Python scipy.misc 模块，toimage() 实例源码

我们从Python开源项目中，提取了以下21个代码示例，用于说明如何使用scipy.misc.toimage()。

项目：Rewrite 作者：kaonashi-tyc | 项目源码 | 文件源码

def render_fonts_image(x, path, img_per_row, unit_scale=True):
    if unit_scale:
        # scale 0-1 matrix back to gray scale bitmaps
        bitmaps = (x * 255.).astype(dtype=np.int16) % 256
    else:
        bitmaps = x
    num_imgs, w, h = x.shape
    assert w == h
    side = int(w)
    width = img_per_row * side
    height = int(np.ceil(float(num_imgs) / img_per_row)) * side
    canvas = np.zeros(shape=(height, width), dtype=np.int16)
    # make the canvas all white
    canvas.fill(255)
    for idx, bm in enumerate(bitmaps):
        x = side * int(idx / img_per_row)
        y = side * int(idx % img_per_row)
        canvas[x: x + side, y: y + side] = bm
    misc.toimage(canvas).save(path)
    return path

项目：Machine-Learning-Coursera-Tensorflow 作者：themech | 项目源码 | 文件源码

def plot_100_images(X):
    """Plot 100 randomly picked digits."""
    width, height = 20, 20
    nrows, ncols = 10, 10
    indices_to_display = np.random.choice(range(X.shape[0]), nrows * ncols)

    big_picture = np.zeros((height * nrows, width * ncols))

    irow, icol = 0, 0
    for idx in indices_to_display:
        if icol == ncols:
            irow += 1
            icol = 0
        iimg = X[idx].reshape(width, height).T # transpose the data set
        big_picture[irow * height:irow * height + iimg.shape[0], icol * width:icol * width + iimg.shape[1]] = iimg
        icol += 1
    fig = plt.figure(figsize=(6, 6))
    img = misc.toimage(big_picture)
    plt.imshow(img, cmap=matplotlib.cm.Greys_r)

    plt.show()

项目：Machine-Learning-Coursera-Tensorflow 作者：themech | 项目源码 | 文件源码

def plot_100_images(X, indices=None):
    """Plot 100 randomly picked digits."""
    width, height = IMAGE_WIDTH, IMAGE_HEIGHT
    nrows, ncols = 10, 10
    if indices is None:
        indices = range(X.shape[0])
    indices_to_display = np.random.choice(indices, nrows * ncols)

    big_picture = np.zeros((height * nrows, width * ncols))

    irow, icol = 0, 0
    for idx in indices_to_display:
        if icol == ncols:
            irow += 1
            icol = 0
        iimg = X[idx].reshape(width, height).T  # transpose the data set
        big_picture[irow * height:irow * height + iimg.shape[0], icol * width:icol * width + iimg.shape[1]] = iimg
        icol += 1
    img = misc.toimage(big_picture)
    plt.imshow(img, cmap=matplotlib.cm.Greys_r)

    plt.show()

项目：pytorch-semseg 作者：meetshah1995 | 项目源码 | 文件源码

def setup(self, pre_encode=False):
        sbd_path = get_data_path('sbd')
        voc_path = get_data_path('pascal')

        target_path = self.root + '/SegmentationClass/pre_encoded/'
        if not os.path.exists(target_path):
            os.makedirs(target_path)

        sbd_train_list = tuple(open(sbd_path + 'dataset/train.txt', 'r'))
        sbd_train_list = [id_.rstrip() for id_ in sbd_train_list]

        self.files['train_aug'] = self.files['train'] + sbd_train_list

        if pre_encode:
            print("Pre-encoding segmentation masks...")
            for i in tqdm(sbd_train_list):
                lbl_path = sbd_path + 'dataset/cls/' + i + '.mat'
                lbl = io.loadmat(lbl_path)['GTcls'][0]['Segmentation'][0].astype(np.int32)
                lbl = m.toimage(lbl, high=lbl.max(), low=lbl.min())
                m.imsave(target_path + i + '.png', lbl)

            for i in tqdm(self.files['trainval']):
                lbl_path = self.root + '/SegmentationClass/' + i + '.png'
                lbl = self.encode_segmap(m.imread(lbl_path))
                lbl = m.toimage(lbl, high=lbl.max(), low=lbl.min())
                m.imsave(target_path + i + '.png', lbl)

项目：structured-output-ae 作者：sbelharbi | 项目源码 | 文件源码

def hide_part(x, part):
    """x shape: 50x50"""
    print x.shape
    if part is "left":
        x[:, :20] = 0.5
    elif part is "right":
        x[:, 30:] = 0.5
    elif part is "up":
        x[:20, :] = 0.5
    elif part is "down":
        x[30:, :] = 0.5
    elif part is "middle":
        x[15:35, :] = 0.5
    # sm.toimage(x).save(str(part)+'.jpg')
    return x

项目：PyCasia 作者：lucaskjaero | 项目源码 | 文件源码

def load_gnt_file(filename):
        """
        Load characters and images from a given GNT file.
        :param filename: The file path to load.
        :return: (image: Pillow.Image.Image, character) tuples
        """

        # Thanks to nhatch for the code to read the GNT file, available at https://github.com/nhatch/casia
        with open(filename, "rb") as f:
            while True:
                packed_length = f.read(4)
                if packed_length == b'':
                    break

                length = struct.unpack("<I", packed_length)[0]
                raw_label = struct.unpack(">cc", f.read(2))
                width = struct.unpack("<H", f.read(2))[0]
                height = struct.unpack("<H", f.read(2))[0]
                photo_bytes = struct.unpack("{}B".format(height * width), f.read(height * width))

                # Comes out as a tuple of chars. Need to be combined. Encoded as gb2312, gotta convert to unicode.
                label = decode(raw_label[0] + raw_label[1], encoding="gb2312")
                # Create an array of bytes for the image, match it to the proper dimensions, and turn it into an image.
                image = toimage(np.array(photo_bytes).reshape(height, width))

                yield image, label

项目：Magic-Pixel 作者：zhwhong | 项目源码 | 文件源码

def histEqual(srcpath, dstpath):
    im_source = Image.open(srcpath)

    if True:
        arr_im_gray = np.array(im_source)
        arr_im_gray_hist = beautyImage(arr_im_gray)
        # figure()
        im_conver = toimage(arr_im_gray_hist, 255, 0, None, None, None, 'L')
        im_conver.save(dstpath, 'jpeg')

# 2.???