Python torchvision.transforms.functional.adjust_brightness() Examples

The following are 20 code examples of torchvision.transforms.functional.adjust_brightness(). 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: transforms.py    From ACDRNet with Apache License 2.0 6 votes vote down vote up 
def get_params(brightness, contrast, saturation, hue):
        transforms = []

        if brightness is not None:
            brightness_factor = random.uniform(brightness[0], brightness[1])
            transforms.append(Lambda_image(lambda img: F.adjust_brightness(img, brightness_factor)))

        if contrast is not None:
            contrast_factor = random.uniform(contrast[0], contrast[1])
            transforms.append(Lambda_image(lambda img: F.adjust_contrast(img, contrast_factor)))

        if saturation is not None:
            saturation_factor = random.uniform(saturation[0], saturation[1])
            transforms.append(Lambda_image(lambda img: F.adjust_saturation(img, saturation_factor)))

        if hue is not None:
            hue_factor = random.uniform(hue[0], hue[1])
            transforms.append(Lambda_image(lambda img: F.adjust_hue(img, hue_factor)))

        random.shuffle(transforms)
        transform = Compose(transforms)

        return transform
Example #2
Source File: cvfunctional.py    From opencv_transforms_torchvision with MIT License 6 votes vote down vote up 
def adjust_brightness(img, brightness_factor):
    """Adjust brightness of an Image.

    Args:
        img (np.ndarray): CV Image to be adjusted.
        brightness_factor (float):  How much to adjust the brightness. Can be
            any non negative number. 0 gives a black image, 1 gives the
            original image while 2 increases the brightness by a factor of 2.

    Returns:
        np.ndarray: Brightness adjusted image.
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be CV Image. Got {}'.format(type(img)))

    im = img.astype(np.float32) * brightness_factor
    im = im.clip(min=0, max=255)
    return im.astype(img.dtype)
Example #3
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 #4
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 #5
Source File: data_transforms.py    From DAVANet with MIT License 6 votes vote down vote up 
def __call__(self, inputs, disps):
        inputs = [Image.fromarray(np.uint8(inp)) for inp in inputs]
        if self.brightness > 0:
            brightness_factor = np.random.uniform(max(0, 1 - self.brightness), 1 + self.brightness)
            inputs = [F.adjust_brightness(inp, brightness_factor) for inp in inputs]

        if self.contrast > 0:
            contrast_factor = np.random.uniform(max(0, 1 - self.contrast), 1 + self.contrast)
            inputs = [F.adjust_contrast(inp, contrast_factor) for inp in inputs]

        if self.saturation > 0:
            saturation_factor = np.random.uniform(max(0, 1 - self.saturation), 1 + self.saturation)
            inputs = [F.adjust_saturation(inp, saturation_factor) for inp in inputs]

        if self.hue > 0:
            hue_factor = np.random.uniform(-self.hue, self.hue)
            inputs = [F.adjust_hue(inp, hue_factor) for inp in inputs]

        inputs = [np.asarray(inp) for inp in inputs]
        inputs = [inp.clip(0,255) for inp in inputs]

        return inputs, disps
Example #6
Source File: data_transforms.py    From STFAN with MIT License 5 votes vote down vote up 
def __call__(self, seq_blur, seq_clear):
        seq_blur  = [Image.fromarray(np.uint8(img)) for img in seq_blur]
        seq_clear = [Image.fromarray(np.uint8(img)) for img in seq_clear]
        if self.brightness > 0:
            brightness_factor = np.random.uniform(max(0, 1 - self.brightness), 1 + self.brightness)
            seq_blur  = [F.adjust_brightness(img, brightness_factor) for img in seq_blur]
            seq_clear = [F.adjust_brightness(img, brightness_factor) for img in seq_clear]

        if self.contrast > 0:
            contrast_factor = np.random.uniform(max(0, 1 - self.contrast), 1 + self.contrast)
            seq_blur  = [F.adjust_contrast(img, contrast_factor) for img in seq_blur]
            seq_clear = [F.adjust_contrast(img, contrast_factor) for img in seq_clear]

        if self.saturation > 0:
            saturation_factor = np.random.uniform(max(0, 1 - self.saturation), 1 + self.saturation)
            seq_blur  = [F.adjust_saturation(img, saturation_factor) for img in seq_blur]
            seq_clear = [F.adjust_saturation(img, saturation_factor) for img in seq_clear]

