def update_canvas_upload_shape(image_string, w, h):
if image_string is None:
raise PreventUpdate
if image_string is not None:
im = image_string_to_PILImage(image_string)
im_h, im_w = im.height, im.width
return round(w / im_w * im_h)
else:
return canvas_height
def update_canvas_upload_scale(image_string):
if image_string is None:
raise PreventUpdate
if image_string is not None:
# very dirty hack, this should be made more robust using regexp
im = image_string_to_PILImage(image_string)
im_h, im_w = im.height, im.width
return canvas_width / im_w
else:
return scale
def update_canvas_upload_shape(image_string, w, h):
if image_string is None:
raise ValueError
if image_string is not None:
# very dirty hack, this should be made more robust using regexp
im = image_string_to_PILImage(image_string)
im_h, im_w = im.height, im.width
return round(w / im_w * im_h)
else:
return canvas_height
def update_figure_upload(image, string, h, s, w, algorithm):
mask = parse_jsonstring(string, shape=(round(h/s), round(w/s)))
if mask.sum() > 0:
if image is None:
im = img
image = img
else:
im = image_string_to_PILImage(image)
im = np.asarray(im)
seg = segmentation_generic(im, mask, mode=algorithm)
else:
if image is None:
image = img
seg = np.zeros((h, w))
return image_with_contour(image, seg, shape=(round(h/s), round(w/s)))
def upload_content(list_image_string, list_filenames, click,
n_rows, n_cols):
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]
res = tile_images(image_list, n_rows, n_cols)
return array_to_data_url(res)
elif click:
import os
filelist = glob('./assets/tile*.jpg')
filelist.sort()
image_list = [io.imread(filename) for filename in filelist[:4]]
res = tile_images(image_list, n_rows, n_cols)
return array_to_data_url(res)
else:
raise PreventUpdate
def update_figure_upload(image, string, h, s, w):
mask = parse_jsonstring(string, shape=(round(h / s), round(w / s)))
if mask.sum() > 0:
if image is None:
im = img
image = img
else:
im = image_string_to_PILImage(image)
im = np.asarray(im)
seg = superpixel_color_segmentation(im, mask)
else:
if image is None:
image = img
seg = np.ones((h, w))
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)
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)
def modify_result(contrast, brightness, image_string):
print('in modify result')
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)
