Python io.imsave函数代码示例

def cut_char(pk):
    page = get_object_or_404(Page, pk=pk)
    page_img_path = page.get_image_path()
    char_lst = Character.objects.filter(page_id=pk)
    image = io.imread(page_img_path, 0)
    binary = binarisation(image)
    binary_image = (binary * 255).astype('ubyte')
    char_dir = settings.CHARACTER_IMAGE_ROOT+ pk+'/'
    if not os.path.exists(char_dir):
        os.makedirs(char_dir)
    for char in char_lst:
        char_image = binary_image[char.top:char.bottom,char.left:char.right]
        char_filename = char.id+'.png'
        char_path = char_dir+char_filename
        try:
            io.imsave(char_path, char_image)
            status = 0
            if is_low_contrast(char_image):
                status = -5
        except:
            char_filename = ''
            status = -6
        char.is_correct = status
        char.image = char_filename
        char.save()
    append_char_stastics.delay(pk)
    return 'cutchar:'+pk

    def save_segmented_image(self, filepath_image, modality='t1c', show=False):
        '''
        Creates an image of original brain with segmentation overlay and save it in ./predictions
        INPUT   (1) str 'filepath_image': filepath to test image for segmentation, including file extension
                (2) str 'modality': imaging modality to use as background. defaults to t1c. options: (flair, t1, t1c, t2)
                (3) bool 'show': If true, shows output image. defaults to False.
        OUTPUT  (1) if show is True, shows image of segmentation results
                (2) if show is false, returns segmented image.
        '''
        modes = {'flair': 0, 't1': 1, 't1c': 2, 't2': 3}

        segmentation = self.predict_image(filepath_image, show=False)
        print 'segmentation = ' + str(segmentation)
        img_mask = np.pad(segmentation, (16, 16), mode='edge')
        ones = np.argwhere(img_mask == 1)
        twos = np.argwhere(img_mask == 2)
        threes = np.argwhere(img_mask == 3)
        fours = np.argwhere(img_mask == 4)

        test_im = io.imread(filepath_image)
        test_back = test_im.reshape(5, 216, 160)[modes[modality]]
        # overlay = mark_boundaries(test_back, img_mask)
        gray_img = img_as_float(test_back)

        # adjust gamma of image
        image = adjust_gamma(color.gray2rgb(gray_img), 0.65)
        sliced_image = image.copy()
        red_multiplier = [1, 0.2, 0.2]
        yellow_multiplier = [1, 1, 0.25]
        green_multiplier = [0.35, 0.75, 0.25]
        blue_multiplier = [0, 0.25, 0.9]

        print str(len(ones))
        print str(len(twos))
        print str(len(threes))
        print str(len(fours))

        # change colors of segmented classes
        for i in xrange(len(ones)):
            sliced_image[ones[i][0]][ones[i][1]] = red_multiplier
        for i in xrange(len(twos)):
            sliced_image[twos[i][0]][twos[i][1]] = green_multiplier
        for i in xrange(len(threes)):
            sliced_image[threes[i][0]][threes[i][1]] = blue_multiplier
        for i in xrange(len(fours)):
            sliced_image[fours[i][0]][fours[i][1]] = yellow_multiplier
        #if show=True show the prediction
        if show:
            print 'Showing...'
            io.imshow(sliced_image)
            plt.show()
        #save the prediction
        print 'Saving...'
        try:
            mkdir_p('./predictions/')
            io.imsave('./predictions/' + os.path.basename(filepath_image) + '.png', sliced_image)
            print 'prediction saved.'
        except:
            io.imsave('./predictions/' + os.path.basename(filepath_image) + '.png', sliced_image)
            print 'prediction saved.'

def produce_smoothed_images(get_component, replace_component, bins, output_path, paths):

    start_img = io.imread(paths[0])
    start_cdf = get_cdf(get_component(start_img), bins)

    end_img = io.imread(paths[-1])
    end_cdf = get_cdf(get_component(end_img), bins)

    delta_cdf = end_cdf - start_cdf

    for i, path in enumerate(paths[1:-1]):
        percentage = i / len(paths[1:-1])
        target_cdf = start_cdf + (delta_cdf * percentage)

        img = io.imread(path)
        values = get_component(img)
        cdf = get_cdf(values, bins)

