Python PIL.Image.HAMMING Examples

def dominant_screen_color(initial_color, func_bounds=lambda: None):
    """
    https://stackoverflow.com/questions/50899692/most-dominant-color-in-rgb-image-opencv-numpy-python
    """
    monitor = get_monitor_bounds(func_bounds)
    if "full" in monitor:
        screenshot = getScreenAsImage()
    else:
        screenshot = getRectAsImage(str2list(monitor, int))

    downscale_width, downscale_height = screenshot.width // 4, screenshot.height // 4
    screenshot = screenshot.resize((downscale_width, downscale_height), Image.HAMMING)

    a = np.array(screenshot)
    a2D = a.reshape(-1, a.shape[-1])
    col_range = (256, 256, 256)  # generically : a2D.max(0)+1
    eval_params = {'a0': a2D[:, 0], 'a1': a2D[:, 1], 'a2': a2D[:, 2],
                   's0': col_range[0], 's1': col_range[1]}
    a1D = ne.evaluate('a0*s0*s1+a1*s0+a2', eval_params)
    color = np.unravel_index(np.bincount(a1D).argmax(), col_range)

    color_hsbk = list(utils.RGBtoHSBK(color, temperature=initial_color[3]))
    # color_hsbk[2] = initial_color[2]  # TODO Decide this
    return color_hsbk 

def __init__(self, size, maintain_aspect_ratio=False, resample='bicubic'):
        self.size = size
        self.maintain_aspect_ratio = maintain_aspect_ratio
        resampling_mapping = {
            'nearest': Image.NEAREST,
            'bilinear': Image.BILINEAR,
            'bicubic': Image.BICUBIC,
            'lanczos': Image.LANCZOS,
            'box': Image.BOX,
            'hamming': Image.HAMMING,
        }
        if resample.lower() not in resampling_mapping.keys():
            raise ValueError(
                "Unknown resampling method '{}'. Allowed values are '{}'"
                .format(resample, "', '".join(resampling_mapping.keys())))
        self.resample = resampling_mapping[resample]
        super().__init__() 

def _load_icons():
        """ Scan the icons cache folder and load the icons into :attr:`icons` for retrieval
        throughout the GUI.

        Returns
        -------
        dict:
            The icons formatted as described in :attr:`icons`

        """
        size = get_config().user_config_dict.get("icon_size", 16)
        size = int(round(size * get_config().scaling_factor))
        icons = dict()
        pathicons = os.path.join(PATHCACHE, "icons")
        for fname in os.listdir(pathicons):
            name, ext = os.path.splitext(fname)
            if ext != ".png":
                continue
            img = Image.open(os.path.join(pathicons, fname))
            img = ImageTk.PhotoImage(img.resize((size, size), resample=Image.HAMMING))
            icons[name] = img
        logger.debug(icons)
        return icons 

def __init__(self, interpolation):
        if Image is None:
            raise ImportError(
                'pillow backend for resize operation requires TensorFlow. Please install it before usage.'
            )
        self._supported_interpolations = {
            'NEAREST': Image.NEAREST,
            'NONE': Image.NONE,
            'BILINEAR': Image.BILINEAR,
            'LINEAR': Image.LINEAR,
            'BICUBIC': Image.BICUBIC,
            'CUBIC': Image.CUBIC,
            'ANTIALIAS': Image.ANTIALIAS,
        }
        try:
            optional_interpolations = {
                'BOX': Image.BOX,
                'LANCZOS': Image.LANCZOS,
                'HAMMING': Image.HAMMING,
            }
            self._supported_interpolations.update(optional_interpolations)
        except AttributeError:
            pass
        super().__init__(interpolation) 

def supported_interpolations(cls):
        if Image is None:
            return {}
        intrp = {
            'NEAREST': Image.NEAREST,
            'NONE': Image.NONE,
            'BILINEAR': Image.BILINEAR,
            'LINEAR': Image.LINEAR,
            'BICUBIC': Image.BICUBIC,
            'CUBIC': Image.CUBIC,
            'ANTIALIAS': Image.ANTIALIAS
        }
        try:
            optional_interpolations = {
                'BOX': Image.BOX,
                'LANCZOS': Image.LANCZOS,
                'HAMMING': Image.HAMMING,
            }
            intrp.update(optional_interpolations)
        except AttributeError:
            pass
        return intrp 

