Python Image.merge方法代碼示例

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def resolve(ctx):
    from PIL import Image
    if isinstance(ctx, list):
        ctx = [ctx[0]]
    net.load_parameters('superres.params', ctx=ctx)
    img = Image.open(opt.resolve_img).convert('YCbCr')
    y, cb, cr = img.split()
    data = mx.nd.expand_dims(mx.nd.expand_dims(mx.nd.array(y), axis=0), axis=0)
    out_img_y = mx.nd.reshape(net(data), shape=(-3, -2)).asnumpy()
    out_img_y = out_img_y.clip(0, 255)
    out_img_y = Image.fromarray(np.uint8(out_img_y[0]), mode='L')

    out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC)
    out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC)
    out_img = Image.merge('YCbCr', [out_img_y, out_img_cb, out_img_cr]).convert('RGB')

    out_img.save('resolved.png') 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def distort_image(im, hue, sat, val):
    im = im.convert('HSV')
    cs = list(im.split())
    cs[1] = cs[1].point(lambda i: i * sat)
    cs[2] = cs[2].point(lambda i: i * val)
    
    def change_hue(x):
        x += hue*255
        if x > 255:
            x -= 255
        if x < 0:
            x += 255
        return x
    cs[0] = cs[0].point(change_hue)
    im = Image.merge(im.mode, tuple(cs))

    im = im.convert('RGB')
    return im 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def distort_image(im, hue, sat, val):
    im = im.convert('HSV')
    cs = list(im.split())
    cs[1] = cs[1].point(lambda i: i * sat)
    cs[2] = cs[2].point(lambda i: i * val)
    
    def change_hue(x):
        x += hue*255
        if x > 255:
            x -= 255
        if x < 0:
            x += 255
        return x
    cs[0] = cs[0].point(change_hue)
    im = Image.merge(im.mode, tuple(cs))

    im = im.convert('RGB')
    #constrain_image(im)
    return im 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def open_base_img(full_profile, res, base_color, color):
    # get base image according to profile and perceptual gray of key color
    base_num = str([0xE0, 0xB0, 0x80, 0x50, 0x20].index(base_color) + 1)

    # open image and convert to Lab
    with Image.open('images/{0}_{1}{2}.png'.format(*full_profile, base_num)) as img:
        key_img = img.resize((int(s * res / 200) for s in img.size), resample=Image.BILINEAR).convert('RGBA')
    if full_profile[1] in ('ISO', 'BIGENTER'): alpha = key_img.split()[-1]
    l, a, b = ImageCms.applyTransform(key_img, rgb2lab_transform).split()

    # convert key color to Lab
    # a and b should be scaled by 128/100, but desaturation looks more natural
    rgb_color = color_objects.sRGBColor(*ImageColor.getrgb(color), is_upscaled=True)
    lab_color = color_conversions.convert_color(rgb_color, color_objects.LabColor)
    l1, a1, b1 = lab_color.get_value_tuple()
    l1, a1, b1 = int(l1 * 256 / 100), int(a1 + 128), int(b1 + 128)

    # change Lab of base image to match that of key color
    l = ImageMath.eval('convert(l + l1 - l_avg, "L")', l=l, l1=l1, l_avg=base_color)
    a = ImageMath.eval('convert(a + a1 - a, "L")', a=a, a1=a1)
    b = ImageMath.eval('convert(b + b1 - b, "L")', b=b, b1=b1)

    key_img = ImageCms.applyTransform(Image.merge('LAB', (l, a, b)), lab2rgb_transform).convert('RGBA')
    if full_profile[1] in ('ISO', 'BIGENTER'): key_img.putalpha(alpha)
    return key_img 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def __call__(self, video_path, frame_indices):
        with h5py.File(video_path, 'r') as f:

            flow_data = []
            for flow in self.flows:
                flow_data.append(f[f'video_{flow}'])

            video = []
            for i in frame_indices:
                if i < len(flow_data[0]):
                    frame = [
                        Image.open(io.BytesIO(video_data[i]))
                        for video_data in flow_data
                    ]
                    frame.append(frame[-1])  # add dummy data into third channel
                    video.append(Image.merge('RGB', frame))

        return video 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def test_consistency_5x5(self):
        source = Image.open("Tests/images/hopper.bmp")
        reference = Image.open("Tests/images/hopper_emboss_more.bmp")
        kernel = ImageFilter.Kernel((5, 5),  # noqa: E127
                                    (-1, -1, -1, -1,  0,
                                     -1, -1, -1,  0,  1,
                                     -1, -1,  0,  1,  1,
                                     -1,  0,  1,  1,  1,
                                      0,  1,  1,  1,  1), 0.3)
        source = source.split() * 2
        reference = reference.split() * 2

        for mode in ['L', 'LA', 'RGB', 'CMYK']:
            self.assert_image_equal(
                Image.merge(mode, source[:len(mode)]).filter(kernel),
                Image.merge(mode, reference[:len(mode)]),
            ) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def test_channels_order(self):
        g = Image.linear_gradient('L')
        im = Image.merge('RGB', [g, g.transpose(Image.ROTATE_90),
                                 g.transpose(Image.ROTATE_180)])

        # Reverse channels by splitting and using table
        self.assert_image_equal(
            Image.merge('RGB', im.split()[::-1]),
            im._new(im.im.color_lut_3d('RGB', Image.LINEAR,
                    3, 2, 2, 2, [
                        0, 0, 0,  0, 0, 1,
                        0, 1, 0,  0, 1, 1,

                        1, 0, 0,  1, 0, 1,
                        1, 1, 0,  1, 1, 1,
                    ]))) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def wedge(self):
        w = Image._wedge()
        w90 = w.rotate(90)

