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https://github.com/qurator-spk/sbb_binarization.git
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SbbBinarizer: refactor (variable names, less instance-wide state)
This commit is contained in:
Konstantin Baierer
parent
a1c8f6f465
commit
1fa581283c
3 changed files with 51 additions and 73 deletions
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@ -16,13 +16,8 @@ def main():
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options = parser.parse_args()
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binarizer = SbbBinarizer(
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image_path=options.image,
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model=options.model,
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patches=options.patches,
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save=options.save
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)
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binarizer.run()
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binarizer = SbbBinarizer(model_dir=options.model)
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binarizer.run(image_path=options.image, patches=options.patches, save=options.save)
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if __name__ == "__main__":
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main()
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@ -39,14 +39,6 @@ class SbbBinarizeProcessor(Processor):
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kwargs['version'] = OCRD_TOOL['version']
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super().__init__(*args, **kwargs)
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def _run_binarizer(self, img):
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return cv2pil(
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SbbBinarizer(
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image=pil2cv(img),
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model=self.model_path,
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patches=self.use_patches,
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save=None).run())
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def process(self):
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"""
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Binarize with sbb_binarization
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@ -56,8 +48,9 @@ class SbbBinarizeProcessor(Processor):
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assert_file_grp_cardinality(self.output_file_grp, 1)
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oplevel = self.parameter['operation_level']
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self.use_patches = self.parameter['patches'] # pylint: disable=attribute-defined-outside-init
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self.model_path = self.parameter['model'] # pylint: disable=attribute-defined-outside-init
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use_patches = self.parameter['patches'] # pylint: disable=attribute-defined-outside-init
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model_path = self.parameter['model'] # pylint: disable=attribute-defined-outside-init
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binarizer = SbbBinarizer(model_dir=self.model_path)
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for n, input_file in enumerate(self.input_files):
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file_id = make_file_id(input_file, self.output_file_grp)
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@ -71,7 +64,7 @@ class SbbBinarizeProcessor(Processor):
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if oplevel == 'page':
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LOG.info("Binarizing on 'page' level in page '%s'", page_id)
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page_image, page_xywh, _ = self.workspace.image_from_page(page, page_id, feature_filter='binarized')
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bin_image = self._run_binarizer(page_image)
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bin_image = cv2pil(binarizer.run(image=pil2cv(page_image), patches=use_patches))
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# update METS (add the image file):
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bin_image_path = self.workspace.save_image_file(bin_image,
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file_id + '.IMG-BIN',
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@ -85,7 +78,7 @@ class SbbBinarizeProcessor(Processor):
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LOG.warning("Page '%s' contains no text/table regions", page_id)
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for region in regions:
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region_image, region_xywh = self.workspace.image_from_segment(region, page_image, page_xywh, feature_filter='binarized')
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region_image_bin = self._run_binarizer(region_image)
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region_image_bin = cv2pil(binarizer.run(image=pil2cv(region_image), patches=use_patches))
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region_image_bin_path = self.workspace.save_image_file(
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region_image_bin,
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"%s_%s.IMG-BIN" % (file_id, region.id),
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@ -100,7 +93,7 @@ class SbbBinarizeProcessor(Processor):
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LOG.warning("Page '%s' contains no text lines", page_id)
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for region_id, line in region_line_tuples:
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line_image, line_xywh = self.workspace.image_from_segment(line, page_image, page_xywh, feature_filter='binarized')
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line_image_bin = self._run_binarizer(line_image)
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line_image_bin = cv2pil(binarizer.run(image=pil2cv(line_image), patches=use_patches))
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line_image_bin_path = self.workspace.save_image_file(
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line_image_bin,
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"%s_%s_%s.IMG-BIN" % (file_id, region_id, line.id),
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@ -22,50 +22,35 @@ def resize_image(img_in, input_height, input_width):
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class SbbBinarizer:
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# TODO use True/False for patches
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def __init__(self, model, image=None, image_path=None, patches='false', save=None):
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if (image is not None and image_path is not None) or \
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(image is None and image_path is None):
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raise ValueError("Must pass either a opencv2 image or an image_path")
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if image is not None:
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self.image = image
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else:
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self.image = cv2.imread(self.image)
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self.patches = patches
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self.save = save
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self.model_dir = model
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def __init__(self, model_dir):
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self.model_dir = model_dir
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def start_new_session_and_model(self):
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def start_new_session(self):
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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self.session = tf.Session(config=config) # tf.InteractiveSession()
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def load_model(self, model_name):
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self.model = load_model(join(self.model_dir, model_name), compile=False)
