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eynollah
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attempting partial revert of `dir_in` mode from c606391c31

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refactor_with_disable-dir-in
Clemens Neudecker 2 months ago
parent 482511adc0
commit ca6090fef1
3 changed files with 294 additions and 292 deletions
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23
eynollah/eynollah/cli.py
Unescape Escape View File

@ -19,12 +19,13 @@ from eynollah.eynollah.eynollah import Eynollah
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--dir_in",
"-di",
help="directory of images",
type=click.Path(exists=True, file_okay=False),
)
# temporary disable directory input
# @click.option(
# "--dir_in",
# "-di",
# help="directory of images",
# type=click.Path(exists=True, file_okay=False),
# )
@click.option(
"--model",
"-m",
@ -143,7 +144,7 @@ from eynollah.eynollah.eynollah import Eynollah
def main(
image,
out,
dir_in,
# dir_in,
model,
save_images,
save_layout,
@ -179,7 +180,7 @@ def main(
eynollah = Eynollah(
image_filename=image,
dir_out=out,
dir_in=dir_in,
# dir_in=dir_in,
dir_models=model,
dir_of_cropped_images=save_images,
dir_of_layout=save_layout,
@ -199,9 +200,9 @@ def main(
light_version=light_version,
ignore_page_extraction=ignore_page_extraction,
)
eynollah.run()
# pcgts = eynollah.run()
# eynollah.writer.write_pagexml(pcgts)
# eynollah.run()
pcgts = eynollah.run()
eynollah.writer.write_pagexml(pcgts)
if __name__ == "__main__":

335
eynollah/eynollah/eynollah.py
Unescape Escape View File

@ -145,11 +145,11 @@ class Eynollah:
def __init__(
self,
dir_models,
image_filename=None,
image_filename,
image_pil=None,
image_filename_stem=None,
dir_out=None,
dir_in=None,
# dir_in=None,
dir_of_cropped_images=None,
dir_of_layout=None,
dir_of_deskewed=None,
@ -171,16 +171,16 @@ class Eynollah:
logger=None,
pcgts=None,
):
if not dir_in:
if image_pil:
self._imgs = self._cache_images(image_pil=image_pil)
else:
self._imgs = self._cache_images(image_filename=image_filename)
if override_dpi:
self.dpi = override_dpi
self.image_filename = image_filename
# if not dir_in:
# if image_pil:
# self._imgs = self._cache_images(image_pil=image_pil)
# else:
# self._imgs = self._cache_images(image_filename=image_filename)
# if override_dpi:
# self.dpi = override_dpi
# self.image_filename = image_filename
self.dir_out = dir_out
self.dir_in = dir_in
# self.dir_in = dir_in
self.dir_of_all = dir_of_all
self.dir_save_page = dir_save_page
self.dir_of_deskewed = dir_of_deskewed
@ -200,21 +200,21 @@ class Eynollah:
self.light_version = light_version
self.ignore_page_extraction = ignore_page_extraction
self.pcgts = pcgts
if not dir_in:
self.plotter = None if not enable_plotting else EynollahPlotter(
dir_out=self.dir_out,
dir_of_all=dir_of_all,
dir_save_page=dir_save_page,
dir_of_deskewed=dir_of_deskewed,
dir_of_cropped_images=dir_of_cropped_images,
dir_of_layout=dir_of_layout,
image_filename_stem=Path(Path(image_filename).name).stem)
self.writer = EynollahXmlWriter(
dir_out=self.dir_out,
image_filename=self.image_filename,
curved_line=self.curved_line,
textline_light=self.textline_light,
pcgts=pcgts)
# if not dir_in:
self.plotter = None if not enable_plotting else EynollahPlotter(
dir_out=self.dir_out,
dir_of_all=dir_of_all,
dir_save_page=dir_save_page,
dir_of_deskewed=dir_of_deskewed,
dir_of_cropped_images=dir_of_cropped_images,
dir_of_layout=dir_of_layout,
image_filename_stem=Path(Path(image_filename).name).stem)
self.writer = EynollahXmlWriter(
dir_out=self.dir_out,
image_filename=self.image_filename,
curved_line=self.curved_line,
textline_light=self.textline_light,
pcgts=pcgts)
self.logger = logger if logger else getLogger('eynollah')
self.dir_models = dir_models
@ -236,39 +236,39 @@ class Eynollah:
self.models = {}
if dir_in and light_version:
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)
set_session(session)
self.model_page = self.our_load_model(self.model_page_dir)
self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
self.model_bin = self.our_load_model(self.model_dir_of_binarization)
self.model_textline = self.our_load_model(self.model_textline_dir)
self.model_region = self.our_load_model(self.model_region_dir_p_ens_light)
self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
self.ls_imgs = os.listdir(self.dir_in)
if dir_in and not light_version:
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)
set_session(session)
self.model_page = self.our_load_model(self.model_page_dir)
self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
self.model_bin = self.our_load_model(self.model_dir_of_binarization)
