Python _png.read_png方法代碼示例

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def get_grey(self, tex, fontsize=None, dpi=None):
        """Return the alpha channel."""
        from matplotlib import _png
        key = tex, self.get_font_config(), fontsize, dpi
        alpha = self.grey_arrayd.get(key)
        if alpha is None:
            pngfile = self.make_png(tex, fontsize, dpi)
            X = _png.read_png(os.path.join(self.texcache, pngfile))
            self.grey_arrayd[key] = alpha = X[:, :, -1]
        return alpha 

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def save_diff_image( expected, actual, output ):
   expectedImage = _png.read_png( expected )
   actualImage = _png.read_png( actual )
   actualImage, expectedImage = crop_to_same(actual, actualImage, expected, expectedImage)
   expectedImage = np.array(expectedImage).astype(np.float)
   actualImage = np.array(actualImage).astype(np.float)
   assert expectedImage.ndim==actualImage.ndim
   assert expectedImage.shape==actualImage.shape
   absDiffImage = abs(expectedImage-actualImage)

   # expand differences in luminance domain
   absDiffImage *= 255 * 10
   save_image_np = np.clip(absDiffImage, 0, 255).astype(np.uint8)
   height, width, depth = save_image_np.shape

   # The PDF renderer doesn't produce an alpha channel, but the
   # matplotlib PNG writer requires one, so expand the array
   if depth == 3:
      with_alpha = np.empty((height, width, 4), dtype=np.uint8)
      with_alpha[:,:,0:3] = save_image_np
      save_image_np = with_alpha

   # Hard-code the alpha channel to fully solid
   save_image_np[:,:,3] = 255

   _png.write_png(save_image_np.tostring(), width, height, output) 

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def save_diff_image(expected, actual, output):
    expectedImage = _png.read_png(expected)
    actualImage = _png.read_png(actual)
    actualImage, expectedImage = crop_to_same(
        actual, actualImage, expected, expectedImage)
    expectedImage = np.array(expectedImage).astype(np.float)
    actualImage = np.array(actualImage).astype(np.float)
    assert expectedImage.ndim == actualImage.ndim
    assert expectedImage.shape == actualImage.shape
    absDiffImage = abs(expectedImage - actualImage)

    # expand differences in luminance domain
    absDiffImage *= 255 * 10
    save_image_np = np.clip(absDiffImage, 0, 255).astype(np.uint8)
    height, width, depth = save_image_np.shape

    # The PDF renderer doesn't produce an alpha channel, but the
    # matplotlib PNG writer requires one, so expand the array
    if depth == 3:
        with_alpha = np.empty((height, width, 4), dtype=np.uint8)
        with_alpha[:, :, 0:3] = save_image_np
        save_image_np = with_alpha

    # Hard-code the alpha channel to fully solid
    save_image_np[:, :, 3] = 255

    _png.write_png(save_image_np.tostring(), width, height, output) 

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def get_grey(self, tex, fontsize=None, dpi=None):
        """Return the alpha channel."""
        key = tex, self.get_font_config(), fontsize, dpi
        alpha = self.grey_arrayd.get(key)
        if alpha is None:
            pngfile = self.make_png(tex, fontsize, dpi)
            X = _png.read_png(os.path.join(self.texcache, pngfile))
            self.grey_arrayd[key] = alpha = X[:, :, -1]
        return alpha 

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def save_diff_image(expected, actual, output):
    '''
    Parameters
    ----------
    expected : str
        File path of expected image.
    actual : str
        File path of actual image.
    output : str
        File path to save difference image to.
    '''
    # Drop alpha channels, similarly to compare_images.
    expectedImage = _png.read_png(expected)[..., :3]
    actualImage = _png.read_png(actual)[..., :3]
    actualImage, expectedImage = crop_to_same(
        actual, actualImage, expected, expectedImage)
    expectedImage = np.array(expectedImage).astype(float)
    actualImage = np.array(actualImage).astype(float)
    if expectedImage.shape != actualImage.shape:
        raise ImageComparisonFailure(
            "Image sizes do not match expected size: {} "
            "actual size {}".format(expectedImage.shape, actualImage.shape))
    absDiffImage = np.abs(expectedImage - actualImage)

    # expand differences in luminance domain
    absDiffImage *= 255 * 10
    save_image_np = np.clip(absDiffImage, 0, 255).astype(np.uint8)
    height, width, depth = save_image_np.shape

    # The PDF renderer doesn't produce an alpha channel, but the
    # matplotlib PNG writer requires one, so expand the array
    if depth == 3:
        with_alpha = np.empty((height, width, 4), dtype=np.uint8)
        with_alpha[:, :, 0:3] = save_image_np
        save_image_np = with_alpha

    # Hard-code the alpha channel to fully solid
    save_image_np[:, :, 3] = 255

    _png.write_png(save_image_np, output) 