def _pil_interp(method):
    if method == 'bicubic':
        return Image.BICUBIC
    elif method == 'lanczos':
        return Image.LANCZOS
    elif method == 'hamming':
        return Image.HAMMING
    else:
        # default bilinear, do we want to allow nearest?
        return Image.BILINEAR 

def avg_screen_color(initial_color, func_bounds=lambda: None):
    """ Capture an image of the monitor defined by func_bounds, then get the average color of the image in HSBK"""
    monitor = get_monitor_bounds(func_bounds)
    if "full" in monitor:
        screenshot = getScreenAsImage()
    else:
        screenshot = getRectAsImage(str2list(monitor, int))
    # Resizing the image to 1x1 pixel will give us the average for the whole image (via HAMMING interpolation)
    color = screenshot.resize((1, 1), Image.HAMMING).getpixel((0, 0))
    color_hsbk = list(utils.RGBtoHSBK(color, temperature=initial_color[3]))
    return color_hsbk 

def test_reduce_hamming(self):
        for mode in ['RGBX', 'RGB', 'La', 'L']:
            case = self.make_case(mode, (8, 8), 0xe1)
            case = case.resize((4, 4), Image.HAMMING)
            data = ('e1 da'
                    'da d3')
            for channel in case.split():
                self.check_case(channel, self.make_sample(data, (4, 4))) 

def test_enlarge_hamming(self):
        for mode in ['RGBX', 'RGB', 'La', 'L']:
            case = self.make_case(mode, (2, 2), 0xe1)
            case = case.resize((4, 4), Image.HAMMING)
            data = ('e1 d2'
                    'd2 c5')
            for channel in case.split():
                self.check_case(channel, self.make_sample(data, (4, 4))) 

def test_levels_rgba(self):
        case = self.make_levels_case('RGBA')
        self.run_levels_case(case.resize((512, 32), Image.BOX))
        self.run_levels_case(case.resize((512, 32), Image.BILINEAR))
        self.run_levels_case(case.resize((512, 32), Image.HAMMING))
        self.run_levels_case(case.resize((512, 32), Image.BICUBIC))
        self.run_levels_case(case.resize((512, 32), Image.LANCZOS)) 

def test_levels_la(self):
        case = self.make_levels_case('LA')
        self.run_levels_case(case.resize((512, 32), Image.BOX))
        self.run_levels_case(case.resize((512, 32), Image.BILINEAR))
        self.run_levels_case(case.resize((512, 32), Image.HAMMING))
        self.run_levels_case(case.resize((512, 32), Image.BICUBIC))
        self.run_levels_case(case.resize((512, 32), Image.LANCZOS)) 

def test_dirty_pixels_rgba(self):
        case = self.make_dirty_case('RGBA', (255, 255, 0, 128), (0, 0, 255, 0))
        self.run_dirty_case(case.resize((20, 20), Image.BOX), (255, 255, 0))
        self.run_dirty_case(case.resize((20, 20), Image.BILINEAR),
                            (255, 255, 0))
        self.run_dirty_case(case.resize((20, 20), Image.HAMMING),
                            (255, 255, 0))
        self.run_dirty_case(case.resize((20, 20), Image.BICUBIC),
                            (255, 255, 0))
        self.run_dirty_case(case.resize((20, 20), Image.LANCZOS),
                            (255, 255, 0)) 

def test_wrong_arguments(self):
        im = hopper()
        for resample in (Image.NEAREST, Image.BOX, Image.BILINEAR,
                         Image.HAMMING, Image.BICUBIC, Image.LANCZOS):
            im.resize((32, 32), resample, (0, 0, im.width, im.height))
            im.resize((32, 32), resample, (20, 20, im.width, im.height))
            im.resize((32, 32), resample, (20, 20, 20, 100))
            im.resize((32, 32), resample, (20, 20, 100, 20))

            with self.assertRaisesRegex(TypeError,
                                        "must be sequence of length 4"):
                im.resize((32, 32), resample, (im.width, im.height))

            with self.assertRaisesRegex(ValueError, "can't be negative"):
                im.resize((32, 32), resample, (-20, 20, 100, 100))
            with self.assertRaisesRegex(ValueError, "can't be negative"):
                im.resize((32, 32), resample, (20, -20, 100, 100))

            with self.assertRaisesRegex(ValueError, "can't be empty"):
                im.resize((32, 32), resample, (20.1, 20, 20, 100))
            with self.assertRaisesRegex(ValueError, "can't be empty"):
                im.resize((32, 32), resample, (20, 20.1, 100, 20))
            with self.assertRaisesRegex(ValueError, "can't be empty"):
                im.resize((32, 32), resample, (20.1, 20.1, 20, 20))

