mirror of
https://github.com/qurator-spk/eynollah.git
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💀 remove dead code from eynollah.py
This commit is contained in:
kba
parent
5171e09c2d
commit
9bcfeab057
4 changed files with 5 additions and 467 deletions
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@ -77,15 +77,14 @@ from .utils.rotate import (
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)
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)
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from .utils.separate_lines import (
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from .utils.separate_lines import (
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return_deskew_slop,
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return_deskew_slop,
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do_work_of_slopes_new,
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do_work_of_slopes_new_curved,
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do_work_of_slopes_new_curved,
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do_work_of_slopes_new_light,
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)
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)
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from .utils.marginals import get_marginals
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from .utils.marginals import get_marginals
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from .utils.resize import resize_image
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from .utils.resize import resize_image
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from .utils.shm import share_ndarray
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from .utils.shm import share_ndarray
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from .utils import (
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from .utils import (
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is_image_filename,
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is_image_filename,
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isNaN,
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crop_image_inside_box,
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crop_image_inside_box,
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box2rect,
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box2rect,
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find_num_col,
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find_num_col,
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@ -269,9 +268,6 @@ class Eynollah:
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key += '_uint8'
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key += '_uint8'
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return self._imgs[key].copy()
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return self._imgs[key].copy()
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def isNaN(self, num):
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return num != num
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def predict_enhancement(self, img):
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def predict_enhancement(self, img):
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self.logger.debug("enter predict_enhancement")
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self.logger.debug("enter predict_enhancement")
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@ -913,136 +909,6 @@ class Eynollah:
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gc.collect()
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gc.collect()
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return prediction_true
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return prediction_true
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def do_padding_with_scale(self, img, scale):
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h_n = int(img.shape[0]*scale)
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w_n = int(img.shape[1]*scale)
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channel0_avg = int( np.mean(img[:,:,0]) )
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channel1_avg = int( np.mean(img[:,:,1]) )
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channel2_avg = int( np.mean(img[:,:,2]) )
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h_diff = img.shape[0] - h_n
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w_diff = img.shape[1] - w_n
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h_start = int(0.5 * h_diff)
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w_start = int(0.5 * w_diff)
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img_res = resize_image(img, h_n, w_n)
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#label_res = resize_image(label, h_n, w_n)
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img_scaled_padded = np.copy(img)
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#label_scaled_padded = np.zeros(label.shape)
