img_height_model=self.img_height img_height_model = self.img_height
if self.patches=='true' or self.patches=='True': if self.patches=='true' or self.patches=='True':
@ -107,149 +104,135 @@ class sbb_binarize:
if index_y_u > img_h: if index_y_u > img_h:
index_y_u = img_h index_y_u = img_h
index_y_d = img_h - img_height_model index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = self.model.predict( label_p_pred = self.model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
if i==0 and j==0: if i == 0 and j == 0:
seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :] 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] seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg 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, prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
:] = seg_color
elif i == nxf-1 and j == nyf-1:
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_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] seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg 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, prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color
:] = seg_color
elif i == 0 and j == nyf-1:
elif i==0 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :] 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] seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg 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, prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color
:] = seg_color
elif i == nxf-1 and j == 0:
elif i==nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 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] seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg 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, prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
:] = seg_color
elif i == 0 and j != 0 and j != nyf-1:
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_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] seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg 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, prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
:] = seg_color
elif i == nxf-1 and j != 0 and j != nyf-1:
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_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] seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg 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, prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
:] = seg_color
elif i != 0 and i != nxf-1 and j == 0:
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_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] seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg 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, prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
:] = seg_color
elif i != 0 and i != nxf-1 and j == nyf-1:
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_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] seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg 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, prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color
:] = seg_color
else: else:
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :] 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] seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg 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, prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
:] = seg_color
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)
else: else:
img_h_page=img.shape[0] img_h_page = img.shape[0]
img_w_page=img.shape[1] img_w_page = img.shape[1]
img = img /float( 255.0) img = img / float(255.0)
img = self.resize_image(img, img_height_model, img_width_model) img = self.resize_image(img, img_height_model, img_width_model)
label_p_pred = self.model.predict( label_p_pred = self.model.predict(
img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_color =np.repeat(seg[:, :, np.newaxis], 3, axis=2) 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 = self.resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)
return prediction_true[:,:,0] return prediction_true[:,:,0]
def run(self): def run(self):
self.start_new_session_and_model() self.start_new_session_and_model()
models_n=os.listdir(self.model_dir) models_n = listdir(self.model_dir)
img_last=0 img_last = 0
for model_in in models_n: for model_in in models_n:
res=self.predict(model_in)
img_fin=np.zeros((res.shape[0],res.shape[1],3) ) res = self.predict(model_in)
res[:,:][res[:,:]==0]=2
res=res-1 img_fin = np.zeros((res.shape[0], res.shape[1], 3))
res=res*255 res[:, :][res[:, :] == 0] = 2
img_fin[:,:,0]=res res = res-1
img_fin[:,:,1]=res res = res*255
img_fin[:,:,2]=res img_fin[:, :, 0] = res
img_fin[:, :, 1] = res
img_fin=img_fin.astype(np.uint8) img_fin[:, :, 2] = res
img_fin=(res[:,:]==0)*255
img_last=img_last+img_fin img_fin = img_fin.astype(np.uint8)
kernel = np.ones((5,5),np.uint8) img_fin = (res[:, :] == 0)*255
img_last[:,:][img_last[:,:]>0]=255 img_last = img_last+img_fin
img_last=(img_last[:,:]==0)*255
if self.save is not None: kernel = np.ones((5, 5), np.uint8)
cv2.imwrite(self.save,img_last) img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0)*255
if self.save:
cv2.imwrite(self.save, img_last)
def main(): def main():
parser=argparse.ArgumentParser() parser = ArgumentParser()
parser.add_argument('-i','--image', dest='inp1', default=None, help='image.') 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('-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('-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.') parser.add_argument('-m', '--model', dest='inp2', default=None, help='models directory.')
options=parser.parse_args() options = parser.parse_args()
possibles=globals() possibles = globals()
possibles.update(locals()) possibles.update(locals())
x=sbb_binarize(options.inp1,options.inp2,options.inp3,options.inp4) x = sbb_binarize(options.inp1, options.inp2, options.inp3, options.inp4)
x.run() x.run()
if __name__=="__main__": if __name__ == "__main__":
main() main()