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update binarization tool
This commit is contained in:
b-vr103
parent
5209dcf46e
commit
e4715b88fa
1 changed files with 135 additions and 387 deletions
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@ -7,18 +7,10 @@ import sys
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import os
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import os
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import numpy as np
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import numpy as np
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import warnings
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import warnings
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import xml.etree.ElementTree as et
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import pandas as pd
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from tqdm import tqdm
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import csv
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import cv2
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import cv2
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import seaborn as sns
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import matplotlib.pyplot as plt
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from keras.models import load_model
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from keras.models import load_model
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import tensorflow as tf
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import tensorflow as tf
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from keras import backend as K
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from skimage.filters import threshold_otsu
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import keras.losses
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with warnings.catch_warnings():
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with warnings.catch_warnings():
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@ -30,147 +22,23 @@ Tool to load model and binarize a given image.
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"""
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"""
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class sbb_binarize:
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class sbb_binarize:
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def __init__(self,image,model, patches='false',save=None, ground_truth=None,weights_dir=None ):
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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.image=image
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self.patches=patches
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self.patches=patches
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self.save=save
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self.save=save
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self.model_dir=model
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self.model_dir=model
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self.ground_truth=ground_truth
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self.weights_dir=weights_dir
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def resize_image(self,img_in,input_height,input_width):
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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 color_images(self,seg):
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ann_u=range(self.n_classes)
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if len(np.shape(seg))==3:
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seg=seg[:,:,0]
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seg_img=np.zeros((np.shape(seg)[0],np.shape(seg)[1],3)).astype(np.uint8)
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colors=sns.color_palette("hls", self.n_classes)
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for c in ann_u:
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c=int(c)
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segl=(seg==c)
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seg_img[:,:,0][seg==c]=c
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seg_img[:,:,1][seg==c]=c
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seg_img[:,:,2][seg==c]=c
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return seg_img
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def otsu_copy_binary(self,img):
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img_r=np.zeros((img.shape[0],img.shape[1],3))
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img1=img[:,:,0]
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#print(img.min())
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#print(img[:,:,0].min())
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#blur = cv2.GaussianBlur(img,(5,5))
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#ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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img_r[:,:,0]=threshold1
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img_r[:,:,1]=threshold1
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img_r[:,:,2]=threshold1
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#img_r=img_r/float(np.max(img_r))*255
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return img_r
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def otsu_copy(self,img):
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img_r=np.zeros((img.shape[0],img.shape[1],3))
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#img1=img[:,:,0]
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#print(img.min())
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#print(img[:,:,0].min())
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#blur = cv2.GaussianBlur(img,(5,5))
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#ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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_, threshold1 = cv2.threshold(img[:,:,0], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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_, threshold2 = cv2.threshold(img[:,:,1], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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_, threshold3 = cv2.threshold(img[:,:,2], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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img_r[:,:,0]=threshold1
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img_r[:,:,1]=threshold2
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img_r[:,:,2]=threshold3
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###img_r=img_r/float(np.max(img_r))*255
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return img_r
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def otsu_org(self,img):
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binary_global = img > threshold_otsu(img)
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binary_global=binary_global*255
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#plt.imshow(binary_sauvola*255,cmap=plt.cm.gray)
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#plt.imshow(binary_global)
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#plt.show()
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#print(np.unique(binary_global))