self.model_textline = self.our_load_model(self.model_textline_dir)
self.model_region = self.our_load_model(self.model_region_dir_p_ens)
self.model_region_p2 = self.our_load_model(self.model_region_dir_p2)
self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
self.model_enhancement = self.our_load_model(self.model_dir_of_enhancement)
self.ls_imgs = os.listdir(self.dir_in)
# if dir_in and light_version:
# config = tf.compat.v1.ConfigProto()
# config.gpu_options.allow_growth = True
# session = tf.compat.v1.Session(config=config)
# set_session(session)
# self.model_page = self.our_load_model(self.model_page_dir)
# self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
# self.model_bin = self.our_load_model(self.model_dir_of_binarization)
# self.model_textline = self.our_load_model(self.model_textline_dir)
# self.model_region = self.our_load_model(self.model_region_dir_p_ens_light)
# self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
# self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
# self.ls_imgs = os.listdir(self.dir_in)
# if dir_in and not light_version:
# config = tf.compat.v1.ConfigProto()
# config.gpu_options.allow_growth = True
# session = tf.compat.v1.Session(config=config)
# set_session(session)
# self.model_page = self.our_load_model(self.model_page_dir)
# self.model_classifier = self.our_load_model(self.model_dir_of_col_classifier)
# self.model_bin = self.our_load_model(self.model_dir_of_binarization)
# self.model_textline = self.our_load_model(self.model_textline_dir)
# self.model_region = self.our_load_model(self.model_region_dir_p_ens)
# self.model_region_p2 = self.our_load_model(self.model_region_dir_p2)
# self.model_region_fl_np = self.our_load_model(self.model_region_dir_fully_np)
# self.model_region_fl = self.our_load_model(self.model_region_dir_fully)
# self.model_enhancement = self.our_load_model(self.model_dir_of_enhancement)
# self.ls_imgs = os.listdir(self.dir_in)
def _cache_images(self, image_filename=None, image_pil=None):
ret = {}
@ -283,9 +283,9 @@ class Eynollah:
ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8)
return ret
def reset_file_name_dir(self, image_filename):
self._imgs = self._cache_images(image_filename=image_filename)
self.image_filename = image_filename
# def reset_file_name_dir(self, image_filename):
# self._imgs = self._cache_images(image_filename=image_filename)
# self.image_filename = image_filename
self.plotter = None if not self.enable_plotting else EynollahPlotter(
dir_out=self.dir_out,
@ -476,9 +476,9 @@ class Eynollah:
img = self.imread()
_, 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)
# if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model(
self.model_dir_of_col_classifier)
if self.input_binary:
img_in = np.copy(img)
img_in = img_in / 255.0
@ -501,10 +501,10 @@ class Eynollah:
img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :]
if not self.dir_in:
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
else:
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
# if not self.dir_in:
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
# else:
# label_p_pred = self.model_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
@ -524,12 +524,12 @@ class Eynollah:
self.logger.info("Detected %s DPI", dpi)
if self.input_binary:
img = self.imread()
if self.dir_in:
prediction_bin = self.do_prediction(True, img, self.model_bin)
else:
# if self.dir_in:
# 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)
prediction_bin = self.do_prediction(True, img, model_bin)
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img, model_bin)
prediction_bin = prediction_bin[:, :, 0]
prediction_bin = (prediction_bin[:, :] == 0) * 1
@ -546,9 +546,9 @@ 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)
# if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model(
self.model_dir_of_col_classifier)
if self.input_binary:
img_in = np.copy(img)
@ -568,10 +568,11 @@ class Eynollah:
img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :]
if self.dir_in:
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
else:
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
# if self.dir_in:
# label_p_pred = self.model_classifier.predict(img_in, verbose=0)
# else:
# label_p_pred = model_num_classifier.predict(img_in, verbose=0)
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
@ -984,13 +985,13 @@ class Eynollah:
if not self.ignore_page_extraction:
img = cv2.GaussianBlur(self.image, (5, 5), 0)
if not self.dir_in:
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
# if not self.dir_in:
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
if not self.dir_in:
img_page_prediction = self.do_prediction(False, img, model_page)
