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more code formatting

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cneud 2024-02-23 22:44:27 +01:00
parent 713b90e084
commit 1a95bca22d
24 changed files with 1408 additions and 1319 deletions
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5
eynollah/eynollah/cli.py
View file

@ -199,8 +199,9 @@ def main(
ignore_page_extraction=ignore_page_extraction,
)
eynollah.run()
#pcgts = eynollah.run()
##eynollah.writer.write_pagexml(pcgts)
# pcgts = eynollah.run()
# eynollah.writer.write_pagexml(pcgts)
if __name__ == "__main__":
main()

2
eynollah/eynollah/ocrd_cli.py
View file

@ -2,10 +2,12 @@ from .processor import EynollahProcessor
from click import command
from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor
@command()
@ocrd_cli_options
def main(*args, **kwargs):
return ocrd_cli_wrap_processor(EynollahProcessor, *args, **kwargs)
if __name__ == '__main__':
main()

89
eynollah/eynollah/plot.py
View file

@ -9,24 +9,25 @@ from .utils import crop_image_inside_box
from .utils.rotate import rotate_image_different
from .utils.resize import resize_image
class EynollahPlotter():
"""
Class collecting all the plotting and image writing methods
"""
def __init__(
self,
*,
dir_out,
dir_of_all,
dir_save_page,
dir_of_deskewed,
dir_of_layout,
dir_of_cropped_images,
image_filename_stem,
image_org=None,
scale_x=1,
scale_y=1,
self,
*,
dir_out,
dir_of_all,
dir_save_page,
dir_of_deskewed,
dir_of_layout,
dir_of_cropped_images,
image_filename_stem,
image_org=None,
scale_x=1,
scale_y=1,
):
self.dir_out = dir_out
self.dir_of_all = dir_of_all
@ -44,22 +45,23 @@ class EynollahPlotter():
if self.dir_of_layout is not None:
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels=['Background' , 'Main text' , 'Image' , 'Separator','Marginalia']
pixels = ['Background', 'Main text', 'Image', 'Separator', 'Marginalia']
values_indexes = [0, 1, 2, 3, 4]
plt.figure(figsize=(40, 40))
plt.rcParams["font.size"] = "40"
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]],
label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in
values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
plt.savefig(os.path.join(self.dir_of_layout, self.image_filename_stem + "_layout_main.png"))
def save_plot_of_layout_main_all(self, text_regions_p, image_page):
if self.dir_of_all is not None:
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels=['Background' , 'Main text' , 'Image' , 'Separator','Marginalia']
pixels = ['Background', 'Main text', 'Image', 'Separator', 'Marginalia']
values_indexes = [0, 1, 2, 3, 4]
plt.figure(figsize=(80, 40))
plt.rcParams["font.size"] = "40"
@ -68,7 +70,9 @@ class EynollahPlotter():
plt.subplot(1, 2, 2)
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]],
label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in
values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_layout_main_and_page.png"))
@ -82,7 +86,9 @@ class EynollahPlotter():
plt.rcParams["font.size"] = "40"
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]],
label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in
values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
plt.savefig(os.path.join(self.dir_of_layout, self.image_filename_stem + "_layout.png"))
@ -99,7 +105,9 @@ class EynollahPlotter():
plt.subplot(1, 2, 2)
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]],
label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in
values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_layout_and_page.png"))
@ -115,7 +123,9 @@ class EynollahPlotter():
plt.subplot(1, 2, 2)
im = plt.imshow(textline_mask_tot_ea[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]],
label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in
values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_textline_and_page.png"))
@ -131,33 +141,36 @@ class EynollahPlotter():
cv2.imwrite(os.path.join(self.dir_of_all, self.image_filename_stem + "_page.png"), image_page)
if self.dir_save_page is not None:
cv2.imwrite(os.path.join(self.dir_save_page, self.image_filename_stem + "_page.png"), image_page)
def save_enhanced_image(self, img_res):
cv2.imwrite(os.path.join(self.dir_out, self.image_filename_stem + "_enhanced.png"), img_res)
def save_plot_of_textline_density(self, img_patch_org):
if self.dir_of_all is not None:
plt.figure(figsize=(80,40))
plt.rcParams['font.size']='50'
plt.subplot(1,2,1)
plt.figure(figsize=(80, 40))
plt.rcParams['font.size'] = '50'
plt.subplot(1, 2, 1)
plt.imshow(img_patch_org)
plt.subplot(1,2,2)
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3),np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))),linewidth=8)
plt.xlabel('Density of textline prediction in direction of X axis',fontsize=60)
plt.ylabel('Height',fontsize=60)
plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
plt.subplot(1, 2, 2)
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3),
np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))), linewidth=8)
plt.xlabel('Density of textline prediction in direction of X axis', fontsize=60)
plt.ylabel('Height', fontsize=60)
plt.yticks([0, len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
plt.gca().invert_yaxis()
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem+'_density_of_textline.png'))
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + '_density_of_textline.png'))
def save_plot_of_rotation_angle(self, angels, var_res):
if self.dir_of_all is not None:
plt.figure(figsize=(60,30))
plt.rcParams['font.size']='50'
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
plt.xlabel('angle',fontsize=50)
plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)
plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))] ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
plt.figure(figsize=(60, 30))
plt.rcParams['font.size'] = '50'
plt.plot(angels, np.array(var_res), '-o', markersize=25, linewidth=4)
plt.xlabel('angle', fontsize=50)
plt.ylabel('variance of sum of rotated textline in direction of x axis', fontsize=50)
plt.plot(angels[np.argmax(var_res)], var_res[np.argmax(np.array(var_res))], '*', markersize=50,
label='Angle of deskewing=' + str("{:.2f}".format(angels[np.argmax(var_res)])) + r'$\degree$')
plt.legend(loc='best')
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem+'_rotation_angle.png'))
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + '_rotation_angle.png'))
def write_images_into_directory(self, img_contours, image_page):
if self.dir_of_cropped_images is not None:
@ -167,9 +180,9 @@ class EynollahPlotter():
box = [x, y, w, h]
croped_page, page_coord = crop_image_inside_box(box, image_page)
croped_page = resize_image(croped_page, int(croped_page.shape[0] / self.scale_y), int(croped_page.shape[1] / self.scale_x))
croped_page = resize_image(croped_page, int(croped_page.shape[0] / self.scale_y),
int(croped_page.shape[1] / self.scale_x))
path = os.path.join(self.dir_of_cropped_images, self.image_filename_stem + "_" + str(index) + ".jpg")
cv2.imwrite(path, croped_page)
index += 1

1
eynollah/eynollah/processor.py
View file

@ -22,6 +22,7 @@ from .utils.pil_cv2 import pil2cv
OCRD_TOOL = loads(resource_string(__name__, 'ocrd-tool.json').decode('utf8'))
class EynollahProcessor(Processor):
def __init__(self, *args, **kwargs):

12
eynollah/eynollah/train/build_model_load_pretrained_weights_and_save.py
View file

@ -1,7 +1,7 @@
import os
import sys
import tensorflow as tf
import keras , warnings
import keras, warnings
from keras.optimizers import *
from sacred import Experiment
from models import *
@ -9,14 +9,12 @@ from utils import *
from metrics import *
def configuration():
gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
if __name__=='__main__':
if __name__ == '__main__':
n_classes = 2
input_height = 224
input_width = 448
@ -24,10 +22,8 @@ if __name__=='__main__':
pretraining = False
dir_of_weights = 'model_bin_sbb_ens.h5'
#configuration()
# configuration()
model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining)
model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining)
model.load_weights(dir_of_weights)
model.save('./name_in_another_python_version.h5')

151
eynollah/eynollah/train/metrics.py
View file

@ -2,8 +2,8 @@ from keras import backend as K
import tensorflow as tf
import numpy as np
def focal_loss(gamma=2., alpha=4.):
def focal_loss(gamma=2., alpha=4.):
gamma = float(gamma)
alpha = float(alpha)
@ -37,8 +37,10 @@ def focal_loss(gamma=2., alpha=4.):
fl = tf.multiply(alpha, tf.multiply(weight, ce))
reduced_fl = tf.reduce_max(fl, axis=1)
return tf.reduce_mean(reduced_fl)
return focal_loss_fixed
def weighted_categorical_crossentropy(weights=None):
""" weighted_categorical_crossentropy
@ -50,15 +52,18 @@ def weighted_categorical_crossentropy(weights=None):
def loss(y_true, y_pred):
labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred)
if weights is not None:
weight_mask = tf.maximum(tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0)
* labels_floats, axis=-1), 1.0)
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss)
return loss
def image_categorical_cross_entropy(y_true, y_pred, weights=None):
"""
:param y_true: tensor of shape (batch_size, height, width) representing the ground truth.
@ -67,35 +72,39 @@ def image_categorical_cross_entropy(y_true, y_pred, weights=None):
"""
labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred)
if weights is not None:
weight_mask = tf.maximum(
tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0)
tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0)
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss)
def class_tversky(y_true, y_pred):
smooth = 1.0#1.00
y_true = K.permute_dimensions(y_true, (3,1,2,0))
y_pred = K.permute_dimensions(y_pred, (3,1,2,0))
def class_tversky(y_true, y_pred):
smooth = 1.0 # 1.00
y_true = K.permute_dimensions(y_true, (3, 1, 2, 0))
y_pred = K.permute_dimensions(y_pred, (3, 1, 2, 0))
y_true_pos = K.batch_flatten(y_true)
y_pred_pos = K.batch_flatten(y_pred)
true_pos = K.sum(y_true_pos * y_pred_pos, 1)
false_neg = K.sum(y_true_pos * (1-y_pred_pos), 1)
false_pos = K.sum((1-y_true_pos)*y_pred_pos, 1)
alpha = 0.2#0.5
beta=0.8
return (true_pos + smooth)/(true_pos + alpha*false_neg + (beta)*false_pos + smooth)
false_neg = K.sum(y_true_pos * (1 - y_pred_pos), 1)
false_pos = K.sum((1 - y_true_pos) * y_pred_pos, 1)
alpha = 0.2 # 0.5
beta = 0.8
return (true_pos + smooth) / (true_pos + alpha * false_neg + (beta) * false_pos + smooth)
def focal_tversky_loss(y_true,y_pred):
def focal_tversky_loss(y_true, y_pred):
pt_1 = class_tversky(y_true, y_pred)
gamma =1.3#4./3.0#1.3#4.0/3.00# 0.75
return K.sum(K.pow((1-pt_1), gamma))
gamma = 1.3 # 4./3.0#1.3#4.0/3.00# 0.75
return K.sum(K.pow((1 - pt_1), gamma))
def generalized_dice_coeff2(y_true, y_pred):
n_el = 1
@ -103,36 +112,41 @@ def generalized_dice_coeff2(y_true, y_pred):
n_el *= int(dim)
n_cl = y_true.shape[-1]
w = K.zeros(shape=(n_cl,))
w = (K.sum(y_true, axis=(0,1,2)))/(n_el)
w = 1/(w**2+0.000001)
numerator = y_true*y_pred
numerator = w*K.sum(numerator,(0,1,2))
w = (K.sum(y_true, axis=(0, 1, 2))) / (n_el)
w = 1 / (w ** 2 + 0.000001)
numerator = y_true * y_pred
numerator = w * K.sum(numerator, (0, 1, 2))
numerator = K.sum(numerator)
denominator = y_true+y_pred
denominator = w*K.sum(denominator,(0,1,2))
denominator = y_true + y_pred
denominator = w * K.sum(denominator, (0, 1, 2))
denominator = K.sum(denominator)
return 2*numerator/denominator
return 2 * numerator / denominator
def generalized_dice_coeff(y_true, y_pred):
axes = tuple(range(1, len(y_pred.shape)-1))
axes = tuple(range(1, len(y_pred.shape) - 1))
Ncl = y_pred.shape[-1]
w = K.zeros(shape=(Ncl,))
w = K.sum(y_true, axis=axes)
w = 1/(w**2+0.000001)
w = 1 / (w ** 2 + 0.000001)
# Compute gen dice coef:
numerator = y_true*y_pred
numerator = w*K.sum(numerator,axes)
numerator = y_true * y_pred
numerator = w * K.sum(numerator, axes)
numerator = K.sum(numerator)
denominator = y_true+y_pred
denominator = w*K.sum(denominator,axes)
denominator = y_true + y_pred
denominator = w * K.sum(denominator, axes)
denominator = K.sum(denominator)
gen_dice_coef = 2*numerator/denominator
gen_dice_coef = 2 * numerator / denominator
return gen_dice_coef
def generalized_dice_loss(y_true, y_pred):
return 1 - generalized_dice_coeff2(y_true, y_pred)
def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
'''
Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.
@ -153,14 +167,16 @@ def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
'''
# skip the batch and class axis for calculating Dice score
axes = tuple(range(1, len(y_pred.shape)-1))
axes = tuple(range(1, len(y_pred.shape) - 1))
numerator = 2. * K.sum(y_pred * y_true, axes)
denominator = K.sum(K.square(y_pred) + K.square(y_true), axes)
return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last = True, mean_per_class=False, verbose=False):
def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last=True, mean_per_class=False,
verbose=False):
"""
Compute mean metrics of two segmentation masks, via Keras.
@ -211,28 +227,28 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
y_pred = K.cast(y_pred, 'float32')
# intersection and union shapes are batch_size * n_classes (values = area in pixels)
axes = (1,2) # W,H axes of each image
axes = (1, 2) # W,H axes of each image
intersection = K.sum(K.abs(y_true * y_pred), axis=axes)
mask_sum = K.sum(K.abs(y_true), axis=axes) + K.sum(K.abs(y_pred), axis=axes)
union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
smooth = .001
iou = (intersection + smooth) / (union + smooth)
dice = 2 * (intersection + smooth)/(mask_sum + smooth)
dice = 2 * (intersection + smooth) / (mask_sum + smooth)
metric = {'iou': iou, 'dice': dice}[metric_name]
# define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise
mask = K.cast(K.not_equal(union, 0), 'float32')
mask = K.cast(K.not_equal(union, 0), 'float32')
if drop_last:
metric = metric[:,:-1]
mask = mask[:,:-1]
metric = metric[:, :-1]
mask = mask[:, :-1]
if verbose:
print('intersection, union')
print(K.eval(intersection), K.eval(union))
print(K.eval(intersection/union))
print(K.eval(intersection / union))
# return mean metrics: remaining axes are (batch, classes)
if flag_naive_mean:
@ -250,6 +266,7 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
return K.mean(non_zero_sum / non_zero_count)
def mean_iou(y_true, y_pred, **kwargs):
"""
Compute mean Intersection over Union of two segmentation masks, via Keras.
@ -257,65 +274,69 @@ def mean_iou(y_true, y_pred, **kwargs):
Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.
"""
return seg_metrics(y_true, y_pred, metric_name='iou', **kwargs)
def Mean_IOU(y_true, y_pred):
nb_classes = K.int_shape(y_pred)[-1]
iou = []
true_pixels = K.argmax(y_true, axis=-1)
pred_pixels = K.argmax(y_pred, axis=-1)
void_labels = K.equal(K.sum(y_true, axis=-1), 0)
for i in range(0, nb_classes): # exclude first label (background) and last label (void)
true_labels = K.equal(true_pixels, i)# & ~void_labels
pred_labels = K.equal(pred_pixels, i)# & ~void_labels
for i in range(0, nb_classes): # exclude first label (background) and last label (void)
true_labels = K.equal(true_pixels, i) # & ~void_labels
pred_labels = K.equal(pred_pixels, i) # & ~void_labels
inter = tf.to_int32(true_labels & pred_labels)
union = tf.to_int32(true_labels | pred_labels)
legal_batches = K.sum(tf.to_int32(true_labels), axis=1)>0
ious = K.sum(inter, axis=1)/K.sum(union, axis=1)
iou.append(K.mean(tf.gather(ious, indices=tf.where(legal_batches)))) # returns average IoU of the same objects
legal_batches = K.sum(tf.to_int32(true_labels), axis=1) > 0
ious = K.sum(inter, axis=1) / K.sum(union, axis=1)
iou.append(K.mean(tf.gather(ious, indices=tf.where(legal_batches)))) # returns average IoU of the same objects
iou = tf.stack(iou)
legal_labels = ~tf.debugging.is_nan(iou)
iou = tf.gather(iou, indices=tf.where(legal_labels))
return K.mean(iou)
def iou_vahid(y_true, y_pred):
nb_classes = tf.shape(y_true)[-1]+tf.to_int32(1)
nb_classes = tf.shape(y_true)[-1] + tf.to_int32(1)
true_pixels = K.argmax(y_true, axis=-1)
pred_pixels = K.argmax(y_pred, axis=-1)
iou = []
for i in tf.range(nb_classes):
tp=K.sum( tf.to_int32( K.equal(true_pixels, i) & K.equal(pred_pixels, i) ) )
fp=K.sum( tf.to_int32( K.not_equal(true_pixels, i) & K.equal(pred_pixels, i) ) )
fn=K.sum( tf.to_int32( K.equal(true_pixels, i) & K.not_equal(pred_pixels, i) ) )
iouh=tp/(tp+fp+fn)
tp = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.equal(pred_pixels, i)))
fp = K.sum(tf.to_int32(K.not_equal(true_pixels, i) & K.equal(pred_pixels, i)))
fn = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.not_equal(pred_pixels, i)))
iouh = tp / (tp + fp + fn)
iou.append(iouh)
return K.mean(iou)
def IoU_metric(Yi,y_predi):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
def IoU_metric(Yi, y_predi):
# mean Intersection over Union
# Mean IoU = TP/(FN + TP + FP)
y_predi = np.argmax(y_predi, axis=3)
y_testi = np.argmax(Yi, axis=3)
IoUs = []
Nclass = int(np.max(Yi)) + 1
for c in range(Nclass):
TP = np.sum( (Yi == c)&(y_predi==c) )
FP = np.sum( (Yi != c)&(y_predi==c) )
FN = np.sum( (Yi == c)&(y_predi != c))
IoU = TP/float(TP + FP + FN)
TP = np.sum((Yi == c) & (y_predi == c))
FP = np.sum((Yi != c) & (y_predi == c))
FN = np.sum((Yi == c) & (y_predi != c))
IoU = TP / float(TP + FP + FN)
IoUs.append(IoU)
return K.cast( np.mean(IoUs) ,dtype='float32' )
return K.cast(np.mean(IoUs), dtype='float32')
def IoU_metric_keras(y_true, y_pred):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
# mean Intersection over Union
# Mean IoU = TP/(FN + TP + FP)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
return IoU_metric(y_true.eval(session=sess), y_pred.eval(session=sess))
def jaccard_distance_loss(y_true, y_pred, smooth=100):
"""
Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
@ -334,5 +355,3 @@ def jaccard_distance_loss(y_true, y_pred, smooth=100):
sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return (1 - jac) * smooth