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img_scaled_padded[:,:,0] = channel0_avg
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img_scaled_padded[:,:,1] = channel1_avg
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img_scaled_padded[:,:,2] = channel2_avg
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img_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = img_res[:,:,:]
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#label_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = label_res[:,:,:]
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return img_scaled_padded#, label_scaled_padded
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def do_prediction_new_concept_scatter_nd(
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self, patches, img, model,
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n_batch_inference=1, marginal_of_patch_percent=0.1,
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thresholding_for_some_classes_in_light_version=False,
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thresholding_for_artificial_class_in_light_version=False):
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self.logger.debug("enter do_prediction_new_concept")
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img_height_model = model.layers[-1].output_shape[1]
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img_width_model = model.layers[-1].output_shape[2]
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if not patches:
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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 / 255.0
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img = resize_image(img, img_height_model, img_width_model)
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label_p_pred = model.predict(img[np.newaxis], verbose=0)
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seg = np.argmax(label_p_pred, axis=3)[0]
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if thresholding_for_artificial_class_in_light_version:
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#seg_text = label_p_pred[0,:,:,1]
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#seg_text[seg_text<0.2] =0
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#seg_text[seg_text>0] =1
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#seg[seg_text==1]=1
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seg_art = label_p_pred[0,:,:,4]
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seg_art[seg_art<0.2] =0
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seg_art[seg_art>0] =1
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seg[seg_art==1]=4
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8)
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return prediction_true
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if img.shape[0] < img_height_model:
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img = resize_image(img, img_height_model, img.shape[1])
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if img.shape[1] < img_width_model:
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img = resize_image(img, img.shape[0], img_width_model)
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self.logger.debug("Patch size: %sx%s", img_height_model, img_width_model)
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##margin = int(marginal_of_patch_percent * img_height_model)
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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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img = img / 255.0
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img = img.astype(np.float16)
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img_h = img.shape[0]
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img_w = img.shape[1]
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stride_x = img_width_model - 100
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stride_y = img_height_model - 100
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one_tensor = tf.ones_like(img)
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img_patches, one_patches = tf.image.extract_patches(
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images=[img, one_tensor],
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sizes=[1, img_height_model, img_width_model, 1],
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strides=[1, stride_y, stride_x, 1],
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rates=[1, 1, 1, 1],
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padding='SAME')
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img_patches = tf.squeeze(img_patches)
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one_patches = tf.squeeze(one_patches)
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img_patches_resh = tf.reshape(img_patches, shape=(img_patches.shape[0] * img_patches.shape[1],
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img_height_model, img_width_model, 3))
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pred_patches = model.predict(img_patches_resh, batch_size=n_batch_inference)
