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 segmentation(string, content, NDVIcontent):
global ROIRGB, ROINDVI
if string:
data = content.encode("utf8").split(b";base64,")[1]
NDVIdata = NDVIcontent.encode("utf8").split(b";base64,")[1]
img = io.BytesIO()
imgNDVI = io.BytesIO()
img.write(base64.b64decode(data))
imgNDVI.write(base64.b64decode(NDVIdata))
img.seek(0)
imgNDVI.seek(0)
i = np.asarray(bytearray(img.read()), dtype=np.uint8)
i = cv2.imdecode(i, cv2.IMREAD_COLOR)
iNDVI = np.asarray(bytearray(imgNDVI.read()), dtype=np.uint8)
iNDVI = cv2.imdecode(iNDVI, cv2.IMREAD_COLOR)
mask = parse_jsonstring(string, (i.shape[0],i.shape[1]))
ret,thresh = cv2.threshold(np.array(mask, dtype=np.uint8), 0, 255, cv2.THRESH_BINARY)
im_floodfill = thresh.copy()
h, w = thresh.shape[:2]
m = np.zeros((h+2, w+2), np.uint8)
cv2.floodFill(im_floodfill, m, (0,0), 255)
im_floodfill_inv = cv2.bitwise_not(im_floodfill)
im_out = thresh | im_floodfill_inv
RGBimg = cv2.bitwise_and(i, i, mask=im_out)
RGBimg = cv2.cvtColor(RGBimg, cv2.COLOR_BGR2RGB)
target_size = (RGBimg.shape[1],(RGBimg.shape[0]))
iNDVI = cv2.resize(iNDVI, target_size)
NDVIimg = cv2.bitwise_and(iNDVI, iNDVI, mask=im_out)
NDVIimg = cv2.cvtColor(NDVIimg, cv2.COLOR_BGR2RGB)
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
cnts = sorted(contours, key=lambda c: cv2.contourArea(c), reverse=True) # finds the largest selection, which is a limitation to multiple selection (will be changed in the future versions)
(x,y,w,h) = cv2.boundingRect(cnts[0])
ROIRGB = RGBimg[y:y+h, x:x+w]
ROINDVI = NDVIimg[y:y+h, x:x+w]
cv2.imwrite(static_image_route+'RGB_cropped.png', cv2.cvtColor(ROIRGB, cv2.COLOR_RGB2BGR ))
cv2.imwrite(static_image_route+'CIR_cropped.png', cv2.cvtColor(ROINDVI, cv2.COLOR_RGB2BGR ))
with ZipFile(static_image_route+'cropped.zip', 'w') as zipObj2:
zipObj2.write(static_image_route+'RGB_cropped.png')
zipObj2.write(static_image_route+'CIR_cropped.png')
location = os.path.join(static_image_route,'cropped.zip')
else:
raise PreventUpdate
return array_to_data_url(img_as_ubyte(ROIRGB)), array_to_data_url(img_as_ubyte(ROINDVI)), html.A(html.Button('Download',style={'display':'block'
,'position':'relative',
'top': '45%','left': '45%',
'font-size': '16px',
'padding': '8px 12px',
'border-radius': '4px',
'text-align': 'center',
'align':'center',
'color':'black',
'font-family':'Times New Roman',
'textAlign':'center'}), href=location) #, html.Div([html.Button('Save Image', id='saveImg')])
def lookup_callback(n_clicks, lookup_val, id_dict, prog_type):
#time.sleep(1000)
try:
print("entered callback")
print(lookup_val)
lookup_val = int(lookup_val)
print(id_dict)
dataset = id_dict["dataset"]
#method = id_dict["dataset"]
pkl = dataset + ".pkl"
if (prog_type == "layer-wise"):
embeddings_dir = embeddings_dir_layer
else:
embeddings_dir = embeddings_dir_epoch
img = pickle.load(open(os.path.join(embeddings_dir, pkl),
"rb"))[lookup_val].astype(np.uint8)
print(img.shape)
child = html.Img(src=array_to_data_url(img_as_ubyte(img)),
style={
"width": "300px",
"height": "300px"
})
return ([child])
except Exception as e:
print(e)
raise PreventUpdate
def interpolate(n_clicks, annotations):
if n_clicks is None:
return dash.no_update
interps = {}
keys = np.sort([int(key) for key in annotations])
z_min = keys[0]
z_max = keys[-1]
volume = 0
for z in np.arange(z_min + 1, z_max, dtype=np.uint8):
i = np.searchsorted(keys, z)
t = (z - keys[i - 1]) / (keys[i] - keys[i - 1])
