""" 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 os 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 sys.stderr = stderr import logging def resize_image(img_in, input_height, input_width): return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) 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(self.model_dir+"/*/", recursive = True) 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): model = load_model(join(self.model_dir, model_name), compile=False) 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, n_batch_inference=5): 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 = np.copy(img_padded) if use_patches: margin = int(0.1 * 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) list_i_s = [] list_j_s = [] list_x_u = [] list_x_d = [] list_y_u = [] list_y_d = [] batch_indexer = 0 img_patch = np.zeros((n_batch_inference, model_height, model_width,3)) 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 list_i_s.append(i) list_j_s.append(j) list_x_u.append(index_x_u) list_x_d.append(index_x_d) list_y_d.append(index_y_d) list_y_u.append(index_y_u) img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :] batch_indexer = batch_indexer + 1 if batch_indexer == n_batch_inference: label_p_pred = model.predict(img_patch,verbose=0) seg = np.argmax(label_p_pred, axis=3) #print(seg.shape, len(seg), len(list_i_s)) indexer_inside_batch = 0 for i_batch, j_batch in zip(list_i_s, list_j_s): seg_in = seg[indexer_inside_batch,:,:] seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2) index_y_u_in = list_y_u[indexer_inside_batch] index_y_d_in = list_y_d[indexer_inside_batch] index_x_u_in = list_x_u[indexer_inside_batch] index_x_d_in = list_x_d[indexer_inside_batch] if i_batch == 0 and j_batch == 0: seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color elif i_batch == nxf - 1 and j_batch == nyf - 1: seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :] prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color elif i_batch == 0 and j_batch == nyf - 1: seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :] prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color elif i_batch == nxf - 1 and j_batch == 0: seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :] prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color else: seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color indexer_inside_batch = indexer_inside_batch +1 list_i_s = [] list_j_s = [] list_x_u = [] list_x_d = [] list_y_u = [] list_y_d = [] batch_indexer = 0 img_patch = np.zeros((n_batch_inference, model_height, model_width,3)) 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, dir_in=None, dir_out=None): print(dir_in,'dir_in') if not dir_in: 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 else: ls_imgs = os.listdir(dir_in) for image_name in ls_imgs: image_stem = image_name.split('.')[0] print(image_name,'image_name') image = cv2.imread(os.path.join(dir_in,image_name) ) 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 cv2.imwrite(os.path.join(dir_out,image_stem+'.png'), img_last)