else:
img_page_prediction = self.do_prediction(False, img, self.model_page)
# if not self.dir_in:
img_page_prediction = self.do_prediction(False, img, model_page)
# else:
# img_page_prediction = self.do_prediction(False, img, self.model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.dilate(thresh, KERNEL, iterations=3)
@ -1036,14 +1037,14 @@ class Eynollah:
img = img.astype(np.uint8)
else:
img = self.imread()
if not self.dir_in:
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
# if not self.dir_in:
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
img = cv2.GaussianBlur(img, (5, 5), 0)
if self.dir_in:
img_page_prediction = self.do_prediction(False, img, self.model_page)
else:
img_page_prediction = self.do_prediction(False, img, model_page)
# if self.dir_in:
# img_page_prediction = self.do_prediction(False, img, self.model_page)
# else:
img_page_prediction = self.do_prediction(False, img, model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
@ -1069,11 +1070,11 @@ class Eynollah:
self.logger.debug("enter extract_text_regions")
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)
else:
model_region = self.model_region_fl if patches else self.model_region_fl_np
# 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)
# else:
# model_region = self.model_region_fl if patches else self.model_region_fl_np
if not patches:
img = otsu_copy_binary(img)
@ -1588,23 +1589,23 @@ 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)
# 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)
img = img.astype(np.uint8)
img_org = np.copy(img)
img_h = img_org.shape[0]
img_w = img_org.shape[1]
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
if not self.dir_in:
prediction_textline = self.do_prediction(patches, img, model_textline)
else:
prediction_textline = self.do_prediction(patches, img, self.model_textline)
# if not self.dir_in:
prediction_textline = self.do_prediction(patches, img, model_textline)
# else:
# prediction_textline = self.do_prediction(patches, img, self.model_textline)
prediction_textline = resize_image(prediction_textline, img_h, img_w)
if not self.dir_in:
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
else:
prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
# if not self.dir_in:
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
# else:
# prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
if self.textline_light:
@ -1681,11 +1682,11 @@ class Eynollah:
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
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)
prediction_bin = self.do_prediction(True, img_resized, model_bin)
else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin)
# if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_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)
prediction_bin = prediction_bin[:, :, 0]
prediction_bin = (prediction_bin[:, :] == 0) * 1
prediction_bin = prediction_bin * 255
@ -1698,11 +1699,11 @@ 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)
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)
# if not self.dir_in:
model_region, session_region = self.start_new_session_and_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)
# plt.imshow(prediction_regions_org[:,:,0])
# plt.show()
@ -1744,17 +1745,17 @@ class Eynollah:
img_height_h = img_org.shape[0]
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)
# if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
ratio_y = 1.3
ratio_x = 1
img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if not self.dir_in:
prediction_regions_org_y = self.do_prediction(True, img, model_region)
else:
prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
# if not self.dir_in:
prediction_regions_org_y = self.do_prediction(True, img, model_region)
# else:
# prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org_y[:,:,0])
@ -1774,24 +1775,24 @@ class Eynollah:
img = resize_image(img_org, int(img_org.shape[0]),
int(img_org.shape[1] * (1.2 if is_image_enhanced else 1)))
if self.dir_in:
prediction_regions_org = self.do_prediction(True, img, self.model_region)
else:
prediction_regions_org = self.do_prediction(True, img, model_region)
# if self.dir_in:
# prediction_regions_org = self.do_prediction(True, img, self.model_region)
# else:
prediction_regions_org = self.do_prediction(True, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
prediction_regions_org = prediction_regions_org[:, :, 0]
prediction_regions_org[(prediction_regions_org[:, :] == 1) & (mask_zeros_y[:, :] == 1)] = 0
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
# if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
if self.dir_in:
prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, 0.2)
else:
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
# if self.dir_in:
# prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, 0.2)
# else:
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
prediction_regions_org2 = resize_image(prediction_regions_org2, img_height_h, img_width_h)
mask_zeros2 = (prediction_regions_org2[:, :, 0] == 0)
@ -1817,11 +1818,11 @@ class Eynollah:
if self.input_binary:
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)
prediction_bin = self.do_prediction(True, img_org, model_bin)
else:
prediction_bin = self.do_prediction(True, img_org, self.model_bin)
# if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_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)
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h)
prediction_bin = prediction_bin[:, :, 0]
@ -1830,17 +1831,17 @@ 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)
# if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
ratio_y = 1
ratio_x = 1
img = resize_image(prediction_bin, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if not self.dir_in:
prediction_regions_org = self.do_prediction(True, img, model_region)
else:
prediction_regions_org = self.do_prediction(True, img, self.model_region)
# if not self.dir_in:
prediction_regions_org = self.do_prediction(True, img, model_region)
# else:
# prediction_regions_org = self.do_prediction(True, img, self.model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
prediction_regions_org = prediction_regions_org[:, :, 0]
@ -1869,11 +1870,11 @@ class Eynollah:
if self.input_binary:
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)
prediction_bin = self.do_prediction(True, img_org, model_bin)
else:
prediction_bin = self.do_prediction(True, img_org, self.model_bin)
# if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_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)
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h)
prediction_bin = prediction_bin[:, :, 0]
@ -1883,8 +1884,8 @@ 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)
# if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
else:
prediction_bin = np.copy(img_org)
@ -1892,10 +1893,10 @@ class Eynollah:
ratio_x = 1
img = resize_image(prediction_bin, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if not self.dir_in:
prediction_regions_org = self.do_prediction(True, img, model_region)
else:
prediction_regions_org = self.do_prediction(True, img, self.model_region)
# if not self.dir_in:
prediction_regions_org = self.do_prediction(True, img, model_region)
# else:
# prediction_regions_org = self.do_prediction(True, img, self.model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
prediction_regions_org = prediction_regions_org[:, :, 0]
@ -3036,13 +3037,13 @@ class Eynollah:
t0_tot = time.time()
if not self.dir_in:
self.ls_imgs = [1]
# if not self.dir_in:
self.ls_imgs = [1]
for img_name in self.ls_imgs:
t0 = time.time()
if self.dir_in:
self.reset_file_name_dir(os.path.join(self.dir_in, img_name))
# if self.dir_in:
# self.reset_file_name_dir(os.path.join(self.dir_in, img_name))
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(
self.light_version)
@ -3077,10 +3078,10 @@ class Eynollah:
pcgts = self.writer.build_pagexml_no_full_layout([], page_coord, [], [], [], [], [], [], [], [], [], [],
cont_page, [], [])
self.logger.info("Job done in %.1fs", time.time() - t1)
if self.dir_in:
self.writer.write_pagexml(pcgts)
continue
else:
# if self.dir_in:
# self.writer.write_pagexml(pcgts)
# continue
# else:
return pcgts
t1 = time.time()
@ -3419,5 +3420,5 @@ class Eynollah:
# return pcgts
self.writer.write_pagexml(pcgts)
# self.logger.info("Job done in %.1fs", time.time() - t0)
if self.dir_in:
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
# if self.dir_in:
# self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)

228
eynollah/eynollah/utils/contour.py
Unescape Escape View File

@ -3,8 +3,8 @@ import numpy as np
from shapely import geometry
from .rotate import rotate_image, rotation_image_new
from multiprocessing import Process, Queue, cpu_count
from multiprocessing import Pool
# from multiprocessing import Process, Queue, cpu_count
# from multiprocessing import Pool
def contours_in_same_horizon(cy_main_hor):
@ -154,95 +154,95 @@ 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 = []
for mv in range(len(contours_per_process)):
index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
# 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 = []
# for mv in range(len(contours_per_process)):
# index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
img_copy = np.zeros(img.shape)