        if self.hue > 0:
            hue_factor = np.random.uniform(-self.hue, self.hue)
            seq_blur  = [F.adjust_hue(img, hue_factor) for img in seq_blur]
            seq_clear = [F.adjust_hue(img, hue_factor) for img in seq_clear]

        seq_blur  = [np.asarray(img) for img in seq_blur]
        seq_clear = [np.asarray(img) for img in seq_clear]

        seq_blur  = [img.clip(0,255) for img in seq_blur]
        seq_clear = [img.clip(0,255) for img in seq_clear]

        return seq_blur, seq_clear
Example #7
Source File: benchmark.py    From albumentations with MIT License 5 votes vote down vote up 
def torchvision_transform(self, img):
        img = torchvision.adjust_hue(img, hue_factor=0.1)
        img = torchvision.adjust_saturation(img, saturation_factor=1.2)
        img = torchvision.adjust_brightness(img, brightness_factor=1.2)
        return img
Example #8
Source File: benchmark.py    From albumentations with MIT License 5 votes vote down vote up 
def torchvision_transform(self, img):
        img = torchvision.adjust_brightness(img, brightness_factor=1.5)
        img = torchvision.adjust_contrast(img, contrast_factor=1.5)
        return img
Example #9
Source File: benchmark.py    From albumentations with MIT License 5 votes vote down vote up 
def torchvision_transform(self, img):
        return torchvision.adjust_brightness(img, brightness_factor=1.5)
Example #10
Source File: ext_transforms.py    From DeepLabV3Plus-Pytorch with MIT License 5 votes vote down vote up 
def get_params(brightness, contrast, saturation, hue):
        """Get a randomized transform to be applied on image.

        Arguments are same as that of __init__.

        Returns:
            Transform which randomly adjusts brightness, contrast and
            saturation in a random order.
        """
        transforms = []

        if brightness is not None:
            brightness_factor = random.uniform(brightness[0], brightness[1])
            transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))

        if contrast is not None:
            contrast_factor = random.uniform(contrast[0], contrast[1])
            transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))

        if saturation is not None:
            saturation_factor = random.uniform(saturation[0], saturation[1])
            transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))

        if hue is not None:
            hue_factor = random.uniform(hue[0], hue[1])
            transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))

        random.shuffle(transforms)
        transform = Compose(transforms)

        return transform
Example #11
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_brightness(img, random.uniform(1 - self.bf, 1 + self.bf)), mask
Example #12
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_brightness(img, 
                                    random.uniform(1 - self.bf, 
                                                   1 + self.bf)), mask
Example #13
Source File: video_transforms.py    From pvse with MIT License 5 votes vote down vote up 
def get_params(brightness, contrast, saturation, hue):
        """Get a randomized transform to be applied on image.

        Arguments are same as that of __init__.

        Returns:
            Transform which randomly adjusts brightness, contrast and
            saturation in a random order.
        """
        transforms = []

        if brightness is not None:
            brightness_factor = random.uniform(brightness[0], brightness[1])
            transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))

        if contrast is not None:
            contrast_factor = random.uniform(contrast[0], contrast[1])
            transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))

        if saturation is not None:
            saturation_factor = random.uniform(saturation[0], saturation[1])
            transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))

        if hue is not None:
            hue_factor = random.uniform(hue[0], hue[1])
            transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))

        random.shuffle(transforms)
        transform = Compose(transforms)

        return transform
Example #14
Source File: video_transforms.py    From DDPAE-video-prediction with MIT License 5 votes vote down vote up 
def get_params(brightness, contrast, saturation, hue):
    """Get a randomized transform to be applied on image.
    Arguments are same as that of __init__.
    Returns:
        Transform which randomly adjusts brightness, contrast and
        saturation in a random order.
    """
    transforms = []
    if brightness > 0:
      brightness_factor = random.uniform(max(0, 1 - brightness), 1 + brightness)
      transforms.append(lambda img: F.adjust_brightness(img, brightness_factor))

    if contrast > 0:
      contrast_factor = random.uniform(max(0, 1 - contrast), 1 + contrast)
      transforms.append(lambda img: F.adjust_contrast(img, contrast_factor))

    if saturation > 0:
      saturation_factor = random.uniform(max(0, 1 - saturation), 1 + saturation)
      transforms.append(lambda img: F.adjust_saturation(img, saturation_factor))