213
eynollah/eynollah/train/models.py
View file

@ -3,19 +3,20 @@ from keras.layers import *
from keras import layers
from keras.regularizers import l2
resnet50_Weights_path='./pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
IMAGE_ORDERING ='channels_last'
MERGE_AXIS=-1
resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
IMAGE_ORDERING = 'channels_last'
MERGE_AXIS = -1
def one_side_pad( x ):
def one_side_pad(x):
x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
if IMAGE_ORDERING == 'channels_first':
x = Lambda(lambda x : x[: , : , :-1 , :-1 ] )(x)
x = Lambda(lambda x: x[:, :, :-1, :-1])(x)
elif IMAGE_ORDERING == 'channels_last':
x = Lambda(lambda x : x[: , :-1 , :-1 , : ] )(x)
x = Lambda(lambda x: x[:, :-1, :-1, :])(x)
return x
def identity_block(input_tensor, kernel_size, filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
@ -37,16 +38,16 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2a')(input_tensor)
x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING ,
x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING,
padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3 , (1, 1), data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(x)
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
x = layers.add([x, input_tensor])
@ -77,20 +78,20 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , strides=strides,
x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING , padding='same',
x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING, padding='same',
name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(x)
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
shortcut = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , strides=strides,
shortcut = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
@ -99,12 +100,11 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
return x
def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
assert input_height%32 == 0
assert input_width%32 == 0
def resnet50_unet_light(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
assert input_height % 32 == 0
assert input_width % 32 == 0
img_input = Input(shape=(input_height,input_width , 3 ))
img_input = Input(shape=(input_height, input_width, 3))
if IMAGE_ORDERING == 'channels_last':
bn_axis = 3
@ -112,19 +112,18 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
bn_axis = 1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
name='conv1')(x)
f1 = x
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x)
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
f2 = one_side_pad(x )
f2 = one_side_pad(x)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
@ -145,78 +144,65 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
f5 = x
if pretraining:
model=Model( img_input , x ).load_weights(resnet50_Weights_path)
model = Model(img_input, x).load_weights(resnet50_Weights_path)
v512_2048 = Conv2D( 512 , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( f5 )
v512_2048 = ( BatchNormalization(axis=bn_axis))(v512_2048)
v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5)
v512_2048 = (BatchNormalization(axis=bn_axis))(v512_2048)
v512_2048 = Activation('relu')(v512_2048)
v512_1024=Conv2D( 512 , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( f4 )
v512_1024 = ( BatchNormalization(axis=bn_axis))(v512_1024)
v512_1024 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f4)
v512_1024 = (BatchNormalization(axis=bn_axis))(v512_1024)
v512_1024 = Activation('relu')(v512_1024)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(v512_2048)
o = ( concatenate([ o ,v512_1024],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
o = ( Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v512_2048)
o = (concatenate([o, v512_1024], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([ o ,f3],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
o = ( Conv2D( 256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f3], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,f2],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING))(o)
o = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay) ) )(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f2], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,f1],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
o = ( Conv2D( 64 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f1], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,img_input],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
o = ( Conv2D( 32 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, img_input], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = Conv2D( n_classes , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( o )
o = ( BatchNormalization(axis=bn_axis))(o)
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(o)
model = Model( img_input , o )
model = Model(img_input, o)
return model
def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
assert input_height%32 == 0
assert input_width%32 == 0
def resnet50_unet(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
assert input_height % 32 == 0
assert input_width % 32 == 0
img_input = Input(shape=(input_height,input_width , 3 ))
img_input = Input(shape=(input_height, input_width, 3))
if IMAGE_ORDERING == 'channels_last':
bn_axis = 3
@ -224,19 +210,18 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
bn_axis = 1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
name='conv1')(x)
f1 = x
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x)
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
f2 = one_side_pad(x )
f2 = one_side_pad(x)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
@ -258,60 +243,52 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
f5 = x
if pretraining:
Model( img_input , x ).load_weights(resnet50_Weights_path)
Model(img_input, x).load_weights(resnet50_Weights_path)
v1024_2048 = Conv2D( 1024 , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( f5 )
v1024_2048 = ( BatchNormalization(axis=bn_axis))(v1024_2048)
v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(
f5)
v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
v1024_2048 = Activation('relu')(v1024_2048)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(v1024_2048)
o = ( concatenate([ o ,f4],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
o = ( Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
o = (concatenate([o, f4], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([ o ,f3],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o)
o = ( Conv2D( 256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f3], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,f2],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING))(o)
o = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay) ) )(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f2], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,f1],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
o = ( Conv2D( 64 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f1], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,img_input],axis=MERGE_AXIS ) )
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o)
o = ( Conv2D( 32 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, img_input], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = Conv2D( n_classes , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( o )
o = ( BatchNormalization(axis=bn_axis))(o)
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(o)
model = Model( img_input , o )
model = Model(img_input, o)
return model