path1 = path_to_indices(annotations[str(keys[i])]["path"])
rr, cc = draw.polygon(path1[:, 1], path1[:, 0])
poly1 = np.zeros((l_lat, l_lat))
poly1[rr, cc] = 1
path2 = path_to_indices(annotations[str(keys[i - 1])]["path"])
rr, cc = draw.polygon(path2[:, 1], path2[:, 0])
poly2 = np.zeros((l_lat, l_lat))
poly2[rr, cc] = 1
poly = (t * poly1 + (1 - t) * poly2) > 0.5
interps[str(z)] = array_to_data_url(
img_as_ubyte(
segmentation.mark_boundaries(img_1[z], poly, mode="thick")))
volume += poly.sum()
volume *= np.abs(np.linalg.det(mat)) / 1000
result = f"The volume of the object is {volume:.0f} cm3"
return interps, result
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 modify_content(n_cl, n_rows, n_cols, overlap, estimate, image_string,
vals):
blending = 0
if vals is not None:
blending = 1 in vals
if image_string is None:
raise PreventUpdate
tiles = untile_images(image_string, n_rows, n_cols)
if estimate is not None and len(estimate) > 0:
overlap_dict = _sort_props_lines(estimate, tiles.shape[2],
tiles.shape[3], n_cols)
else:
overlap_dict = None
canvas = register_tiles(
tiles,
n_rows,
n_cols,
overlap_global=overlap,
overlap_local=overlap_dict,
pad=np.max(tiles.shape[2:]),
blending=blending,
)
return array_to_data_url(canvas)
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 make_figure(img_array):
img_uri = array_to_data_url(img_array)
width = img.shape[1]
height = img.shape[0]
fig = go.Figure()
# Add trace
fig.add_trace(go.Scatter(x=[], y=[]))
# Add images
fig.add_layout_image(
dict(source=img_uri,
xref="x",
yref="y",
x=0,
y=0,
sizex=width,
sizey=height,
sizing="contain",
layer="below"))
fig.update_layout(template=None)
fig.update_xaxes(showgrid=False,
range=(0, width),
showticklabels=False,
zeroline=False)
fig.update_yaxes(showgrid=False,
scaleanchor='x',
range=(height, 0),
showticklabels=False,
zeroline=False)
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
return fig
def update_data(string):
if string:
mask = parse_jsonstring(string,
io.imread(filename, as_gray=True).shape)
else:
raise PreventUpdate
return array_to_data_url((255 * mask).astype(np.uint8))
def segmentation(string):
if string:
mask = parse_jsonstring(string, img.shape)
seg = watershed_segmentation(img, mask)
src = color.label2rgb(seg, image=img)
else:
raise PreventUpdate
return array_to_data_url(img_as_ubyte(src))
def send_file(filename,mime_type=None):
try:
content = array_to_data_url(img_as_ubyte(ROIRGB))
data = content.encode("utf8").split(b";base64,")[1]
imgtowrite = io.BytesIO(base64.decodebytes(data))
c = base64.b64encode(imgtowrite.read()).decode()
return dict(content=c, filename=filename,mime_type=mime_type, base64=True)
except Exception: pass
def update_figure(labs):
if labs:
new_labels = np.array(labs)
overlay = segmentation.mark_boundaries(img, new_labels)
overlay = img_as_ubyte(overlay)
return array_to_data_url(overlay)
else:
raise PreventUpdate
def segmentation(string):
if string:
mask = parse_jsonstring(string,
io.imread(filename, as_gray=True).shape)
seg = watershed_segmentation(io.imread(filename, as_gray=True), mask)
src = color.label2rgb(seg, image=io.imread(filename, as_gray=True))
else:
raise PreventUpdate
return array_to_data_url(img_as_ubyte(src))
def load_slice(n, n_slider, full_cut, res_state):
if n_slider is None:
return dash.no_update
if full_cut is not None:
print(full_cut['index'])
if full_cut is not None and full_cut[
'index'] == n_slider and res_state == 'coarse':
