caer.transforms¶
caer.transforms consists of position and color-based transforms for use.
- caer.transforms.adjust_brightness(tens, coeff, rgb=True)[source]¶
Adjust the brightness of an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise(rgb=True) >> filtered = caer.transforms.adjust_brightness(tens, coeff=1.4, rgb=True) >> filtered (427, 640, 3)
- caer.transforms.adjust_contrast(tens, contrast_factor)[source]¶
Adjust contrast of an image.
- Parameters
- Returns
Contrast adjusted image.
- Return type
caer.Tensor
- caer.transforms.adjust_gamma(tens, gamma, gain=1)[source]¶
Perform gamma correction on an image.
Also known as Power Law Transform. Intensities in RGB mode are adjusted based on the following equation: .. math:
I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma}
See `Gamma Correction`_ for more details. .. _Gamma Correction: https://en.wikipedia.org/wiki/Gamma_correction
- Parameters
Examples:
>> tens = caer.data.sunrise(rgb=True) >> filtered = caer.transforms.adjust_gamma(tens, gamma=1.5, rgb=True) >> filtered (427, 640, 3)
- Return type
Tensor
- caer.transforms.adjust_hue(tens, hue_factor)[source]¶
Adjust hue of an image.
The image hue is adjusted by converting the image to HSV and cyclically shifting the intensities in the hue channel (H). The image is then converted back to original image mode.
See `Hue`_ for more details. .. _Hue: https://en.wikipedia.org/wiki/Hue
- Parameters
tens¶ (Tensor) – Any valid
caer.Tensor
.hue_factor¶ (float) – How much to shift the hue channel. Should be in the range [-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in HSV space in positive and negative direction respectively. 0 means no shift. Therefore, both -0.5 and 0.5 will give an image with complementary colors while 0 gives the original image.
- Return type
Tensor
- Returns
Hue adjusted image.
Examples:
>> tens = caer.data.sunrise(rgb=True) >> filtered = caer.transforms.adjust_hue(tens, hue_factor=1, rgb=True) >> filtered (427, 640, 3)
- caer.transforms.adjust_saturation(tens, saturation_factor)[source]¶
Adjust color saturation of an image.
- Parameters
- Return type
Tensor
- Returns
Saturation-adjusted image.
Examples:
>> tens = caer.data.sunrise(rgb=True) >> filtered = caer.transforms.adjust_saturation(tens, saturation_factor=1.5, rgb=True) >> filtered (427, 640, 3)
- caer.transforms.affine(tens, angle, translate, scale, shear, interpolation='bilinear', mode=0, fillcolor=0)[source]¶
Apply affine transformation on the image keeping image center invariant.
- Parameters
tens¶ (Tensor) – Any valid
caer.Tensor
.angle¶ (float or int) – Rotation angle in degrees between -180 and 180, clockwise direction.
translate¶ (list or tuple of integers) – Horizontal and vertical translations (post-rotation translation)
scale¶ (float) – Overall scale
shear¶ (float) – Shear angle value in degrees between -180 to 180, clockwise direction.
interpolation¶ (int, str) – Interpolation to use for resizing. Defaults to “bilinear”. Supports “bilinear”, “bicubic”, “area”, “nearest”.
mode¶ (int, str) – Method for filling in border regions. Defaults to
constant
meaning areas outside the image are filled with a value (val, default 0). Supports"replicate"
,"reflect"
,"reflect-101"
.fillcolor¶ (int) – Optional fill color for the area outside the transform in the output image. Default: 0
- Return type
Tensor
- caer.transforms.brighten(tens, coeff=- 1, rgb=True)[source]¶
Brighten an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise(rgb=True) >> filtered = caer.transforms.brighten(tens, coeff=-1, rgb=True) >> filtered (427, 640, 3)
- caer.transforms.correct_exposure(tens, rgb=True)[source]¶
Correct the exposure of an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise(rgb=True) >> filtered = caer.transforms.correct_exposure(tens, rgb=True) >> filtered (427, 640, 3)
- caer.transforms.darken(tens, darkness_coeff=- 1)[source]¶
Darken an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise(rgb=True) >> filtered = caer.transforms.darken(tens, coeff=-1, rgb=True) >> filtered (427, 640, 3)
- caer.transforms.equalize(tens, mask=None, by_channels=True)[source]¶
Equalize the image histogram.
- Parameters
tens¶ (Tensor) – RGB or grayscale image.
mask¶ (Tensor) – An optional mask. If given, only the pixels selected by the mask are included in the analysis. Maybe 1 channel or 3 channel array.
by_channels¶ (bool) – If True, use equalization by channels separately, else convert image to YCbCr representation and use equalization by Y channel.
- Return type
Tensor
- Returns
Equalized image (Tensor)
Examples:
>> tens = caer.data.beverages() >> equalized = caer.equalize(tens, mask=None) >> equalized.shape (427,640,3)
- caer.transforms.hflip(tens)[source]¶
Flip an image horizontally.
- Parameters
tens¶ (Tensor) – Image to be flipped.
- Return type
Tensor
- Returns
Flipped image.
- caer.transforms.hvflip(tens)[source]¶
Flip an image both horizontally and vertically.
- Parameters
tens¶ (Tensor) – Image to be flipped.
- Return type
Tensor
- Returns
Flipped image.