        # In order to match the length of "bins" for the interpolation below
        # we prepend a 0
        target_cdf = numpy.insert(target_cdf, 0, 0)
        cdf = numpy.insert(cdf, 0, 0)

        matched = match(values, cdf, target_cdf, bins)
        matched = matched.reshape(values.shape)

        img = replace_component(img, matched)

        result_path = os.path.join(output_path, os.path.basename(path))
        io.imsave(result_path, img)
        print('Done with', result_path)

def rawFrameToImageFile(image, filename):
    """Writes a single raw image frame to image file.
    The file type must be given, e.g. png or jpg.
    The image need not be scaled beforehand, it is done prior 
    to writing out the image. Could be one of
    BMP, JPG, JPEG, PNG, PPM, TIFF, XBM, XPM)
    but the file types available depends
    on the QT imsave plugin in use.

    Args:
        | image (np.ndarray): two-dimensional array representing an image
        | filename (string): name of file to be written to, with extension

    Returns:
        | Nothing

    Raises:
        | No exception is raised.
    """
    #normalise input image (img) data to between 0 and 1
    from scipy import ndimage

    image = (image - ndimage.minimum(image)) / (ndimage.maximum(image) - ndimage.minimum(image))

    # http://scikit-image.org/docs/dev/api/skimage.io.html#imsave
    import skimage.io as io
    io.imsave(filename, image) 

def detect(path):

	input_image = io.imread(path)
	gnb = joblib.load('/home/qburst/Desktop/Emotion_detection/ED_dist/Classifier/gnb.pkl')
	face_count, features, faces = p.feature_extraction(input_image)
	if face_count:
		emotions = gnb.predict(features)
		print emotions
		for d, emotion in zip(faces, emotions):
			print d, emotion
			if emotion == 0:
				cv2.rectangle(input_image, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 0), 2)

			elif emotion == 1:
                                cv2.rectangle(input_image, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 255), 3)


			elif emotion == 2:
                                cv2.rectangle(input_image, (d.left(), d.top()), (d.right(), d.bottom()), (255, 255, 0), 3)


			elif emotion == 3:
				cv2.rectangle(input_image, (d.left(), d.top()), (d.right(), d.bottom()), (255, 0, 0), 3)

				
		io.imsave('Detected/emotion.jpg', input_image)
		return face_count, 'Detected/emotion.jpg'
	else:
	 	return face_count, 'err'

def run_quadrant_stitch(fns, re_string='(.*)_(s[1-4])_(w[1-3]).*',
                        re_quadrant_group=1):
    """Read images, stitched them, and write out to same directory.

    Parameters
    ----------
    fns : list of string
        The filenames to be processed.
    re_string : string, optional
        The regular expression to match the filename.
    re_quadrant_group : int, optional
        The group from the re.match object that will contain quadrant info.

    Returns
    -------
    fns_out : list of string
        The output filenames
    """
    qd = group_by_quadrant(fns, re_string, re_quadrant_group)
    fns_out = []
    for fn_pattern, fns in qd.items():
        new_filename = '_'.join(fn_pattern) + '_stitched.tif'
        ims = map(io.imread, sorted(fns))
        im = quadrant_stitch(*ims)
        io.imsave(new_filename, im)
        fns_out.append(new_filename)
    return fns_out

 def roundtrip(self, dtype, x):
     f = NamedTemporaryFile(suffix='.tif')
     fname = f.name
     f.close()
     imsave(fname, x)
     y = imread(fname)
     assert_array_equal(x, y)

    def produce(solution_number, gsd, name=None):
        print("{}/{}".format(solution_number+1, number_of_solutions))
        cam_left = model.fexternal(solution_number)[0]
        cam_right = model.fexternal(solution_number)[1]

        corners_left  = project_corners(model.finternal(solution_number), cam_left , pixel_size(model.finternal(solution_number)), image_shape, elevation)
        corners_right = project_corners(model.finternal(solution_number), cam_right, pixel_size(model.finternal(solution_number)), image_shape, elevation)

        world_rect = WorldRect.from_points(np.vstack([corners_left, corners_right]))