        (px, h) = w.size

        r = Image.new('L', (px*3, h))
        g = r.copy()
        b = r.copy()

        r.paste(w, (0, 0))
        r.paste(w90, (px, 0))

        g.paste(w90, (0, 0))
        g.paste(w,  (2*px, 0))

        b.paste(w, (px, 0))
        b.paste(w90, (2*px, 0))

        img = Image.merge('RGB', (r, g, b))

        return img 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def _fry(img):
		e = ImageEnhance.Sharpness(img)
		img = e.enhance(100)
		e = ImageEnhance.Contrast(img)
		img = e.enhance(100)
		e = ImageEnhance.Brightness(img)
		img = e.enhance(.27)
		r, b, g = img.split()
		e = ImageEnhance.Brightness(r)
		r = e.enhance(4)
		e = ImageEnhance.Brightness(g)
		g = e.enhance(1.75)
		e = ImageEnhance.Brightness(b)
		b = e.enhance(.6)
		img = Image.merge('RGB', (r, g, b))
		e = ImageEnhance.Brightness(img)
		img = e.enhance(1.5)
		temp = BytesIO()
		temp.name = 'deepfried.png'
		img.save(temp)
		temp.seek(0)
		return temp 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def save_to_disk(self, filename, format='.png'):
        """Save this image to disk (requires PIL installed)."""
        filename = _append_extension(filename, format)

        try:
            from PIL import Image as PImage
        except ImportError:
            raise RuntimeError(
                'cannot import PIL, make sure pillow package is installed')

        image = PImage.frombytes(
            mode='RGBA',
            size=(self.width, self.height),
            data=self.raw_data,
            decoder_name='raw')
        color = image.split()
        image = PImage.merge("RGB", color[2::-1])

        folder = os.path.dirname(filename)
        if not os.path.isdir(folder):
            os.makedirs(folder)
        image.save(filename, quality=100) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def from_png_to_bmp(png_path, output_path=BMP_IMAGE_TEST_TO_PATH):
    """
    Convert a png_path image into a bmp 3-channel one and return the path to the converted image
    :param png_path: path of the image
    :param output_path: path in which we save the image
    :return: the file path
    """
    # convert a .png image file to a .bmp image file using PIL
    file_name = os.path.splitext(png_path)[0] \
        .split("/")[-1]
    file_in = png_path
    img = Image.open(file_in)

    file_out = os.path.join(output_path, str(file_name), str(file_name) + '.bmp')
    len(img.split())  # test
    if len(img.split()) == 4:
        # prevent IOError: cannot write mode RGBA as BMP
        r, g, b, a = img.split()
        img = Image.merge("RGB", (r, g, b))
        img.save(file_out)
    else:
        img.save(file_out)
    return file_out 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def save_to_disk(self, filename):
        """Save this image to disk (requires PIL installed)."""
        filename = _append_extension(filename, '.png')

        try:
            from PIL import Image as PImage
        except ImportError:
            raise RuntimeError(
                'cannot import PIL, make sure pillow package is installed')

        image = PImage.frombytes(
            mode='RGBA',
            size=(self.width, self.height),
            data=self.raw_data,
            decoder_name='raw')
        color = image.split()
        image = PImage.merge("RGB", color[2::-1])

        folder = os.path.dirname(filename)
        if not os.path.isdir(folder):
            os.makedirs(folder)
        image.save(filename) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def distort_image(im, hue, sat, val):
	im = im.convert('HSV')
	cs = list(im.split())
	cs[1] = cs[1].point(lambda i: i * sat)
	cs[2] = cs[2].point(lambda i: i * val)
	def change_hue(x):
		x += hue*255
		if x > 255:
			x -= 255
		if x < 0:
			x += 255
		return x
	cs[0] = cs[0].point(change_hue)
	im = Image.merge(im.mode, tuple(cs))
	im = im.convert('RGB')
	return im

# generate random scale. 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def perform_inference(sym, arg_params, aux_params, input_img, img_cb, img_cr):
    """Perform inference on image using mxnet"""
    metadata = onnx_mxnet.get_model_metadata('super_resolution.onnx')
    data_names = [input_name[0] for input_name in metadata.get('input_tensor_data')]
    # create module
    mod = mx.mod.Module(symbol=sym, data_names=data_names, label_names=None)
    mod.bind(for_training=False, data_shapes=[(data_names[0], input_img.shape)])
    mod.set_params(arg_params=arg_params, aux_params=aux_params)

    # run inference
    batch = namedtuple('Batch', ['data'])
    mod.forward(batch([mx.nd.array(input_img)]))

    # Save the result
    img_out_y = Image.fromarray(np.uint8(mod.get_outputs()[0][0][0].
                                         asnumpy().clip(0, 255)), mode='L')

    result_img = Image.merge(
        "YCbCr", [img_out_y,
                  img_cb.resize(img_out_y.size, Image.BICUBIC),
                  img_cr.resize(img_out_y.size, Image.BICUBIC)]).convert("RGB")
    output_img_dim = 672
    assert result_img.size == (output_img_dim, output_img_dim)
    LOGGER.info("Super Resolution example success.")
    result_img.save("super_res_output.jpg")
    return result_img 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import merge [as 別名]
def color(src, target):
    num_pixels = src.size[0] * src.size[1]
    colors = src.getcolors(num_pixels)
    rgb = sum(c[0] * c[1][0] for c in colors), sum(c[0] * c[1][1] for c in colors), sum(
        c[0] * c[1][2] for c in colors)
    rgb = rgb[0] / num_pixels, rgb[1] / num_pixels, rgb[2] / num_pixels
    bands = target.split()
    for i, v in enumerate(rgb):
        out = bands[i].point(lambda p: int(p * v / 255))
        bands[i].paste(out)
    return Image.merge(target.mode, bands)