Python torchvision.transforms.functional.adjust_saturation() Examples

def adjust_saturation(img, saturation_factor):
    """Adjust color saturation of an image.

    Args:
        img (np.ndarray): CV Image to be adjusted.
        saturation_factor (float):  How much to adjust the saturation. 0 will
            give a gray image, 1 will give the original image while
            2 will enhance the saturation by a factor of 2.

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

    im = img.astype(np.float32)
    degenerate = cv2.cvtColor(cv2.cvtColor(im, cv2.COLOR_RGB2GRAY), cv2.COLOR_GRAY2RGB)
    im = (1-saturation_factor) * degenerate + saturation_factor * im
    im = im.clip(min=0, max=255)
    return im.astype(img.dtype) 

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) 

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) 

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 

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 

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 

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 

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 

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 

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 

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 

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 

def __call__(self, img, mask):
        assert img.size == mask.size
        return tf.adjust_saturation(img, 
                                    random.uniform(1 - self.saturation, 
                                                   1 + self.saturation)), mask 

def __call__(self, img, mask):
        assert img.size == mask.size
        return (
            tf.adjust_saturation(img, random.uniform(1 - self.saturation, 1 + self.saturation)),
            mask,
        ) 

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 

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