img_copy = cv2.fillPoly(img_copy, pts=[contours_per_process[mv]], color=(1, 1, 1))
# img_copy = np.zeros(img.shape)
# img_copy = cv2.fillPoly(img_copy, pts=[contours_per_process[mv]], color=(1, 1, 1))
img_copy = rotation_image_new(img_copy, -slope_first)
# img_copy = rotation_image_new(img_copy, -slope_first)
img_copy = img_copy.astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
# img_copy = img_copy.astype(np.uint8)
# imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
# ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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])
# 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])
# 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):
# def get_textregion_contours_in_org_image_multi(cnts, img, slope_first):
num_cores = cpu_count()
queue_of_all_params = Queue()
processes = []
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])]
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 = []
all_index_text_con = []
for i in range(num_cores):
list_all_par = queue_of_all_params.get(True)
contours_for_sub_process = list_all_par[0]
indexes_for_sub_process = list_all_par[1]
for j in range(len(contours_for_sub_process)):
cnts_org.append(contours_for_sub_process[j])
all_index_text_con.append(indexes_for_sub_process[j])
for i in range(num_cores):
processes[i].join()
print(all_index_text_con)
return cnts_org
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))
# plt.imshow(img_copy)
# plt.show()
# print(img.shape,'img')
img_copy = rotation_image_new(img_copy, -slope_first)
# print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.show()
img_copy = img_copy.astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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])
# 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))])
# num_cores = cpu_count()
# queue_of_all_params = Queue()
# processes = []
# 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])]
# 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 = []
# all_index_text_con = []
# for i in range(num_cores):
# list_all_par = queue_of_all_params.get(True)
# contours_for_sub_process = list_all_par[0]
# indexes_for_sub_process = list_all_par[1]
# for j in range(len(contours_for_sub_process)):
# cnts_org.append(contours_for_sub_process[j])
# all_index_text_con.append(indexes_for_sub_process[j])
# for i in range(num_cores):
# processes[i].join()
# print(all_index_text_con)
# return cnts_org
# 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))
# # plt.imshow(img_copy)
# # plt.show()
# # print(img.shape,'img')
# img_copy = rotation_image_new(img_copy, -slope_first)
# # print(img_copy.shape,'img_copy')
# # plt.imshow(img_copy)
# # plt.show()
# img_copy = img_copy.astype(np.uint8)
# imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
# ret, thresh = cv2.threshold(imgray, 0, 255, 0)
# cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 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])
# # 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))])
return cnts_org
# return cnts_org
def get_textregion_contours_in_org_image(cnts, img, slope_first):
@ -276,42 +276,42 @@ 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):
# def get_textregion_contours_in_org_image_light(cnts, img, slope_first):
h_o = img.shape[0]
w_o = img.shape[1]
# 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_org = []
# 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))
# plt.imshow(img_copy)
# plt.show()
# print(img.shape,'img')
img_copy = rotation_image_new(img_copy, -slope_first)
# print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.show()
img_copy = img_copy.astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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])
# print(np.shape(cont_int[0]))
cnts_org.append(cont_int[0]*3)
return cnts_org
# 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_org = []
# # 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))
# # plt.imshow(img_copy)
# # plt.show()
# # print(img.shape,'img')
# img_copy = rotation_image_new(img_copy, -slope_first)
# # print(img_copy.shape,'img_copy')
# # plt.imshow(img_copy)
# # plt.show()
# img_copy = img_copy.astype(np.uint8)
# imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
# ret, thresh = cv2.threshold(imgray, 0, 255, 0)
# cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 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])
# # print(np.shape(cont_int[0]))
# cnts_org.append(cont_int[0]*3)
# return cnts_org
def return_contours_of_interested_textline(region_pre_p, pixel):

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