            with self.assertRaisesRegex(ValueError, "can't exceed"):
                im.resize((32, 32), resample, (0, 0, im.width + 1, im.height))
            with self.assertRaisesRegex(ValueError, "can't exceed"):
                im.resize((32, 32), resample, (0, 0, im.width, im.height + 1)) 

def test_reduce_filters(self):
        for f in [Image.NEAREST, Image.BOX, Image.BILINEAR,
                  Image.HAMMING, Image.BICUBIC, Image.LANCZOS]:
            r = self.resize(hopper("RGB"), (15, 12), f)
            self.assertEqual(r.mode, "RGB")
            self.assertEqual(r.size, (15, 12)) 

def test_enlarge_filters(self):
        for f in [Image.NEAREST, Image.BOX, Image.BILINEAR,
                  Image.HAMMING, Image.BICUBIC, Image.LANCZOS]:
            r = self.resize(hopper("RGB"), (212, 195), f)
            self.assertEqual(r.mode, "RGB")
            self.assertEqual(r.size, (212, 195)) 

def test_endianness(self):
        # Make an image with one colored pixel, in one channel.
        # When resized, that channel should be the same as a GS image.
        # Other channels should be unaffected.
        # The R and A channels should not swap, which is indicative of
        # an endianness issues.

        samples = {
            'blank': Image.new('L', (2, 2), 0),
            'filled': Image.new('L', (2, 2), 255),
            'dirty': Image.new('L', (2, 2), 0),
        }
        samples['dirty'].putpixel((1, 1), 128)

        for f in [Image.NEAREST, Image.BOX, Image.BILINEAR,
                  Image.HAMMING, Image.BICUBIC, Image.LANCZOS]:
            # samples resized with current filter
            references = {
                name: self.resize(ch, (4, 4), f)
                for name, ch in samples.items()
            }

            for mode, channels_set in [
                ('RGB', ('blank', 'filled', 'dirty')),
                ('RGBA', ('blank', 'blank', 'filled', 'dirty')),
                ('LA', ('filled', 'dirty')),
            ]:
                for channels in set(permutations(channels_set)):
                    # compile image from different channels permutations
                    im = Image.merge(mode, [samples[ch] for ch in channels])
                    resized = self.resize(im, (4, 4), f)

                    for i, ch in enumerate(resized.split()):
                        # check what resized channel in image is the same
                        # as separately resized channel
                        self.assert_image_equal(ch, references[channels[i]]) 

def test_enlarge_zero(self):
        for f in [Image.NEAREST, Image.BOX, Image.BILINEAR,
                  Image.HAMMING, Image.BICUBIC, Image.LANCZOS]:
            r = self.resize(Image.new('RGB', (0, 0), "white"), (212, 195), f)
            self.assertEqual(r.mode, "RGB")
            self.assertEqual(r.size, (212, 195))
            self.assertEqual(r.getdata()[0], (0, 0, 0)) 

def _pil_interp(method):
    if method == 'bicubic':
        return Image.BICUBIC
    elif method == 'lanczos':
        return Image.LANCZOS
    elif method == 'hamming':
        return Image.HAMMING
    else:
        # default bilinear, do we want to allow nearest?
        return Image.BILINEAR 

def __init__(self):
        ImageUtilsBackend.__init__(self)
        if hasattr(Image, "HAMMING"):  # version >3.4.0
            self._interpolations_map["hamming"] = Image.HAMMING

        if hasattr(Image, "BOX"):  # version >3.4.0
            self._interpolations_map["box"] = Image.BOX

        if hasattr(Image, "LANCZOS"):  # version >1.1.3
            self._interpolations_map["lanczos"] = Image.LANCZOS 

def resize(self, image, size):
        resamples = [Image.NEAREST, Image.BILINEAR, Image.HAMMING, \
                     Image.BICUBIC, Image.LANCZOS]
        resample = random.choice(resamples)
        return image.resize(size, resample=resample) 

def resize_image(
        self,
        width: int,
        height: int,
        extension: str = "png",
        resample: str = "nearest",
        quality: int = 100,
    ):
        """Resize an image. 
        
        Args:
            width (int): Required. Width in pixels
            height (int): Required. Height in pixels
            extension (str, optional): File extension of loaded image. Defaults to png
            resample (str, optional): Resample rate. Defaults to "nearest".
            quality (int, optional): Quality of output. Defaults to 100.
        
