Python torchvision.transforms.functional.adjust_hue() Examples

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 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 adjust_hue(img, hue_factor):
    """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.

    `hue_factor` is the amount of shift in H channel and must be in the
    interval `[-0.5, 0.5]`.

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

    Args:
        img (np.ndarray): CV Image to be adjusted.
        hue_factor (float):  How much to shift the hue channel. Should be in
            [-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.

    Returns:
        np.ndarray: Hue adjusted image.
    """
    if not(-0.5 <= hue_factor <= 0.5):
        raise ValueError('hue_factor is not in [-0.5, 0.5].'.format(hue_factor))

    if not _is_numpy_image(img):
        raise TypeError('img should be CV Image. Got {}'.format(type(img)))

    im = img.astype(np.uint8)
    hsv = cv2.cvtColor(im, cv2.COLOR_RGB2HSV_FULL)
    hsv[..., 0] += np.uint8(hue_factor * 255)

    im = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB_FULL)
    return im.astype(img.dtype) 

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_hue(img, random.uniform(-self.hue, 
                                                  self.hue)), mask 

def __call__(self, img, mask):
        assert img.size == mask.size
        return tf.adjust_hue(img, random.uniform(-self.hue, self.hue)), 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