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self.img_height = self.model.layers[len(self.model.layers)-1].output_shape[1]
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self.img_width = self.model.layers[len(self.model.layers)-1].output_shape[2]
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self.n_classes = self.model.layers[len(self.model.layers)-1].output_shape[3]
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def end_session(self):
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self.session.close()
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del self.model
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del self.session
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def predict(self,model_name):
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self.load_model(model_name)
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img = self.image
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img_width_model = self.img_width
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img_height_model = self.img_height
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def load_model(self, model_name):
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model = load_model(join(self.model_dir, model_name), compile=False)
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model_height = model.layers[len(model.layers)-1].output_shape[1]
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model_width = model.layers[len(model.layers)-1].output_shape[2]
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n_classes = model.layers[len(model.layers)-1].output_shape[3]
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return model, model_height, model_width, n_classes
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if self.patches in ('true', 'True'):
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def predict(self, model_name, img, patches):
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model, model_height, model_width, n_classes = self.load_model(model_name)
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margin = int(0.1 * img_width_model)
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if patches in ('true', 'True'):
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width_mid = img_width_model - 2 * margin
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height_mid = img_height_model - 2 * margin
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margin = int(0.1 * model_width)
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width_mid = model_width - 2 * margin
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height_mid = model_height - 2 * margin
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img = img / float(255.0)
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@ -93,28 +78,28 @@ class SbbBinarizer:
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + img_width_model
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index_x_u = index_x_d + model_width
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + img_width_model
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index_x_u = index_x_d + model_width
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if j == 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + img_height_model
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index_y_u = index_y_d + model_height
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elif j > 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + img_height_model
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index_y_u = index_y_d + model_height
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - img_width_model
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index_x_d = img_w - model_width
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - img_height_model
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index_y_d = img_h - model_height
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = self.model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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@ -189,10 +174,9 @@ class SbbBinarizer:
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img_h_page = img.shape[0]
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img_w_page = img.shape[1]
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img = img / float(255.0)
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img = resize_image(img, img_height_model, img_width_model)
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img = resize_image(img, model_height, model_width)
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label_p_pred = self.model.predict(
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img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
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label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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@ -200,29 +184,35 @@ class SbbBinarizer:
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prediction_true = prediction_true.astype(np.uint8)
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return prediction_true[:,:,0]
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def run(self):
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self.start_new_session_and_model()
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models_n = listdir(self.model_dir)
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# TODO use True/False for patches
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def run(self, image=None, image_path=None, save=None, patches='false'):
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if (image is not None and image_path is not None) or \
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(image is None and image_path is None):
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raise ValueError("Must pass either a opencv2 image or an image_path")
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if image_path is not None:
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image = cv2.imread(image)
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self.start_new_session()
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list_of_model_files = listdir(self.model_dir)
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img_last = 0
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for model_in in models_n:
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for model_in in list_of_model_files:
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res = self.predict(model_in)
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res = self.predict(model_in, image, patches)
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img_fin = np.zeros((res.shape[0], res.shape[1], 3))
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res[:, :][res[:, :] == 0] = 2
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res = res-1
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res = res*255
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res = res - 1
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res = res * 255
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img_fin[:, :, 0] = res
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img_fin[:, :, 1] = res
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img_fin[:, :, 2] = res
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img_fin = img_fin.astype(np.uint8)
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img_fin = (res[:, :] == 0)*255
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img_last = img_last+img_fin
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img_fin = (res[:, :] == 0) * 255
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img_last = img_last + img_fin
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kernel = np.ones((5, 5), np.uint8)
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img_last[:, :][img_last[:, :] > 0] = 255
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img_last = (img_last[:, :] == 0)*255
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if self.save:
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cv2.imwrite(self.save, img_last)
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img_last = (img_last[:, :] == 0) * 255
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if save:
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cv2.imwrite(save, img_last)
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return img_last
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