(self,
*data_key_batch,
normalize=True,
seq_len=None,
**kwargs)
| 67 | return self.load_image_batch(data_key, data_key2, **kwargs) |
| 68 | |
| 69 | def load_image_batch(self, |
| 70 | *data_key_batch, |
| 71 | normalize=True, |
| 72 | seq_len=None, |
| 73 | **kwargs): |
| 74 | seq_len = self.seq_len if seq_len is None else seq_len |
| 75 | imgs = [] |
| 76 | for data_key in data_key_batch: |
| 77 | img = self._load_image(data_key) |
| 78 | imgs.append(img) |
| 79 | w, h = imgs[0].size |
| 80 | dh, dw = self.downsample[1:] |
| 81 | |
| 82 | # compute output size |
| 83 | scale = min(1., np.sqrt(seq_len / ((h / dh) * (w / dw)))) |
| 84 | oh = int(h * scale) // dh * dh |
| 85 | ow = int(w * scale) // dw * dw |
| 86 | assert (oh // dh) * (ow // dw) <= seq_len |
| 87 | imgs = [self._image_preprocess(img, oh, ow, normalize) for img in imgs] |
| 88 | return *imgs, (oh, ow) |
| 89 | |
| 90 | |
| 91 | class VaceVideoProcessor(object): |
no test coverage detected