def inference(self, image_path, image_index): im_data = Data.load_data(image_path=image_path, input_size=self.input_size) im_data = np.expand_dims(im_data, axis=0) result, summary_now = self.sess.run([self.features[-1], self.summary_op], feed_dict={self.image_placeholder: im_data}) self.summary_writer.add_summary(summary_now, global_step=image_index) print(result) pass
def inference(self, image_path, image_index, save_path=None): im_data = Data.load_data(image_path=image_path, input_size=self.input_size) im_data = np.expand_dims(im_data, axis=0) pred_segment_r, summary_now = self.sess.run([self.pred_segment, self.summary_op], feed_dict={self.image_placeholder: im_data}) self.summary_writer.add_summary(summary_now, global_step=image_index) s_image = Image.fromarray(np.asarray(np.squeeze(pred_segment_r) * 255, dtype=np.uint8)) if save_path is None: s_image.show() else: Tools.new_dir(save_path) s_image.convert("L").save("{}/{}.bmp".format(save_path, os.path.splitext(os.path.basename(image_path))[0])) pass