""" Tool to load model and binarize a given image. """ from os import listdir from os.path import join from warnings import catch_warnings, simplefilter import numpy as np import cv2 from keras.models import load_model import tensorflow as tf # XXX better to set env var before tensorflow import to suppress those specific warnings with catch_warnings(): simplefilter("ignore") def resize_image(img_in, input_height, input_width): return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST) class SbbBinarizer: # TODO use True/False for patches def __init__(self, model, image=None, image_path=None, patches='false', save=None): if not(image or image_path) or (image and image_path): raise ValueError("Must pass either a PIL image or an image_path") if image: self.image = image else: self.image = cv2.imread(self.image) self.patches = patches self.save = save self.model_dir = model def start_new_session_and_model(self): config = tf.ConfigProto() config.gpu_options.allow_growth = True self.session = tf.Session(config=config) # tf.InteractiveSession() def load_model(self, model_name): self.model = load_model(join(self.model_dir, model_name), compile=False) self.img_height = self.model.layers[len(self.model.layers)-1].output_shape[1] self.img_width = self.model.layers[len(self.model.layers)-1].output_shape[2] self.n_classes = self.model.layers[len(self.model.layers)-1].output_shape[3] def end_session(self): self.session.close() del self.model del self.session def predict(self,model_name): self.load_model(model_name) img = self.image img_width_model = self.img_width img_height_model = self.img_height if self.patches in ('true', 'True'): margin = int(0.1 * 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) 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 + img_width_model elif i > 0: 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 elif j > 0: 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 = self.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) 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.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, img_height_model, img_width_model) label_p_pred = self.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): self.start_new_session_and_model() models_n = listdir(self.model_dir) img_last = 0 for model_in in models_n: res = self.predict(model_in) 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 self.save: cv2.imwrite(self.save, img_last) return img_last