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one_patches = tf.reshape(one_patches, shape=(img_patches.shape[0] * img_patches.shape[1],
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img_height_model, img_width_model, 3))
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x = tf.range(img.shape[1])
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y = tf.range(img.shape[0])
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x, y = tf.meshgrid(x, y)
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indices = tf.stack([y, x], axis=-1)
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indices_patches = tf.image.extract_patches(
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images=tf.expand_dims(indices, axis=0),
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sizes=[1, img_height_model, img_width_model, 1],
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strides=[1, stride_y, stride_x, 1],
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rates=[1, 1, 1, 1],
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padding='SAME')
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indices_patches = tf.squeeze(indices_patches)
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indices_patches = tf.reshape(indices_patches, shape=(img_patches.shape[0] * img_patches.shape[1],
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img_height_model, img_width_model, 2))
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margin_y = int( 0.5 * (img_height_model - stride_y) )
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margin_x = int( 0.5 * (img_width_model - stride_x) )
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mask_margin = np.zeros((img_height_model, img_width_model))
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mask_margin[margin_y:img_height_model - margin_y,
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margin_x:img_width_model - margin_x] = 1
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indices_patches_array = indices_patches.numpy()
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for i in range(indices_patches_array.shape[0]):
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indices_patches_array[i,:,:,0] = indices_patches_array[i,:,:,0]*mask_margin
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indices_patches_array[i,:,:,1] = indices_patches_array[i,:,:,1]*mask_margin
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reconstructed = tf.scatter_nd(
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indices=indices_patches_array,
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updates=pred_patches,
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shape=(img.shape[0], img.shape[1], pred_patches.shape[-1])).numpy()
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prediction_true = np.argmax(reconstructed, axis=2).astype(np.uint8)
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gc.collect()
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return np.repeat(prediction_true[:, :, np.newaxis], 3, axis=2)
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def do_prediction_new_concept(
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def do_prediction_new_concept(
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self, patches, img, model,
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self, patches, img, model,
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n_batch_inference=1, marginal_of_patch_percent=0.1,
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n_batch_inference=1, marginal_of_patch_percent=0.1,
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@ -1615,41 +1481,6 @@ class Eynollah:
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all_box_coord,
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all_box_coord,
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slopes)
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slopes)
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def get_slopes_and_deskew_new_light(self, contours, contours_par, textline_mask_tot, boxes, slope_deskew):
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if not len(contours):
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return [], [], []
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self.logger.debug("enter get_slopes_and_deskew_new_light")
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with share_ndarray(textline_mask_tot) as textline_mask_tot_shared:
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assert self.executor
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results = self.executor.map(partial(do_work_of_slopes_new_light,
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textline_mask_tot_ea=textline_mask_tot_shared,
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slope_deskew=slope_deskew,
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logger=self.logger,),
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boxes, contours, contours_par)
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results = list(results) # exhaust prior to release