313
eynollah/eynollah/train/pagexml2img.py
View file

@ -1,6 +1,6 @@
#! /usr/bin/env python3
__version__= '1.0'
__version__ = '1.0'
import argparse
import sys
@ -14,235 +14,260 @@ import cv2
with warnings.catch_warnings():
warnings.simplefilter("ignore")
__doc__=\
"""
__doc__ = \
"""
tool to extract 2d or 3d RGB images from page xml data. In former case output will be 1
2D image array which each class has filled with a pixel value. In the case of 3D RGB image
each class will be defined with a RGB value and beside images a text file of classes also will be produced.
This classes.txt file is required for dhsegment tool.
"""
class pagexml2img:
def __init__(self,dir_in, out_dir,output_type):
self.dir=dir_in
self.output_dir=out_dir
self.output_type=output_type
def __init__(self, dir_in, out_dir, output_type):
self.dir = dir_in
self.output_dir = out_dir
self.output_type = output_type
def get_content_of_dir(self):
"""
Listing all ground truth page xml files. All files are needed to have xml format.
"""
gt_all=os.listdir(self.dir)
self.gt_list=[file for file in gt_all if file.split('.')[ len(file.split('.'))-1 ]=='xml' ]
gt_all = os.listdir(self.dir)
self.gt_list = [file for file in gt_all if file.split('.')[len(file.split('.')) - 1] == 'xml']
def get_images_of_ground_truth(self):
"""
Reading the page xml files and write the ground truth images into given output directory.
"""
if self.output_type=='3d' or self.output_type=='3D':
classes=np.array([ [0,0,0, 1, 0, 0, 0, 0],
[255,0,0, 0, 1, 0, 0, 0],
[0,255,0, 0, 0, 1, 0, 0],
[0,0,255, 0, 0, 0, 1, 0],
[0,255,255, 0, 0, 0, 0, 1] ])
if self.output_type == '3d' or self.output_type == '3D':
classes = np.array([[0, 0, 0, 1, 0, 0, 0, 0],
[255, 0, 0, 0, 1, 0, 0, 0],
[0, 255, 0, 0, 0, 1, 0, 0],
[0, 0, 255, 0, 0, 0, 1, 0],
[0, 255, 255, 0, 0, 0, 0, 1]])
for index in tqdm(range(len(self.gt_list))):
try:
tree1 = ET.parse(self.dir+'/'+self.gt_list[index])
root1=tree1.getroot()
alltags=[elem.tag for elem in root1.iter()]
link=alltags[0].split('}')[0]+'}'
tree1 = ET.parse(self.dir + '/' + self.gt_list[index])
root1 = tree1.getroot()
alltags = [elem.tag for elem in root1.iter()]
link = alltags[0].split('}')[0] + '}'
region_tags=np.unique([x for x in alltags if x.endswith('Region')])
region_tags = np.unique([x for x in alltags if x.endswith('Region')])
for jj in root1.iter(link+'Page'):
y_len=int(jj.attrib['imageHeight'])
x_len=int(jj.attrib['imageWidth'])
for jj in root1.iter(link + 'Page'):
y_len = int(jj.attrib['imageHeight'])
x_len = int(jj.attrib['imageWidth'])
co_text=[]
co_sep=[]
co_img=[]
co_table=[]
co_text = []
co_sep = []
co_img = []
co_table = []
for tag in region_tags:
if tag.endswith('}TextRegion') or tag.endswith('}Textregion') or tag.endswith('}textRegion') or tag.endswith('}textregion'):
if tag.endswith('}TextRegion') or tag.endswith('}Textregion') or tag.endswith(
'}textRegion') or tag.endswith('}textregion'):
for nn in root1.iter(tag):
for co_it in nn.iter(link+'Coords'):
if bool(co_it.attrib)==False:
c_t_in=[]
for ll in nn.iter(link+'Point'):
c_t_in.append([ int(np.float(ll.attrib['x'])) , int(np.float(ll.attrib['y'])) ])
for co_it in nn.iter(link + 'Coords'):
if bool(co_it.attrib) == False:
c_t_in = []
for ll in nn.iter(link + 'Point'):
c_t_in.append(
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
co_text.append(np.array(c_t_in))
print(co_text)
elif bool(co_it.attrib)==True and 'points' in co_it.attrib.keys():
p_h=co_it.attrib['points'].split(' ')
co_text.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
p_h = co_it.attrib['points'].split(' ')
co_text.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
elif tag.endswith('}ImageRegion') or tag.endswith('}Imageregion') or tag.endswith('}imageRegion') or tag.endswith('}imageregion'):
elif tag.endswith('}ImageRegion') or tag.endswith('}Imageregion') or tag.endswith(
'}imageRegion') or tag.endswith('}imageregion'):
for nn in root1.iter(tag):
for co_it in nn.iter(link+'Coords'):
if bool(co_it.attrib)==False:
c_i_in=[]
for ll in nn.iter(link+'Point'):
c_i_in.append([ int(np.float(ll.attrib['x'])) , int(np.float(ll.attrib['y'])) ])
for co_it in nn.iter(link + 'Coords'):
if bool(co_it.attrib) == False:
c_i_in = []
for ll in nn.iter(link + 'Point'):
c_i_in.append(
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
co_img.append(np.array(c_i_in))
elif bool(co_it.attrib)==True and 'points' in co_it.attrib.keys():
p_h=co_it.attrib['points'].split(' ')
co_img.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
p_h = co_it.attrib['points'].split(' ')
co_img.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
elif tag.endswith('}SeparatorRegion') or tag.endswith('}Separatorregion') or tag.endswith('}separatorRegion') or tag.endswith('}separatorregion'):
elif tag.endswith('}SeparatorRegion') or tag.endswith('}Separatorregion') or tag.endswith(
'}separatorRegion') or tag.endswith('}separatorregion'):
for nn in root1.iter(tag):
for co_it in nn.iter(link+'Coords'):
if bool(co_it.attrib)==False:
c_s_in=[]
for ll in nn.iter(link+'Point'):
c_s_in.append([ int(np.float(ll.attrib['x'])) , int(np.float(ll.attrib['y'])) ])
for co_it in nn.iter(link + 'Coords'):
if bool(co_it.attrib) == False:
c_s_in = []
for ll in nn.iter(link + 'Point'):
c_s_in.append(
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
co_sep.append(np.array(c_s_in))
elif bool(co_it.attrib)==True and 'points' in co_it.attrib.keys():
p_h=co_it.attrib['points'].split(' ')
co_sep.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
p_h = co_it.attrib['points'].split(' ')
co_sep.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
elif tag.endswith('}TableRegion') or tag.endswith('}tableRegion') or tag.endswith('}Tableregion') or tag.endswith('}tableregion'):
elif tag.endswith('}TableRegion') or tag.endswith('}tableRegion') or tag.endswith(
'}Tableregion') or tag.endswith('}tableregion'):
for nn in root1.iter(tag):
for co_it in nn.iter(link+'Coords'):
if bool(co_it.attrib)==False:
c_ta_in=[]
for ll in nn.iter(link+'Point'):
c_ta_in.append([ int(np.float(ll.attrib['x'])) , int(np.float(ll.attrib['y'])) ])
for co_it in nn.iter(link + 'Coords'):
if bool(co_it.attrib) == False:
c_ta_in = []
for ll in nn.iter(link + 'Point'):
c_ta_in.append(
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
co_table.append(np.array(c_ta_in))
elif bool(co_it.attrib)==True and 'points' in co_it.attrib.keys():
p_h=co_it.attrib['points'].split(' ')
co_table.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
p_h = co_it.attrib['points'].split(' ')
co_table.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
else:
pass
img = np.zeros( (y_len,x_len,3) )
img_poly=cv2.fillPoly(img, pts =co_text, color=(255,0,0))
img_poly=cv2.fillPoly(img, pts =co_img, color=(0,255,0))
img_poly=cv2.fillPoly(img, pts =co_sep, color=(0,0,255))
img_poly=cv2.fillPoly(img, pts =co_table, color=(0,255,255))
img = np.zeros((y_len, x_len, 3))
img_poly = cv2.fillPoly(img, pts=co_text, color=(255, 0, 0))
img_poly = cv2.fillPoly(img, pts=co_img, color=(0, 255, 0))
img_poly = cv2.fillPoly(img, pts=co_sep, color=(0, 0, 255))
img_poly = cv2.fillPoly(img, pts=co_table, color=(0, 255, 255))
try:
cv2.imwrite(self.output_dir+'/'+self.gt_list[index].split('-')[1].split('.')[0]+'.png',img_poly )
cv2.imwrite(self.output_dir + '/' + self.gt_list[index].split('-')[1].split('.')[0] + '.png',
img_poly)
except:
cv2.imwrite(self.output_dir+'/'+self.gt_list[index].split('.')[0]+'.png',img_poly )
cv2.imwrite(self.output_dir + '/' + self.gt_list[index].split('.')[0] + '.png', img_poly)
except:
pass
np.savetxt(self.output_dir+'/../classes.txt',classes)
np.savetxt(self.output_dir + '/../classes.txt', classes)
if self.output_type=='2d' or self.output_type=='2D':
if self.output_type == '2d' or self.output_type == '2D':
for index in tqdm(range(len(self.gt_list))):
try:
tree1 = ET.parse(self.dir+'/'+self.gt_list[index])
root1=tree1.getroot()
alltags=[elem.tag for elem in root1.iter()]
link=alltags[0].split('}')[0]+'}'
tree1 = ET.parse(self.dir + '/' + self.gt_list[index])
root1 = tree1.getroot()
alltags = [elem.tag for elem in root1.iter()]
link = alltags[0].split('}')[0] + '}'
region_tags=np.unique([x for x in alltags if x.endswith('Region')])
region_tags = np.unique([x for x in alltags if x.endswith('Region')])
for jj in root1.iter(link+'Page'):
y_len=int(jj.attrib['imageHeight'])
x_len=int(jj.attrib['imageWidth'])
for jj in root1.iter(link + 'Page'):
y_len = int(jj.attrib['imageHeight'])
x_len = int(jj.attrib['imageWidth'])
co_text=[]
co_sep=[]
co_img=[]
co_table=[]
co_text = []
co_sep = []
co_img = []
co_table = []
for tag in region_tags:
if tag.endswith('}TextRegion') or tag.endswith('}Textregion') or tag.endswith('}textRegion') or tag.endswith('}textregion'):
if tag.endswith('}TextRegion') or tag.endswith('}Textregion') or tag.endswith(
'}textRegion') or tag.endswith('}textregion'):
for nn in root1.iter(tag):
for co_it in nn.iter(link+'Coords'):
if bool(co_it.attrib)==False:
c_t_in=[]
for ll in nn.iter(link+'Point'):
c_t_in.append([ int(np.float(ll.attrib['x'])) , int(np.float(ll.attrib['y'])) ])
for co_it in nn.iter(link + 'Coords'):
if bool(co_it.attrib) == False:
c_t_in = []
for ll in nn.iter(link + 'Point'):
c_t_in.append(
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
co_text.append(np.array(c_t_in))
print(co_text)
elif bool(co_it.attrib)==True and 'points' in co_it.attrib.keys():
p_h=co_it.attrib['points'].split(' ')
co_text.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
p_h = co_it.attrib['points'].split(' ')
co_text.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
elif tag.endswith('}ImageRegion') or tag.endswith('}Imageregion') or tag.endswith('}imageRegion') or tag.endswith('}imageregion'):
elif tag.endswith('}ImageRegion') or tag.endswith('}Imageregion') or tag.endswith(
'}imageRegion') or tag.endswith('}imageregion'):
for nn in root1.iter(tag):
for co_it in nn.iter(link+'Coords'):
if bool(co_it.attrib)==False:
c_i_in=[]
for ll in nn.iter(link+'Point'):
c_i_in.append([ int(np.float(ll.attrib['x'])) , int(np.float(ll.attrib['y'])) ])
for co_it in nn.iter(link + 'Coords'):
if bool(co_it.attrib) == False:
c_i_in = []
for ll in nn.iter(link + 'Point'):
c_i_in.append(
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
co_img.append(np.array(c_i_in))
elif bool(co_it.attrib)==True and 'points' in co_it.attrib.keys():
p_h=co_it.attrib['points'].split(' ')
co_img.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
p_h = co_it.attrib['points'].split(' ')
co_img.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
elif tag.endswith('}SeparatorRegion') or tag.endswith('}Separatorregion') or tag.endswith('}separatorRegion') or tag.endswith('}separatorregion'):
elif tag.endswith('}SeparatorRegion') or tag.endswith('}Separatorregion') or tag.endswith(
'}separatorRegion') or tag.endswith('}separatorregion'):
for nn in root1.iter(tag):
for co_it in nn.iter(link+'Coords'):
if bool(co_it.attrib)==False:
c_s_in=[]
for ll in nn.iter(link+'Point'):
c_s_in.append([ int(np.float(ll.attrib['x'])) , int(np.float(ll.attrib['y'])) ])
for co_it in nn.iter(link + 'Coords'):
if bool(co_it.attrib) == False:
c_s_in = []
for ll in nn.iter(link + 'Point'):
c_s_in.append(
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
co_sep.append(np.array(c_s_in))
elif bool(co_it.attrib)==True and 'points' in co_it.attrib.keys():
p_h=co_it.attrib['points'].split(' ')
co_sep.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
p_h = co_it.attrib['points'].split(' ')
co_sep.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
elif tag.endswith('}TableRegion') or tag.endswith('}tableRegion') or tag.endswith('}Tableregion') or tag.endswith('}tableregion'):
elif tag.endswith('}TableRegion') or tag.endswith('}tableRegion') or tag.endswith(
'}Tableregion') or tag.endswith('}tableregion'):
for nn in root1.iter(tag):
for co_it in nn.iter(link+'Coords'):
if bool(co_it.attrib)==False:
c_ta_in=[]
for ll in nn.iter(link+'Point'):
c_ta_in.append([ int(np.float(ll.attrib['x'])) , int(np.float(ll.attrib['y'])) ])
for co_it in nn.iter(link + 'Coords'):
if bool(co_it.attrib) == False:
c_ta_in = []
for ll in nn.iter(link + 'Point'):
c_ta_in.append(
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
co_table.append(np.array(c_ta_in))
elif bool(co_it.attrib)==True and 'points' in co_it.attrib.keys():
p_h=co_it.attrib['points'].split(' ')
co_table.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ] for x in p_h] ) )
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
p_h = co_it.attrib['points'].split(' ')
co_table.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
else:
pass
img = np.zeros( (y_len,x_len) )
img_poly=cv2.fillPoly(img, pts =co_text, color=(1,1,1))
img_poly=cv2.fillPoly(img, pts =co_img, color=(2,2,2))
img_poly=cv2.fillPoly(img, pts =co_sep, color=(3,3,3))
img_poly=cv2.fillPoly(img, pts =co_table, color=(4,4,4))
img = np.zeros((y_len, x_len))
img_poly = cv2.fillPoly(img, pts=co_text, color=(1, 1, 1))
img_poly = cv2.fillPoly(img, pts=co_img, color=(2, 2, 2))
img_poly = cv2.fillPoly(img, pts=co_sep, color=(3, 3, 3))
img_poly = cv2.fillPoly(img, pts=co_table, color=(4, 4, 4))
try:
cv2.imwrite(self.output_dir+'/'+self.gt_list[index].split('-')[1].split('.')[0]+'.png',img_poly )
cv2.imwrite(self.output_dir + '/' + self.gt_list[index].split('-')[1].split('.')[0] + '.png',
img_poly)
except:
cv2.imwrite(self.output_dir+'/'+self.gt_list[index].split('.')[0]+'.png',img_poly )
cv2.imwrite(self.output_dir + '/' + self.gt_list[index].split('.')[0] + '.png', img_poly)
except:
pass
def run(self):
self.get_content_of_dir()
self.get_images_of_ground_truth()
def main():
parser=argparse.ArgumentParser()
parser = argparse.ArgumentParser()
parser.add_argument('-dir_in','--dir_in', dest='inp1', default=None, help='directory of page-xml files')
parser.add_argument('-dir_out','--dir_out', dest='inp2', default=None, help='directory where ground truth images would be written')
parser.add_argument('-type','--type', dest='inp3', default=None, help='this defines how output should be. A 2d image array or a 3d image array encoded with RGB color. Just pass 2d or 3d. The file will be saved one directory up. 2D image array is 3d but only information of one channel would be enough since all channels have the same values.')
options=parser.parse_args()
parser.add_argument('-dir_in', '--dir_in', dest='inp1', default=None, help='directory of page-xml files')
parser.add_argument('-dir_out', '--dir_out', dest='inp2', default=None,
help='directory where ground truth images would be written')
parser.add_argument('-type', '--type', dest='inp3', default=None,
help='this defines how output should be. A 2d image array or a 3d image array encoded with RGB color. Just pass 2d or 3d. The file will be saved one directory up. 2D image array is 3d but only information of one channel would be enough since all channels have the same values.')
options = parser.parse_args()
possibles=globals()
possibles = globals()
possibles.update(locals())
x=pagexml2img(options.inp1,options.inp2,options.inp3)
x = pagexml2img(options.inp1, options.inp2, options.inp3)
x.run()
if __name__=="__main__":
if __name__ == "__main__":
main()

229
eynollah/eynollah/train/train.py
View file

@ -2,7 +2,7 @@ import os
import sys
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
import keras , warnings
import keras, warnings
from keras.optimizers import *
from sacred import Experiment
from models import *
@ -11,20 +11,21 @@ from metrics import *
from keras.models import load_model
from tqdm import tqdm
def configuration():
keras.backend.clear_session()
tf.reset_default_graph()
warnings.filterwarnings('ignore')
os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction=0.95#0.95
config.gpu_options.visible_device_list="0"
config.gpu_options.per_process_gpu_memory_fraction = 0.95 # 0.95
config.gpu_options.visible_device_list = "0"
set_session(tf.Session(config=config))
def get_dirs_or_files(input_data):
if os.path.isdir(input_data):
image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/')
@ -33,206 +34,188 @@ def get_dirs_or_files(input_data):
assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input)
return image_input, labels_input
ex = Experiment()
@ex.config
def config_params():
n_classes=None # Number of classes. If your case study is binary case the set it to 2 and otherwise give your number of cases.
n_epochs=1
input_height=224*1
input_width=224*1
weight_decay=1e-6 # Weight decay of l2 regularization of model layers.
n_batch=1 # Number of batches at each iteration.
learning_rate=1e-4
patches=False # Make patches of image in order to use all information of image. In the case of page
n_classes = None # Number of classes. If your case study is binary case the set it to 2 and otherwise give your number of cases.
n_epochs = 1
input_height = 224 * 1
input_width = 224 * 1
weight_decay = 1e-6 # Weight decay of l2 regularization of model layers.
n_batch = 1 # Number of batches at each iteration.
learning_rate = 1e-4
patches = False # Make patches of image in order to use all information of image. In the case of page
# extraction this should be set to false since model should see all image.
augmentation=False
flip_aug=False # Flip image (augmentation).
blur_aug=False # Blur patches of image (augmentation).
scaling=False # Scaling of patches (augmentation) will be imposed if this set to true.
binarization=False # Otsu thresholding. Used for augmentation in the case of binary case like textline prediction. For multicases should not be applied.
dir_train=None # Directory of training dataset (sub-folders should be named images and labels).
dir_eval=None # Directory of validation dataset (sub-folders should be named images and labels).
dir_output=None # Directory of output where the model should be saved.
pretraining=False # Set true to load pretrained weights of resnet50 encoder.
scaling_bluring=False
scaling_binarization=False
scaling_flip=False
thetha=[10,-10]
blur_k=['blur','guass','median'] # Used in order to blur image. Used for augmentation.
scales= [ 0.5, 2 ] # Scale patches with these scales. Used for augmentation.
flip_index=[0,1,-1] # Flip image. Used for augmentation.
continue_training = False # If
augmentation = False
flip_aug = False # Flip image (augmentation).
blur_aug = False # Blur patches of image (augmentation).
scaling = False # Scaling of patches (augmentation) will be imposed if this set to true.
binarization = False # Otsu thresholding. Used for augmentation in the case of binary case like textline prediction. For multicases should not be applied.
dir_train = None # Directory of training dataset (sub-folders should be named images and labels).
dir_eval = None # Directory of validation dataset (sub-folders should be named images and labels).
dir_output = None # Directory of output where the model should be saved.
pretraining = False # Set true to load pretrained weights of resnet50 encoder.
scaling_bluring = False
scaling_binarization = False
scaling_flip = False
thetha = [10, -10]
blur_k = ['blur', 'guass', 'median'] # Used in order to blur image. Used for augmentation.
scales = [0.5, 2] # Scale patches with these scales. Used for augmentation.
flip_index = [0, 1, -1] # Flip image. Used for augmentation.
continue_training = False # If
index_start = 0
dir_of_start_model = ''
is_loss_soft_dice = False
weighted_loss = False
data_is_provided = False
@ex.automain
def run(n_classes,n_epochs,input_height,
input_width,weight_decay,weighted_loss,
index_start,dir_of_start_model,is_loss_soft_dice,
n_batch,patches,augmentation,flip_aug
,blur_aug,scaling, binarization,
blur_k,scales,dir_train,data_is_provided,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,continue_training,
flip_index,dir_eval ,dir_output,pretraining,learning_rate):
def run(n_classes, n_epochs, input_height,
input_width, weight_decay, weighted_loss,
index_start, dir_of_start_model, is_loss_soft_dice,
n_batch, patches, augmentation, flip_aug,
blur_aug, scaling, binarization,
blur_k, scales, dir_train, data_is_provided,
scaling_bluring, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip, continue_training,
flip_index, dir_eval, dir_output, pretraining, learning_rate):
if data_is_provided:
dir_train_flowing=os.path.join(dir_output,'train')
dir_eval_flowing=os.path.join(dir_output,'eval')
dir_train_flowing = os.path.join(dir_output, 'train')
dir_eval_flowing = os.path.join(dir_output, 'eval')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels')
dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images')
dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels')
dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels')
dir_flow_eval_imgs = os.path.join(dir_eval_flowing, 'images')
dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels')
configuration()
else:
dir_img,dir_seg=get_dirs_or_files(dir_train)
dir_img_val,dir_seg_val=get_dirs_or_files(dir_eval)
dir_img, dir_seg = get_dirs_or_files(dir_train)
dir_img_val, dir_seg_val = get_dirs_or_files(dir_eval)
# make first a directory in output for both training and evaluations in order to flow data from these directories.
dir_train_flowing=os.path.join(dir_output,'train')
dir_eval_flowing=os.path.join(dir_output,'eval')
dir_train_flowing = os.path.join(dir_output, 'train')
dir_eval_flowing = os.path.join(dir_output, 'eval')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images/')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels/')
dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images/')
dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels/')
dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images/')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels/')
dir_flow_eval_imgs = os.path.join(dir_eval_flowing, 'images/')
dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels/')
if os.path.isdir(dir_train_flowing):
os.system('rm -rf '+dir_train_flowing)
os.system('rm -rf ' + dir_train_flowing)
os.makedirs(dir_train_flowing)
else:
os.makedirs(dir_train_flowing)
if os.path.isdir(dir_eval_flowing):
os.system('rm -rf '+dir_eval_flowing)
os.system('rm -rf ' + dir_eval_flowing)
os.makedirs(dir_eval_flowing)
else:
os.makedirs(dir_eval_flowing)
os.mkdir(dir_flow_train_imgs)
os.mkdir(dir_flow_train_labels)
os.mkdir(dir_flow_eval_imgs)
os.mkdir(dir_flow_eval_labels)
#set the gpu configuration
# set the gpu configuration
configuration()
#writing patches into a sub-folder in order to be flowed from directory.
provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
# writing patches into a sub-folder in order to be flowed from directory.
provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=augmentation,patches=patches)
input_height, input_width, blur_k, blur_aug,
flip_aug, binarization, scaling, scales, flip_index,
scaling_bluring, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip,
augmentation=augmentation, patches=patches)
provide_patches(dir_img_val,dir_seg_val,dir_flow_eval_imgs,
provide_patches(dir_img_val, dir_seg_val, dir_flow_eval_imgs,
dir_flow_eval_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=False,patches=patches)
input_height, input_width, blur_k, blur_aug,
flip_aug, binarization, scaling, scales, flip_index,
scaling_bluring, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip,
augmentation=False, patches=patches)
if weighted_loss:
weights=np.zeros(n_classes)
weights = np.zeros(n_classes)
if data_is_provided:
for obj in os.listdir(dir_flow_train_labels):
try:
label_obj=cv2.imread(dir_flow_train_labels+'/'+obj)
label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
label_obj = cv2.imread(dir_flow_train_labels + '/' + obj)
label_obj_one_hot = get_one_hot(label_obj, label_obj.shape[0], label_obj.shape[1], n_classes)
weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0)
except:
pass
else:
for obj in os.listdir(dir_seg):
try:
label_obj=cv2.imread(dir_seg+'/'+obj)
label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
label_obj = cv2.imread(dir_seg + '/' + obj)
label_obj_one_hot = get_one_hot(label_obj, label_obj.shape[0], label_obj.shape[1], n_classes)
weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0)
except:
pass
weights = 1.00 / weights
weights=1.00/weights
weights=weights/float(np.sum(weights))
weights=weights/float(np.min(weights))
weights=weights/float(np.sum(weights))
weights = weights / float(np.sum(weights))
weights = weights / float(np.min(weights))
weights = weights / float(np.sum(weights))
if continue_training:
if is_loss_soft_dice:
model = load_model (dir_of_start_model, compile = True, custom_objects={'soft_dice_loss': soft_dice_loss})
model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss})
if weighted_loss:
model = load_model (dir_of_start_model, compile = True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
model = load_model(dir_of_start_model, compile=True,
custom_objects={'loss': weighted_categorical_crossentropy(weights)})
if not is_loss_soft_dice and not weighted_loss:
model = load_model (dir_of_start_model, compile = True)
model = load_model(dir_of_start_model, compile=True)
else:
#get our model.
# get our model.
index_start = 0
model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining)
#if you want to see the model structure just uncomment model summary.
#model.summary()
model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining)
# if you want to see the model structure just uncomment model summary.
# model.summary()
if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy',
optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
if is_loss_soft_dice:
model.compile(loss=soft_dice_loss,
optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
if weighted_loss:
model.compile(loss=weighted_categorical_crossentropy(weights),
optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
#generating train and evaluation data
train_gen = data_gen(dir_flow_train_imgs,dir_flow_train_labels, batch_size = n_batch,
input_height=input_height, input_width=input_width,n_classes=n_classes )
val_gen = data_gen(dir_flow_eval_imgs,dir_flow_eval_labels, batch_size = n_batch,
input_height=input_height, input_width=input_width,n_classes=n_classes )
# generating train and evaluation data
train_gen = data_gen(dir_flow_train_imgs, dir_flow_train_labels, batch_size=n_batch,
input_height=input_height, input_width=input_width, n_classes=n_classes)
val_gen = data_gen(dir_flow_eval_imgs, dir_flow_eval_labels, batch_size=n_batch,
input_height=input_height, input_width=input_width, n_classes=n_classes)
for i in tqdm(range(index_start, n_epochs+index_start)):
for i in tqdm(range(index_start, n_epochs + index_start)):
model.fit_generator(
train_gen,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch)-1,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
validation_data=val_gen,
validation_steps=1,
epochs=1)
model.save(dir_output+'/'+'model_'+str(i)+'.h5')
#os.system('rm -rf '+dir_train_flowing)
#os.system('rm -rf '+dir_eval_flowing)
#model.save(dir_output+'/'+'model'+'.h5')
model.save(dir_output + '/' + 'model_' + str(i) + '.h5')
# os.system('rm -rf '+dir_train_flowing)
# os.system('rm -rf '+dir_eval_flowing)
# model.save(dir_output+'/'+'model'+'.h5')