return dash.no_update
return dict(cut=array_to_data_url(img[int(n_slider)]), index=int(n_slider))
def update_data(string):
pprint(string)
if string:
mask = parse_jsonstring(string, (canvas_width, canvas_height))
else:
raise PreventUpdate
return array_to_data_url((255 * mask).astype(np.uint8))
def save_segmentation(labs, save_mode):
if labs:
new_labels = np.array(labs)
np.save('labels.npy', new_labels)
if save_mode == 'png':
color_labels = color.label2rgb(new_labels)
uri = array_to_data_url(new_labels, dtype=np.uint8)
return uri
else:
raise PreventUpdate
def create_images(size_distr_json, pred_json_copy):
"""Creates all the relevant base64 image strings"""
size_distr = json.loads(size_distr_json)
pred_copy = json.loads(pred_json_copy)
# create black and white image
pred = 255 * np.asarray(pred_copy['yimage_list'], dtype=np.uint8)
encoded_pred = array_to_data_url(pred)
# create blue and gold overlay
lookup = np.asarray([[153, 153, 0], [45, 0, 78]], dtype=np.uint8)
colour = lookup[pred_copy['yimage_list']]
encoded_colour = (
pred_copy['content_type']
+ ','
+ cu.numpy_2_b64(colour)
)
# create labeled image
unique = size_distr['unique_list']
labeled = np.asarray(size_distr['labeled_list'])
flattened_colour_arr = np.linspace(
0,
256 ** 3 - 1,
num=len(unique) + 1,
dtype=np.int32
)
# represent values in flattened_color_arr as three digit number with
# base 256 to create a color array with shape
# (num unique values including background, 3)
colours_labeled = np.zeros((len(unique) + 1, 3), dtype=np.uint8)
for i in range(len(colours_labeled)):
colours_labeled[i] = np.array([
(flattened_colour_arr[i] // (256 ** 2)) % 256,
(flattened_colour_arr[i] // (256)) % 256,
flattened_colour_arr[i] % 256
])
# create a lookup table using colors array and convert 2D labeled array
# into 3D rgb array
lookup_labeled = np.zeros((len(unique) + 1, 3), dtype=np.uint8)
lookup_labeled[np.unique(labeled - 1)] = colours_labeled
rgb_labeled = lookup_labeled[labeled - 1]
# convert from numpy array to base64 image
# rgb = np.asarray(data['rgb_pred_list'], dtype=np.uint8)
encoded_rgb = (
size_distr['content_type']
+ ','
+ cu.numpy_2_b64(rgb_labeled)
)
return [encoded_pred, encoded_colour, encoded_rgb]
def test_canvas_undo_redo(dash_duo):
h, w = 10, 10
overlay = np.zeros((h, w), dtype=np.uint8)
overlay = img_as_ubyte(overlay)
# Set up a small app. This could probably be made into a fixture.
app = dash.Dash(__name__)
app.layout = html.Div([
dcc.Store(id='cache', data=''),
dash_canvas.DashCanvas(id="canvas",
width=w,
height=h,
image_content=array_to_data_url(overlay),
goButtonTitle="save")
])
data_saved = []
@app.callback(Output('cache', 'data'), [Input("canvas", "trigger")],
[State("canvas", "json_data")])
def update_overlay(flag, data):
data_saved.append(data)
dash_duo.start_server(app)
# At application startup, a black 10x10 image is shown. When we click
# save, we expect a non-trivial JSON object representing this image. We
# assert that we get this object, but we don't dig into it.
btn = _get_button_by_title(dash_duo, "Save")
btn.click()
objs_1 = json.loads(data_saved[-1])['objects']
assert len(objs_1) > 0
# When we click "undo", the image disappears. We check that we get an
# empty JSON representation back.
btn = _get_button_by_title(dash_duo, "Undo")
btn.click()
btn = _get_button_by_title(dash_duo, "Save")
btn.click()
objs_2 = json.loads(data_saved[-1])['objects']
assert objs_2 == []
