def inference(self, img):
h, w = img.shape[:2]
img_var = img.astype(np.float32)
semantic_predictions = self.sess.run(self.predictions,
feed_dict={self.input_images: img_var})
pred = np.squeeze(semantic_predictions[0])
pred_cls_idx = np.argmax(pred, axis=2)
res_figures = sly.prediction_to_sly_bitmaps(self.out_class_mapping, pred_cls_idx)
res_ann = sly.Annotation.new_with_objects((w, h), res_figures)
return res_ann
def _determine_input_data(self):
project_fs = sly.ProjectFS.from_disk_dir_project(self.helper.paths.project_dir)
logger.info('Project structure has been read. Samples: {}.'.format(project_fs.pr_structure.image_cnt))
self.in_project_fs = project_fs
self.inf_feeder = sly.InferenceFeederFactory.create(
self.config, self.helper.in_project_meta, self.train_classes
)
if self.inf_feeder.expected_result == sly.InfResultsToFeeder.FIGURES:
self._postproc = lambda a, pred: sly.prediction_to_sly_bitmaps(a, np.argmax(pred, axis=2))
elif self.inf_feeder.expected_result == sly.InfResultsToFeeder.SEGMENTATION:
self._postproc = lambda a, b: (a, b)
else:
raise NotImplementedError()
def inference(self, img):
h, w = img.shape[:2]
x = cv2.resize(img, tuple(self.input_size_wh))
x = input_image_normalizer(x)
x = torch.stack([x], 0) # add dim #0 (batch size 1)
x = cuda_variable(x, volatile=True)
output = self.model(x)
output = functional.softmax(output, dim=1)
output = output.data.cpu().numpy()[0] # from batch to 3d
pred = np.transpose(output, (1, 2, 0))
pred = cv2.resize(pred, (w, h), cv2.INTER_LINEAR)
pred_cls_idx = np.argmax(pred, axis=2)
res_figures = sly.prediction_to_sly_bitmaps(self.out_class_mapping,
pred_cls_idx)
res_ann = sly.Annotation.new_with_objects((w, h), res_figures)
return res_ann