        Returns:
            Chepy: The Chepy object. 

        Examples:
            >>> c = Chepy("image.png").load_file().resize_image(256, 256, "png")
            >>> c.write_to_file("/path/to/file.png", as_binary=True)
        """
        fh = io.BytesIO()
        if resample == "nearest":
            resample = Image.NEAREST
        elif resample == "antialias":
            resample = Image.ANTIALIAS
        elif resample == "bilinear":
            resample = Image.BILINEAR
        elif resample == "box":
            resample = Image.BOX
        elif resample == "hamming":
            resample = Image.HAMMING
        else:  # pragma: no cover
            raise TypeError(
                "Valid resampling options are: nearest, antialias, bilinear, box and hamming"
            )
        image = Image.open(self._load_as_file())
        resized = image.resize((width, height), resample=resample)
        resized.save(fh, extension, quality=quality)
        self.state = fh.getvalue()
        return self 

def resize(img, boxes, size, max_size=1000, random_interpolation=False):
    '''Resize the input PIL image to given size.

    If boxes is not None, resize boxes accordingly.

    Args:
      img: (PIL.Image) image to be resized.
      boxes: (tensor) object boxes, sized [#obj,4].
      size: (tuple or int)
        - if is tuple, resize image to the size.
        - if is int, resize the shorter side to the size while maintaining the aspect ratio.
      max_size: (int) when size is int, limit the image longer size to max_size.
                This is essential to limit the usage of GPU memory.
      random_interpolation: (bool) randomly choose a resize interpolation method.

    Returns:
      img: (PIL.Image) resized image.
      boxes: (tensor) resized boxes.

    Example:
    >> img, boxes = resize(img, boxes, 600)  # resize shorter side to 600
    >> img, boxes = resize(img, boxes, (500,600))  # resize image size to (500,600)
    >> img, _ = resize(img, None, (500,600))  # resize image only
    '''
    w, h = img.size
    if isinstance(size, int):
        size_min = min(w,h)
        size_max = max(w,h)
        sw = sh = float(size) / size_min
        if sw * size_max > max_size:
            sw = sh = float(max_size) / size_max
        ow = int(w * sw + 0.5)
        oh = int(h * sh + 0.5)
    else:
        ow, oh = size
        sw = float(ow) / w
        sh = float(oh) / h

    method = random.choice([
        Image.BOX,
        Image.NEAREST,
        Image.HAMMING,
        Image.BICUBIC,
        Image.LANCZOS,
        Image.BILINEAR]) if random_interpolation else Image.BILINEAR
    img = img.resize((ow,oh), method)
    if boxes is not None:
        boxes = boxes * torch.Tensor([sw,sh,sw,sh])
    return img, boxes 

def resize(img, boxes, size, max_size=1000, random_interpolation=False):
    '''Resize the input PIL image to given size.

    If boxes is not None, resize boxes accordingly.

    Args:
      img: (PIL.Image) image to be resized.
      boxes: (tensor) object boxes, sized [#obj,4].
      size: (tuple or int)
        - if is tuple, resize image to the size.
        - if is int, resize the shorter side to the size while maintaining the aspect ratio.
      max_size: (int) when size is int, limit the image longer size to max_size.
                This is essential to limit the usage of GPU memory.
      random_interpolation: (bool) randomly choose a resize interpolation method.

    Returns:
      img: (PIL.Image) resized image.
      boxes: (tensor) resized boxes.

    Example:
    >> img, boxes = resize(img, boxes, 600)  # resize shorter side to 600
    >> img, boxes = resize(img, boxes, (500,600))  # resize image size to (500,600)
    >> img, _ = resize(img, None, (500,600))  # resize image only
    '''
    w, h = img.size
    if isinstance(size, int):
        size_min = min(w,h)
        size_max = max(w,h)
        sw = sh = float(size) / size_min
        if sw * size_max > max_size:
            sw = sh = float(max_size) / size_max
        ow = int(w * sw + 0.5)
        oh = int(h * sh + 0.5)
    else:
        ow, oh = size
        sw = float(ow) / w
        sh = float(oh) / h

    method = random.choice([
        Image.BOX,
        Image.NEAREST,
        Image.HAMMING,
        Image.BICUBIC,
        Image.LANCZOS,
        Image.BILINEAR]) if random_interpolation else Image.BILINEAR
    img = img.resize((ow,oh), method)
    if boxes is not None:
        boxes = boxes * torch.tensor([sw,sh,sw,sh])
    return img, boxes