# When we click "redo", the original 10x10 black image is restored.
btn = _get_button_by_title(dash_duo, "Redo")
btn.click()
btn = _get_button_by_title(dash_duo, "Save")
btn.click()
objs_3 = json.loads(data_saved[-1])['objects']
assert objs_1 == objs_3
def segmentation(string, content, NDVIcontent):
if string:
data = content.encode("utf8").split(b";base64,")[1]
NDVIdata = NDVIcontent.encode("utf8").split(b";base64,")[1]
img = io.BytesIO()
imgNDVI = io.BytesIO()
img.write(base64.b64decode(data))
imgNDVI.write(base64.b64decode(NDVIdata))
img.seek(0)
imgNDVI.seek(0)
i = np.asarray(bytearray(img.read()), dtype=np.uint8)
i = cv2.imdecode(i, cv2.IMREAD_COLOR)
iNDVI = np.asarray(bytearray(imgNDVI.read()), dtype=np.uint8)
iNDVI = cv2.imdecode(iNDVI, cv2.IMREAD_COLOR)
mask = parse_jsonstring(string, (i.shape[0],i.shape[1]))
ret,thresh = cv2.threshold(np.array(mask, dtype=np.uint8), 0, 255, cv2.THRESH_BINARY)
im_floodfill = thresh.copy()
h, w = thresh.shape[:2]
m = np.zeros((h+2, w+2), np.uint8)
cv2.floodFill(im_floodfill, m, (0,0), 255)
im_floodfill_inv = cv2.bitwise_not(im_floodfill)
im_out = thresh | im_floodfill_inv
RGBimg = cv2.bitwise_and(i, i, mask=im_out)
RGBimg = cv2.cvtColor(RGBimg, cv2.COLOR_BGR2RGB)
target_size = (RGBimg.shape[1],(RGBimg.shape[0]))
iNDVI = cv2.resize(iNDVI, target_size)
NDVIimg = cv2.bitwise_and(iNDVI, iNDVI, mask=im_out)
NDVIimg = cv2.cvtColor(NDVIimg, cv2.COLOR_BGR2RGB)
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
cnts = sorted(contours, key=lambda c: cv2.contourArea(c), reverse=True) # finds the largest selection, which is a limitation to multiple selection (will be changed in the future versions)
(x,y,w,h) = cv2.boundingRect(cnts[0])
ROIRGB = RGBimg[y:y+h, x:x+w]
ROINDVI = NDVIimg[y:y+h, x:x+w]
else:
raise PreventUpdate
return array_to_data_url(img_as_ubyte(ROIRGB)), array_to_data_url(img_as_ubyte(ROINDVI))
def classify(alteration, image):
prevent_update(image)
mask = parse_jsonstring(alteration)
with open('bug.txt', 'w') as f:
f.write(str(mask))
image2 = array_to_data_url((255 * mask).astype(np.uint8))
if alteration:
return str(
google_classify(
combine(convert_base46(image), convert_base46(image2))))
return str(google_classify(convert_base46(image)))
def make_empty_found_segments():
""" fstc_slices is initialized to a bunch of images containing nothing (clear pixels) """
found_segs_tensor = np.zeros_like(img)
# convert to a colored image (but it will just be colored "clear")
fst_colored = image_utils.label_to_colors(
found_segs_tensor,
colormap=["#000000", "#8A2BE2"],
alpha=[0, 128],
color_class_offset=0,
)
fstc_slices = [[
array_to_data_url(np.moveaxis(fst_colored, 0, j)[i])
for i in range(np.moveaxis(fst_colored, 0, j).shape[0])
] for j in range(NUM_DIMS_DISPLAYED)]
return fstc_slices
def show_draw(string):
if not string:
raise PreventUpdate
# Convert image string to displayable image
mask = parse_jsonstring(string, shape)
mask = (255 * mask).astype(np.uint8)
mask = cv2.resize(mask, (400, 100), interpolation=cv2.INTER_AREA)
mask = cv2.threshold(mask, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
captcha_img = array_to_data_url(mask)
# Show image and save image string
return captcha_img, string
def draw_shapes_react(
drawn_shapes_data,
image_display_top_figure,
image_display_side_figure,
current_render_id,
):
if any(
[
e is None
for e in [
drawn_shapes_data,
image_display_top_figure,
image_display_side_figure,
]
]
):
return dash.no_update
t1 = time.time()
found_segs_tensor = shapes_to_segs(
drawn_shapes_data, image_display_top_figure, image_display_side_figure,
)
t2 = time.time()
PRINT("Time to convert shapes to segments:", t2 - t1)
# convert to a colored image
fst_colored = image_utils.label_to_colors(
found_segs_tensor,
colormap=["#8A2BE2"],
alpha=[128],
# we map label 0 to the color #000000 using no_map_zero, so we start at
# color_class 1
color_class_offset=1,