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#textline_polygons, box_coord, slopes = zip(*results)
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self.logger.debug("exit get_slopes_and_deskew_new_light")
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return tuple(zip(*results))
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def get_slopes_and_deskew_new(self, contours, contours_par, textline_mask_tot, boxes, slope_deskew):
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if not len(contours):
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return [], [], []
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self.logger.debug("enter get_slopes_and_deskew_new")
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with share_ndarray(textline_mask_tot) as textline_mask_tot_shared:
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assert self.executor
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results = self.executor.map(partial(do_work_of_slopes_new,
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textline_mask_tot_ea=textline_mask_tot_shared,
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slope_deskew=slope_deskew,
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MAX_SLOPE=MAX_SLOPE,
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KERNEL=KERNEL,
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logger=self.logger,
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plotter=self.plotter,),
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boxes, contours, contours_par)
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results = list(results) # exhaust prior to release
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#textline_polygons, box_coord, slopes = zip(*results)
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self.logger.debug("exit get_slopes_and_deskew_new")
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return tuple(zip(*results))
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def get_slopes_and_deskew_new_curved(self, contours_par, textline_mask_tot, boxes,
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def get_slopes_and_deskew_new_curved(self, contours_par, textline_mask_tot, boxes,
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mask_texts_only, num_col, scale_par, slope_deskew):
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mask_texts_only, num_col, scale_par, slope_deskew):
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if not len(contours_par):
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if not len(contours_par):
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@ -1875,145 +1706,6 @@ class Eynollah:
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img_bin,
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img_bin,
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confidence_matrix)
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confidence_matrix)
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def get_regions_from_xy_2models(self,img,is_image_enhanced, num_col_classifier):
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self.logger.debug("enter get_regions_from_xy_2models")
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erosion_hurts = False
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img_org = np.copy(img)
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img_height_h = img_org.shape[0]
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img_width_h = img_org.shape[1]
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ratio_y=1.3
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ratio_x=1
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img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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prediction_regions_org_y = self.do_prediction(True, img, self.model_zoo.get("region"))
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prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h )
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#plt.imshow(prediction_regions_org_y[:,:,0])
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#plt.show()
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prediction_regions_org_y = prediction_regions_org_y[:,:,0]
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mask_zeros_y = (prediction_regions_org_y[:,:]==0)*1
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##img_only_regions_with_sep = ( (prediction_regions_org_y[:,:] != 3) & (prediction_regions_org_y[:,:] != 0) )*1
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img_only_regions_with_sep = (prediction_regions_org_y == 1).astype(np.uint8)
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try:
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img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=20)
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_, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
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img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