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binary_global=np.repeat(binary_global[:, :, np.newaxis], 3, axis=2)
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plt.imshow(binary_global)
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plt.show()
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print(binary_global.shape)
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return binary_global
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def soft_dice_loss(self,y_true, y_pred, epsilon=1e-6):
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axes = tuple(range(1, len(y_pred.shape)-1))
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numerator = 2. * K.sum(y_pred * y_true, axes)
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denominator = K.sum(K.square(y_pred) + K.square(y_true), axes)
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return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
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def weighted_categorical_crossentropy(self,weights=None):
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def loss(y_true, y_pred):
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labels_floats = tf.cast(y_true, tf.float32)
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per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
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if weights is not None:
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weight_mask = tf.maximum(tf.reduce_max(tf.constant(
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np.array(weights, dtype=np.float32)[None, None, None])
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* labels_floats, axis=-1), 1.0)
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per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
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return tf.reduce_mean(per_pixel_loss)
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return self.loss
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def IoU(self,Yi,y_predi):
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## mean Intersection over Union
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## Mean IoU = TP/(FN + TP + FP)
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IoUs = []
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Nclass = np.unique(Yi)
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for c in Nclass:
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TP = np.sum( (Yi == c)&(y_predi==c) )
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FP = np.sum( (Yi != c)&(y_predi==c) )
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FN = np.sum( (Yi == c)&(y_predi != c))
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IoU = TP/float(TP + FP + FN)
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if self.n_classes>2:
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print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c,TP,FP,FN,IoU))
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IoUs.append(IoU)
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if self.n_classes>2:
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mIoU = np.mean(IoUs)
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print("_________________")
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print("Mean IoU: {:4.3f}".format(mIoU))
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return mIoU
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elif self.n_classes==2:
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mIoU = IoUs[1]
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print("_________________")
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print("IoU: {:4.3f}".format(mIoU))
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return mIoU
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def start_new_session_and_model(self):
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def start_new_session_and_model(self):
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config = tf.ConfigProto()
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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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#keras.losses.custom_loss = self.weighted_categorical_crossentropy
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def load_model(self,model_name):
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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(self.model_dir+'/'+model_name , compile=False)
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#if self.weights_dir!=None:
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# print('man burdayammmmaaa')
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# self.model.load_weights(self.weights_dir)
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self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1]
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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.img_width=self.model.layers[len(self.model.layers)-1].output_shape[2]
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@ -183,51 +51,20 @@ class sbb_binarize:
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del self.model
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del self.model
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del self.session
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del self.session
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def predict(self,model_name):
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def predict(self,model_name):
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#self.start_new_session_and_model(model_name)
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self.load_model(model_name)
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self.load_model(model_name)
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if self.patches=='true' or self.patches=='True':
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print(self.patches,'gadaaiikk')
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#def textline_contours(img,input_width,input_height,n_classes,model):
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img=cv2.imread(self.image)
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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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margin = int(0.1 * img_width_model)
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width_mid = img_width_model - 2 * margin
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height_mid = img_height_model - 2 * margin
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img = img / float(255.0)
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if img.shape[0]<self.img_height:
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img=cv2.resize( img, ( img.shape[1],self.img_width) ,interpolation=cv2.INTER_NEAREST)
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if img.shape[1]<self.img_width:
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img=cv2.resize( img, ( self.img_height,img.shape[0]) ,interpolation=cv2.INTER_NEAREST)
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margin=True
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if margin:
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kernel = np.ones((5,5),np.uint8)
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width=self.img_width
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height=self.img_height
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#offset=int(.1*width)
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offset=int(0.1*width)
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width_mid=width-2*offset
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height_mid=height-2*offset
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#img= cv2.medianBlur(img, 5)
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#img = cv2.GaussianBlur(img,(5,5),0)
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#img= cv2.medianBlur(img, 5)
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#img= cv2.medianBlur(img, 5)
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#img= cv2.medianBlur(img, 5)
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#img= cv2.medianBlur(img, 5)
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#img=self.otsu_copy_binary(img)
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#img=self.otsu_org(img)
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img=img.astype(np.uint8)
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#for i in range(10):
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# img= cv2.medianBlur(img, 3)
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img=img/255.0
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img_h = img.shape[0]
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img_h = img.shape[0]
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img_w = img.shape[1]
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img_w = img.shape[1]
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@ -252,218 +89,132 @@ class sbb_binarize:
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if i == 0:
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if i == 0:
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index_x_d = i * width_mid
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index_x_d = i * width_mid
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index_x_u=index_x_d+width#(i+1)*width
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index_x_u = index_x_d + img_width_model
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elif i > 0:
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elif i > 0:
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index_x_d = i * width_mid
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index_x_d = i * width_mid
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index_x_u=index_x_d+width#(i+1)*width
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index_x_u = index_x_d + img_width_model
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if j == 0:
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if j == 0:
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index_y_d = j * height_mid
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index_y_d = j * height_mid
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index_y_u=index_y_d+height#(j+1)*height
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index_y_u = index_y_d + img_height_model
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elif j > 0:
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elif j > 0:
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index_y_d = j * height_mid
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index_y_d = j * height_mid
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index_y_u=index_y_d+height#(j+1)*height
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index_y_u = index_y_d + img_height_model
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if index_x_u > img_w:
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_u = img_w
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index_x_d=img_w-width
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index_x_d = img_w - img_width_model
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if index_y_u > img_h:
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_u = img_h
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index_y_d=img_h-height
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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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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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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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img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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#print(np.unique(label_p_pred))
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th3=label_p_pred[0,:,:,1]
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th3=th3*255
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th3=th3.astype(np.uint8)
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#print(np.unique(th3))
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ret3,th3 = cv2.threshold(th3,30,250,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color=self.color_images(seg)
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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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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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seg_color=seg_color[offset:seg_color.shape[0]-offset,offset:seg_color.shape[1]-offset,:]
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elif i==nxf-1 and j==nyf-1:
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seg=seg[offset:seg.shape[0]-offset,offset:seg.shape[1]-offset]
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
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th3=th3[offset:th3.shape[0]-offset,offset:th3.shape[1]-offset]
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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+offset:index_y_u-offset,index_x_d+offset:index_x_u-offset]=seg
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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+offset:index_y_u-offset,index_x_d+offset:index_x_u-offset,:]=seg_color
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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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y_predi = mask_true
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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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#print(np.unique(mask_true))
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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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#find_contours(mask_true)
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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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#y_testi = label[:,:,0]#np.argmax(label.reshape(1,label.shape[0],label.shape[1],label.shape[2]), axis=3)
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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, :]
|
||||||
|
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:
|
||||||
|
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]
|
||||||
|
|
||||||
#y_predi=cv2.erode(y_predi,kernel,iterations=3)