685
eynollah/eynollah/train/utils.py
View file

@ -10,18 +10,17 @@ import imutils
import math
def bluring(img_in,kind):
if kind=='guass':
img_blur = cv2.GaussianBlur(img_in,(5,5),0)
elif kind=="median":
img_blur = cv2.medianBlur(img_in,5)
elif kind=='blur':
img_blur=cv2.blur(img_in,(5,5))
def bluring(img_in, kind):
if kind == 'guass':
img_blur = cv2.GaussianBlur(img_in, (5, 5), 0)
elif kind == "median":
img_blur = cv2.medianBlur(img_in, 5)
elif kind == 'blur':
img_blur = cv2.blur(img_in, (5, 5))
return img_blur
def elastic_transform(image, alpha, sigma,seedj, random_state=None):
def elastic_transform(image, alpha, sigma, seedj, random_state=None):
"""Elastic deformation of images as described in [Simard2003]_.
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
@ -37,461 +36,459 @@ def elastic_transform(image, alpha, sigma,seedj, random_state=None):
dz = np.zeros_like(dx)
x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))
indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1))
distored_image = map_coordinates(image, indices, order=1, mode='reflect')
return distored_image.reshape(image.shape)
def rotation_90(img):
img_rot=np.zeros((img.shape[1],img.shape[0],img.shape[2]))
img_rot[:,:,0]=img[:,:,0].T
img_rot[:,:,1]=img[:,:,1].T
img_rot[:,:,2]=img[:,:,2].T
img_rot = np.zeros((img.shape[1], img.shape[0], img.shape[2]))
img_rot[:, :, 0] = img[:, :, 0].T
img_rot[:, :, 1] = img[:, :, 1].T
img_rot[:, :, 2] = img[:, :, 2].T
return img_rot
def rotatedRectWithMaxArea(w, h, angle):
"""
"""
Given a rectangle of size wxh that has been rotated by 'angle' (in
radians), computes the width and height of the largest possible
axis-aligned rectangle (maximal area) within the rotated rectangle.
"""
if w <= 0 or h <= 0:
return 0,0
if w <= 0 or h <= 0:
return 0, 0
width_is_longer = w >= h
side_long, side_short = (w,h) if width_is_longer else (h,w)
width_is_longer = w >= h
side_long, side_short = (w, h) if width_is_longer else (h, w)
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2.*sin_a*cos_a*side_long or abs(sin_a-cos_a) < 1e-10:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5*side_short
wr,hr = (x/sin_a,x/cos_a) if width_is_longer else (x/cos_a,x/sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a*cos_a - sin_a*sin_a
wr,hr = (w*cos_a - h*sin_a)/cos_2a, (h*cos_a - w*sin_a)/cos_2a
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2. * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5 * side_short
wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a * cos_a - sin_a * sin_a
wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
return wr,hr
return wr, hr
def rotate_max_area(image,rotated, rotated_label,angle):
def rotate_max_area(image, rotated, rotated_label, angle):
""" image: cv2 image matrix object
angle: in degree
"""
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0],
math.radians(angle))
h, w, _ = rotated.shape
y1 = h//2 - int(hr/2)
y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr)
x1 = w//2 - int(wr/2)
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2],rotated_label[y1:y2, x1:x2]
def rotation_not_90_func(img,label,thetha):
rotated=imutils.rotate(img,thetha)
rotated_label=imutils.rotate(label,thetha)
return rotate_max_area(img, rotated,rotated_label,thetha)
return rotated[y1:y2, x1:x2], rotated_label[y1:y2, x1:x2]
def rotation_not_90_func(img, label, thetha):
rotated = imutils.rotate(img, thetha)
rotated_label = imutils.rotate(label, thetha)
return rotate_max_area(img, rotated, rotated_label, thetha)
def color_images(seg, n_classes):
ann_u=range(n_classes)
if len(np.shape(seg))==3:
seg=seg[:,:,0]
ann_u = range(n_classes)
if len(np.shape(seg)) == 3:
seg = seg[:, :, 0]
seg_img=np.zeros((np.shape(seg)[0],np.shape(seg)[1],3)).astype(float)
colors=sns.color_palette("hls", n_classes)
seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
colors = sns.color_palette("hls", n_classes)
for c in ann_u:
c=int(c)
segl=(seg==c)
seg_img[:,:,0]+=segl*(colors[c][0])
seg_img[:,:,1]+=segl*(colors[c][1])
seg_img[:,:,2]+=segl*(colors[c][2])
c = int(c)
segl = (seg == c)
seg_img[:, :, 0] += segl * (colors[c][0])
seg_img[:, :, 1] += segl * (colors[c][1])
seg_img[:, :, 2] += segl * (colors[c][2])
return seg_img
def resize_image(seg_in,input_height,input_width):
return cv2.resize(seg_in,(input_width,input_height),interpolation=cv2.INTER_NEAREST)
def get_one_hot(seg,input_height,input_width,n_classes):
seg=seg[:,:,0]
seg_f=np.zeros((input_height, input_width,n_classes))
def resize_image(seg_in, input_height, input_width):
return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def get_one_hot(seg, input_height, input_width, n_classes):
seg = seg[:, :, 0]
seg_f = np.zeros((input_height, input_width, n_classes))
for j in range(n_classes):
seg_f[:,:,j]=(seg==j).astype(int)
seg_f[:, :, j] = (seg == j).astype(int)
return seg_f
def IoU(Yi,y_predi):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
def IoU(Yi, y_predi):
# mean Intersection over Union
# Mean IoU = TP/(FN + TP + FP)
IoUs = []
classes_true=np.unique(Yi)
classes_true = np.unique(Yi)
for c in classes_true:
TP = np.sum( (Yi == c)&(y_predi==c) )
FP = np.sum( (Yi != c)&(y_predi==c) )
FN = np.sum( (Yi == c)&(y_predi != c))
IoU = TP/float(TP + FP + FN)
print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c,TP,FP,FN,IoU))
TP = np.sum((Yi == c) & (y_predi == c))
FP = np.sum((Yi != c) & (y_predi == c))
FN = np.sum((Yi == c) & (y_predi != c))
IoU = TP / float(TP + FP + FN)
print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c, TP, FP, FN, IoU))
IoUs.append(IoU)
mIoU = np.mean(IoUs)
print("_________________")
print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU
def data_gen(img_folder, mask_folder, batch_size,input_height, input_width,n_classes):
def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes):
c = 0
n = [f for f in os.listdir(img_folder) if not f.startswith('.')]# os.listdir(img_folder) #List of training images
n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images
random.shuffle(n)
while True:
img = np.zeros((batch_size, input_height, input_width, 3)).astype('float')
mask = np.zeros((batch_size, input_height, input_width, n_classes)).astype('float')
for i in range(c, c+batch_size): #initially from 0 to 16, c = 0.
#print(img_folder+'/'+n[i])
for i in range(c, c + batch_size): # initially from 0 to 16, c = 0.
# print(img_folder+'/'+n[i])
try:
filename=n[i].split('.')[0]
filename = n[i].split('.')[0]
train_img = cv2.imread(img_folder+'/'+n[i])/255.
train_img = cv2.resize(train_img, (input_width, input_height),interpolation=cv2.INTER_NEAREST)# Read an image from folder and resize
train_img = cv2.imread(img_folder + '/' + n[i]) / 255.
train_img = cv2.resize(train_img, (input_width, input_height),
interpolation=cv2.INTER_NEAREST) # Read an image from folder and resize
img[i-c] = train_img #add to array - img[0], img[1], and so on.
train_mask = cv2.imread(mask_folder+'/'+filename+'.png')
#print(mask_folder+'/'+filename+'.png')
#print(train_mask.shape)
train_mask = get_one_hot( resize_image(train_mask,input_height,input_width),input_height,input_width,n_classes)
#train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
img[i - c] = train_img # add to array - img[0], img[1], and so on.
train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
# print(mask_folder+'/'+filename+'.png')
# print(train_mask.shape)
train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
n_classes)
# train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
mask[i-c] = train_mask
mask[i - c] = train_mask
except:
img[i-c] = np.ones((input_height, input_width, 3)).astype('float')
mask[i-c] = np.zeros((input_height, input_width, n_classes)).astype('float')
img[i - c] = np.ones((input_height, input_width, 3)).astype('float')
mask[i - c] = np.zeros((input_height, input_width, n_classes)).astype('float')
c+=batch_size
if(c+batch_size>=len(os.listdir(img_folder))):
c=0
c += batch_size
if c + batch_size >= len(os.listdir(img_folder)):
c = 0
random.shuffle(n)
yield img, mask
def otsu_copy(img):
img_r=np.zeros(img.shape)
img1=img[:,:,0]
img2=img[:,:,1]
img3=img[:,:,2]
_, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
img_r[:,:,0]=threshold1
img_r[:,:,1]=threshold1
img_r[:,:,2]=threshold1
img_r = np.zeros(img.shape)
img1 = img[:, :, 0]
img2 = img[:, :, 1]
img3 = img[:, :, 2]
_, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
_, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
_, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_r[:, :, 0] = threshold1
img_r[:, :, 1] = threshold1
img_r[:, :, 2] = threshold1
return img_r
def get_patches(dir_img_f,dir_seg_f,img,label,height,width,indexer):
if img.shape[0]<height or img.shape[1]<width:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer):
if img.shape[0] < height or img.shape[1] < width:
img, label = do_padding(img, label, height, width)
nxf=img_w/float(width)
nyf=img_h/float(height)
img_h = img.shape[0]
img_w = img.shape[1]
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf = img_w / float(width)
nyf = img_h / float(height)
nxf=int(nxf)
nyf=int(nyf)
if nxf > int(nxf):
nxf = int(nxf) + 1
if nyf > int(nyf):
nyf = int(nyf) + 1
nxf = int(nxf)
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width
index_x_u=(i+1)*width
index_x_d = i * width
index_x_u = (i + 1) * width
index_y_d=j*height
index_y_u=(j+1)*height
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
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_patch = label[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_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
return indexer
def do_padding(img,label,height,width):
height_new=img.shape[0]
width_new=img.shape[1]
h_start=0
w_start=0
if img.shape[0]<height:
h_start=int( abs(height-img.shape[0])/2. )
height_new=height
if img.shape[1]<width:
w_start=int( abs(width-img.shape[1])/2. )
width_new=width
img_new=np.ones((height_new,width_new,img.shape[2])).astype(float)*255
label_new=np.zeros((height_new,width_new,label.shape[2])).astype(float)
img_new[h_start:h_start+img.shape[0],w_start:w_start+img.shape[1],:]=np.copy(img[:,:,:])
label_new[h_start:h_start+label.shape[0],w_start:w_start+label.shape[1],:]=np.copy(label[:,:,:])
return img_new,label_new
def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,n_patches,scaler):
if img.shape[0]<height or img.shape[1]<width:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
height_scale=int(height*scaler)
width_scale=int(width*scaler)
nxf=img_w/float(width_scale)
nyf=img_h/float(height_scale)
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width_scale
index_x_u=(i+1)*width_scale
index_y_d=j*height_scale
index_y_u=(j+1)*height_scale
if index_x_u>img_w:
index_x_u=img_w
index_x_d=img_w-width_scale
if index_y_u>img_h:
index_y_u=img_h
index_y_d=img_h-height_scale
img_patch=img[index_y_d:index_y_u,index_x_d:index_x_u,:]
label_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
img_patch=resize_image(img_patch,height,width)
label_patch=resize_image(label_patch,height,width)
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
return indexer
def get_patches_num_scale_new(dir_img_f,dir_seg_f,img,label,height,width,indexer,scaler):
img=resize_image(img,int(img.shape[0]*scaler),int(img.shape[1]*scaler))
label=resize_image(label,int(label.shape[0]*scaler),int(label.shape[1]*scaler))
if img.shape[0]<height or img.shape[1]<width:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
height_scale=int(height*1)
width_scale=int(width*1)
nxf=img_w/float(width_scale)
nyf=img_h/float(height_scale)
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width_scale
index_x_u=(i+1)*width_scale
index_y_d=j*height_scale
index_y_u=(j+1)*height_scale
if index_x_u>img_w:
index_x_u=img_w
index_x_d=img_w-width_scale
if index_y_u>img_h:
index_y_u=img_h
index_y_d=img_h-height_scale
img_patch=img[index_y_d:index_y_u,index_x_d:index_x_u,:]
label_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
#img_patch=resize_image(img_patch,height,width)
#label_patch=resize_image(label_patch,height,width)
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
indexer += 1
return indexer
def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
def do_padding(img, label, height, width):
height_new = img.shape[0]
width_new = img.shape[1]
h_start = 0
w_start = 0
if img.shape[0] < height:
h_start = int(abs(height - img.shape[0]) / 2.)
height_new = height
if img.shape[1] < width:
w_start = int(abs(width - img.shape[1]) / 2.)
width_new = width
img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255
label_new = np.zeros((height_new, width_new, label.shape[2])).astype(float)
img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :])
label_new[h_start:h_start + label.shape[0], w_start:w_start + label.shape[1], :] = np.copy(label[:, :, :])
return img_new, label_new
def get_patches_num_scale(dir_img_f, dir_seg_f, img, label, height, width, indexer, n_patches, scaler):
if img.shape[0] < height or img.shape[1] < width:
img, label = do_padding(img, label, height, width)
img_h = img.shape[0]
img_w = img.shape[1]
height_scale = int(height * scaler)
width_scale = int(width * scaler)
nxf = img_w / float(width_scale)
nyf = img_h / float(height_scale)
if nxf > int(nxf):
nxf = int(nxf) + 1
if nyf > int(nyf):
nyf = int(nyf) + 1
nxf = int(nxf)
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d = i * width_scale
index_x_u = (i + 1) * width_scale
index_y_d = j * height_scale
index_y_u = (j + 1) * height_scale
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - width_scale
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - height_scale
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
img_patch = resize_image(img_patch, height, width)
label_patch = resize_image(label_patch, height, width)
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
indexer += 1
return indexer
def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, indexer, scaler):
img = resize_image(img, int(img.shape[0] * scaler), int(img.shape[1] * scaler))
label = resize_image(label, int(label.shape[0] * scaler), int(label.shape[1] * scaler))
if img.shape[0] < height or img.shape[1] < width:
img, label = do_padding(img, label, height, width)
img_h = img.shape[0]
img_w = img.shape[1]
height_scale = int(height * 1)
width_scale = int(width * 1)
nxf = img_w / float(width_scale)
nyf = img_h / float(height_scale)
if nxf > int(nxf):
nxf = int(nxf) + 1
if nyf > int(nyf):
nyf = int(nyf) + 1
nxf = int(nxf)
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d = i * width_scale
index_x_u = (i + 1) * width_scale
index_y_d = j * height_scale
index_y_u = (j + 1) * height_scale
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - width_scale
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - height_scale
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
# img_patch=resize_image(img_patch,height,width)
# label_patch=resize_image(label_patch,height,width)
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
indexer += 1
return indexer
def provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=False,patches=False):
input_height, input_width, blur_k, blur_aug,
flip_aug, binarization, scaling, scales, flip_index,
scaling_bluring, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip,
augmentation=False, patches=False):
imgs_cv_train = np.array(os.listdir(dir_img))
segs_cv_train = np.array(os.listdir(dir_seg))
imgs_cv_train=np.array(os.listdir(dir_img))
segs_cv_train=np.array(os.listdir(dir_seg))
indexer=0
for im, seg_i in tqdm(zip(imgs_cv_train,segs_cv_train)):
img_name=im.split('.')[0]
indexer = 0
for im, seg_i in tqdm(zip(imgs_cv_train, segs_cv_train)):
img_name = im.split('.')[0]
if not patches:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png', resize_image(cv2.imread(dir_img+'/'+im),input_height,input_width ) )
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png' , resize_image(cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width ) )
indexer+=1
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_img + '/' + im), input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
indexer += 1
if augmentation:
if flip_aug:
for f_i in flip_index:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
resize_image(cv2.flip(cv2.imread(dir_img+'/'+im),f_i),input_height,input_width) )
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
resize_image(cv2.flip(cv2.imread(dir_img + '/' + im), f_i), input_height,
input_width))
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png' ,
resize_image(cv2.flip(cv2.imread(dir_seg+'/'+img_name+'.png'),f_i),input_height,input_width) )
indexer+=1
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
input_height, input_width))
indexer += 1
if blur_aug:
for blur_i in blur_k:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
(resize_image(bluring(cv2.imread(dir_img+'/'+im),blur_i),input_height,input_width) ) )
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png' ,
resize_image(cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width) )
indexer+=1
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
(resize_image(bluring(cv2.imread(dir_img + '/' + im), blur_i), input_height,
input_width)))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height,
input_width))
indexer += 1
if binarization:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
resize_image(otsu_copy( cv2.imread(dir_img+'/'+im)),input_height,input_width ))
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png',
resize_image( cv2.imread(dir_seg+'/'+img_name+'.png'),input_height,input_width ))
indexer+=1
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
indexer += 1
if patches:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
cv2.imread(dir_img+'/'+im),cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
cv2.imread(dir_img + '/' + im), cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer)
if augmentation:
if rotation:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
rotation_90( cv2.imread(dir_img+'/'+im) ),
rotation_90( cv2.imread(dir_seg+'/'+img_name+'.png') ),
input_height,input_width,indexer=indexer)
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
rotation_90(cv2.imread(dir_img + '/' + im)),
rotation_90(cv2.imread(dir_seg + '/' + img_name + '.png')),
input_height, input_width, indexer=indexer)
if rotation_not_90:
for thetha_i in thetha:
img_max_rotated,label_max_rotated=rotation_not_90_func(cv2.imread(dir_img+'/'+im),cv2.imread(dir_seg+'/'+img_name+'.png'),thetha_i)
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
img_max_rotated,
label_max_rotated,
input_height,input_width,indexer=indexer)
img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/' + im),
cv2.imread(
dir_seg + '/' + img_name + '.png'),
thetha_i)
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
img_max_rotated,
label_max_rotated,
input_height, input_width, indexer=indexer)
if flip_aug:
for f_i in flip_index:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
cv2.flip( cv2.imread(dir_img+'/'+im) , f_i),
cv2.flip( cv2.imread(dir_seg+'/'+img_name+'.png') ,f_i),
input_height,input_width,indexer=indexer)
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
cv2.flip(cv2.imread(dir_img + '/' + im), f_i),
cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
input_height, input_width, indexer=indexer)
if blur_aug:
for blur_i in blur_k:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
bluring( cv2.imread(dir_img+'/'+im) , blur_i),
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
bluring(cv2.imread(dir_img + '/' + im), blur_i),
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer)
if scaling:
for sc_ind in scales:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
cv2.imread(dir_img+'/'+im) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
cv2.imread(dir_img + '/' + im),
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer, scaler=sc_ind)
if binarization:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)),
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer)
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
otsu_copy(cv2.imread(dir_img + '/' + im)),
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer)
if scaling_bluring:
for sc_ind in scales:
for blur_i in blur_k:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
bluring( cv2.imread(dir_img+'/'+im) , blur_i) ,
cv2.imread(dir_seg+'/'+img_name+'.png') ,
input_height,input_width,indexer=indexer,scaler=sc_ind)
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
bluring(cv2.imread(dir_img + '/' + im), blur_i),
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer,
scaler=sc_ind)
if scaling_binarization:
for sc_ind in scales:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
otsu_copy(cv2.imread(dir_img + '/' + im)),
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer, scaler=sc_ind)
if scaling_flip:
for sc_ind in scales:
for f_i in flip_index:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels,
cv2.flip( cv2.imread(dir_img+'/'+im) , f_i) ,
cv2.flip(cv2.imread(dir_seg+'/'+img_name+'.png') ,f_i) ,
input_height,input_width,indexer=indexer,scaler=sc_ind)
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
cv2.flip(cv2.imread(dir_img + '/' + im), f_i),
cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'),
f_i),
input_height, input_width, indexer=indexer,
scaler=sc_ind)

