def update_segmentation(toggle, string, s, h, w, children, mode):
if len(children) == 0:
labs = labels
else:
labs = np.asarray(children)
mask = parse_jsonstring(string, shape=(height, width))
new_labels = modify_segmentation(labs, mask, img=img, mode=mode)
return new_labels
def update_data(string):
if string:
imageArray = parse_jsonstring(string)
imageCropped = imageArray[0:width, 0:height]
rescaledImageArray = rescale(imageCropped, (0.14, 0.14))
prediction = predict(rescaledImageArray)
else:
raise PreventUpdate
return "Your number is: " + str(prediction)
def update_segmentation(toggle, string, s, h, w, children, mode):
print("updating")
if len(children) == 0:
labs = labels
else:
labs = np.asarray(children)
with open('data.json', 'w') as fp:
json.dump(string, fp)
mask = parse_jsonstring(string, shape=(height, width))
new_labels = modify_segmentation(labs, mask, img=img, mode=mode)
return new_labels
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 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)