| |
| |
|
|
| import numpy as np |
| import torch |
|
|
| from .utils import convert_to_numpy |
|
|
|
|
| class FaceAnnotator: |
| def __init__(self, cfg, device=None): |
| from insightface.app import FaceAnalysis |
| self.return_raw = cfg.get('RETURN_RAW', True) |
| self.return_mask = cfg.get('RETURN_MASK', False) |
| self.return_dict = cfg.get('RETURN_DICT', False) |
| self.multi_face = cfg.get('MULTI_FACE', True) |
| pretrained_model = cfg['PRETRAINED_MODEL'] |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device |
| self.device_id = self.device.index if self.device.type == 'cuda' else None |
| ctx_id = self.device_id if self.device_id is not None else 0 |
| self.model = FaceAnalysis(name=cfg.MODEL_NAME, root=pretrained_model, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
| self.model.prepare(ctx_id=ctx_id, det_size=(640, 640)) |
|
|
| def forward(self, image=None, return_mask=None, return_dict=None): |
| return_mask = return_mask if return_mask is not None else self.return_mask |
| return_dict = return_dict if return_dict is not None else self.return_dict |
| image = convert_to_numpy(image) |
| |
| faces = self.model.get(image) |
| if self.return_raw: |
| return faces |
| else: |
| crop_face_list, mask_list = [], [] |
| if len(faces) > 0: |
| if not self.multi_face: |
| faces = faces[:1] |
| for face in faces: |
| x_min, y_min, x_max, y_max = face['bbox'].tolist() |
| crop_face = image[int(y_min): int(y_max) + 1, int(x_min): int(x_max) + 1] |
| crop_face_list.append(crop_face) |
| mask = np.zeros_like(image[:, :, 0]) |
| mask[int(y_min): int(y_max) + 1, int(x_min): int(x_max) + 1] = 255 |
| mask_list.append(mask) |
| if not self.multi_face: |
| crop_face_list = crop_face_list[0] |
| mask_list = mask_list[0] |
| if return_mask: |
| if return_dict: |
| return {'image': crop_face_list, 'mask': mask_list} |
| else: |
| return crop_face_list, mask_list |
| else: |
| return crop_face_list |
| else: |
| return None |
|
|
