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eynollah
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🎨 unncesssary if True

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pull/19/head
Konstantin Baierer 4 years ago
parent 68d5c0d523
commit a65caa4d25
1 changed files with 79 additions and 92 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -171,93 +171,81 @@ class eynollah:
if img.shape[1] < img_width_model:
img = cv2.resize(img, (img_height_model, img.shape[0]), interpolation=cv2.INTER_NEAREST)
margin = int(0 * img_width_model)
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float(255.0)
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
else:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
else:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg = label_p_pred[0, :, :, :]
seg = seg * 255
if i == 0 and j == 0:
seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg
elif i == 0 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j == 0:
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
elif i == 0 and j != 0 and j != nyf - 1:
seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j != 0 and j != nyf - 1:
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
elif i != 0 and i != nxf - 1 and j == 0:
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
elif i != 0 and i != nxf - 1 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg
else:
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
margin = True
if margin:
kernel = np.ones((5, 5), np.uint8)
margin = int(0 * img_width_model)
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float(255.0)
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
else:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
else:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg = label_p_pred[0, :, :, :]
seg = seg * 255
if i == 0 and j == 0:
seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg
elif i == 0 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j == 0:
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
elif i == 0 and j != 0 and j != nyf - 1:
seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
elif i == nxf - 1 and j != 0 and j != nyf - 1:
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
elif i != 0 and i != nxf - 1 and j == 0:
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
elif i != 0 and i != nxf - 1 and j == nyf - 1:
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg
else:
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
prediction_true = prediction_true.astype(int)
del model_enhancement
del session_enhancemnet
prediction_true = prediction_true.astype(int)
return prediction_true
return prediction_true
def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred):
self.logger.debug("enter calculate_width_height_by_columns")
@ -1252,7 +1240,6 @@ class eynollah:
id_indexer_l = 0
if len(found_polygons_text_region) > 0:
self.xml_reading_order(page, order_of_texts, id_of_texts, id_of_marginalia, found_polygons_marginals)
for mm in range(len(found_polygons_text_region)):
textregion = ET.SubElement(page, 'TextRegion')
textregion.set('id', 'r%s' % id_indexer)
@ -1282,9 +1269,9 @@ class eynollah:
points_co += ','
points_co += str(int((all_found_texline_polygons[mm][j][l][1] + page_coord[0]) / self.scale_y))
else:
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ','
points_co = points_co + str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
points_co += str(int((all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]) / self.scale_y))
elif curved_line and abs(slopes[mm]) > 45:
if len(all_found_texline_polygons[mm][j][l]) == 2:
points_co += str(int((all_found_texline_polygons[mm][j][l][0] + all_box_coord[mm][2] + page_coord[2]) / self.scale_x))
@ -1298,7 +1285,6 @@ class eynollah:
if l < len(all_found_texline_polygons[mm][j]) - 1:
points_co += ' '
coord.set('points', points_co)
add_textequiv(textregion)
for mm in range(len(found_polygons_marginals)):
@ -2002,12 +1988,13 @@ class eynollah:
text_regions_p = text_regions_p_1[:, :] # long_short_region[:,:]#self.get_regions_from_2_models(image_page)
text_regions_p = np.array(text_regions_p)
if num_col_classifier == 1 or num_col_classifier == 2:
if num_col_classifier in (1, 2):
try:
regions_without_seperators = (text_regions_p[:, :] == 1) * 1
regions_without_seperators = regions_without_seperators.astype(np.uint8)
text_regions_p = get_marginals(rotate_image(regions_without_seperators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=self.kernel)
except:
except Exception as e:
self.logger.error("exception %s", e)
pass
if self.plotter:

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