""" Tool to load model and binarize a given image. """ import sys from glob import glob from os import environ, devnull from os.path import join from warnings import catch_warnings, simplefilter import numpy as np from PIL import Image import cv2 environ['TF_CPP_MIN_LOG_LEVEL'] = '3' stderr = sys.stderr sys.stderr = open(devnull, 'w') import tensorflow as tf from tensorflow.keras.models import load_model from tensorflow.python.keras import backend as tensorflow_backend from tensorflow.keras import layers import tensorflow.keras.losses from tensorflow.keras.layers import * sys.stderr = stderr import logging projection_dim = 64 patch_size = 1 num_patches =14*14 def resize_image(img_in, input_height, input_width): return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) class Patches(layers.Layer): def __init__(self, **kwargs): super(Patches, self).__init__() self.patch_size = patch_size def call(self, images): batch_size = tf.shape(images)[0] patches = tf.image.extract_patches( images=images, sizes=[1, self.patch_size, self.patch_size, 1], strides=[1, self.patch_size, self.patch_size, 1], rates=[1, 1, 1, 1], padding="VALID", ) patch_dims = patches.shape[-1] patches = tf.reshape(patches, [batch_size, -1, patch_dims]) return patches def get_config(self): config = super().get_config().copy() config.update({ 'patch_size': self.patch_size, }) return config class PatchEncoder(layers.Layer): def __init__(self, **kwargs): super(PatchEncoder, self).__init__() self.num_patches = num_patches self.projection = layers.Dense(units=projection_dim) self.position_embedding = layers.Embedding( input_dim=num_patches, output_dim=projection_dim ) def call(self, patch): positions = tf.range(start=0, limit=self.num_patches, delta=1) encoded = self.projection(patch) + self.position_embedding(positions) return encoded def get_config(self): config = super().get_config().copy() config.update({ 'num_patches': self.num_patches, 'projection': self.projection, 'position_embedding': self.position_embedding, }) return config class SbbBinarizer: def __init__(self, model_dir, logger=None): self.model_dir = model_dir self.log = logger if logger else logging.getLogger('SbbBinarizer') self.start_new_session() self.model_files = glob('%s/*.h5' % self.model_dir) self.models = [] for model_file in self.model_files: self.models.append(self.load_model(model_file)) def start_new_session(self): config = tf.compat.v1.ConfigProto() config.gpu_options.allow_growth = True self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession() tensorflow_backend.set_session(self.session) def end_session(self): tensorflow_backend.clear_session() self.session.close() del self.session def load_model(self, model_name): try: model = load_model(join(self.model_dir, model_name), compile=False) self.margin_percent = 0.1 except: model = load_model(join(self.model_dir, model_name) , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches}) self.margin_percent = 0.15 model_height = model.layers[len(model.layers)-1].output_shape[1] model_width = model.layers[len(model.layers)-1].output_shape[2] n_classes = model.layers[len(model.layers)-1].output_shape[3] return model, model_height, model_width, n_classes def predict(self, model_in, img, use_patches): tensorflow_backend.set_session(self.session) model, model_height, model_width, n_classes = model_in img_org_h = img.shape[0] img_org_w = img.shape[1] if img.shape[0] < model_height and img.shape[1] >= model_width: img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] )) index_start_h = int( abs( img.shape[0] - model_height) /2.) index_start_w = 0 img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:] elif img.shape[0] >= model_height and img.shape[1] < model_width: img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] )) index_start_h = 0 index_start_w = int( abs( img.shape[1] - model_width) /2.) img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:] elif img.shape[0] < model_height and img.shape[1] < model_width: img_padded = np.zeros(( model_height, model_width, img.shape[2] )) index_start_h = int( abs( img.shape[0] - model_height) /2.) index_start_w = int( abs( img.shape[1] - model_width) /2.) img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:] else: index_start_h = 0 index_start_w = 0 img_padded = np.copy(img) img_org_h_pad = img_padded.shape[0] img_org_w_pad = img_padded.shape[1] index_start_h_alw = 100 index_start_w_alw = 100 img_padded_alw = np.zeros(( img_padded.shape[0]+2*index_start_h_alw, img.shape[1]+2*index_start_w_alw, img.shape[2] )) img_padded_alw [ 0: index_start_h_alw, index_start_w_alw: index_start_w_alw+img_padded.shape[1], : ] = img_padded[:index_start_h_alw,:,:] img_padded_alw [ index_start_h_alw: index_start_h_alw+img_padded.shape[0], 0:index_start_w_alw, : ] = img_padded[:,0:index_start_w_alw,:] img_padded_alw [ img_padded_alw.shape[0]-index_start_h_alw: img_padded_alw.shape[0], index_start_w_alw: index_start_w_alw+img_padded.shape[1], : ] = img_padded[img_padded.shape[0]-index_start_h_alw:img_padded.shape[0],:,:] img_padded_alw [ index_start_h_alw: index_start_h_alw+img_padded.shape[0],img_padded_alw.shape[1]-index_start_w_alw: img_padded_alw.shape[1], : ] = img_padded[:,img_padded.shape[1]-index_start_w_alw:img_padded.shape[1],:] img_padded_alw [ index_start_h_alw: index_start_h_alw+img_padded.shape[0], index_start_w_alw: index_start_w_alw+img_padded.shape[1], : ] = img_padded[:,:,:] img = np.copy(img_padded_alw) if use_patches: margin = int(self.margin_percent * model_width) width_mid = model_width - 2 * margin height_mid = model_height - 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) if nxf > int(nxf): nxf = int(nxf) + 1 else: nxf = int(nxf) if nyf > int(nyf): nyf = int(nyf) + 1 else: nyf = 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 + model_width elif i > 0: index_x_d = i * width_mid index_x_u = index_x_d + model_width if j == 0: index_y_d = j * height_mid index_y_u = index_y_d + model_height elif j > 0: index_y_d = j * height_mid index_y_u = index_y_d + model_height if index_x_u > img_w: index_x_u = img_w index_x_d = img_w - model_width if index_y_u > img_h: index_y_u = img_h index_y_d = img_h - model_height img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] #h_res = int( img_patch.shape[0]/1.05) #w_res = int( img_patch.shape[1]/1.05) #img_patch_resize = resize_image(img_patch, h_res, w_res) #img_patch_resized_padded =np.ones((img_patch.shape[0],img_patch.shape[1],img_patch.shape[2])).astype(float)#self.do_padding() #h_start=int( abs(img_patch.shape[0]-img_patch_resize.shape[0])/2. ) #w_start=int( abs(img_patch.shape[1]-img_patch_resize.shape[1])/2. ) #img_patch_resized_padded[h_start:h_start+img_patch_resize.shape[0],w_start:w_start+img_patch_resize.shape[1],:]=np.copy(img_patch_resize[:,:,:]) #label_p_pred_padded = model.predict(img_patch_resized_padded.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) #seg = np.argmax(label_p_pred, axis=3)[0] #label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) seg = np.argmax(label_p_pred, axis=3)[0] seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) #seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) if i == 0 and j == 0: seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :] seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin] mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color elif i == nxf-1 and j == nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :] seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0] mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color elif i == 0 and j == nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color elif i == nxf-1 and j == 0: seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :] seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0] mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color elif i == 0 and j != 0 and j != nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color elif i == nxf-1 and j != 0 and j != nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :] seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0] mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color elif i != 0 and i != nxf-1 and j == 0: seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :] seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin] mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color elif i != 0 and i != nxf-1 and j == nyf-1: seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color else: seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :] seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin] mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color prediction_true = prediction_true[index_start_h_alw: index_start_h_alw+img_org_h_pad, index_start_w_alw: index_start_w_alw+img_org_w_pad,:] prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:] prediction_true = prediction_true.astype(np.uint8) else: img_h_page = img.shape[0] img_w_page = img.shape[1] img = img / float(255.0) img = resize_image(img, model_height, model_width) label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) seg = np.argmax(label_p_pred, axis=3)[0] seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) prediction_true = resize_image(seg_color, img_h_page, img_w_page) prediction_true = prediction_true.astype(np.uint8) return prediction_true[:,:,0] def run(self, image=None, image_path=None, save=None, use_patches=False): if (image is not None and image_path is not None) or \ (image is None and image_path is None): raise ValueError("Must pass either a opencv2 image or an image_path") if image_path is not None: image = cv2.imread(image_path) img_last = 0 for n, (model, model_file) in enumerate(zip(self.models, self.model_files)): self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files))) res = self.predict(model, image, use_patches) img_fin = np.zeros((res.shape[0], res.shape[1], 3)) res[:, :][res[:, :] == 0] = 2 res = res - 1 res = res * 255 img_fin[:, :, 0] = res img_fin[:, :, 1] = res img_fin[:, :, 2] = res img_fin = img_fin.astype(np.uint8) img_fin = (res[:, :] == 0) * 255 img_last = img_last + img_fin kernel = np.ones((5, 5), np.uint8) img_last[:, :][img_last[:, :] > 0] = 255 img_last = (img_last[:, :] == 0) * 255 if save: cv2.imwrite(save, img_last) return img_last