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prediction_regions_org = self.do_prediction(True, img, self.model_zoo.get("region"))
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prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
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prediction_regions_org=prediction_regions_org[:,:,0]
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prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
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img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
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prediction_regions_org2 = self.do_prediction(True, img, self.model_zoo.get("region_p2"), marginal_of_patch_percent=0.2)
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prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
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mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
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mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
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text_sume_early = (prediction_regions_org[:,:] == 1).sum()
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prediction_regions_org_copy = np.copy(prediction_regions_org)
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prediction_regions_org_copy[(prediction_regions_org_copy[:,:]==1) & (mask_zeros2[:,:]==1)] = 0
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text_sume_second = ((prediction_regions_org_copy[:,:]==1)*1).sum()
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rate_two_models = 100. * text_sume_second / text_sume_early
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self.logger.info("ratio_of_two_models: %s", rate_two_models)
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if not(is_image_enhanced and rate_two_models < RATIO_OF_TWO_MODEL_THRESHOLD):
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prediction_regions_org = np.copy(prediction_regions_org_copy)
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prediction_regions_org[(mask_lines2[:,:]==1) & (prediction_regions_org[:,:]==0)]=3
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mask_lines_only=(prediction_regions_org[:,:]==3)*1
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prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2)
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prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2)
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if rate_two_models<=40:
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if self.input_binary:
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prediction_bin = np.copy(img_org)
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else:
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prediction_bin = self.do_prediction(True, img_org, self.model_zoo.get("binarization"), n_batch_inference=5)
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prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
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prediction_bin = 255 * (prediction_bin[:,:,0]==0)
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prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
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ratio_y=1
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ratio_x=1
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img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
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prediction_regions_org = self.do_prediction(True, img, self.model_zoo.get("region"))
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prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
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prediction_regions_org=prediction_regions_org[:,:,0]
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mask_lines_only=(prediction_regions_org[:,:]==3)*1
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mask_texts_only=(prediction_regions_org[:,:]==1)*1
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mask_images_only=(prediction_regions_org[:,:]==2)*1
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polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
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polygons_seplines = filter_contours_area_of_image(
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mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
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|
||||||
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only, 1, 0.00001)
|
|
||||||
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only, 1, 0.00001)
|
|
||||||
|
|
||||||
text_regions_p_true = np.zeros(prediction_regions_org.shape)
|
|
||||||
text_regions_p_true = cv2.fillPoly(text_regions_p_true,pts = polygons_of_only_lines, color=(3, 3, 3))
|
|
||||||
text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
|
|
||||||
|
|
||||||
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
|
|
||||||
|
|
||||||
self.logger.debug("exit get_regions_from_xy_2models")