|
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
|
||||||
y_predi=cv2.resize( y_predi, ( img.shape[1],img.shape[0]) ,interpolation=cv2.INTER_NEAREST)
|
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
|
||||||
return y_predi
|
:] = 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]
|
||||||
|
|
||||||
if not margin:
|
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
|
||||||
|
|
||||||
kernel = np.ones((5,5),np.uint8)
|
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]
|
||||||
|
|
||||||
width=self.img_width
|
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
|
||||||
height=self.img_height
|
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]
|
||||||
|
|
||||||
#img = cv2.medianBlur(img,5)
|
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
|
||||||
img=self.otsu_copy_binary(img)
|
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
|
||||||
|
:] = seg_color
|
||||||
|
|
||||||
#img=cv2.bilateralFilter(img,9,75,75)
|
|
||||||
img = cv2.GaussianBlur(img,(5,5),0)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
img=img/255.0
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
img_h=img.shape[0]
|
|
||||||
img_w=img.shape[1]
|
|
||||||
|
|
||||||
prediction_true=np.zeros((img_h,img_w,3))
|
|
||||||
mask_true=np.zeros((img_h,img_w))
|
|
||||||
nxf=img_w/float(width)
|
|
||||||
nyf=img_h/float(height)
|
|
||||||
|
|
||||||
if nxf>int(nxf):
|
|
||||||
nxf=int(nxf)+1
|
|
||||||
else:
|
else:
|
||||||
nxf=int(nxf)
|
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 = prediction_true.astype(np.uint8)
|
||||||
|
|
||||||
if nyf>int(nyf):
|
|
||||||
nyf=int(nyf)+1
|
|
||||||
else:
|
else:
|
||||||
nyf=int(nyf)
|
img_h_page=img.shape[0]
|
||||||
|
img_w_page=img.shape[1]
|
||||||
print(nxf,nyf)
|
img = img /float( 255.0)
|
||||||
for i in range(nxf):
|
img = self.resize_image(img, img_height_model, img_width_model)
|
||||||
for j in range(nyf):
|
|
||||||
index_x_d=i*width
|
|
||||||
index_x_u=(i+1)*width
|
|
||||||
|
|
||||||
index_y_d=j*height
|
|
||||||
index_y_u=(j+1)*height
|
|
||||||
|
|
||||||
if index_x_u>img_w:
|
|
||||||
index_x_u=img_w
|
|
||||||
index_x_d=img_w-width
|
|
||||||
if index_y_u>img_h:
|
|
||||||
index_y_u=img_h
|
|
||||||
index_y_d=img_h-height
|
|
||||||
|
|
||||||
|
|
||||||
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]) )
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
seg=np.argmax(label_p_pred,axis=3)[0]
|
|
||||||
|
|
||||||
|
|
||||||
seg_color=self.color_images(seg)
|
|
||||||
|
|
||||||
###seg_color=color_images_diva(seg,n_classes)
|
|
||||||
|
|
||||||
|
|
||||||
mask_true[index_y_d:index_y_u,index_x_d:index_x_u]=seg
|
|
||||||
prediction_true[index_y_d:index_y_u,index_x_d:index_x_u,:]=seg_color
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#mask_true=color_images(mask_true,n_classes)
|
|
||||||
y_predi = mask_true
|
|
||||||
|
|
||||||
#print(np.unique(mask_true))
|
|
||||||
#find_contours(mask_true)
|
|
||||||
|
|
||||||
#y_testi = label[:,:,0]#np.argmax(label.reshape(1,label.shape[0],label.shape[1],label.shape[2]), axis=3)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#y_predi=cv2.erode(y_predi,kernel,iterations=3)
|
|
||||||
y_predi=cv2.resize( y_predi, ( img.shape[1],img.shape[0]) ,interpolation=cv2.INTER_NEAREST)
|
|
||||||
#self.end_session()
|
|
||||||
return y_predi
|
|
||||||
|
|
||||||
#def extract_page(img,input_width,input_height,n_classes,model):
|
|
||||||
if self.patches=='false' or self.patches=='False':
|
|
||||||
|
|
||||||
img=cv2.imread(self.image,0)
|
|
||||||
img_org_height=img.shape[0]
|
|
||||||
img_org_width=img.shape[1]
|
|
||||||
#kernel = np.ones((5,5),np.uint8)
|
|
||||||
|
|
||||||
width=self.img_width
|
|
||||||
height=self.img_height
|
|
||||||
#for _ in range(1):
|
|
||||||
#img = cv2.medianBlur(img,5)
|
|
||||||
|
|
||||||
img=self.otsu_org(img)
|
|
||||||
#img=img.astype(np.uint8)
|
|
||||||
img=img.astype(np.uint8)
|
|
||||||
#img = cv2.medianBlur(img,5)
|
|
||||||
#img=img.astype(np.uint8)
|
|
||||||
#img = cv2.GaussianBlur(img,(5,5),0)
|
|
||||||
#img=self.otsu_copy_binary(img)
|
|
||||||
img=img.astype(np.uint8)
|
|
||||||
|
|
||||||
img=img/255.0
|
|
||||||
img=self.resize_image(img,self.img_height,self.img_width)
|
|
||||||
|
|
||||||
|
|
||||||
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]
|
||||||
print(np.shape(seg),np.unique(seg))
|
seg_color =np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
||||||
|
prediction_true = self.resize_image(seg_color, img_h_page, img_w_page)
|
||||||
plt.imshow(seg*255)
|
prediction_true = prediction_true.astype(np.uint8)
|
||||||
plt.show()
|
return prediction_true[:,:,0]
|
||||||
seg_color=self.color_images(seg)
|
|
||||||
print(np.unique(seg_color))
|
|
||||||
|
|
||||||
|
|
||||||
#imgs = seg_color#/np.max(seg_color)*255#np.repeat(seg_color[:, :, np.newaxis], 3, axis=2)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
y_predi=cv2.resize( seg_color, ( img_org_width,img_org_height) ,interpolation=cv2.INTER_NEAREST)
|
|
||||||
return y_predi
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
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=os.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)
|
res=self.predict(model_in)
|
||||||
if self.ground_truth!=None:
|
|
||||||
gt_img=cv2.imread(self.ground_truth)
|
|
||||||
print(np.shape(gt_img),np.shape(res))
|
|
||||||
#self.IoU(gt_img[:,:,0],res)
|
|
||||||
#print(np.unique(res))
|
|
||||||
|
|
||||||
img_fin=np.zeros((res.shape[0],res.shape[1],3) )
|
img_fin=np.zeros((res.shape[0],res.shape[1],3) )
|
||||||
res[:,:][res[:,:]==0]=2
|
res[:,:][res[:,:]==0]=2
|
||||||
|
|
@ -479,24 +230,21 @@ class sbb_binarize:
|
||||||
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
|
||||||
#img_fin= cv2.medianBlur(img_fin, 5)
|
|
||||||
if self.save is not None:
|
if self.save is not None:
|
||||||
cv2.imwrite('./'+self.save,img_last)
|
cv2.imwrite(self.save,img_last)
|
||||||
def main():
|
def main():
|
||||||
parser=argparse.ArgumentParser()
|
parser=argparse.ArgumentParser()
|
||||||
|
|
||||||
parser.add_argument('-i','--image', dest='inp1', default=None, help='directory of alto files which have to be transformed.')
|
parser.add_argument('-i','--image', dest='inp1', default=None, help='image.')
|
||||||
parser.add_argument('-p','--patches', dest='inp3', default=False, help='use patches of image for prediction or should image resize be applied to be fit for model. this parameter should be true or false')
|
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 agive name in the same directory you are. The name and format should be given (0045.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='model directory and name should be provided here.')
|
parser.add_argument('-m','--model', dest='inp2', default=None, help='models directory.')
|
||||||
parser.add_argument('-gt','--groundtruth', dest='inp5', default=None, help='ground truth directory if you want to see the iou of prediction.')
|
|
||||||
parser.add_argument('-mw','--model_weights', dest='inp6', default=None, help='previous model weights which are saved.')
|
|
||||||
|
|
||||||
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,options.inp5,options.inp6)
|
x=sbb_binarize(options.inp1,options.inp2,options.inp3,options.inp4)
|
||||||
x.run()
|
x.run()
|
||||||
|
|
||||||
if __name__=="__main__":
|
if __name__=="__main__":
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue