mirror of
https://github.com/qurator-spk/eynollah.git
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360 lines
18 KiB
Python
360 lines
18 KiB
Python
"""
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Tool to load model and binarize a given image.
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"""
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import sys
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from glob import glob
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import os
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import logging
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import numpy as np
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from PIL import Image
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import cv2
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from ocrd_utils import tf_disable_interactive_logs
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tf_disable_interactive_logs()
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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from tensorflow.python.keras import backend as tensorflow_backend
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from .utils import is_image_filename
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def resize_image(img_in, input_height, input_width):
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return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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class SbbBinarizer:
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def __init__(self, model_dir, logger=None):
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self.model_dir = model_dir
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self.logger = logger if logger else logging.getLogger('SbbBinarizer')
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self.start_new_session()
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self.model_files = glob(self.model_dir+"/*/", recursive = True)
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self.models = []
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for model_file in self.model_files:
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self.models.append(self.load_model(model_file))
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def start_new_session(self):
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
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tensorflow_backend.set_session(self.session)
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def end_session(self):
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tensorflow_backend.clear_session()
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self.session.close()
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del self.session
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def load_model(self, model_name):
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model = load_model(os.path.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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def predict(self, model_in, img, use_patches, n_batch_inference=5):
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tensorflow_backend.set_session(self.session)
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model, model_height, model_width, n_classes = model_in
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img_org_h = img.shape[0]
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img_org_w = img.shape[1]
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if img.shape[0] < model_height and img.shape[1] >= model_width:
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img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
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index_start_h = int( abs( img.shape[0] - model_height) /2.)
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index_start_w = 0
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img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
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elif img.shape[0] >= model_height and img.shape[1] < model_width:
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img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
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index_start_h = 0
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index_start_w = int( abs( img.shape[1] - model_width) /2.)
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img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
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elif img.shape[0] < model_height and img.shape[1] < model_width:
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img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
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index_start_h = int( abs( img.shape[0] - model_height) /2.)
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index_start_w = int( abs( img.shape[1] - model_width) /2.)
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img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
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else:
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index_start_h = 0
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index_start_w = 0
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img_padded = np.copy(img)
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img = np.copy(img_padded)
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if use_patches:
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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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img_h = img.shape[0]
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img_w = img.shape[1]
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prediction_true = np.zeros((img_h, img_w, 3))
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mask_true = np.zeros((img_h, img_w))
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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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else:
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nxf = int(nxf)
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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else:
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nyf = int(nyf)
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
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for i in range(nxf):
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for j in range(nyf):
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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 + 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 + 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 + 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 + 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 - 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 - model_height
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list_i_s.append(i)
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list_j_s.append(j)
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list_x_u.append(index_x_u)
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list_x_d.append(index_x_d)
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list_y_d.append(index_y_d)
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list_y_u.append(index_y_u)
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img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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batch_indexer = batch_indexer + 1
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if batch_indexer == n_batch_inference:
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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#print(seg.shape, len(seg), len(list_i_s))
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch,:,:]
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seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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if i_batch == 0 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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else:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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indexer_inside_batch = indexer_inside_batch +1
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
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elif i==(nxf-1) and j==(nyf-1):
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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#print(seg.shape, len(seg), len(list_i_s))
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch,:,:]
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seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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if i_batch == 0 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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else:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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indexer_inside_batch = indexer_inside_batch +1
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
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prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
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prediction_true = prediction_true.astype(np.uint8)
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else:
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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, model_height, model_width)
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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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prediction_true = resize_image(seg_color, img_h_page, img_w_page)
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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, image_path=None, output=None, dir_in=None, use_patches=False, overwrite=False):
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if dir_in:
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ls_imgs = [(os.path.join(dir_in, image_filename),
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os.path.join(output, os.path.splitext(image_filename)[0] + '.png'))
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for image_filename in filter(is_image_filename,
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os.listdir(dir_in))]
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else:
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ls_imgs = [(image_path, output)]
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for input_path, output_path in ls_imgs:
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print(input_path, 'image_name')
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if os.path.exists(output_path):
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if overwrite:
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self.logger.warning("will overwrite existing output file '%s'", output_ptah)
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else:
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self.logger.warning("will skip input for existing output file '%s'", output_path)
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image = cv2.imread(input_path)
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result = self.run_single(image, use_patches)
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cv2.imwrite(output_path, result)
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def run_single(self, image: np.ndarray, use_patches=False):
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img_last = 0
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for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
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self.logger.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
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res = self.predict(model, image, use_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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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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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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return img_last
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