diff --git a/sbb_binarize/sbb_binarize.py b/sbb_binarize/sbb_binarize.py index f701a1c..7faf1be 100644 --- a/sbb_binarize/sbb_binarize.py +++ b/sbb_binarize/sbb_binarize.py @@ -1,60 +1,57 @@ -#! /usr/bin/env python3 +""" +Tool to load model and binarize a given image. +""" -__version__= '1.0' +from argparse import ArgumentParser +from os import listdir +from os.path import join +from warnings import catch_warnings, simplefilter -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. -""" +# XXX better to set env var before tensorflow import to suppress those specific warnings +with catch_warnings(): + simplefilter("ignore") class sbb_binarize: - def __init__(self,image,model, patches='false',save=None ): - self.image=image - self.patches=patches - self.save=save - self.model_dir=model + + # TODO use True/False for patches + def __init__(self, image, model, patches='false', save=None): + 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) - + 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() + 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() + 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_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 + img = cv2.imread(self.image) + img_width_model = self.img_width + img_height_model = self.img_height if self.patches=='true' or self.patches=='True': @@ -107,149 +104,135 @@ class sbb_binarize: if index_y_u > img_h: index_y_u = img_h index_y_d = img_h - img_height_model - - img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - label_p_pred = self.model.predict( - img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) + label_p_pred = self.model.predict(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_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 = 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 - prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, - :] = seg_color - - elif i==nxf-1 and j==nyf-1: + prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color + + 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] 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 - - elif i==0 and j==nyf-1: + prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color + + 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] 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 - - elif i==nxf-1 and j==0: + prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color + + 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] 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: + 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] 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: + 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] 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: + 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] 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: + 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] 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 + prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color 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] 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 + prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color 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_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) label_p_pred = self.model.predict( img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) 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 = prediction_true.astype(np.uint8) return prediction_true[:,:,0] def run(self): self.start_new_session_and_model() - models_n=os.listdir(self.model_dir) - img_last=0 + models_n = listdir(self.model_dir) + img_last = 0 for model_in in models_n: - - res=self.predict(model_in) - - 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) + + res = self.predict(model_in) + + 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: + cv2.imwrite(self.save, img_last) + 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.') - - options=parser.parse_args() - - possibles=globals() + parser = 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.') + + options = parser.parse_args() + + possibles = globals() 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() -if __name__=="__main__": +if __name__ == "__main__": main() - - - -