|
|
||||||
return text_regions_p_true, erosion_hurts, polygons_seplines, polygons_of_only_texts
|
|
||||||
except:
|
|
||||||
if self.input_binary:
|
|
||||||
prediction_bin = np.copy(img_org)
|
|
||||||
prediction_bin = self.do_prediction(True, img_org, self.model_zoo.get("binarization"), n_batch_inference=5)
|
|
||||||
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
|
|
||||||
prediction_bin = 255 * (prediction_bin[:,:,0]==0)
|
|
||||||
prediction_bin = np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
|
|
||||||
else:
|
|
||||||
prediction_bin = np.copy(img_org)
|
|
||||||
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))
|
|
||||||
prediction_regions_org = self.do_prediction(True, img, self.model_zoo.get("region"))
|
|
||||||
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
|
|
||||||
prediction_regions_org=prediction_regions_org[:,:,0]
|
|
||||||
|
|
||||||
#mask_lines_only=(prediction_regions_org[:,:]==3)*1
|
|
||||||
#img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
|
|
||||||
|
|
||||||
#prediction_regions_org = self.do_prediction(True, img, self.model_zoo.get_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
|
|
||||||
|
|
||||||
mask_lines_only = (prediction_regions_org == 3)*1
|
|
||||||
mask_texts_only = (prediction_regions_org == 1)*1
|
|
||||||
mask_images_only= (prediction_regions_org == 2)*1
|
|
||||||
|
|
||||||
polygons_seplines, hir_seplines = return_contours_of_image(mask_lines_only)
|
|
||||||
polygons_seplines = filter_contours_area_of_image(
|
|
||||||
mask_lines_only, polygons_seplines, hir_seplines, max_area=1, min_area=0.00001, dilate=1)
|
|
||||||
|
|
||||||
polygons_of_only_texts = return_contours_of_interested_region(mask_texts_only,1,0.00001)
|
|
||||||
polygons_of_only_lines = return_contours_of_interested_region(mask_lines_only,1,0.00001)
|
|
||||||
|
|
||||||
text_regions_p_true = np.zeros(prediction_regions_org.shape)
|
|
||||||
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_lines, color=(3,3,3))
|
|
||||||
|
|
||||||
text_regions_p_true[:,:][mask_images_only[:,:] == 1] = 2
|
|
||||||
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
|
|
||||||
|
|
||||||
erosion_hurts = True
|
|
||||||
self.logger.debug("exit get_regions_from_xy_2models")
|
|
||||||
return text_regions_p_true, erosion_hurts, polygons_seplines, polygons_of_only_texts
|
|
||||||
|
|
||||||
def do_order_of_regions(
|
def do_order_of_regions(
|
||||||
self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
|
self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
|
||||||
|
|
||||||
|
|
@ -2259,7 +1951,7 @@ class Eynollah:
|
||||||
|
|
||||||
img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=indiv)
|
img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=indiv)
|
||||||
|
|
||||||
if not self.isNaN(slope_mean_hor):
|
if not isNaN(slope_mean_hor):
|
||||||
image_revised_last = np.zeros(image_regions_eraly_p.shape[:2])
|
image_revised_last = np.zeros(image_regions_eraly_p.shape[:2])
|
||||||
for i in range(len(boxes)):
|
for i in range(len(boxes)):
|
||||||
box_ys = slice(*boxes[i][2:4])
|
box_ys = slice(*boxes[i][2:4])
|
||||||
|
|
@ -2455,52 +2147,6 @@ class Eynollah:
|
||||||
|
|
||||||
return page_coord, image_page, textline_mask_tot_ea, img_bin_light, cont_page
|
return page_coord, image_page, textline_mask_tot_ea, img_bin_light, cont_page
|
||||||
|
|
||||||
def run_graphics_and_columns(
|
|
||||||
self, text_regions_p_1,
|
|
||||||
num_col_classifier, num_column_is_classified, erosion_hurts):
|
|
||||||
|
|
||||||
t_in_gr = time.time()
|
|
||||||
img_g = self.imread(grayscale=True, uint8=True)
|
|
||||||
|
|
||||||
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
|
|
||||||
img_g3 = img_g3.astype(np.uint8)
|
|
||||||
img_g3[:, :, 0] = img_g[:, :]
|
|
||||||
img_g3[:, :, 1] = img_g[:, :]
|
|
||||||
img_g3[:, :, 2] = img_g[:, :]
|
|
||||||
|
|
||||||
image_page, page_coord, cont_page = self.extract_page()
|
|
||||||
|
|
||||||
if self.tables:
|
|
||||||
table_prediction = self.get_tables_from_model(image_page, num_col_classifier)
|
|
||||||
else:
|
|
||||||
table_prediction = np.zeros((image_page.shape[0], image_page.shape[1])).astype(np.int16)
|
|
||||||
|
|
||||||
if self.plotter:
|
|
||||||
self.plotter.save_page_image(image_page)
|
|
||||||
|
|
||||||
text_regions_p_1 = text_regions_p_1[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
|
|
||||||
mask_images = (text_regions_p_1[:, :] == 2) * 1
|
|
||||||
mask_images = mask_images.astype(np.uint8)
|
|
||||||
mask_images = cv2.erode(mask_images[:, :], KERNEL, iterations=10)
|
|
||||||
mask_lines = (text_regions_p_1[:, :] == 3) * 1
|
|
||||||
mask_lines = mask_lines.astype(np.uint8)
|
|
||||||
img_only_regions_with_sep = ((text_regions_p_1[:, :] != 3) & (text_regions_p_1[:, :] != 0)) * 1
|
|
||||||
img_only_regions_with_sep = img_only_regions_with_sep.astype(np.uint8)
|
|
||||||
|
|
||||||
if erosion_hurts:
|
|
||||||
img_only_regions = np.copy(img_only_regions_with_sep[:,:])
|
|
||||||
else:
|
|
||||||
img_only_regions = cv2.erode(img_only_regions_with_sep[:,:], KERNEL, iterations=6)
|
|
||||||
try:
|
|
