qurator-spk/sbb_binarization
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Code Issues Releases Wiki Activity

Improved loading of models to allow providing a directory, added a few type-hints and improved the code-style a little bit by running an auto-formatter on the entire file.

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Alexander Pacha 2022-08-22 14:23:50 +02:00
parent f11d0b0bf7
commit b0a8b613e8
1 changed files with 57 additions and 61 deletions
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118
sbb_binarize/sbb_binarize.py
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@ -1,42 +1,43 @@
"""
Tool to load model and binarize a given image.
"""
import argparse
import sys
from glob import glob
from os import environ, devnull
from os.path import join
from warnings import catch_warnings, simplefilter
from pathlib import Path
from typing import Union
import numpy as np
from PIL import Image
import cv2
import numpy as np
environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
stderr = sys.stderr
sys.stderr = open(devnull, 'w')
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.python.keras import backend as tensorflow_backend
sys.stderr = stderr
import logging
def resize_image(img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
class SbbBinarizer:
def __init__(self, model_dir, logger=None):
self.model_dir = model_dir
def __init__(self, model_dir: Union[str, Path], logger=None):
model_dir = Path(model_dir)
self.log = logger if logger else logging.getLogger('SbbBinarizer')
self.start_new_session()
self.model_files = glob('%s/*.h5' % self.model_dir)
self.model_files = list([str(p.absolute()) for p in model_dir.rglob("*.h5")])
if not self.model_files:
raise ValueError(f"No models found in {self.model_dir}")
raise ValueError(f"No models found in {str(model_dir)}")
self.models = []
for model_file in self.model_files:
self.models.append(self.load_model(model_file))
@ -53,54 +54,51 @@ class SbbBinarizer:
self.session.close()
del self.session
def load_model(self, model_name):
model = load_model(model_name, compile=False)
model_height = model.layers[len(model.layers)-1].output_shape[1]
model_width = model.layers[len(model.layers)-1].output_shape[2]
n_classes = model.layers[len(model.layers)-1].output_shape[3]
def load_model(self, model_path: str):
model = load_model(model_path, compile=False)
model_height = model.layers[len(model.layers) - 1].output_shape[1]
model_width = model.layers[len(model.layers) - 1].output_shape[2]
n_classes = model.layers[len(model.layers) - 1].output_shape[3]
return model, model_height, model_width, n_classes
def predict(self, model_in, img, use_patches):
tensorflow_backend.set_session(self.session)
model, model_height, model_width, n_classes = model_in
img_org_h = img.shape[0]
img_org_w = img.shape[1]
if img.shape[0] < model_height and img.shape[1] >= model_width:
img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
index_start_h = int( abs( img.shape[0] - model_height) /2.)
img_padded = np.zeros((model_height, img.shape[1], img.shape[2]))
index_start_h = int(abs(img.shape[0] - model_height) / 2.)
index_start_w = 0
img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
img_padded[index_start_h: index_start_h + img.shape[0], :, :] = img[:, :, :]
elif img.shape[0] >= model_height and img.shape[1] < model_width:
img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
index_start_h = 0
index_start_w = int( abs( img.shape[1] - model_width) /2.)
img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
img_padded = np.zeros((img.shape[0], model_width, img.shape[2]))
index_start_h = 0
index_start_w = int(abs(img.shape[1] - model_width) / 2.)
img_padded[:, index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
elif img.shape[0] < model_height and img.shape[1] < model_width:
img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
index_start_h = int( abs( img.shape[0] - model_height) /2.)
index_start_w = int( abs( img.shape[1] - model_width) /2.)
img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
img_padded = np.zeros((model_height, model_width, img.shape[2]))
index_start_h = int(abs(img.shape[0] - model_height) / 2.)
index_start_w = int(abs(img.shape[1] - model_width) / 2.)
img_padded[index_start_h: index_start_h + img.shape[0], index_start_w: index_start_w + img.shape[1], :] = img[:, :, :]
else:
index_start_h = 0
index_start_w = 0
index_start_w = 0
img_padded = np.copy(img)
img = np.copy(img_padded)
if use_patches:
@ -109,7 +107,6 @@ class SbbBinarizer:
width_mid = model_width - 2 * margin
height_mid = model_height - 2 * margin
img = img / float(255.0)
img_h = img.shape[0]
@ -169,49 +166,49 @@ class SbbBinarizer:
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
elif i == nxf-1 and j == nyf-1:
elif i == nxf - 1 and j == nyf - 1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color
elif i == 0 and j == nyf-1:
elif i == 0 and j == nyf - 1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color
elif i == nxf-1 and j == 0:
elif i == nxf - 1 and j == 0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
elif i == 0 and j != 0 and j != nyf-1:
elif i == 0 and j != 0 and j != nyf - 1:
seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
elif i == nxf-1 and j != 0 and j != nyf-1:
elif i == nxf - 1 and j != 0 and j != nyf - 1:
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
elif i != 0 and i != nxf-1 and j == 0:
elif i != 0 and i != nxf - 1 and j == 0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
elif i != 0 and i != nxf-1 and j == nyf-1:
elif i != 0 and i != nxf - 1 and j == nyf - 1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
@ -224,10 +221,8 @@ class SbbBinarizer:
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
prediction_true = prediction_true[index_start_h: index_start_h + img_org_h, index_start_w: index_start_w + img_org_w, :]
prediction_true = prediction_true.astype(np.uint8)
else:
@ -242,17 +237,16 @@ class SbbBinarizer:
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8)
return prediction_true[:,:,0]
return prediction_true[:, :, 0]
def run(self, image=None, image_path=None, save=None, use_patches=False):
if (image is not None and image_path is not None) or \
(image is None and image_path is None):
if (image is not None and image_path is not None) or (image is None and image_path is None):
raise ValueError("Must pass either a opencv2 image or an image_path")
if image_path is not None:
image = cv2.imread(image_path)
img_last = 0
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
self.log.info(f"Predicting with model {model_file} [{n + 1}/{len(self.model_files)}]")
res = self.predict(model, image, use_patches)
@ -272,5 +266,7 @@ class SbbBinarizer:
img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0) * 255
if save:
# Create the output directory (and if necessary it's parents) if it doesn't exist already
Path(save).parent.mkdir(parents=True, exist_ok=True)
cv2.imwrite(save, img_last)
return img_last