| |
| |
| import numpy as np |
| import torch |
| from scipy import ndimage |
|
|
| from .utils import convert_to_numpy |
|
|
|
|
| class SAMImageAnnotator: |
| def __init__(self, cfg, device=None): |
| try: |
| from segment_anything import sam_model_registry, SamPredictor |
| from segment_anything.utils.transforms import ResizeLongestSide |
| except: |
| import warnings |
| warnings.warn("please pip install sam package, or you can refer to models/VACE-Annotators/sam/segment_anything-1.0-py3-none-any.whl") |
| self.task_type = cfg.get('TASK_TYPE', 'input_box') |
| self.return_mask = cfg.get('RETURN_MASK', False) |
| self.transform = ResizeLongestSide(1024) |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device |
| seg_model = sam_model_registry[cfg.get('MODEL_NAME', 'vit_b')](checkpoint=cfg['PRETRAINED_MODEL']).eval().to(self.device) |
| self.predictor = SamPredictor(seg_model) |
|
|
| def forward(self, |
| image, |
| input_box=None, |
| mask=None, |
| task_type=None, |
| return_mask=None): |
| task_type = task_type if task_type is not None else self.task_type |
| return_mask = return_mask if return_mask is not None else self.return_mask |
| mask = convert_to_numpy(mask) if mask is not None else None |
|
|
| if task_type == 'mask_point': |
| if len(mask.shape) == 3: |
| scribble = mask.transpose(2, 1, 0)[0] |
| else: |
| scribble = mask.transpose(1, 0) |
| labeled_array, num_features = ndimage.label(scribble >= 255) |
| centers = ndimage.center_of_mass(scribble, labeled_array, |
| range(1, num_features + 1)) |
| point_coords = np.array(centers) |
| point_labels = np.array([1] * len(centers)) |
| sample = { |
| 'point_coords': point_coords, |
| 'point_labels': point_labels |
| } |
| elif task_type == 'mask_box': |
| if len(mask.shape) == 3: |
| scribble = mask.transpose(2, 1, 0)[0] |
| else: |
| scribble = mask.transpose(1, 0) |
| labeled_array, num_features = ndimage.label(scribble >= 255) |
| centers = ndimage.center_of_mass(scribble, labeled_array, |
| range(1, num_features + 1)) |
| centers = np.array(centers) |
| |
| x_min = centers[:, 0].min() |
| x_max = centers[:, 0].max() |
| y_min = centers[:, 1].min() |
| y_max = centers[:, 1].max() |
| bbox = np.array([x_min, y_min, x_max, y_max]) |
| sample = {'box': bbox} |
| elif task_type == 'input_box': |
| if isinstance(input_box, list): |
| input_box = np.array(input_box) |
| sample = {'box': input_box} |
| elif task_type == 'mask': |
| sample = {'mask_input': mask[None, :, :]} |
| else: |
| raise NotImplementedError |
|
|
| self.predictor.set_image(image) |
| masks, scores, logits = self.predictor.predict( |
| multimask_output=False, |
| **sample |
| ) |
| sorted_ind = np.argsort(scores)[::-1] |
| masks = masks[sorted_ind] |
| scores = scores[sorted_ind] |
| logits = logits[sorted_ind] |
| |
| if return_mask: |
| return masks[0] |
| else: |
| ret_data = { |
| "masks": masks, |
| "scores": scores, |
| "logits": logits |
| } |
| return ret_data |