74
eynollah/eynollah/utils/contour.py
View file

@ -5,6 +5,8 @@ from shapely import geometry
from .rotate import rotate_image, rotation_image_new
from multiprocessing import Process, Queue, cpu_count
from multiprocessing import Pool
def contours_in_same_horizon(cy_main_hor):
X1 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
X2 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
@ -22,6 +24,7 @@ def contours_in_same_horizon(cy_main_hor):
all_args.append(list(set(list_h)))
return np.unique(np.array(all_args, dtype=object))
def find_contours_mean_y_diff(contours_main):
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
@ -42,10 +45,11 @@ def get_text_region_boxes_by_given_contours(contours):
del contours
return boxes, contours_new
def filter_contours_area_of_image(image, contours, hierarchy, max_area, min_area):
found_polygons_early = list()
for jv,c in enumerate(contours):
for jv, c in enumerate(contours):
if len(c) < 3: # A polygon cannot have less than 3 points
continue
@ -55,17 +59,18 @@ def filter_contours_area_of_image(image, contours, hierarchy, max_area, min_area
found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint))
return found_polygons_early
def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, min_area):
found_polygons_early = list()
for jv,c in enumerate(contours):
for jv, c in enumerate(contours):
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
# area = cv2.contourArea(c)
area = polygon.area
##print(np.prod(thresh.shape[:2]))
# print(np.prod(thresh.shape[:2]))
# Check that polygon has area greater than minimal area
# print(hierarchy[0][jv][3],hierarchy )
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 :
@ -73,6 +78,7 @@ def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, m
found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32))
return found_polygons_early
def find_new_features_of_contours(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
@ -107,25 +113,27 @@ def find_new_features_of_contours(contours_main):
# dis_x=np.abs(x_max_main-x_min_main)
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
def find_features_of_contours(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
areas_main=np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main=[(M_main[j]['m10']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
x_min_main=np.array([np.min(contours_main[j][:,0,0]) for j in range(len(contours_main))])
x_max_main=np.array([np.max(contours_main[j][:,0,0]) for j in range(len(contours_main))])
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
return y_min_main, y_max_main
def return_parent_contours(contours, hierarchy):
contours_parent = [contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1]
return contours_parent
def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
# pixels of images are identified by 5
@ -145,6 +153,7 @@ def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
return contours_imgs
def do_work_of_contours_in_image(queue_of_all_params, contours_per_process, indexes_r_con_per_pro, img, slope_first):
cnts_org_per_each_subprocess = []
index_by_text_region_contours = []
@ -165,10 +174,9 @@ def do_work_of_contours_in_image(queue_of_all_params, contours_per_process, inde
cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
cnts_org_per_each_subprocess.append(cont_int[0])
queue_of_all_params.put([ cnts_org_per_each_subprocess, index_by_text_region_contours])
queue_of_all_params.put([cnts_org_per_each_subprocess, index_by_text_region_contours])
def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
@ -180,10 +188,10 @@ def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
nh = np.linspace(0, len(cnts), num_cores + 1)
indexes_by_text_con = np.array(range(len(cnts)))
for i in range(num_cores):
contours_per_process = cnts[int(nh[i]) : int(nh[i + 1])]
indexes_text_con_per_process = indexes_by_text_con[int(nh[i]) : int(nh[i + 1])]
contours_per_process = cnts[int(nh[i]): int(nh[i + 1])]
indexes_text_con_per_process = indexes_by_text_con[int(nh[i]): int(nh[i + 1])]
processes.append(Process(target=do_work_of_contours_in_image, args=(queue_of_all_params, contours_per_process, indexes_text_con_per_process, img,slope_first )))
processes.append(Process(target=do_work_of_contours_in_image, args=(queue_of_all_params, contours_per_process, indexes_text_con_per_process, img, slope_first)))
for i in range(num_cores):
processes[i].start()
cnts_org = []
@ -200,7 +208,9 @@ def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
print(all_index_text_con)
return cnts_org
def loop_contour_image(index_l, cnts,img, slope_first):
def loop_contour_image(index_l, cnts, img, slope_first):
img_copy = np.zeros(img.shape)
img_copy = cv2.fillPoly(img_copy, pts=[cnts[index_l]], color=(1, 1, 1))
@ -209,7 +219,7 @@ def loop_contour_image(index_l, cnts,img, slope_first):
# print(img.shape,'img')
img_copy = rotation_image_new(img_copy, -slope_first)
##print(img_copy.shape,'img_copy')
# print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.show()
@ -224,15 +234,17 @@ def loop_contour_image(index_l, cnts,img, slope_first):
# print(np.shape(cont_int[0]))
return cont_int[0]
def get_textregion_contours_in_org_image_multi2(cnts, img, slope_first):
cnts_org = []
# print(cnts,'cnts')
with Pool(cpu_count()) as p:
cnts_org = p.starmap(loop_contour_image, [(index_l,cnts, img,slope_first) for index_l in range(len(cnts))])
cnts_org = p.starmap(loop_contour_image, [(index_l, cnts, img, slope_first) for index_l in range(len(cnts))])
return cnts_org
def get_textregion_contours_in_org_image(cnts, img, slope_first):
cnts_org = []
@ -246,7 +258,7 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
# print(img.shape,'img')
img_copy = rotation_image_new(img_copy, -slope_first)
##print(img_copy.shape,'img_copy')
# print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.show()
@ -263,17 +275,18 @@ def get_textregion_contours_in_org_image(cnts, img, slope_first):
return cnts_org
def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
h_o = img.shape[0]
w_o = img.shape[1]
img = cv2.resize(img, (int(img.shape[1]/3.), int(img.shape[0]/3.)), interpolation=cv2.INTER_NEAREST)
##cnts = list( (np.array(cnts)/2).astype(np.int16) )
#cnts = cnts/2
cnts = [(i/ 3).astype(np.int32) for i in cnts]
# cnts = list( (np.array(cnts)/2).astype(np.int16) )
# cnts = cnts/2
cnts = [(i / 3).astype(np.int32) for i in cnts]
cnts_org = []
#print(cnts,'cnts')
# print(cnts,'cnts')
for i in range(len(cnts)):
img_copy = np.zeros(img.shape)
img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
@ -283,7 +296,7 @@ def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
# print(img.shape,'img')
img_copy = rotation_image_new(img_copy, -slope_first)
##print(img_copy.shape,'img_copy')
# print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.show()
@ -300,6 +313,7 @@ def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
return cnts_org
def return_contours_of_interested_textline(region_pre_p, pixel):
# pixels of images are identified by 5
@ -317,6 +331,7 @@ def return_contours_of_interested_textline(region_pre_p, pixel):
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hierarchy, max_area=1, min_area=0.000000003)
return contours_imgs
def return_contours_of_image(image):
if len(image.shape) == 2:
@ -329,6 +344,7 @@ def return_contours_of_image(image):
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
return contours, hierarchy
def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_size=0.00003):
# pixels of images are identified by 5
@ -348,6 +364,7 @@ def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_si
return contours_imgs
def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area, max_area):
# pixels of images are identified by 5
@ -367,4 +384,3 @@ def return_contours_of_interested_region_by_size(region_pre_p, pixel, min_area,
img_ret = np.zeros((region_pre_p.shape[0], region_pre_p.shape[1], 3))
img_ret = cv2.fillPoly(img_ret, pts=contours_imgs, color=(1, 1, 1))
return img_ret[:, :, 0]

