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| # coding: utf-8 | |
| import os.path as osp | |
| import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) | |
| import torch | |
| import numpy as np | |
| import onnxruntime | |
| from .timer import Timer | |
| from .rprint import rlog | |
| from .crop import crop_image, _transform_pts | |
| def make_abs_path(fn): | |
| return osp.join(osp.dirname(osp.realpath(__file__)), fn) | |
| def to_ndarray(obj): | |
| if isinstance(obj, torch.Tensor): | |
| return obj.cpu().numpy() | |
| elif isinstance(obj, np.ndarray): | |
| return obj | |
| else: | |
| return np.array(obj) | |
| class LandmarkRunner(object): | |
| """landmark runner""" | |
| def __init__(self, **kwargs): | |
| ckpt_path = kwargs.get('ckpt_path') | |
| onnx_provider = kwargs.get('onnx_provider', 'cuda') # 默认用cuda | |
| device_id = kwargs.get('device_id', 0) | |
| self.dsize = kwargs.get('dsize', 224) | |
| self.timer = Timer() | |
| if onnx_provider.lower() == 'cuda': | |
| self.session = onnxruntime.InferenceSession( | |
| ckpt_path, providers=[ | |
| ('CUDAExecutionProvider', {'device_id': device_id}) | |
| ] | |
| ) | |
| else: | |
| opts = onnxruntime.SessionOptions() | |
| opts.intra_op_num_threads = 4 # 默认线程数为 4 | |
| self.session = onnxruntime.InferenceSession( | |
| ckpt_path, providers=['CPUExecutionProvider'], | |
| sess_options=opts | |
| ) | |
| def _run(self, inp): | |
| out = self.session.run(None, {'input': inp}) | |
| return out | |
| def run(self, img_rgb: np.ndarray, lmk=None): | |
| if lmk is not None: | |
| crop_dct = crop_image(img_rgb, lmk, dsize=self.dsize, scale=1.5, vy_ratio=-0.1) | |
| img_crop_rgb = crop_dct['img_crop'] | |
| else: | |
| # NOTE: force resize to 224x224, NOT RECOMMEND! | |
| img_crop_rgb = cv2.resize(img_rgb, (self.dsize, self.dsize)) | |
| scale = max(img_rgb.shape[:2]) / self.dsize | |
| crop_dct = { | |
| 'M_c2o': np.array([ | |
| [scale, 0., 0.], | |
| [0., scale, 0.], | |
| [0., 0., 1.], | |
| ], dtype=np.float32), | |
| } | |
| inp = (img_crop_rgb.astype(np.float32) / 255.).transpose(2, 0, 1)[None, ...] # HxWx3 (BGR) -> 1x3xHxW (RGB!) | |
| out_lst = self._run(inp) | |
| out_pts = out_lst[2] | |
| # 2d landmarks 203 points | |
| lmk = to_ndarray(out_pts[0]).reshape(-1, 2) * self.dsize # scale to 0-224 | |
| lmk = _transform_pts(lmk, M=crop_dct['M_c2o']) | |
| return lmk | |
| def warmup(self): | |
| self.timer.tic() | |
| dummy_image = np.zeros((1, 3, self.dsize, self.dsize), dtype=np.float32) | |
| _ = self._run(dummy_image) | |
| elapse = self.timer.toc() | |
| rlog(f'LandmarkRunner warmup time: {elapse:.3f}s') | |