        tile = FlatTile(world_rect, gsd)
        tile.draw_cam_trace(corners_left)
        tile.draw_cam_trace(corners_right)
        tile.project_camera(model.finternal(solution_number), cam_left, elevation, left)
        tile.project_camera(model.finternal(solution_number), cam_right, elevation, right)
        tile.draw_observations(model.finternal(solution_number), cam_left, elevation, data_set.rows, data_set.cols, model.features.edges[0].obs_a)
        tile.draw_observations(model.finternal(solution_number), cam_right, elevation, data_set.rows, data_set.cols, model.features.edges[0].obs_b)
        tile.draw_obs_pair(model.finternal(solution_number),
                           cam_left,
                           cam_right,
                           elevation,
                           data_set.rows,
                           data_set.cols,
                           model.features.edges[0].obs_a,
                           model.features.edges[0].obs_b)

        if name is None:
            name = "iteration{}.jpg".format(solution_number)
        io.imsave(os.path.join(tile_dir, name), tile.image)

def main():
    # get a list of image filenames in a directory
    base_directory = "/path/to/image/directory"

    # create an output_directory
    output_directory = os.path.join(base_directory, "output")
    if not os.path.exists(output_directory):
        os.mkdir(output_directory)

    # string formatting with leading zeros - we'll use this later
    output_filename_template = os.path.join(output_directory, "output {:03d}.png")

    # this will work if base_directory does not end with '/'
    filenames = glob(base_directory + "/*.png")

    # but it's better to safely join paths
    filenames = glob(os.path.join(base_directory, "*.png"))

    # will often want to process these in order
    filenames = sorted(glob(os.path.join(base_directory, "*.png")))

    # process each image
    for i, filename in enumerate(filenames):
        image = img_as_float(imread(filename))
        result = vignette(image)
        imsave(output_filename_template.format(i), result)

def gen_ablation(imgIds = [], mode = 'blackout', ct = None, out_path="tmp", **args):
    """Perform specified ablation on every image specified by the imgIds list.
    If no imgId is specified, will randomly sample an image with text.
    return (imgId, old_img, new_img) list"""
    imgs = ct.loadImgs(imgIds)
    results = []
    for idx, img in enumerate(imgs):
        print("Ablating image {}/{}".format(idx+1, len(imgIds)))
        ori_file_name = '%s/%s/%s'%(DATA_PATH,DATA_TYPE,img['file_name'])
        orig = io.imread(ori_file_name)
        annIds = ct.getAnnIds(imgIds=img['id'])
        anns = ct.loadAnns(annIds)

        if len(anns)==0:
            print("[WARNING] Weirdly sampled an image without text contents:{}".format(img['file_name']))

        running = orig
        for ann in anns:
            bbox = ann['bbox'] #format: [x,y,width,height]
            if mode=='blackout':
                running = blackout(running, bbox)
            elif mode=='gaussian':
                running = gaussian(running, bbox, ksize=args['ksize'], sigma = args['sigma'])
            elif mode=='median':
                running = median(running, bbox, width=args['width'])
        out_file_name = os.path.join(CD, "..", out_path, "%s_%s"%(mode, img['file_name']))
        io.imsave(out_file_name, running)
        results.append((img['id'], ori_file_name, out_file_name))
    return results

def ablate(imgIds = [], mode ='destroy', out_path="tmp", coco = coco, ct = None,  **args):
    """[ablation entry point 2.0]
    Created to accomodate background-destroying ablation. Will dispatch all
    old ablations (gaussian, blackout, & median) to gen_ablation."""

    if ct is None:
        ct = coco_text.COCO_Text(os.path.join(CD, 'COCO_Text.json'))
    if imgIds == []:
        imgIds = ct.getImgIds(imgIds=ct.train, catIds=[('legibility','legible')])
        imgIds = [imgIds[np.random.randint(0,len(imgIds))]]

    #dispatch to old ablation entry point
    if mode in ['gaussian', 'blackout', 'median']:
        return gen_ablation(imgIds, mode, ct, out_path=out_path, **args)