    if hue > 0:
      hue_factor = random.uniform(-hue, hue)
      transforms.append(lambda img: F.adjust_hue(img, hue_factor))

    random.shuffle(transforms)

    return transforms
Example #15
Source File: photometric_augmentation.py    From GIFT with Apache License 2.0 5 votes vote down vote up 
def random_brightness(image, max_change=0.3):
    return np.asarray(adjust_brightness(Image.fromarray(image),max_change))
Example #16
Source File: transform.py    From ocr-pytorch with MIT License 5 votes vote down vote up 
def __call__(self, img, target):
        factor = random.uniform(-self.factor, self.factor)
        img = F.adjust_brightness(img, 1 + factor)

        return img, target
Example #17
Source File: utils.py    From a-PyTorch-Tutorial-to-Object-Detection with MIT License 5 votes vote down vote up 
def photometric_distort(image):
    """
    Distort brightness, contrast, saturation, and hue, each with a 50% chance, in random order.

    :param image: image, a PIL Image
    :return: distorted image
    """
    new_image = image

    distortions = [FT.adjust_brightness,
                   FT.adjust_contrast,
                   FT.adjust_saturation,
                   FT.adjust_hue]

    random.shuffle(distortions)

    for d in distortions:
        if random.random() < 0.5:
            if d.__name__ is 'adjust_hue':
                # Caffe repo uses a 'hue_delta' of 18 - we divide by 255 because PyTorch needs a normalized value
                adjust_factor = random.uniform(-18 / 255., 18 / 255.)
            else:
                # Caffe repo uses 'lower' and 'upper' values of 0.5 and 1.5 for brightness, contrast, and saturation
                adjust_factor = random.uniform(0.5, 1.5)

            # Apply this distortion
            new_image = d(new_image, adjust_factor)

    return new_image
Example #18
Source File: transforms.py    From PPGNet with MIT License 5 votes vote down vote up 
def __call__(self, img, pt):
        transforms = [
            tf.adjust_brightness,
            tf.adjust_contrast,
            tf.adjust_saturation
            ]
        random.shuffle(transforms)
        for t in transforms:
            img = t(img, (np.random.rand() - 0.5) * 2 * self.factor + 1)

        return img, pt
Example #19
Source File: augmentation.py    From DPC with MIT License 5 votes vote down vote up 
def get_params(brightness, contrast, saturation, hue):
        """Get a randomized transform to be applied on image.
        Arguments are same as that of __init__.
        Returns:
            Transform which randomly adjusts brightness, contrast and
            saturation in a random order.
        """
        transforms = []

        if brightness is not None:
            brightness_factor = random.uniform(brightness[0], brightness[1])
            transforms.append(torchvision.transforms.Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))

        if contrast is not None:
            contrast_factor = random.uniform(contrast[0], contrast[1])
            transforms.append(torchvision.transforms.Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))

        if saturation is not None:
            saturation_factor = random.uniform(saturation[0], saturation[1])
            transforms.append(torchvision.transforms.Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))

        if hue is not None:
            hue_factor = random.uniform(hue[0], hue[1])
            transforms.append(torchvision.transforms.Lambda(lambda img: F.adjust_hue(img, hue_factor)))

        random.shuffle(transforms)
        transform = torchvision.transforms.Compose(transforms)

        return transform
Example #20
Source File: utils.py    From ICDAR-2019-SROIE with MIT License 5 votes vote down vote up 
def photometric_distort(image):
    """
    Distort brightness, contrast, saturation, and hue, each with a 50% chance, in random order.

    :param image: image, a PIL Image
    :return: distorted image
    """
    new_image = image

    distortions = [FT.adjust_brightness,
                   FT.adjust_contrast,
                   FT.adjust_saturation,
                   FT.adjust_hue]

    random.shuffle(distortions)

    for d in distortions:
        if random.random() < 0.5:
            if d.__name__ is 'adjust_hue':
                # Caffe repo uses a 'hue_delta' of 18 - we divide by 255 because PyTorch needs a normalized value
                adjust_factor = random.uniform(-18 / 255., 18 / 255.)
            else:
                # Caffe repo uses 'lower' and 'upper' values of 0.5 and 1.5 for brightness, contrast, and saturation
                adjust_factor = random.uniform(0.5, 1.5)

            # Apply this distortion
            new_image = d(new_image, adjust_factor)

    return new_image