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