Python torchvision.transforms.functional.adjust_gamma() Examples

The following are 8 code examples of torchvision.transforms.functional.adjust_gamma(). 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 torchvision.transforms.functional , or try the search function .
Example #1
Source File: cvfunctional.py    From opencv_transforms_torchvision with MIT License 6 votes vote down vote up 
def cv_transform(img):
    # img = resize(img, size=(100, 300))
    # img = to_tensor(img)
    # img = normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    # img = pad(img, padding=(10, 10, 20, 20), fill=(255, 255, 255), padding_mode='constant')
    # img = pad(img, padding=(100, 100, 100, 100), fill=5, padding_mode='symmetric')
    # img = crop(img, -40, -20, 1000, 1000)
    # img = center_crop(img, (310, 300))
    # img = resized_crop(img, -10.3, -20, 330, 220, (500, 500))
    # img = hflip(img)
    # img = vflip(img)
    # tl, tr, bl, br, center = five_crop(img, 100)
    # img = adjust_brightness(img, 2.1)
    # img = adjust_contrast(img, 1.5)
    # img = adjust_saturation(img, 2.3)
    # img = adjust_hue(img, 0.5)
    # img = adjust_gamma(img, gamma=3, gain=0.1)
    # img = rotate(img, 10, resample='BILINEAR', expand=True, center=None)
    # img = to_grayscale(img, 3)
    # img = affine(img, 10, (0, 0), 1, 0, resample='BICUBIC', fillcolor=(255,255,0))
    # img = gaussion_noise(img)
    # img = poisson_noise(img)
    img = salt_and_pepper(img)
    return to_tensor(img)
Example #2
Source File: cvfunctional.py    From opencv_transforms_torchvision with MIT License 6 votes vote down vote up 
def pil_transform(img):
    # img = functional.resize(img, size=(100, 300))
    # img = functional.to_tensor(img)
    # img = functional.normalize(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    # img = functional.pad(img, padding=(10, 10, 20, 20), fill=(255, 255, 255), padding_mode='constant')
    # img = functional.pad(img, padding=(100, 100, 100, 100), padding_mode='symmetric')
    # img = functional.crop(img, -40, -20, 1000, 1000)
    # img = functional.center_crop(img, (310, 300))
    # img = functional.resized_crop(img, -10.3, -20, 330, 220, (500, 500))
    # img = functional.hflip(img)
    # img = functional.vflip(img)
    # tl, tr, bl, br, center = functional.five_crop(img, 100)
    # img = functional.adjust_brightness(img, 2.1)
    # img = functional.adjust_contrast(img, 1.5)
    # img = functional.adjust_saturation(img, 2.3)
    # img = functional.adjust_hue(img, 0.5)
    # img = functional.adjust_gamma(img, gamma=3, gain=0.1)
    # img = functional.rotate(img, 10, resample=PIL.Image.BILINEAR, expand=True, center=None)
    # img = functional.to_grayscale(img, 3)
    # img = functional.affine(img, 10, (0, 0), 1, 0, resample=PIL.Image.BICUBIC, fillcolor=(255,255,0))

    return functional.to_tensor(img)
Example #3
Source File: cvfunctional.py    From opencv_transforms_torchvision with MIT License 5 votes vote down vote up 
def adjust_gamma(img, gamma, gain=1):
    """Perform gamma correction on an image.

    Also known as Power Law Transform. Intensities in RGB mode are adjusted
    based on the following equation:

        I_out = 255 * gain * ((I_in / 255) ** gamma)

    See https://en.wikipedia.org/wiki/Gamma_correction for more details.

    Args:
        img (np.ndarray): CV Image to be adjusted.
        gamma (float): Non negative real number. gamma larger than 1 make the
            shadows darker, while gamma smaller than 1 make dark regions
            lighter.
        gain (float): The constant multiplier.
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be CV Image. Got {}'.format(type(img)))

    if gamma < 0:
        raise ValueError('Gamma should be a non-negative real number')

    im = img.astype(np.float32)
    im = 255. * gain * np.power(im / 255., gamma)
    im = im.clip(min=0., max=255.)
    return im.astype(img.dtype)
Example #4
Source File: representations.py    From SharpNet with GNU General Public License v3.0 5 votes vote down vote up 
def gamma(self, gamma_ratio):
        self.data = TF.adjust_gamma(self.data, gamma_ratio, gain=1)
Example #5
Source File: augmentations.py    From CAG_UDA with MIT License 5 votes vote down vote up 
def __call__(self, img, mask):
        assert img.size == mask.size
        return tf.adjust_gamma(img, random.uniform(1, 1 + self.gamma)), mask
Example #6
Source File: augmentations.py    From pytorch-semseg with MIT License 5 votes vote down vote up 
def __call__(self, img, mask):
        assert img.size == mask.size
        return tf.adjust_gamma(img, random.uniform(1, 1 + self.gamma)), mask
Example #7
Source File: transforms.py    From NWPU-Crowd-Sample-Code with MIT License 5 votes vote down vote up 
def __call__(self, img):
        if random.random() < 0.5:
            gamma = random.uniform(self.gamma_range[0],self.gamma_range[1])
            return  TrF.adjust_gamma(img, gamma)
        else: 
            return img

# ===============================label tranforms============================
Example #8
Source File: benchmark.py    From albumentations with MIT License 5 votes vote down vote up 
def torchvision_transform(self, img):
        return torchvision.adjust_gamma(img, gamma=0.5)