qurator-spk/sbb_binarization
1
0
Fork 0
mirror of https://github.com/qurator-spk/sbb_binarization.git synced 2025-06-11 21:29:56 +02:00
Code Issues Releases Wiki Activity

🎨 clean up code

Browse source
This commit is contained in:
Konstantin Baierer 2020-10-15 15:33:30 +02:00
parent 150f03154f
commit 71d44408b3
1 changed files with 101 additions and 118 deletions
Download patch file Download diff file Expand all files Collapse all files

87
sbb_binarize/sbb_binarize.py
View file

@ -1,27 +1,24 @@
#! /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:
# TODO use True/False for patches
def __init__(self, image, model, patches='false', save=None): def __init__(self, image, model, patches='false', save=None):
self.image = image self.image = image
self.patches = patches self.patches = patches
@ -36,9 +33,10 @@ class sbb_binarize:
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):
self.model = load_model(self.model_dir+'/'+model_name , compile=False)
def load_model(self,model_name):
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]
@ -46,10 +44,9 @@ class sbb_binarize:
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)
@ -108,12 +105,9 @@ class sbb_binarize:
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]
@ -124,72 +118,63 @@ class sbb_binarize:
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)
@ -210,7 +195,7 @@ class sbb_binarize:
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:
@ -227,13 +212,15 @@ class sbb_binarize:
img_fin = img_fin.astype(np.uint8) img_fin = img_fin.astype(np.uint8)
img_fin = (res[:, :] == 0)*255 img_fin = (res[:, :] == 0)*255
img_last = img_last+img_fin img_last = img_last+img_fin
kernel = np.ones((5, 5), np.uint8) kernel = np.ones((5, 5), np.uint8)
img_last[:, :][img_last[:, :] > 0] = 255 img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0)*255 img_last = (img_last[:, :] == 0)*255
if self.save is not None: if self.save:
cv2.imwrite(self.save, img_last) 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.')
@ -249,7 +236,3 @@ def main():
if __name__ == "__main__": if __name__ == "__main__":
main() main()