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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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@ -1,60 +1,57 @@
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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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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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self.save=save
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self.model_dir=model
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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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self.save = save
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self.model_dir = model
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def resize_image(self,img_in,input_height,input_width):
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return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST)
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return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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def start_new_session_and_model(self):
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config = tf.ConfigProto()
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config.gpu_options.allow_growth=True
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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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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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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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self.n_classes=self.model.layers[len(self.model.layers)-1].output_shape[3]
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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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self.n_classes = self.model.layers[len(self.model.layers)-1].output_shape[3]
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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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img_width_model=self.img_width
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img_height_model=self.img_height
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img = cv2.imread(self.image)
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img_width_model = self.img_width
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img_height_model = self.img_height
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if self.patches=='true' or self.patches=='True':
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@ -108,148 +105,134 @@ 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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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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if i==0 and j==0:
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if i == 0 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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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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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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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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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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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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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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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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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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else:
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img_h_page=img.shape[0]
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img_w_page=img.shape[1]
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img = img /float( 255.0)
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img_h_page = img.shape[0]
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img_w_page = img.shape[1]
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img = img / float(255.0)
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img = self.resize_image(img, img_height_model, img_width_model)
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label_p_pred = self.model.predict(
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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]
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seg_color =np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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prediction_true = self.resize_image(seg_color, img_h_page, img_w_page)
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prediction_true = prediction_true.astype(np.uint8)
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return prediction_true[:,:,0]
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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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img_last=0
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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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res=self.predict(model_in)
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res = self.predict(model_in)
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img_fin=np.zeros((res.shape[0],res.shape[1],3) )
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res[:,:][res[:,:]==0]=2
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res=res-1
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res=res*255
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img_fin[:,:,0]=res
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img_fin[:,:,1]=res
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img_fin[:,:,2]=res
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img_fin = np.zeros((res.shape[0], res.shape[1], 3))
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res[:, :][res[:, :] == 0] = 2
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res = res-1
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res = res*255
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img_fin[:, :, 0] = res
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img_fin[:, :, 1] = res
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img_fin[:, :, 2] = res
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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:
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cv2.imwrite(self.save, img_last)
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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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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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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).')
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parser.add_argument('-m','--model', dest='inp2', default=None, help='models directory.')
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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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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).')
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parser.add_argument('-m', '--model', dest='inp2', default=None, help='models directory.')
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options=parser.parse_args()
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options = parser.parse_args()
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possibles=globals()
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possibles = globals()
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possibles.update(locals())
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x=sbb_binarize(options.inp1,options.inp2,options.inp3,options.inp4)
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x = sbb_binarize(options.inp1, options.inp2, options.inp3, options.inp4)
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x.run()
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if __name__=="__main__":
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if __name__ == "__main__":
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main()
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