Convert Image to Other Formats

_, x = cv2.VideoCapture('files/interstellar-waves-edit.mp4').read()
type(x)
x.shape

numpy.ndarray

(480, 720, 3)

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as_tensor[source]

as_tensor(image, dtype:Union[FloatTensor, HalfTensor, NoneType]='FloatTensor', normalise:bool=False)

Convert numpy array to torch tensor

type(as_tensor(x))
as_tensor(x, torch.HalfTensor).dtype
as_tensor(x).shape
as_tensor(x).mean(), as_tensor(x, normalise=True).mean()

torch.Tensor

torch.float16

torch.Size([480, 720, 3])

(tensor(36.6933), tensor(0.1439))

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bgr2rgb[source]

bgr2rgb(x:ndarray)

convert cv2 generated image array from BGR to RGB

type(bgr2rgb(x))
imshow(bgr2rgb(x))

numpy.ndarray

<matplotlib.image.AxesImage at 0x132c83940>

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bgr2hsv[source]

bgr2hsv(x:ndarray)

convert cv2 generated image array from BGR to HSV

type(bgr2hsv(x))
(bgr2hsv(x)).shape

numpy.ndarray

(480, 720, 3)

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lapply[source]

lapply(x:Any, func:Callable)

Apply func to each element in list x

type(x)
[type(i) for i in lapply([x, x, x], as_tensor)]

numpy.ndarray

[torch.Tensor, torch.Tensor, torch.Tensor]

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Resize Image

resize[source]

resize(image, height=None, width=None, keep_aspect_ratio=True, scale_factor=1.0)

Resize by scale_factor if preserving aspect ratio else resize by custom height and width

resize(x).shape # scale_factor = 1. by default
resize(x, scale_factor=1.5).shape
resize(x, width=200, height=100, keep_aspect_ratio=False).shape

(480, 720, 3)

(720, 1080, 3)

(100, 200, 3)