1
eynollah/eynollah/utils/counter.py
View file

@ -3,6 +3,7 @@ from collections import Counter
REGION_ID_TEMPLATE = 'region_%04d'
LINE_ID_TEMPLATE = 'region_%04d_line_%04d'
class EynollahIdCounter():
def __init__(self, region_idx=0, line_idx=0):

177
eynollah/eynollah/utils/drop_capitals.py
View file

@ -6,6 +6,7 @@ from .contour import (
return_parent_contours,
)
def adhere_drop_capital_region_into_corresponding_textline(
text_regions_p,
polygons_of_drop_capitals,
@ -26,7 +27,7 @@ def adhere_drop_capital_region_into_corresponding_textline(
img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
for j_cont in range(len(contours_only_text_parent)):
img_con_all[all_box_coord[j_cont][0] : all_box_coord[j_cont][1], all_box_coord[j_cont][2] : all_box_coord[j_cont][3], 0] = (j_cont + 1) * 3
img_con_all[all_box_coord[j_cont][0]: all_box_coord[j_cont][1], all_box_coord[j_cont][2]: all_box_coord[j_cont][3], 0] = (j_cont + 1) * 3
# img_con_all=cv2.fillPoly(img_con_all,pts=[contours_only_text_parent[j_cont]],color=((j_cont+1)*3,(j_cont+1)*3,(j_cont+1)*3))
# plt.imshow(img_con_all[:,:,0])
@ -44,7 +45,7 @@ def adhere_drop_capital_region_into_corresponding_textline(
# plt.imshow(img_con[:,:,0])
# plt.show()
##img_con=cv2.dilate(img_con, kernel, iterations=30)
# img_con=cv2.dilate(img_con, kernel, iterations=30)
# plt.imshow(img_con[:,:,0])
# plt.show()
@ -185,7 +186,7 @@ def adhere_drop_capital_region_into_corresponding_textline(
# contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
# print(np.shape(contours_biggest),'contours_biggest')
# print(np.shape(all_found_textline_polygons[int(region_final)][arg_min]))
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
# contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_textline_polygons[int(region_final)][arg_min] = contours_biggest
except:
pass
@ -230,7 +231,7 @@ def adhere_drop_capital_region_into_corresponding_textline(
contours_biggest[:, 0, 0] = contours_biggest[:, 0, 0] # -all_box_coord[int(region_final)][2]
contours_biggest[:, 0, 1] = contours_biggest[:, 0, 1] # -all_box_coord[int(region_final)][0]
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
# contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
all_found_textline_polygons[int(region_final)][arg_min] = contours_biggest
# all_found_textline_polygons[int(region_final)][arg_min]=contours_biggest
@ -239,49 +240,49 @@ def adhere_drop_capital_region_into_corresponding_textline(
else:
pass
##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
###print(all_box_coord[j_cont])
###print(cx_t)
###print(cy_t)
###print(cx_d[i_drop])
###print(cy_d[i_drop])
##y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
# cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[int(region_final)])
# ##print(all_box_coord[j_cont])
# ##print(cx_t)
# ##print(cy_t)
# ##print(cx_d[i_drop])
# ##print(cy_d[i_drop])
# #y_lines=all_box_coord[int(region_final)][0]+np.array(cy_t)
##y_lines[y_lines<y_min_d[i_drop]]=0
###print(y_lines)
# #y_lines[y_lines<y_min_d[i_drop]]=0
# ##print(y_lines)
##arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
###print(arg_min)
# #arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
# ##print(arg_min)
##cnt_nearest=np.copy(all_found_textline_polygons[int(region_final)][arg_min])
##cnt_nearest[:,0,0]=all_found_textline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
##cnt_nearest[:,0,1]=all_found_textline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
# #cnt_nearest=np.copy(all_found_textline_polygons[int(region_final)][arg_min])
# #cnt_nearest[:,0,0]=all_found_textline_polygons[int(region_final)][arg_min][:,0,0]#+all_box_coord[int(region_final)][2]
# #cnt_nearest[:,0,1]=all_found_textline_polygons[int(region_final)][arg_min][:,0,1]#+all_box_coord[int(region_final)][0]
##img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
##img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
##img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
# #img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
# #img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
# #img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
##img_textlines=img_textlines.astype(np.uint8)
# #img_textlines=img_textlines.astype(np.uint8)
##plt.imshow(img_textlines)
##plt.show()
##imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
# #plt.imshow(img_textlines)
# #plt.show()
# #imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
# #ret, thresh = cv2.threshold(imgray, 0, 255, 0)
##contours_combined,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# #contours_combined,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
##print(len(contours_combined),'len textlines mixed')
##areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
# #print(len(contours_combined),'len textlines mixed')
# #areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
##contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
# #contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
###print(np.shape(contours_biggest))
###print(contours_biggest[:])
##contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
##contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
# ##print(np.shape(contours_biggest))
# ##print(contours_biggest[:])
# #contours_biggest[:,0,0]=contours_biggest[:,0,0]#-all_box_coord[int(region_final)][2]
# #contours_biggest[:,0,1]=contours_biggest[:,0,1]#-all_box_coord[int(region_final)][0]
##contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
##all_found_textline_polygons[int(region_final)][arg_min]=contours_biggest
# #contours_biggest=contours_biggest.reshape(np.shape(contours_biggest)[0],np.shape(contours_biggest)[2])
# #all_found_textline_polygons[int(region_final)][arg_min]=contours_biggest
else:
if len(region_with_intersected_drop) > 1:
@ -399,71 +400,72 @@ def adhere_drop_capital_region_into_corresponding_textline(
else:
pass
#####for i_drop in range(len(polygons_of_drop_capitals)):
#####for j_cont in range(len(contours_only_text_parent)):
#####img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
#####img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
#####img_con=cv2.fillPoly(img_con,pts=[contours_only_text_parent[j_cont]],color=(255,255,255))
# ####for i_drop in range(len(polygons_of_drop_capitals)):
# ####for j_cont in range(len(contours_only_text_parent)):
# ####img_con=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
# ####img_con=cv2.fillPoly(img_con,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
# ####img_con=cv2.fillPoly(img_con,pts=[contours_only_text_parent[j_cont]],color=(255,255,255))
#####img_con=img_con.astype(np.uint8)
######imgray = cv2.cvtColor(img_con, cv2.COLOR_BGR2GRAY)
######ret, thresh = cv2.threshold(imgray, 0, 255, 0)
# ####img_con=img_con.astype(np.uint8)
# #####imgray = cv2.cvtColor(img_con, cv2.COLOR_BGR2GRAY)
# #####ret, thresh = cv2.threshold(imgray, 0, 255, 0)
######contours_new,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# #####contours_new,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#####contours_new,hir_new=return_contours_of_image(img_con)
#####contours_new_parent=return_parent_contours( contours_new,hir_new)
######plt.imshow(img_con)
######plt.show()
#####try:
#####if len(contours_new_parent)==1:
######print(all_found_textline_polygons[j_cont][0])
#####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[j_cont])
######print(all_box_coord[j_cont])
######print(cx_t)
######print(cy_t)
######print(cx_d[i_drop])
######print(cy_d[i_drop])
#####y_lines=all_box_coord[j_cont][0]+np.array(cy_t)
# ####contours_new,hir_new=return_contours_of_image(img_con)
# ####contours_new_parent=return_parent_contours( contours_new,hir_new)
# #####plt.imshow(img_con)
# #####plt.show()
# ####try:
# ####if len(contours_new_parent)==1:
# #####print(all_found_textline_polygons[j_cont][0])
# ####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contours(all_found_textline_polygons[j_cont])
# #####print(all_box_coord[j_cont])
# #####print(cx_t)
# #####print(cy_t)
# #####print(cx_d[i_drop])
# #####print(cy_d[i_drop])
# ####y_lines=all_box_coord[j_cont][0]+np.array(cy_t)
######print(y_lines)
# #####print(y_lines)
#####arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
######print(arg_min)
# ####arg_min=np.argmin(np.abs(y_lines-y_min_d[i_drop]) )
# #####print(arg_min)
#####cnt_nearest=np.copy(all_found_textline_polygons[j_cont][arg_min])
#####cnt_nearest[:,0]=all_found_textline_polygons[j_cont][arg_min][:,0]+all_box_coord[j_cont][2]
#####cnt_nearest[:,1]=all_found_textline_polygons[j_cont][arg_min][:,1]+all_box_coord[j_cont][0]
# ####cnt_nearest=np.copy(all_found_textline_polygons[j_cont][arg_min])
# ####cnt_nearest[:,0]=all_found_textline_polygons[j_cont][arg_min][:,0]+all_box_coord[j_cont][2]
# ####cnt_nearest[:,1]=all_found_textline_polygons[j_cont][arg_min][:,1]+all_box_coord[j_cont][0]
#####img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
#####img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
#####img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
# ####img_textlines=np.zeros((text_regions_p.shape[0],text_regions_p.shape[1],3))
# ####img_textlines=cv2.fillPoly(img_textlines,pts=[cnt_nearest],color=(255,255,255))
# ####img_textlines=cv2.fillPoly(img_textlines,pts=[polygons_of_drop_capitals[i_drop] ],color=(255,255,255))
#####img_textlines=img_textlines.astype(np.uint8)
#####imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
#####ret, thresh = cv2.threshold(imgray, 0, 255, 0)
# ####img_textlines=img_textlines.astype(np.uint8)
# ####imgray = cv2.cvtColor(img_textlines, cv2.COLOR_BGR2GRAY)
# ####ret, thresh = cv2.threshold(imgray, 0, 255, 0)
#####contours_combined,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# ####contours_combined,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#####areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
# ####areas_cnt_text=np.array([cv2.contourArea(contours_combined[j]) for j in range(len(contours_combined))])
#####contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
# ####contours_biggest=contours_combined[np.argmax(areas_cnt_text)]
######print(np.shape(contours_biggest))
######print(contours_biggest[:])
#####contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[j_cont][2]
#####contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[j_cont][0]
# #####print(np.shape(contours_biggest))
# #####print(contours_biggest[:])
# ####contours_biggest[:,0,0]=contours_biggest[:,0,0]-all_box_coord[j_cont][2]
# ####contours_biggest[:,0,1]=contours_biggest[:,0,1]-all_box_coord[j_cont][0]
#####all_found_textline_polygons[j_cont][arg_min]=contours_biggest
######print(contours_biggest)
######plt.imshow(img_textlines[:,:,0])
######plt.show()
#####else:
#####pass
#####except:
#####pass
# ####all_found_textline_polygons[j_cont][arg_min]=contours_biggest
# #####print(contours_biggest)
# #####plt.imshow(img_textlines[:,:,0])
# #####plt.show()
# ####else:
# ####pass
# ####except:
# ####pass
return all_found_textline_polygons
def filter_small_drop_capitals_from_no_patch_layout(layout_no_patch, layout1):
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
@ -489,7 +491,7 @@ def filter_small_drop_capitals_from_no_patch_layout(layout_no_patch, layout1):
if iou_of_box_and_contoure > 60 and weigh_to_height_ratio < 1.2 and height_to_weight_ratio < 2:
map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1]))
map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w]
map_of_drop_contour_bb[y: y + h, x: x + w] = layout1[y: y + h, x: x + w]
if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15:
contours_drop_parent_final.append(contours_drop_parent[jj])
@ -499,4 +501,3 @@ def filter_small_drop_capitals_from_no_patch_layout(layout_no_patch, layout1):
layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4))
return layout_no_patch

304
eynollah/eynollah/utils/marginals.py
View file

@ -3,250 +3,226 @@ import cv2
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
from .contour import find_new_features_of_contours, return_contours_of_interested_region
from .resize import resize_image
from .rotate import rotate_image
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None):
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
mask_marginals=mask_marginals.astype(np.uint8)
mask_marginals = np.zeros((text_with_lines.shape[0], text_with_lines.shape[1]))
mask_marginals = mask_marginals.astype(np.uint8)
text_with_lines = text_with_lines.astype(np.uint8)
# text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
text_with_lines=text_with_lines.astype(np.uint8)
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
text_with_lines_eroded = cv2.erode(text_with_lines, kernel, iterations=5)
text_with_lines_eroded=cv2.erode(text_with_lines,kernel,iterations=5)
if text_with_lines.shape[0]<=1500:
if text_with_lines.shape[0] <= 1500:
pass
elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,kernel,iterations=5)
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
elif text_with_lines.shape[0] > 1500 and text_with_lines.shape[0] <= 1800:
text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.5), text_with_lines.shape[1])
text_with_lines = cv2.erode(text_with_lines, kernel, iterations=5)
text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0],
text_with_lines_eroded.shape[1])
else:
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
text_with_lines=cv2.erode(text_with_lines,kernel,iterations=7)
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.8), text_with_lines.shape[1])
text_with_lines = cv2.erode(text_with_lines, kernel, iterations=7)
text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0],
text_with_lines_eroded.shape[1])
text_with_lines_y = text_with_lines.sum(axis=0)
text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0)
text_with_lines_y=text_with_lines.sum(axis=0)
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
thickness_along_y_percent = text_with_lines_y_eroded.max() / (float(text_with_lines.shape[0])) * 100
thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
# print(thickness_along_y_percent,'thickness_along_y_percent')
#print(thickness_along_y_percent,'thickness_along_y_percent')
if thickness_along_y_percent<30:
min_textline_thickness=8
elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
min_textline_thickness=20
if thickness_along_y_percent < 30:
min_textline_thickness = 8
elif thickness_along_y_percent >= 30 and thickness_along_y_percent < 50:
min_textline_thickness = 20
else:
min_textline_thickness=40
min_textline_thickness = 40
if thickness_along_y_percent >= 14:
text_with_lines_y_rev = -1 * text_with_lines_y[:]
# print(text_with_lines_y)
# print(text_with_lines_y_rev)
if thickness_along_y_percent>=14:
# plt.plot(text_with_lines_y)
# plt.show()
text_with_lines_y_rev=-1*text_with_lines_y[:]
#print(text_with_lines_y)
#print(text_with_lines_y_rev)
text_with_lines_y_rev = text_with_lines_y_rev - np.min(text_with_lines_y_rev)
# plt.plot(text_with_lines_y_rev)
# plt.show()
sigma_gaus = 1
region_sum_0 = gaussian_filter1d(text_with_lines_y, sigma_gaus)
region_sum_0_rev = gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
# plt.plot(region_sum_0_rev)
# plt.show()
region_sum_0_updown = region_sum_0[len(region_sum_0)::-1]
#plt.plot(text_with_lines_y)
#plt.show()
first_nonzero = (next((i for i, x in enumerate(region_sum_0) if x), None))
last_nonzero = (next((i for i, x in enumerate(region_sum_0_updown) if x), None))
last_nonzero = len(region_sum_0) - last_nonzero
text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
#plt.plot(text_with_lines_y_rev)
#plt.show()
sigma_gaus=1
region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
#plt.plot(region_sum_0_rev)
#plt.show()
region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))
last_nonzero=len(region_sum_0)-last_nonzero
##img_sum_0_smooth_rev=-region_sum_0
mid_point=(last_nonzero+first_nonzero)/2.
one_third_right=(last_nonzero-mid_point)/3.0
one_third_left=(mid_point-first_nonzero)/3.0
#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
# img_sum_0_smooth_rev=-region_sum_0
mid_point = (last_nonzero + first_nonzero) / 2.
one_third_right = (last_nonzero - mid_point) / 3.0
one_third_left = (mid_point - first_nonzero) / 3.0
# img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
peaks = np.array(peaks)
peaks=np.array(peaks)
# print(region_sum_0[peaks])
# #plt.plot(region_sum_0)
# #plt.plot(peaks,region_sum_0[peaks],'*')
# #plt.show()
# print(first_nonzero,last_nonzero,peaks)
peaks = peaks[(peaks > first_nonzero) & (peaks < last_nonzero)]
# print(first_nonzero,last_nonzero,peaks)
#print(region_sum_0[peaks])
##plt.plot(region_sum_0)
##plt.plot(peaks,region_sum_0[peaks],'*')
##plt.show()
#print(first_nonzero,last_nonzero,peaks)
peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
# print(region_sum_0[peaks]<10)
# ###peaks=peaks[region_sum_0[peaks]<25 ]
#print(first_nonzero,last_nonzero,peaks)
#print(region_sum_0[peaks]<10)
####peaks=peaks[region_sum_0[peaks]<25 ]
#print(region_sum_0[peaks])
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
#print(peaks)
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col==1:
peaks_right=peaks[peaks>mid_point]
peaks_left=peaks[peaks<mid_point]
if num_col==2:
peaks_right=peaks[peaks>(mid_point+one_third_right)]
peaks_left=peaks[peaks<(mid_point-one_third_left)]
# print(region_sum_0[peaks])
peaks = peaks[region_sum_0[peaks] < min_textline_thickness]
# print(peaks)
# print(first_nonzero,last_nonzero,one_third_right,one_third_left)
if num_col == 1:
peaks_right = peaks[peaks > mid_point]
peaks_left = peaks[peaks < mid_point]
if num_col == 2:
peaks_right = peaks[peaks > (mid_point + one_third_right)]
peaks_left = peaks[peaks < (mid_point - one_third_left)]
try:
point_right=np.min(peaks_right)
point_right = np.min(peaks_right)
except:
point_right=last_nonzero
point_right = last_nonzero
try:
point_left=np.max(peaks_left)
point_left = np.max(peaks_left)
except:
point_left=first_nonzero
point_left = first_nonzero
#print(point_left,point_right)
#print(text_regions.shape)
if point_right>=mask_marginals.shape[1]:
point_right=mask_marginals.shape[1]-1
# print(point_left,point_right)
# print(text_regions.shape)
if point_right >= mask_marginals.shape[1]:
point_right = mask_marginals.shape[1] - 1
try:
mask_marginals[:,point_left:point_right]=1
mask_marginals[:, point_left:point_right] = 1
except:
mask_marginals[:,:]=1
mask_marginals[:, :] = 1
#print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated=rotate_image(mask_marginals,-slope_deskew)
# print(mask_marginals.shape,point_left,point_right,'nadosh')
mask_marginals_rotated = rotate_image(mask_marginals, -slope_deskew)
#print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
# print(mask_marginals_rotated.shape,'nadosh')
mask_marginals_rotated_sum = mask_marginals_rotated.sum(axis=0)
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
mask_marginals_rotated_sum[mask_marginals_rotated_sum != 0] = 1
index_x = np.array(range(len(mask_marginals_rotated_sum))) + 1
index_x_interest=index_x[mask_marginals_rotated_sum==1]
index_x_interest = index_x[mask_marginals_rotated_sum == 1]
min_point_of_left_marginal=np.min(index_x_interest)-16
max_point_of_right_marginal=np.max(index_x_interest)+16
min_point_of_left_marginal = np.min(index_x_interest) - 16
max_point_of_right_marginal = np.max(index_x_interest) + 16
if min_point_of_left_marginal<0:
min_point_of_left_marginal=0
if max_point_of_right_marginal>=text_regions.shape[1]:
max_point_of_right_marginal=text_regions.shape[1]-1
if min_point_of_left_marginal < 0:
min_point_of_left_marginal = 0
if max_point_of_right_marginal >= text_regions.shape[1]:
max_point_of_right_marginal = text_regions.shape[1] - 1
# print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
# print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
# plt.imshow(mask_marginals)
# plt.show()
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
#plt.imshow(mask_marginals)
#plt.show()
# plt.imshow(mask_marginals_rotated)
# plt.show()
#plt.imshow(mask_marginals_rotated)
#plt.show()
text_regions[(mask_marginals_rotated[:, :] != 1) & (text_regions[:, :] == 1)] = 4
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
# plt.imshow(text_regions)
# plt.show()
#plt.imshow(text_regions)
#plt.show()
pixel_img = 4
min_area_text = 0.00001
polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
pixel_img=4
min_area_text=0.00001
polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = find_new_features_of_contours(
polygons_of_marginals)
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals)
text_regions[(text_regions[:, :] == 4)] = 1
text_regions[(text_regions[:,:]==4)]=1
marginlas_should_be_main_text = []
marginlas_should_be_main_text=[]
x_min_marginals_left=[]
x_min_marginals_right=[]
x_min_marginals_left = []
x_min_marginals_right = []
for i in range(len(cx_text_only)):
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
if x_width_mar>16 and y_height_mar/x_width_mar<18:
x_width_mar = abs(x_min_text_only[i] - x_max_text_only[i])
y_height_mar = abs(y_min_text_only[i] - y_max_text_only[i])
# print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
if x_width_mar > 16 and y_height_mar / x_width_mar < 18:
marginlas_should_be_main_text.append(polygons_of_marginals[i])
if x_min_text_only[i]<(mid_point-one_third_left):
x_min_marginals_left_new=x_min_text_only[i]
if len(x_min_marginals_left)==0:
if x_min_text_only[i] < (mid_point - one_third_left):
x_min_marginals_left_new = x_min_text_only[i]
if len(x_min_marginals_left) == 0:
x_min_marginals_left.append(x_min_marginals_left_new)
else:
x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
x_min_marginals_left[0] = min(x_min_marginals_left[0], x_min_marginals_left_new)
else:
x_min_marginals_right_new=x_min_text_only[i]
if len(x_min_marginals_right)==0:
x_min_marginals_right_new = x_min_text_only[i]
if len(x_min_marginals_right) == 0:
x_min_marginals_right.append(x_min_marginals_right_new)
else:
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
x_min_marginals_right[0] = min(x_min_marginals_right[0], x_min_marginals_right_new)
if len(x_min_marginals_left)==0:
x_min_marginals_left=[0]
if len(x_min_marginals_right)==0:
x_min_marginals_right=[text_regions.shape[1]-1]
if len(x_min_marginals_left) == 0:
x_min_marginals_left = [0]
if len(x_min_marginals_right) == 0:
x_min_marginals_right = [text_regions.shape[1] - 1]
# print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
# print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions = cv2.fillPoly(text_regions, pts=marginlas_should_be_main_text, color=(4, 4))
# print(np.unique(text_regions))
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
# text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
# text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
text_regions[:, :int(min_point_of_left_marginal)][text_regions[:, :int(min_point_of_left_marginal)] == 1] = 0
text_regions[:, int(max_point_of_right_marginal):][text_regions[:, int(max_point_of_right_marginal):] == 1] = 0
#print(np.unique(text_regions))
# ##text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
# ##text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
# plt.plot(region_sum_0)
# plt.plot(peaks,region_sum_0[peaks],'*')
# plt.show()
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
# plt.imshow(text_regions)
# plt.show()
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
#plt.plot(region_sum_0)
#plt.plot(peaks,region_sum_0[peaks],'*')
#plt.show()
#plt.imshow(text_regions)
#plt.show()
#sys.exit()
# sys.exit()
else:
pass
return text_regions

5
eynollah/eynollah/utils/pil_cv2.py
View file

@ -5,15 +5,18 @@ from cv2 import COLOR_GRAY2BGR, COLOR_RGB2BGR, COLOR_BGR2RGB, cvtColor, imread
# from sbb_binarization
def cv2pil(img):
return Image.fromarray(np.array(cvtColor(img, COLOR_BGR2RGB)))
def pil2cv(img):
# from ocrd/workspace.py
color_conversion = COLOR_GRAY2BGR if img.mode in ('1', 'L') else COLOR_RGB2BGR
color_conversion = COLOR_GRAY2BGR if img.mode in ('1', 'L') else COLOR_RGB2BGR
pil_as_np_array = np.array(img).astype('uint8') if img.mode == '1' else np.array(img)
return cvtColor(pil_as_np_array, color_conversion)
def check_dpi(img):
try:
if isinstance(img, Image.Image):

1
eynollah/eynollah/utils/resize.py
View file

@ -1,4 +1,5 @@
import cv2
def resize_image(img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)

12
eynollah/eynollah/utils/rotate.py
View file

@ -3,6 +3,7 @@ import math
import imutils
import cv2
def rotatedRectWithMaxArea(w, h, angle):
if w <= 0 or h <= 0:
return 0, 0
@ -25,6 +26,7 @@ def rotatedRectWithMaxArea(w, h, angle):
return wr, hr
def rotate_max_area_new(image, rotated, angle):
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
@ -34,17 +36,20 @@ def rotate_max_area_new(image, rotated, angle):
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2]
def rotation_image_new(img, thetha):
rotated = imutils.rotate(img, thetha)
return rotate_max_area_new(img, rotated, thetha)
def rotate_image(img_patch, slope):
(h, w) = img_patch.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, slope, 1.0)
return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
def rotate_image_different( img, slope):
def rotate_image_different(img, slope):
# img = cv2.imread('images/input.jpg')
num_rows, num_cols = img.shape[:2]
@ -52,6 +57,7 @@ def rotate_image_different( img, slope):
img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
return img_rotation
def rotate_max_area(image, rotated, rotated_textline, rotated_layout, rotated_table_prediction, angle):
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
@ -61,6 +67,7 @@ def rotate_max_area(image, rotated, rotated_textline, rotated_layout, rotated_ta
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_table_prediction[y1:y2, x1:x2]
def rotation_not_90_func(img, textline, text_regions_p_1, table_prediction, thetha):
rotated = imutils.rotate(img, thetha)
rotated_textline = imutils.rotate(textline, thetha)
@ -68,6 +75,7 @@ def rotation_not_90_func(img, textline, text_regions_p_1, table_prediction, thet
rotated_table_prediction = imutils.rotate(table_prediction, thetha)
return rotate_max_area(img, rotated, rotated_textline, rotated_layout, rotated_table_prediction, thetha)
def rotation_not_90_func_full_layout(img, textline, text_regions_p_1, text_regions_p_fully, thetha):
rotated = imutils.rotate(img, thetha)
rotated_textline = imutils.rotate(textline, thetha)
@ -75,6 +83,7 @@ def rotation_not_90_func_full_layout(img, textline, text_regions_p_1, text_regio
rotated_layout_full = imutils.rotate(text_regions_p_fully, thetha)
return rotate_max_area_full_layout(img, rotated, rotated_textline, rotated_layout, rotated_layout_full, thetha)
def rotate_max_area_full_layout(image, rotated, rotated_textline, rotated_layout, rotated_layout_full, angle):
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
@ -83,4 +92,3 @@ def rotate_max_area_full_layout(image, rotated, rotated_textline, rotated_layout
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_layout_full[y1:y2, x1:x2]

3
eynollah/eynollah/utils/xml.py
View file

@ -29,6 +29,7 @@ from ocrd_models.ocrd_page import (
to_xml)
def create_page_xml(imageFilename, height, width):
now = datetime.now()
pcgts = PcGtsType(
@ -46,6 +47,7 @@ def create_page_xml(imageFilename, height, width):
))
return pcgts
def xml_reading_order(page, order_of_texts, id_of_marginalia):
region_order = ReadingOrderType()
og = OrderedGroupType(id="ro357564684568544579089")
@ -59,6 +61,7 @@ def xml_reading_order(page, order_of_texts, id_of_marginalia):
og.add_RegionRefIndexed(RegionRefIndexedType(index=str(region_counter.get('region')), regionRef=id_marginal))
region_counter.inc('region')
def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point):
indexes_sorted = np.array(indexes_sorted)
index_of_types = np.array(index_of_types)

183
eynollah/eynollah/writer.py
View file

@ -8,21 +8,22 @@ from .utils.counter import EynollahIdCounter
from ocrd_utils import getLogger
from ocrd_models.ocrd_page import (
BorderType,
CoordsType,
PcGtsType,
TextLineType,
TextRegionType,
ImageRegionType,
TableRegionType,
SeparatorRegionType,
to_xml
)
BorderType,
CoordsType,
PcGtsType,
TextLineType,
TextRegionType,
ImageRegionType,
TableRegionType,
SeparatorRegionType,
to_xml
)
import numpy as np
class EynollahXmlWriter():
def __init__(self, *, dir_out, image_filename, curved_line,textline_light, pcgts=None):
def __init__(self, *, dir_out, image_filename, curved_line, textline_light, pcgts=None):
self.logger = getLogger('eynollah.writer')
self.counter = EynollahIdCounter()
self.dir_out = dir_out
@ -30,10 +31,10 @@ class EynollahXmlWriter():
self.curved_line = curved_line
self.textline_light = textline_light
self.pcgts = pcgts
self.scale_x = None # XXX set outside __init__
self.scale_y = None # XXX set outside __init__
self.height_org = None # XXX set outside __init__
self.width_org = None # XXX set outside __init__
self.scale_x = None # XXX set outside __init__
self.scale_y = None # XXX set outside __init__
self.height_org = None # XXX set outside __init__
self.width_org = None # XXX set outside __init__
@property
def image_filename_stem(self):
@ -50,11 +51,12 @@ class EynollahXmlWriter():
else:
points_page_print += str(int((contour[0][0]) / self.scale_x))
points_page_print += ','
points_page_print += str(int((contour[0][1] ) / self.scale_y))
points_page_print += str(int((contour[0][1]) / self.scale_y))
points_page_print = points_page_print + ' '
return points_page_print[:-1]
def serialize_lines_in_marginal(self, marginal_region, all_found_textline_polygons_marginals, marginal_idx, page_coord, all_box_coord_marginals, slopes_marginals, counter):
def serialize_lines_in_marginal(self, marginal_region, all_found_textline_polygons_marginals, marginal_idx,
page_coord, all_box_coord_marginals, slopes_marginals, counter):
for j in range(len(all_found_textline_polygons_marginals[marginal_idx])):
coords = CoordsType()
textline = TextLineType(id=counter.next_line_id, Coords=coords)
@ -63,37 +65,54 @@ class EynollahXmlWriter():
for l in range(len(all_found_textline_polygons_marginals[marginal_idx][j])):
if not (self.curved_line or self.textline_light):
if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2:
textline_x_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x) )
textline_y_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y) )
textline_x_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] +
all_box_coord_marginals[marginal_idx][2] + page_coord[
2]) / self.scale_x))
textline_y_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] +
all_box_coord_marginals[marginal_idx][0] + page_coord[
0]) / self.scale_y))
else:
textline_x_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x) )
textline_y_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y) )
textline_x_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] +
all_box_coord_marginals[marginal_idx][2] + page_coord[
2]) / self.scale_x))
textline_y_coord = max(0, int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] +
all_box_coord_marginals[marginal_idx][0] + page_coord[
0]) / self.scale_y))
points_co += str(textline_x_coord)
points_co += ','
points_co += str(textline_y_coord)
if (self.curved_line or self.textline_light) and np.abs(slopes_marginals[marginal_idx]) <= 45:
if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2:
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + page_coord[2]) / self.scale_x))
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + page_coord[
2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + page_coord[0]) / self.scale_y))
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + page_coord[
0]) / self.scale_y))
else:
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] +
page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + page_coord[0]) / self.scale_y))
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] +
page_coord[0]) / self.scale_y))
elif (self.curved_line or self.textline_light) and np.abs(slopes_marginals[marginal_idx]) > 45:
if len(all_found_textline_polygons_marginals[marginal_idx][j][l]) == 2:
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0] +
all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][1] +
all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][0] +
all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
points_co += str(int((all_found_textline_polygons_marginals[marginal_idx][j][l][0][1] +
all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
points_co += ' '
coords.set_points(points_co[:-1])
def serialize_lines_in_region(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord, slopes, counter):
def serialize_lines_in_region(self, text_region, all_found_textline_polygons, region_idx, page_coord, all_box_coord,
slopes, counter):
self.logger.debug('enter serialize_lines_in_region')
for j in range(len(all_found_textline_polygons[region_idx])):
coords = CoordsType()
@ -104,11 +123,15 @@ class EynollahXmlWriter():
for idx_contour_textline, contour_textline in enumerate(all_found_textline_polygons[region_idx][j]):
if not (self.curved_line or self.textline_light):
if len(contour_textline) == 2:
textline_x_coord = max(0, int((contour_textline[0] + region_bboxes[2] + page_coord[2]) / self.scale_x))
textline_y_coord = max(0, int((contour_textline[1] + region_bboxes[0] + page_coord[0]) / self.scale_y))
textline_x_coord = max(0, int((contour_textline[0] + region_bboxes[2] + page_coord[
2]) / self.scale_x))
textline_y_coord = max(0, int((contour_textline[1] + region_bboxes[0] + page_coord[
0]) / self.scale_y))
else:
textline_x_coord = max(0, int((contour_textline[0][0] + region_bboxes[2] + page_coord[2]) / self.scale_x))
textline_y_coord = max(0, int((contour_textline[0][1] + region_bboxes[0] + page_coord[0]) / self.scale_y))
textline_x_coord = max(0, int((contour_textline[0][0] + region_bboxes[2] + page_coord[
2]) / self.scale_x))
textline_y_coord = max(0, int((contour_textline[0][1] + region_bboxes[0] + page_coord[
0]) / self.scale_y))
points_co += str(textline_x_coord)
points_co += ','
points_co += str(textline_y_coord)
@ -121,16 +144,18 @@ class EynollahXmlWriter():
else:
points_co += str(int((contour_textline[0][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((contour_textline[0][1] + page_coord[0])/self.scale_y))
points_co += str(int((contour_textline[0][1] + page_coord[0]) / self.scale_y))
elif (self.curved_line or self.textline_light) and np.abs(slopes[region_idx]) > 45:
if len(contour_textline)==2:
points_co += str(int((contour_textline[0] + region_bboxes[2] + page_coord[2])/self.scale_x))
if len(contour_textline) == 2:
points_co += str(int((contour_textline[0] + region_bboxes[2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((contour_textline[1] + region_bboxes[0] + page_coord[0])/self.scale_y))
points_co += str(int((contour_textline[1] + region_bboxes[0] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((contour_textline[0][0] + region_bboxes[2]+page_coord[2])/self.scale_x))
points_co += str(
int((contour_textline[0][0] + region_bboxes[2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((contour_textline[0][1] + region_bboxes[0]+page_coord[0])/self.scale_y))
points_co += str(
int((contour_textline[0][1] + region_bboxes[0] + page_coord[0]) / self.scale_y))
points_co += ' '
coords.set_points(points_co[:-1])
@ -140,7 +165,11 @@ class EynollahXmlWriter():
with open(out_fname, 'w') as f:
f.write(to_xml(pcgts))
def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts, all_found_textline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml, found_polygons_tables):
def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts,
all_found_textline_polygons, all_box_coord, found_polygons_text_region_img,
found_polygons_marginals, all_found_textline_polygons_marginals,
all_box_coord_marginals, slopes, slopes_marginals, cont_page,
polygons_lines_to_be_written_in_xml, found_polygons_tables):
self.logger.debug('enter build_pagexml_no_full_layout')
# create the file structure
@ -156,25 +185,31 @@ class EynollahXmlWriter():
for mm in range(len(found_polygons_text_region)):
textregion = TextRegionType(id=counter.next_region_id, type_='paragraph',
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region[mm], page_coord)),
)
Coords=CoordsType(
points=self.calculate_polygon_coords(found_polygons_text_region[mm],
page_coord)),
)
page.add_TextRegion(textregion)
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter)
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord,
slopes, counter)
for mm in range(len(found_polygons_marginals)):
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals[mm], page_coord)))
Coords=CoordsType(
points=self.calculate_polygon_coords(found_polygons_marginals[mm],
page_coord)))
page.add_TextRegion(marginal)
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, counter)
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord,
all_box_coord_marginals, slopes_marginals, counter)
for mm in range(len(found_polygons_text_region_img)):
img_region = ImageRegionType(id=counter.next_region_id, Coords=CoordsType())
page.add_ImageRegion(img_region)
points_co = ''
for lmm in range(len(found_polygons_text_region_img[mm])):
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,0] + page_coord[2]) / self.scale_x))
points_co += str(int((found_polygons_text_region_img[mm][lmm, 0, 0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
points_co += str(int((found_polygons_text_region_img[mm][lmm, 0, 1] + page_coord[0]) / self.scale_y))
points_co += ' '
img_region.get_Coords().set_points(points_co[:-1])
@ -183,9 +218,9 @@ class EynollahXmlWriter():
page.add_SeparatorRegion(sep_hor)
points_co = ''
for lmm in range(len(polygons_lines_to_be_written_in_xml[mm])):
points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm,0,0] ) / self.scale_x))
points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm, 0, 0]) / self.scale_x))
points_co += ','
points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm,0,1] ) / self.scale_y))
points_co += str(int((polygons_lines_to_be_written_in_xml[mm][lmm, 0, 1]) / self.scale_y))
points_co += ' '
sep_hor.get_Coords().set_points(points_co[:-1])
for mm in range(len(found_polygons_tables)):
@ -193,15 +228,21 @@ class EynollahXmlWriter():
page.add_TableRegion(tab_region)
points_co = ''
for lmm in range(len(found_polygons_tables[mm])):
points_co += str(int((found_polygons_tables[mm][lmm,0,0] + page_coord[2]) / self.scale_x))
points_co += str(int((found_polygons_tables[mm][lmm, 0, 0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((found_polygons_tables[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
points_co += str(int((found_polygons_tables[mm][lmm, 0, 1] + page_coord[0]) / self.scale_y))
points_co += ' '
tab_region.get_Coords().set_points(points_co[:-1])
return pcgts
def build_pagexml_full_layout(self, found_polygons_text_region, found_polygons_text_region_h, page_coord, order_of_texts, id_of_texts, all_found_textline_polygons, all_found_textline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_textline_polygons_marginals, all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page, polygons_lines_to_be_written_in_xml):
def build_pagexml_full_layout(self, found_polygons_text_region, found_polygons_text_region_h, page_coord,
order_of_texts, id_of_texts, all_found_textline_polygons,
all_found_textline_polygons_h, all_box_coord, all_box_coord_h,
found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals,
found_polygons_marginals, all_found_textline_polygons_marginals,
all_box_coord_marginals, slopes, slopes_h, slopes_marginals, cont_page,
polygons_lines_to_be_written_in_xml):
self.logger.debug('enter build_pagexml_full_layout')
# create the file structure
@ -216,35 +257,48 @@ class EynollahXmlWriter():
for mm in range(len(found_polygons_text_region)):
textregion = TextRegionType(id=counter.next_region_id, type_='paragraph',
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region[mm], page_coord)))
Coords=CoordsType(
points=self.calculate_polygon_coords(found_polygons_text_region[mm],
page_coord)))
page.add_TextRegion(textregion)
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord, slopes, counter)
self.serialize_lines_in_region(textregion, all_found_textline_polygons, mm, page_coord, all_box_coord,
slopes, counter)
self.logger.debug('len(found_polygons_text_region_h) %s', len(found_polygons_text_region_h))
for mm in range(len(found_polygons_text_region_h)):
textregion = TextRegionType(id=counter.next_region_id, type_='header',
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_h[mm], page_coord)))
Coords=CoordsType(
points=self.calculate_polygon_coords(found_polygons_text_region_h[mm],
page_coord)))
page.add_TextRegion(textregion)
self.serialize_lines_in_region(textregion, all_found_textline_polygons_h, mm, page_coord, all_box_coord_h, slopes_h, counter)
self.serialize_lines_in_region(textregion, all_found_textline_polygons_h, mm, page_coord, all_box_coord_h,
slopes_h, counter)
for mm in range(len(found_polygons_marginals)):
marginal = TextRegionType(id=counter.next_region_id, type_='marginalia',
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_marginals[mm], page_coord)))
Coords=CoordsType(
points=self.calculate_polygon_coords(found_polygons_marginals[mm],
page_coord)))
page.add_TextRegion(marginal)
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, counter)
self.serialize_lines_in_marginal(marginal, all_found_textline_polygons_marginals, mm, page_coord,
all_box_coord_marginals, slopes_marginals, counter)
for mm in range(len(found_polygons_drop_capitals)):
page.add_TextRegion(TextRegionType(id=counter.next_region_id, type_='drop-capital',
Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_drop_capitals[mm], page_coord))))
Coords=CoordsType(points=self.calculate_polygon_coords(
found_polygons_drop_capitals[mm], page_coord))))
for mm in range(len(found_polygons_text_region_img)):
page.add_ImageRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_text_region_img[mm], page_coord))))
page.add_ImageRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(
points=self.calculate_polygon_coords(found_polygons_text_region_img[mm], page_coord))))
for mm in range(len(polygons_lines_to_be_written_in_xml)):
page.add_SeparatorRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(polygons_lines_to_be_written_in_xml[mm], [0 , 0, 0, 0]))))
page.add_SeparatorRegion(ImageRegionType(id=counter.next_region_id, Coords=CoordsType(
points=self.calculate_polygon_coords(polygons_lines_to_be_written_in_xml[mm], [0, 0, 0, 0]))))
for mm in range(len(found_polygons_tables)):
page.add_TableRegion(TableRegionType(id=counter.next_region_id, Coords=CoordsType(points=self.calculate_polygon_coords(found_polygons_tables[mm], page_coord))))
page.add_TableRegion(TableRegionType(id=counter.next_region_id, Coords=CoordsType(
points=self.calculate_polygon_coords(found_polygons_tables[mm], page_coord))))
return pcgts
@ -260,6 +314,5 @@ class EynollahXmlWriter():
coords += str(int((value_bbox[0][0] + page_coord[2]) / self.scale_x))
coords += ','
coords += str(int((value_bbox[0][1] + page_coord[0]) / self.scale_y))
coords=coords + ' '
coords = coords + ' '
return coords[:-1]

5
tests/base.py
View file

@ -10,12 +10,14 @@ from unittest import TestCase as VanillaTestCase, skip, main as unittests_main
import pytest
from ocrd_utils import disableLogging, initLogging
def main(fn=None):
if fn:
sys.exit(pytest.main([fn]))
else:
unittests_main()
class TestCase(VanillaTestCase):
@classmethod
@ -26,6 +28,7 @@ class TestCase(VanillaTestCase):
disableLogging()
initLogging()
class CapturingTestCase(TestCase):
"""
A TestCase that needs to capture stderr/stdout and invoke click CLI.
@ -42,7 +45,7 @@ class CapturingTestCase(TestCase):
"""
self.capture_out_err() # XXX snapshot just before executing the CLI
code = 0
sys.argv[1:] = args # XXX necessary because sys.argv reflects pytest args not cli args
sys.argv[1:] = args # XXX necessary because sys.argv reflects pytest args not cli args
try:
cli.main(args=args)
except SystemExit as e:

4
tests/test_counter.py
View file

@ -1,6 +1,7 @@
from tests.base import main
from eynollah.eynollah.utils.counter import EynollahIdCounter
def test_counter_string():
c = EynollahIdCounter()
assert c.next_region_id == 'region_0001'
@ -11,6 +12,7 @@ def test_counter_string():
assert c.region_id(999) == 'region_0999'
assert c.line_id(999, 888) == 'region_0999_line_0888'
def test_counter_init():
c = EynollahIdCounter(region_idx=2)
assert c.get('region') == 2
@ -19,6 +21,7 @@ def test_counter_init():
c.reset()
assert c.get('region') == 2
def test_counter_methods():
c = EynollahIdCounter()
assert c.get('region') == 0
@ -29,5 +32,6 @@ def test_counter_methods():
c.inc('region', -9)
assert c.get('region') == 1
if __name__ == '__main__':
main(__file__)

2
tests/test_dpi.py
View file

@ -3,9 +3,11 @@ from pathlib import Path
from eynollah.eynollah.utils.pil_cv2 import check_dpi
from tests.base import main
def test_dpi():
fpath = str(Path(__file__).parent.joinpath('resources', 'kant_aufklaerung_1784_0020.tif'))
assert 230 == check_dpi(cv2.imread(fpath))
if __name__ == '__main__':
main(__file__)

2
tests/test_run.py
View file

@ -8,6 +8,7 @@ testdir = Path(__file__).parent.resolve()
EYNOLLAH_MODELS = environ.get('EYNOLLAH_MODELS', str(testdir.joinpath('..', 'models_eynollah').resolve()))
class TestEynollahRun(TestCase):
def test_full_run(self):
@ -20,5 +21,6 @@ class TestEynollahRun(TestCase):
print(code, out, err)
assert not code
if __name__ == '__main__':
main(__file__)

2
tests/test_xml.py
View file

@ -4,11 +4,13 @@ from ocrd_models.ocrd_page import to_xml
PAGE_2019 = 'http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15'
def test_create_xml():
pcgts = create_page_xml('/path/to/img.tif', 100, 100)
xmlstr = to_xml(pcgts)
assert 'xmlns:pc="%s"' % PAGE_2019 in xmlstr
assert 'Metadata' in xmlstr
if __name__ == '__main__':
main([__file__])