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eynollah
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adapt to one-arg start_new_session_and_model and rename load_model

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refactoring-2024-08-merged
kba 4 months ago
parent 8c4bfa229f
commit 762a7a058e
2 changed files with 33 additions and 25 deletions
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8
Makefile
Unescape Escape View File

@ -1,4 +1,9 @@
EYNOLLAH_MODELS ?= $(PWD)/models_eynollah
ifneq (,$(wildcard $(HOME)/.local/share/ocrd-resources/ocrd-eynollah-segment/default))
EYNOLLAH_MODELS = $(HOME)/.local/share/ocrd-resources/ocrd-eynollah-segment/default
else
EYNOLLAH_MODELS = $(PWD)/models_eynollah
endif
export EYNOLLAH_MODELS
# BEGIN-EVAL makefile-parser --make-help Makefile
@ -47,4 +52,5 @@ smoke-test:
# Run unit tests
test:
echo $(EYNOLLAH_MODELS)
pytest tests

50
qurator/eynollah/eynollah.py
Unescape Escape View File

@ -19,10 +19,15 @@ from ocrd import OcrdPage
import cv2
import numpy as np
from scipy.signal import find_peaks
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter1d
from qurator.eynollah.utils.tf import tf, PatchEncoder, Patches
from .utils.tf import (
PatchEncoder,
Patches,
load_model,
set_session,
tf,
)
from .utils.contour import (
filter_contours_area_of_image,
@ -252,7 +257,7 @@ class Eynollah():
def predict_enhancement(self, img):
self.logger.debug("enter predict_enhancement")
model_enhancement, session_enhancement = self.start_new_session_and_model(self.model_dir_of_enhancement)
model_enhancement = self.load_model(self.model_dir_of_enhancement)
img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1]
img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2]
@ -411,7 +416,7 @@ class Eynollah():
_, page_coord = self.early_page_for_num_of_column_classification(img)
if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
model_num_classifier = self.load_model(self.model_dir_of_col_classifier)
if self.input_binary:
img_in = np.copy(img)
img_in = img_in / 255.0
@ -461,7 +466,7 @@ class Eynollah():
prediction_bin = self.do_prediction(True, img, self.model_bin)
else:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
model_bin = self.load_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img, model_bin)
prediction_bin=prediction_bin[:,:,0]
@ -480,7 +485,7 @@ class Eynollah():
t1 = time.time()
_, page_coord = self.early_page_for_num_of_column_classification(img_bin)
if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
model_num_classifier = self.load_model(self.model_dir_of_col_classifier)
if self.input_binary:
img_in = np.copy(img)
@ -574,16 +579,13 @@ class Eynollah():
self.writer.height_org = self.height_org
self.writer.width_org = self.width_org
def start_new_session_and_model(self, model_dir) -> tf.keras.Model:
def load_model(self, model_dir) -> tf.keras.Model:
self.logger.debug("enter start_new_session_and_model (model_dir=%s)", model_dir)
#gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
#gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=7.7, allow_growth=True)
#session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
physical_devices = tf.config.list_physical_devices('GPU')
try:
for device in physical_devices:
tf.config.experimental.set_memory_growth(device, True)
except:
except ValueError:
self.logger.warning("no GPU device available")
if model_dir.endswith('.h5') and Path(model_dir[:-3]).exists():
@ -895,7 +897,7 @@ class Eynollah():
img = cv2.GaussianBlur(self.image, (5, 5), 0)
if not self.dir_in:
model_page = self.start_new_session_and_model(self.model_page_dir)
model_page = self.load_model(self.model_page_dir)
if not self.dir_in:
img_page_prediction = self.do_prediction(False, img, model_page)
@ -943,7 +945,7 @@ class Eynollah():
else:
img = self.imread()
if not self.dir_in:
model_page = self.start_new_session_and_model(self.model_page_dir)
model_page = self.load_model(self.model_page_dir)
img = cv2.GaussianBlur(img, (5, 5), 0)
if self.dir_in:
@ -976,7 +978,7 @@ class Eynollah():
img_height_h = img.shape[0]
img_width_h = img.shape[1]
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
model_region = self.load_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
else:
model_region = self.model_region_fl if patches else self.model_region_fl_np
@ -1443,7 +1445,7 @@ class Eynollah():
def textline_contours(self, img, patches, scaler_h, scaler_w):
self.logger.debug('enter textline_contours')
if not self.dir_in:
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir if patches else self.model_textline_dir_np)
model_textline = self.load_model(self.model_textline_dir if patches else self.model_textline_dir_np)
img = img.astype(np.uint8)
img_org = np.copy(img)
img_h = img_org.shape[0]
@ -1535,7 +1537,7 @@ class Eynollah():
img_resized = resize_image(img,img_h_new, img_w_new )
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
model_bin = self.load_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_resized, model_bin)
else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin)
@ -1554,7 +1556,7 @@ class Eynollah():
textline_mask_tot_ea = self.run_textline(img_bin)
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
model_region = self.load_model(self.model_region_dir_p_ens_light)
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
else:
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region)
@ -1599,7 +1601,7 @@ class Eynollah():
img_width_h = img_org.shape[1]
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
model_region = self.load_model(self.model_region_dir_p_ens)
ratio_y=1.3
ratio_x=1
@ -1638,7 +1640,7 @@ class Eynollah():
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
model_region = self.load_model(self.model_region_dir_p2)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
@ -1677,7 +1679,7 @@ class Eynollah():
prediction_bin = np.copy(img_org)
else:
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
model_bin = self.load_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, model_bin)
else:
prediction_bin = self.do_prediction(True, img_org, self.model_bin)
@ -1690,7 +1692,7 @@ class Eynollah():
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
model_region = self.load_model(self.model_region_dir_p_ens)
ratio_y=1
ratio_x=1
@ -1730,7 +1732,7 @@ class Eynollah():
prediction_bin = np.copy(img_org)
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
model_bin = self.load_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, model_bin)
else:
prediction_bin = self.do_prediction(True, img_org, self.model_bin)
@ -1745,7 +1747,7 @@ class Eynollah():
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
model_region = self.load_model(self.model_region_dir_p_ens)
else:
prediction_bin = np.copy(img_org)
@ -2266,7 +2268,7 @@ class Eynollah():
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
model_region, session_region = self.start_new_session_and_model(self.model_tables)
model_region = self.load_model(self.model_tables)
patches = False

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