Python mmcv.imdenormalize() Examples

The following are 24 code examples of mmcv.imdenormalize(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module mmcv , or try the search function .
Example #1
Source File: misc.py    From mmdetection with Apache License 2.0 6 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    """Convert tensor to images.

    Args:
        tensor (torch.Tensor): Tensor that contains multiple images
        mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0).
        std (tuple[float], optional): Standard deviation of images.
            Defaults to (1, 1, 1).
        to_rgb (bool, optional): Whether convert the images to RGB format.
            Defaults to True.

    Returns:
        list[np.ndarray]: A list that contains multiple images.
    """
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #2
Source File: misc.py    From Reasoning-RCNN with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #3
Source File: misc.py    From AugFPN with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #4
Source File: misc.py    From ttfnet with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #5
Source File: misc.py    From CenterNet with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #6
Source File: misc.py    From hrnet with MIT License 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #7
Source File: misc.py    From mmaction with Apache License 2.0 5 votes vote down vote up 
def tensor2video_snaps(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_videos = tensor.size(0)
    num_frames = tensor.size(2)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    video_snaps = []
    for vid_id in range(num_videos):
        img = tensor[vid_id, :, num_frames //
                     2, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        video_snaps.append(np.ascontiguousarray(img))
    return video_snaps
Example #8
Source File: eda.py    From kaggle-imaterialist with MIT License 5 votes vote down vote up 
def main():
    args = parse_args()
    os.makedirs(args.output, exist_ok=True)
    cfg = Config.fromfile(args.config)
    dataset = get_dataset(cfg.data.train)
    for i in tqdm(np.random.randint(0, len(dataset), 500)):
        data = dataset[i]
        img = data['img'].data.numpy().transpose(1, 2, 0)
        masks = data['gt_masks'].data.transpose(1, 2, 0).astype(bool)
        bboxes = data['gt_bboxes'].data.numpy()
        img = mmcv.imdenormalize(img, mean=cfg.img_norm_cfg.mean, std=cfg.img_norm_cfg.std, to_bgr=False)
        img = draw_masks(img, masks).astype(np.uint8)
        draw_bounding_boxes_on_image_array(img, bboxes, use_normalized_coordinates=False, thickness=5)
        cv2.imwrite(osp.join(args.output, f'{i}_{np.random.randint(0, 10000)}.jpg'), img[..., ::-1])
Example #9
Source File: misc.py    From kaggle-imaterialist with MIT License 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #10
Source File: misc.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #11
Source File: misc.py    From Cascade-RPN with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #12
Source File: misc.py    From FoveaBox with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #13
Source File: misc.py    From Libra_R-CNN with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #14
Source File: misc.py    From IoU-Uniform-R-CNN with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #15
Source File: misc.py    From RDSNet with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #16
Source File: misc.py    From Grid-R-CNN with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #17
Source File: misc.py    From kaggle-kuzushiji-recognition with MIT License 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #18
Source File: misc.py    From PolarMask with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #19
Source File: misc.py    From mmdetection_with_SENet154 with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #20
Source File: test_photometric.py    From mmcv with Apache License 2.0 5 votes vote down vote up 
def test_imdenormalize(self):
        norm_img = (self.img[:, :, ::-1] - self.mean) / self.std
        rgb_baseline = (norm_img * self.std + self.mean)
        bgr_baseline = rgb_baseline[:, :, ::-1]
        img = mmcv.imdenormalize(norm_img, self.mean, self.std)
        assert np.allclose(img, bgr_baseline)
        img = mmcv.imdenormalize(norm_img, self.mean, self.std, to_bgr=False)
        assert np.allclose(img, rgb_baseline)
Example #21
Source File: misc.py    From mmcv with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    """Convert tensor to 3-channel images.

    Args:
        tensor (torch.Tensor): Tensor that contains multiple images, shape (
            N, C, H, W).
        mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0).
        std (tuple[float], optional): Standard deviation of images.
            Defaults to (1, 1, 1).
        to_rgb (bool, optional): Whether the tensor was converted to RGB
            format in the first place. If so, convert it back to BGR.
            Defaults to True.

    Returns:
        list[np.ndarray]: A list that contains multiple images.
    """

    if torch is None:
        raise RuntimeError('pytorch is not installed')
    assert torch.is_tensor(tensor) and tensor.ndim == 4
    assert len(mean) == 3
    assert len(std) == 3

    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #22
Source File: misc.py    From mmdetection-annotated with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #23
Source File: misc.py    From GCNet with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs
Example #24
Source File: misc.py    From AerialDetection with Apache License 2.0 5 votes vote down vote up 
def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True):
    num_imgs = tensor.size(0)
    mean = np.array(mean, dtype=np.float32)
    std = np.array(std, dtype=np.float32)
    imgs = []
    for img_id in range(num_imgs):
        img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0)
        img = mmcv.imdenormalize(
            img, mean, std, to_bgr=to_rgb).astype(np.uint8)
        imgs.append(np.ascontiguousarray(img))
    return imgs