def infer_on_img(img, input_size, model):
x = cv2.resize(img, input_size[::-1])
x = input_image_normalizer(x)
output = model(x.unsqueeze_(0).cuda())
output = functional.softmax(output, dim=1)
output = output.data.cpu().numpy()[0]
return output
def inference(self, img, ann):
resized_img = cv2.resize(img, self.input_size[::-1])
model_input = input_image_normalizer(resized_img)
pixelwise_probas_array = pytorch_inference.infer_per_pixel_scores_single_image(
self.model, model_input, img.shape[:2])
labels = raw_to_labels.segmentation_array_to_sly_bitmaps(
self.out_class_mapping, np.argmax(pixelwise_probas_array, axis=2))
pixelwise_scores_labels = raw_to_labels.segmentation_scores_to_per_class_labels(
self.out_class_mapping, pixelwise_probas_array)
return Annotation(ann.img_size, labels=labels, pixelwise_scores_labels=pixelwise_scores_labels)
def _infer_on_img(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)
res = self._postproc(self.out_class_mapping, pred)
return res
def _infer_on_img_legacy(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.sigmoid(output)
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 = pred[..., 0] >= 0.5 # use only one channel, fixed threshold
pred[..., 1] = pred[..., 0]
pred[..., 0] = 1 - pred[..., 1]
res = self._postproc(self.out_class_mapping, pred)
return res
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