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def save_diff_image(expected, actual, output):
    '''
    Parameters
    ----------
    expected : str
        File path of expected image.
    actual : str
        File path of actual image.
    output : str
        File path to save difference image to.
    '''
    # Drop alpha channels, similarly to compare_images.
    from matplotlib import _png
    expected_image = _png.read_png(expected)[..., :3]
    actual_image = _png.read_png(actual)[..., :3]
    actual_image, expected_image = crop_to_same(
        actual, actual_image, expected, expected_image)
    expected_image = np.array(expected_image).astype(float)
    actual_image = np.array(actual_image).astype(float)
    if expected_image.shape != actual_image.shape:
        raise ImageComparisonFailure(
            "Image sizes do not match expected size: {} "
            "actual size {}".format(expected_image.shape, actual_image.shape))
    abs_diff_image = np.abs(expected_image - actual_image)

    # expand differences in luminance domain
    abs_diff_image *= 255 * 10
    save_image_np = np.clip(abs_diff_image, 0, 255).astype(np.uint8)
    height, width, depth = save_image_np.shape

    # The PDF renderer doesn't produce an alpha channel, but the
    # matplotlib PNG writer requires one, so expand the array
    if depth == 3:
        with_alpha = np.empty((height, width, 4), dtype=np.uint8)
        with_alpha[:, :, 0:3] = save_image_np
        save_image_np = with_alpha

    # Hard-code the alpha channel to fully solid
    save_image_np[:, :, 3] = 255

    _png.write_png(save_image_np, output) 

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def get_grey(self, tex, fontsize=None, dpi=None):
        """Return the alpha channel."""
        key = tex, self.get_font_config(), fontsize, dpi
        alpha = self.grey_arrayd.get(key)
        if alpha is None:
            pngfile = self.make_png(tex, fontsize, dpi)
            X = read_png(os.path.join(self.texcache, pngfile))
            self.grey_arrayd[key] = alpha = X[:, :, -1]
        return alpha 

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def save_diff_image(expected, actual, output):
    expectedImage = _png.read_png(expected)
    actualImage = _png.read_png(actual)
    actualImage, expectedImage = crop_to_same(
        actual, actualImage, expected, expectedImage)
    expectedImage = np.array(expectedImage).astype(float)
    actualImage = np.array(actualImage).astype(float)
    if expectedImage.shape != actualImage.shape:
        raise ImageComparisonFailure(
            "Image sizes do not match expected size: {0} "
            "actual size {1}".format(expectedImage.shape, actualImage.shape))
    absDiffImage = np.abs(expectedImage - actualImage)

    # expand differences in luminance domain
    absDiffImage *= 255 * 10
    save_image_np = np.clip(absDiffImage, 0, 255).astype(np.uint8)
    height, width, depth = save_image_np.shape

    # The PDF renderer doesn't produce an alpha channel, but the
    # matplotlib PNG writer requires one, so expand the array
    if depth == 3:
        with_alpha = np.empty((height, width, 4), dtype=np.uint8)
        with_alpha[:, :, 0:3] = save_image_np
        save_image_np = with_alpha

    # Hard-code the alpha channel to fully solid
    save_image_np[:, :, 3] = 255

    _png.write_png(save_image_np, output) 

# 需要導入模塊: from matplotlib import _png [as 別名]
# 或者: from matplotlib._png import read_png [as 別名]
def get_grey(self, tex, fontsize=None, dpi=None):
        """returns the alpha channel"""
        key = tex, self.get_font_config(), fontsize, dpi
        alpha = self.grey_arrayd.get(key)

        if alpha is None:
            pngfile = self.make_png(tex, fontsize, dpi)
            X = read_png(os.path.join(self.texcache, pngfile))

            if rcParams['text.dvipnghack'] is not None:
                hack = rcParams['text.dvipnghack']
            else:
                if TexManager._dvipng_hack_alpha is None:
                    TexManager._dvipng_hack_alpha = dvipng_hack_alpha()
                hack = TexManager._dvipng_hack_alpha

            if hack:
                # hack the alpha channel
                # dvipng assumed a constant background, whereas we want to
                # overlay these rasters with antialiasing over arbitrary
                # backgrounds that may have other figure elements under them.
                # When you set dvipng -bg Transparent, it actually makes the
                # alpha channel 1 and does the background compositing and
                # antialiasing itself and puts the blended data in the rgb
                # channels.  So what we do is extract the alpha information
                # from the red channel, which is a blend of the default dvipng
                # background (white) and foreground (black).  So the amount of
                # red (or green or blue for that matter since white and black
                # blend to a grayscale) is the alpha intensity.  Once we
                # extract the correct alpha information, we assign it to the
                # alpha channel properly and let the users pick their rgb.  In
                # this way, we can overlay tex strings on arbitrary
                # backgrounds with antialiasing
                #
                # red = alpha*red_foreground + (1-alpha)*red_background
                #
                # Since the foreground is black (0) and the background is
                # white (1) this reduces to red = 1-alpha or alpha = 1-red
                #alpha = npy.sqrt(1-X[:,:,0]) # should this be sqrt here?
                alpha = 1 - X[:, :, 0]
            else:
                alpha = X[:, :, -1]

            self.grey_arrayd[key] = alpha
        return alpha