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https://github.com/qurator-spk/sbb_binarization.git
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🎨 clean up code
This commit is contained in:
Konstantin Baierer
parent
150f03154f
commit
71d44408b3
1 changed files with 101 additions and 118 deletions
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@ -1,27 +1,24 @@
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#! /usr/bin/env python3
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__version__= '1.0'
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import argparse
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import sys
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import os
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import numpy as np
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import warnings
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import cv2
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from keras.models import load_model
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import tensorflow as tf
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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__doc__=\
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"""
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Tool to load model and binarize a given image.
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"""
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from argparse import ArgumentParser
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from os import listdir
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from os.path import join
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from warnings import catch_warnings, simplefilter
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import numpy as np
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import cv2
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from keras.models import load_model
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import tensorflow as tf
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# XXX better to set env var before tensorflow import to suppress those specific warnings
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with catch_warnings():
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simplefilter("ignore")
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class sbb_binarize:
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# TODO use True/False for patches
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def __init__(self, image, model, patches='false', save=None):
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self.image = image
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self.patches = patches
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@ -36,9 +33,10 @@ class sbb_binarize:
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config.gpu_options.allow_growth = True
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self.session = tf.Session(config=config) # tf.InteractiveSession()
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def load_model(self,model_name):
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self.model = load_model(self.model_dir+'/'+model_name , compile=False)
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def load_model(self,model_name):
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self.model = load_model(join(self.model_dir, model_name), compile=False)
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self.img_height = self.model.layers[len(self.model.layers)-1].output_shape[1]
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self.img_width = self.model.layers[len(self.model.layers)-1].output_shape[2]
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@ -46,10 +44,9 @@ class sbb_binarize:
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def end_session(self):
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self.session.close()
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del self.model
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del self.session
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def predict(self,model_name):
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self.load_model(model_name)
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img = cv2.imread(self.image)
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@ -108,12 +105,9 @@ class sbb_binarize:
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index_y_u = img_h
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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(
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img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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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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@ -124,72 +118,63 @@ class sbb_binarize:
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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
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
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:] = seg_color
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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:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
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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
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
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:] = seg_color
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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:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
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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
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
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:] = seg_color
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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:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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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
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
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:] = seg_color
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i == 0 and j != 0 and j != nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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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
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
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:] = seg_color
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j != 0 and j != nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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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
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
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:] = seg_color
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i != 0 and i != nxf-1 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
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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
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
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:] = seg_color
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
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elif i != 0 and i != nxf-1 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
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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
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
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:] = seg_color
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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:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
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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
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
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:] = seg_color
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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)
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@ -210,7 +195,7 @@ class sbb_binarize:
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def run(self):
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self.start_new_session_and_model()
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models_n=os.listdir(self.model_dir)
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models_n = listdir(self.model_dir)
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img_last = 0
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for model_in in models_n:
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@ -227,13 +212,15 @@ class sbb_binarize:
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img_fin = img_fin.astype(np.uint8)
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img_fin = (res[:, :] == 0)*255
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img_last = img_last+img_fin
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kernel = np.ones((5, 5), np.uint8)
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img_last[:, :][img_last[:, :] > 0] = 255
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img_last = (img_last[:, :] == 0)*255
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if self.save is not None:
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if self.save:
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cv2.imwrite(self.save, img_last)
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def main():
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parser=argparse.ArgumentParser()
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parser = ArgumentParser()
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parser.add_argument('-i', '--image', dest='inp1', default=None, help='image.')
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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.')
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@ -249,7 +236,3 @@ def main():
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if __name__ == "__main__":
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main()
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