- caer.transforms.pad(tens, padding, fill=0, padding_mode='constant')[source]¶
Pad the given image on all sides with specified padding mode and fill value.
- Parameters
tens¶ (Tensor) – image to be padded.
padding¶ (int or tuple) – Padding on each border. If a single int is provided this is used to pad all borders. If tuple of length 2 is provided this is the padding on left/right and top/bottom respectively. If a tuple of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.
fill¶ (
int
) – Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constantpadding_mode¶ –
Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant. - constant: pads with a constant value, this value is specified with fill - edge: pads with the last value on the edge of the image - reflect: pads with reflection of image (without repeating the last value on the edge)
padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2]
- symmetric: pads with reflection of image (repeating the last value on the edge)
padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3]
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
- caer.transforms.posterize(tens, bits)[source]¶
Reduce the number of bits for each color channel in the image.
- Parameters
- Return type
Tensor
- Returns
Image with reduced color channels (Tensor)
Examples:
>> tens = caer.data.sunrise() >> posterized = caer.posterize(tens, bits=4) >> posterized.shape (427,640,3)
- caer.transforms.rand_flip(tens)[source]¶
Randomly flip an image vertically or horizontally.
- Parameters
tens¶ (Tensor) – Image to be flipped.
- Return type
Tensor
- Returns
Flipped image.
- caer.transforms.random_brightness(tens)[source]¶
Add random brightness to an image.
- Parameters
tens¶ (Tensor) – Any regular
caer.Tensor
.- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise() >> filtered = caer.transforms.random_brightness(tens, rgb=True) >> filtered (427, 640, 3)
- caer.transforms.rotate(tens, angle, rotPoint=None)[source]¶
Rotates an given image by an angle around a particular rotation point (if provided) or centre otherwise.
- Return type
Tensor
- caer.transforms.sim_autumn(tens)[source]¶
Simulate autumn conditions on an image.
- Parameters
tens¶ (Tensor) – Any regular
caer.Tensor
.- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise >> filtered = caer.filters.sim_autumn(tens) >> filtered (427, 640, 3)
- caer.transforms.sim_fog(tens, fog_coeff=- 1)[source]¶
Simulate foggy conditions on an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise >> filtered = caer.filters.sim_fog(tens, fog_coeff=-1) >> filtered (427, 640, 3)
- caer.transforms.sim_gravel(tens, rectangular_roi=(- 1, - 1, - 1, - 1), num_patches=8)[source]¶
Simulate gravelly conditions on an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise >> filtered = caer.filters.sim_gravel(tens) >> filtered (427, 640, 3)
- caer.transforms.sim_motion_blur(tens, speed_coeff=- 1)[source]¶
Simulate motion-blur conditions on an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise >> filtered = caer.filters.sim_motion_blur(tens, speed_coeff=-1) >> filtered (427, 640, 3)
- caer.transforms.sim_rain(tens, slant=- 1, drop_length=20, drop_width=1, drop_color=(200, 200, 200), rain_type='None')[source]¶
Simulate rainy conditions on an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise >> filtered = caer.filters.sim_rain(tens) >> filtered (427, 640, 3)
- caer.transforms.sim_shadow(tens, num_shadows=1, rectangular_roi=(- 1, - 1, - 1, - 1), shadow_dimension=5)[source]¶
Simulate shadowy conditions on an image.
- Parameters
tens¶ (Tensor) – Any regular
caer.Tensor
.num_shadows¶ (int) – Number of shadows to work with. Value must be between 1 and 10.
rectangular_roi¶ (tuple) – Rectanglar co-ordinates of the intended region of interest. Default: (-1,-1,-1,-1).
shadow_dimensions¶ (int) – Number of shadow dimensions. Value must be > 3.
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise >> filtered = caer.filters.sim_shadow(tens) >> filtered (427, 640, 3)
- caer.transforms.sim_snow(tens, snow_coeff=- 1)[source]¶
Simulate snowy conditions on an image.
- Parameters
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise >> filtered = caer.filters.sim_snow(tens, snow_coeff=-1) >> filtered (427, 640, 3)
- caer.transforms.sim_sun_flare(tens, flare_center=- 1, angle=- 1, num_flare_circles=8, src_radius=400, src_color=(255, 255, 255))[source]¶
Add a source of light (flare) on an specific region of an image.
- Parameters
tens¶ (Tensor) – Any regular
caer.Tensor
.flare_center¶ (int) – Center of the flare. Default: -1.
angle¶ (int) – Angle of the flare. Default: -1
num_flare_circles¶ (int) – Number of flare circles to operate on.
src_radius¶ (int) – Intended size of the flare
src_color¶ (tuple) – Intended source color of the flare
- Return type
Tensor
- Returns
Tensor of shape
(height, width, channels)
.
Examples:
>> tens = caer.data.sunrise >> filtered = caer.filters.sim_sun_flare(tens) >> filtered (427, 640, 3)
- caer.transforms.solarize(tens, threshold=128)[source]¶
Invert all pixel values above a threshold.
- Parameters
- Return type
Tensor
- Returns
Solarized image (Tensor)
Examples:
>> tens = caer.data.sunrise() >> solarized = caer.solarize(tens, threshold=128) >> solarized.shape (427,640,3)