def upload_content(list_image_string, list_filenames, click, downsample,
n_rows, n_cols):
downsample = int(downsample)
if list_image_string is not None:
order = np.argsort(list_filenames)
image_list = [
np.asarray(image_string_to_PILImage(list_image_string[i]))
for i in order
]
if downsample > 1:
ratio = 1.0 / downsample
multichannel = image_list[0].ndim > 2
image_list = [
transform.rescale(image,
ratio,
multichannel=multichannel,
preserve_range=True).astype(np.uint8)
for image in image_list
]
res = tile_images(image_list, n_rows, n_cols)
return array_to_data_url(res)
elif click:
res = demo_data()
tmp = array_to_data_url(res)
return tmp
raise PreventUpdate
def remove_background(json_data, image):
if json_data:
# ➊ imageが値をもつ場合、それを基に画像のnumpy.ndarrayに変換する
if image:
image_array = image_string_to_PILImage(image)
image_array = np.asarray(image_array)
# imageが値をもたない場合、imreadで画像を読み込む
else:
image_array = io.imread(image_path)
# ➋ 画像のアレイのサイズを変数shapeに代入する
shape = image_array.shape[:2]
# ➌ 書き込みのJSONデータをパースし、ブール値に変換する
try:
mask = parse_jsonstring(json_data, shape=shape)
except IndexError:
raise PreventUpdate
if mask.sum() > 0: # ➍
seg = superpixel_color_segmentation(image_array, mask)
else:
seg = np.ones(shape)
filled_image = np.copy(image_array)
filled_image[np.logical_not(seg)] = np.array([255, 255, 255],
dtype="uint8") # ➎
return array_to_data_url(filled_image) # ➏
else:
PreventUpdate
def modify_result(contrast, brightness, image_string):
if image_string is None:
raise PreventUpdate
img = np.asarray(image_string_to_PILImage(image_string))
img = contrast_adjust(img, contrast)
img = brightness_adjust(img, brightness)
return array_to_data_url(img)
def update_figure_upload(string, image, algorithm):
print("update figure")
if string:
if image is None:
im = img
image = img
else:
im = image_string_to_PILImage(image)
im = np.asarray(im)
shape = im.shape[:2]
mask = parse_jsonstring(string, shape=shape)
if mask.sum() > 0:
seg = segmentation_generic(im, mask, mode=algorithm)
else:
seg = np.zeros(shape)
return image_with_contour(im, seg, shape=shape)
else:
raise PreventUpdate
def update_figure_upload(string, image, height, width, birdeye,
calculation):
if string:
if image is None:
im = img_app3
else:
im = image_string_to_PILImage(image)
im = np.asarray(im)
dat, locsgrid, locs, gray, output_image = solve_sudoku(
im,
beyeview=birdeye,
digit_h=(height[0], height[1]),
digit_w=(width[0], width[1]))
if calculation:
dat = compute(locsgrid, locs, gray, output_image)
return array_to_data_url(dat)
else:
raise PreventUpdate
def update_figure_upload(string, image):
if string:
if image is None:
im = img_app3
else:
im = image_string_to_PILImage(image)
im = np.asarray(im)
shape = im.shape[:2]
try:
mask = parse_jsonstring(string, shape=shape)
except IndexError:
raise PreventUpdate
if mask.sum() > 0:
seg = superpixel_color_segmentation(im, mask)
else:
seg = np.ones(shape)
fill_value = 255 * np.ones(3, dtype=np.uint8)
dat = np.copy(im)
dat[np.logical_not(seg)] = fill_value
return array_to_data_url(dat)
else:
raise PreventUpdate
def untile_images(image_string, n_rows, n_cols):
big_im = np.asarray(image_string_to_PILImage(image_string))
tiles = [np.split(im, n_cols, axis=1) for im in np.split(big_im, n_rows)]
return np.array(tiles)