    #else do destroy_bg
    if coco is None:
        coco = COCO('%s/annotations/instances_%s.json'%(DATA_PATH,DATA_TYPE))
    imgs = coco.loadImgs(imgIds)
    results = []
    for idx, img in enumerate(imgs):
        print("Ablating image {}/{} with id {} ".format(idx+1, len(imgIds), img['id']))
        ori_file_name = os.path.join(CD, DATA_PATH, DATA_TYPE, img['file_name'])
        orig = io.imread(ori_file_name)

        if mode == 'destroy':
            ablt = destroy_bg(orig, img['id'], coco, **args)
        elif mode == 'median_bg':
            ablt = median_bg(orig, img['id'], coco, **args)

        out_file_name = os.path.join(CD, "..", out_path, "%s_%s"%(mode, img['file_name']))
        io.imsave(out_file_name, ablt)

        results.append((img['id'], ori_file_name, out_file_name))
    return results

def run_illum(args):
    """Run illumination correction.

    Parameters
    ----------
    args : argparse.Namespace
        The arguments parsed by the argparse library.
    """
    if args.file_list is not None:
        args.images.extend([fn.rstrip() for fn in args.file_list])
    il = pre.find_background_illumination(args.images, args.radius,
                                          args.quantile, args.stretchlim,
                                          args.use_mask, args.mask_offset,
                                          args.mask_close, args.mask_erode)
    if args.verbose:
        print 'illumination field:', type(il), il.dtype, il.min(), il.max()
    if args.save_illumination is not None:
        io.imsave(args.save_illumination, il / il.max())
    base_fns = [pre.basefn(fn) for fn in args.images]
    ims_out = [fn + args.output_suffix for fn in base_fns]
    mask_fns = [fn + '.mask.tif' for fn in base_fns]
    ims = (io.imread(fn) for fn in args.images)
    for im, fout, mask_fn in it.izip(ims, ims_out, mask_fns):
        if os.path.isfile(mask_fn):
            mask = io.imread(mask_fn).astype(bool)
        else:
            mask = np.ones(im.shape, bool)
        im = pre.correct_image_illumination(im, il,
                                            args.stretchlim_output, mask)
        io.imsave(fout, im)

    def save_to_file(self):
        """Save current image to file.

        The current behavior is not ideal: It saves the image displayed on
        screen, so all images will be converted to RGB, and the image size is
        not preserved (resizing the viewer window will alter the size of the
        saved image).
        """
        filename = dialogs.save_file_dialog()
        if filename is None:
            return
        if len(self.ax.images) == 1:
            io.imsave(filename, self.image)
        else:
            underlay = mpl_image_to_rgba(self.ax.images[0])
            overlay = mpl_image_to_rgba(self.ax.images[1])
            alpha = overlay[:, :, 3]

            # alpha can be set by channel of array or by a scalar value.
            # Prefer the alpha channel, but fall back to scalar value.
            if np.all(alpha == 1):
                alpha = np.ones_like(alpha) * self.ax.images[1].get_alpha()

            alpha = alpha[:, :, np.newaxis]
            composite = (overlay[:, :, :3] * alpha +
                         underlay[:, :, :3] * (1 - alpha))
            io.imsave(filename, composite)

def run(imfile, N, sigma, mu):
    N = 2 if N is None else int(N)
    sigma = 1.0 if sigma is None else float(sigma)
    mu = 10.0 if mu is None else float(mu)

    # read image
    im0 = imread(imfile, as_grey=True)

    # rescale to a common size
    scale = 1e6 / float(im0.size)
    im = rescale(im0, (scale, scale))

    # estimate illumination profile
    proc0 = NonUniformIllumination(N=N, sigma=sigma, mu=mu)

    comp = proc0(im)
    illum = proc0.profile

    # # resize to original size
    # illum = rescale(illum, (1.0/scale, 1.0/scale))
    # illum = np.resize(illum, im0.shape)

    fname = os.path.splitext(imfile)

    illum = (illum - illum.min()) / (illum.max() - illum.min())
    imsave(fname[0] + '-illum' + fname[1], illum)

    comp = (comp - comp.min()) / (comp.max() - comp.min())
    imsave(fname[0] + '-comp' + fname[1], comp)

    return

def deflicker():
    #bins = numpy.arange(0, 1, 0.01)
    bins = 256

    for path, img in zip(paths, use_first(paths, bins)):
        output_path = os.path.join(output_directory, os.path.basename(path))
        io.imsave(output_path, img)