labels_contiguous=True,
no_map_zero=True,
)
t3 = time.time()
PRINT("Time to convert from labels to colored image:", t3 - t2)
fstc_slices = [
[
array_to_data_url(s) if np.any(s != 0) else blank_seg_slices[j]
for s in np.moveaxis(fst_colored, 0, j)
]
for j in range(NUM_DIMS_DISPLAYED)
]
t4 = time.time()
PRINT("Time to convert to data URLs:", t4 - t3)
PRINT("Total time to compute 2D annotations:", t4 - t1)
return fstc_slices, current_render_id + 1
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_data(string):
if string:
try:
mask = parse_jsonstring(string,
shape=(canvas_height, canvas_width))
except:
return "Out of Bounding Box, click clear button and try again"
# np.savetxt('data.csv', mask) use this to save the canvas annotations as a numpy array
# Invert True and False
mask = (~mask.astype(bool)).astype(int)
image_string = array_to_data_url((255 * mask).astype(np.uint8))
# this is from canvas.utils.image_string_to_PILImage(image_string)
img = Image.open(BytesIO(base64.b64decode(image_string[22:])))
text = "{}".format(
pytesseract.image_to_string(img, lang="eng", config="--psm 6"))
return text
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
dat = np.load(LOAD_SUPERPIXEL)
segl = dat["segl"]
seg = dat["seg"]
else:
# partition image
segl, seg = make_seg_image(img)
if len(SAVE_SUPERPIXEL) > 0:
np.savez(SAVE_SUPERPIXEL, segl=segl, seg=seg)
exit(0)
seg_img = img_as_ubyte(segl)
img_slices, seg_slices = [
[
# top
[array_to_data_url(im[i, :, :]) for i in range(im.shape[0])],
# side
[array_to_data_url(im[:, i, :]) for i in range(im.shape[1])],
] for im in [img, seg_img]
]
# initially no slices have been found so we don't draw anything
found_seg_slices = make_empty_found_segments()
# store encoded blank slices for each view to save recomputing them for slices
# containing no colored pixels
blank_seg_slices = [found_seg_slices[0][0], found_seg_slices[1][0]]
app = dash.Dash(__name__)
server = app.server
top_fig, side_fig = [
make_default_figure(
import dash_html_components as html
import numpy as np
from dash.dependencies import Input, Output
from dash.exceptions import PreventUpdate
from dash_canvas import DashCanvas
from dash_canvas.utils import (
array_to_data_url,
image_string_to_PILImage,
parse_jsonstring,
superpixel_color_segmentation,
)
from skimage import color, io
image_path = "img/bird2.png"
default_image = io.imread(image_path)
image_string = array_to_data_url(default_image)
app = dash.Dash(__name__)
app.layout = html.Div([
html.Div(
[
DashCanvas(
id="first-image",
filename=image_string,
width=500,
lineWidth=5,
lineColor="lime",
goButtonTitle="remove",
)
],
style={
def update_data(string):
if string:
mask = parse_jsonstring(string, shape)
else:
raise PreventUpdate
return array_to_data_url((255 * mask).astype(np.uint8))
def fill_tab(tab):
if tab == "canvas-tab":
return [
dash_canvas.DashCanvas(
id="canvas-stitch",
width=canvas_width,
height=canvas_height,
scale=scale,
lineWidth=2,
lineColor="red",
tool="line",
hide_buttons=["pencil"],
image_content=array_to_data_url(
np.zeros((height, width), dtype=np.uint8)),
goButtonTitle="Estimate translation",
),
html.Div(
children=[
html.Div(
image_upload_zone("upload-stitch",
multiple=True,
width="100px"))
],
className="upload_zone",
id="upload",
),
]
elif tab == "result-tab":
return [
dcc.Loading(
id="loading-1",
children=[
html.Img(
id="stitching-result",
src=array_to_data_url(
np.zeros((height, width), dtype=np.uint8)),
width=canvas_width,
)
],
type="circle",
),
html.Div(
[
html.Label("Contrast"),
dcc.Slider(id="contrast-stitch",
min=0,
max=1,
step=0.02,
value=0.5),
],
className="result_slider",
),
html.Div(
[
html.Label("Brightness"),
dcc.Slider(id="brightness-stitch",
min=0,
max=1,
step=0.02,
value=0.5),
],
className="result_slider",
),
]
return [
html.Img(id="bla",
src=app.get_asset_url("stitch_demo.gif"),
width=canvas_width)
]