||||||
num_col, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
|
|
||||||
num_col = num_col + 1
|
|
||||||
if not num_column_is_classified:
|
|
||||||
num_col_classifier = num_col + 1
|
|
||||||
except Exception as why:
|
|
||||||
self.logger.error(why)
|
|
||||||
num_col = None
|
|
||||||
return (num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines,
|
|
||||||
text_regions_p_1, cont_page, table_prediction)
|
|
||||||
|
|
||||||
def run_enhancement(self):
|
def run_enhancement(self):
|
||||||
t_in = time.time()
|
t_in = time.time()
|
||||||
|
|
@ -2518,10 +2164,7 @@ class Eynollah:
|
||||||
else:
|
else:
|
||||||
self.get_image_and_scales_after_enhancing(img_org, img_res)
|
self.get_image_and_scales_after_enhancing(img_org, img_res)
|
||||||
else:
|
else:
|
||||||
if self.allow_enhancement:
|
self.get_image_and_scales(img_org, img_res, scale)
|
||||||
self.get_image_and_scales(img_org, img_res, scale)
|
|
||||||
else:
|
|
||||||
self.get_image_and_scales(img_org, img_res, scale)
|
|
||||||
if self.allow_scaling:
|
if self.allow_scaling:
|
||||||
img_org, img_res, is_image_enhanced = \
|
img_org, img_res, is_image_enhanced = \
|
||||||
self.resize_image_with_column_classifier(is_image_enhanced, img_bin)
|
self.resize_image_with_column_classifier(is_image_enhanced, img_bin)
|
||||||
|
|
@ -3019,42 +2662,6 @@ class Eynollah:
|
||||||
region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))]
|
region_ids = ['region_%04d' % i for i in range(len(co_text_all_org))]
|
||||||
return ordered, region_ids
|
return ordered, region_ids
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def return_list_of_contours_with_desired_order(self, ls_cons, sorted_indexes):
|
|
||||||
return list(np.array(ls_cons)[np.array(sorted_indexes)])
|
|
||||||
|
|
||||||
def return_it_in_two_groups(self, x_differential):
|
|
||||||
split = [ind if x_differential[ind]!=x_differential[ind+1] else -1
|
|
||||||
for ind in range(len(x_differential)-1)]
|
|
||||||
split_masked = list( np.array(split[:])[np.array(split[:])!=-1] )
|
|
||||||
if 0 not in split_masked:
|
|
||||||
split_masked.insert(0, -1)
|
|
||||||
split_masked.append(len(x_differential)-1)
|
|
||||||
|
|
||||||
split_masked = np.array(split_masked) +1
|
|
||||||
|
|
||||||
sums = [np.sum(x_differential[split_masked[ind]:split_masked[ind+1]])
|
|
||||||
for ind in range(len(split_masked)-1)]
|
|
||||||
|
|
||||||
indexes_to_bec_changed = [ind if (np.abs(sums[ind-1]) > np.abs(sums[ind]) and
|
|
||||||
np.abs(sums[ind+1]) > np.abs(sums[ind])) else -1
|
|
||||||
for ind in range(1,len(sums)-1)]
|
|
||||||
indexes_to_bec_changed_filtered = np.array(indexes_to_bec_changed)[np.array(indexes_to_bec_changed)!=-1]
|
|
||||||
|
|
||||||
x_differential_new = np.copy(x_differential)
|
|
||||||
for i in indexes_to_bec_changed_filtered:
|
|
||||||
i_slice = slice(split_masked[i], split_masked[i+1])
|
|
||||||
x_differential_new[i_slice] = -1 * np.array(x_differential)[i_slice]
|
|
||||||
|
|
||||||
return x_differential_new
|
|
||||||
|
|
||||||
def filter_contours_inside_a_bigger_one(self, contours, contours_d_ordered, image,
|
def filter_contours_inside_a_bigger_one(self, contours, contours_d_ordered, image,
|
||||||
marginal_cnts=None, type_contour="textregion"):
|
marginal_cnts=None, type_contour="textregion"):
|
||||||
if type_contour == "textregion":
|
if type_contour == "textregion":
|
||||||
|
|
|
||||||
|
|
@ -10,14 +10,14 @@ Image enhancer. The output can be written as same scale of input or in new predi
|
||||||
import logging
|
import logging
|
||||||
import os
|
import os
|
||||||
import time
|
import time
|
||||||
from typing import Dict, Optional
|
from typing import Optional
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
import gc
|
import gc
|
||||||
|
|
||||||
import cv2
|
import cv2
|
||||||
from keras.models import Model
|
from keras.models import Model
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import tensorflow as tf
|
import tensorflow as tf # type: ignore
|
||||||
from skimage.morphology import skeletonize
|
from skimage.morphology import skeletonize
|
||||||
|
|
||||||
from .model_zoo import EynollahModelZoo
|
from .model_zoo import EynollahModelZoo
|
||||||
|
|
@ -27,7 +27,6 @@ from .utils import (
|
||||||
is_image_filename,
|
is_image_filename,
|
||||||
crop_image_inside_box
|
crop_image_inside_box
|
||||||
)
|
)
|
||||||
from .patch_encoder import PatchEncoder, Patches
|
|
||||||
|
|
||||||
DPI_THRESHOLD = 298
|
DPI_THRESHOLD = 298
|
||||||
KERNEL = np.ones((5, 5), np.uint8)
|
KERNEL = np.ones((5, 5), np.uint8)
|
||||||
|
|
@ -91,9 +90,6 @@ class Enhancer:
|
||||||
key += '_uint8'
|
key += '_uint8'
|
||||||
return self._imgs[key].copy()
|
return self._imgs[key].copy()
|
||||||
|
|
||||||
def isNaN(self, num):
|
|
||||||
return num != num
|
|
||||||
|
|
||||||
def predict_enhancement(self, img):
|
def predict_enhancement(self, img):
|
||||||
self.logger.debug("enter predict_enhancement")
|
self.logger.debug("enter predict_enhancement")
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -5,8 +5,6 @@ import numpy as np
|
||||||
import cv2
|
import cv2
|
||||||
from scipy.signal import find_peaks
|
from scipy.signal import find_peaks
|
||||||
from scipy.ndimage import gaussian_filter1d
|
from scipy.ndimage import gaussian_filter1d
|
||||||
from multiprocessing import Process, Queue, cpu_count
|
|
||||||
from multiprocessing import Pool
|
|
||||||
from .rotate import rotate_image
|
from .rotate import rotate_image
|
||||||
from .resize import resize_image
|
from .resize import resize_image
|
||||||
from .contour import (
|
from .contour import (
|
||||||
|
|
@ -20,9 +18,7 @@ from .contour import (
|
||||||
from .shm import share_ndarray, wrap_ndarray_shared
|
from .shm import share_ndarray, wrap_ndarray_shared
|
||||||
from . import (
|
from . import (
|
||||||
find_num_col_deskew,
|
find_num_col_deskew,
|
||||||
crop_image_inside_box,
|
|
||||||
box2rect,
|
box2rect,
|
||||||
box2slice,
|
|
||||||
)
|
)
|
||||||
|
|
||||||
def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
|
def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
|
||||||
|
|
@ -1590,65 +1586,6 @@ def get_smallest_skew(img, sigma_des, angles, logger=None, plotter=None, map=map
|
||||||
var = 0
|
var = 0
|
||||||
return angle, var
|
return angle, var
|
||||||
|
|
||||||
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
|
|
||||||
def do_work_of_slopes_new(
|
|
||||||
box_text, contour, contour_par,
|
|
||||||
textline_mask_tot_ea=None, slope_deskew=0.0,
|
|
||||||
logger=None, MAX_SLOPE=999, KERNEL=None, plotter=None
|
|
||||||
):
|
|
||||||
if KERNEL is None:
|
|
||||||
KERNEL = np.ones((5, 5), np.uint8)
|
|
||||||
if logger is None:
|
|
||||||
logger = getLogger(__package__)
|
|
||||||
logger.debug('enter do_work_of_slopes_new')
|
|
||||||
|
|
||||||
x, y, w, h = box_text
|
|
||||||
crop_coor = box2rect(box_text)
|
|
||||||
mask_textline = np.zeros(textline_mask_tot_ea.shape)
|
|
||||||
mask_textline = cv2.fillPoly(mask_textline, pts=[contour], color=(1,1,1))
|
|
||||||
all_text_region_raw = textline_mask_tot_ea * mask_textline
|
|
||||||
all_text_region_raw = all_text_region_raw[y: y + h, x: x + w].astype(np.uint8)
|
|
||||||
img_int_p = all_text_region_raw[:,:]
|
|
||||||
img_int_p = cv2.erode(img_int_p, KERNEL, iterations=2)
|
|
||||||
|
|
||||||
if not np.prod(img_int_p.shape) or img_int_p.shape[0] /img_int_p.shape[1] < 0.1:
|
|
||||||
slope = 0
|
|
||||||
slope_for_all = slope_deskew
|
|
||||||
all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w]
|
|
||||||
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text, 0)
|
|
||||||
else:
|
|
||||||
try:
|
|
||||||
textline_con, hierarchy = return_contours_of_image(img_int_p)
|
|
||||||
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con,
|
|
||||||
hierarchy,
|
|
||||||
max_area=1, min_area=0.00008)
|
|
||||||
y_diff_mean = find_contours_mean_y_diff(textline_con_fil) if len(textline_con_fil) > 1 else np.NaN
|
|
||||||
if np.isnan(y_diff_mean):
|
|
||||||
slope_for_all = MAX_SLOPE
|
|
||||||
else:
|
|
||||||
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
|
|
||||||
img_int_p[img_int_p > 0] = 1
|
|
||||||
slope_for_all = return_deskew_slop(img_int_p, sigma_des, logger=logger, plotter=plotter)
|
|
||||||
if abs(slope_for_all) <= 0.5:
|
|
||||||
slope_for_all = slope_deskew
|
|
||||||
except:
|
|
||||||
logger.exception("cannot determine angle of contours")
|
|
||||||
slope_for_all = MAX_SLOPE
|
|
||||||
|
|
||||||
if slope_for_all == MAX_SLOPE:
|
|
||||||
slope_for_all = slope_deskew
|
|
||||||
slope = slope_for_all
|
|
||||||
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
|
|
||||||
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contour_par], color=(1, 1, 1))
|
|
||||||
|
|
||||||
all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w].copy()
|
|
||||||
mask_only_con_region = mask_only_con_region[y: y + h, x: x + w]
|
|
||||||
|
|
||||||
all_text_region_raw[mask_only_con_region == 0] = 0
|
|
||||||
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text)
|
|
||||||
|
|
||||||
return cnt_clean_rot, crop_coor, slope
|
|
||||||
|
|
||||||
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
|
@wrap_ndarray_shared(kw='textline_mask_tot_ea')
|
||||||
@wrap_ndarray_shared(kw='mask_texts_only')
|
@wrap_ndarray_shared(kw='mask_texts_only')
|
||||||
def do_work_of_slopes_new_curved(
|
def do_work_of_slopes_new_curved(
|
||||||
|
|
|
||||||
|
|
@ -4,7 +4,6 @@ from pathlib import Path
|
||||||
import os.path
|
import os.path
|
||||||
from typing import Optional
|
from typing import Optional
|
||||||
import logging
|
import logging
|
||||||
import xml.etree.ElementTree as ET
|
|
||||||
from .utils.xml import create_page_xml, xml_reading_order
|
from .utils.xml import create_page_xml, xml_reading_order
|
||||||
from .utils.counter import EynollahIdCounter
|
from .utils.counter import EynollahIdCounter
|
||||||
|
|
||||||
|
|
@ -19,7 +18,6 @@ from ocrd_models.ocrd_page import (
|
||||||
SeparatorRegionType,
|
SeparatorRegionType,
|
||||||
to_xml
|
to_xml
|
||||||
)
|
)
|
||||||
import numpy as np
|
|
||||||
|
|
||||||
class EynollahXmlWriter:
|
class EynollahXmlWriter:
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue