def update_uploaded_image(list_of_contents, n_clicks, filename, style,
style_weight, storage, session_id):
print("update_uploaded_image")
storage = json.loads(storage)
# if an image was loaded
if list_of_contents is not None:
# if the callback was triggered by the upload-image module
if "upload-image" in dash.callback_context.triggered[0].get('prop_id'):
print('list of contents')
storage["filename"] = filename
string = list_of_contents.split(";base64,")[-1]
storage["image_signature"] = string[0:200]
storage["image_string"] = list_of_contents
return [
html.Img(id="image", src=list_of_contents),
html.Div(id="div-storage",
children=json.dumps(storage),
style={"display": "none"})
]
else:
print('not list of contents')
if style is not None:
print('style')
# CREATE the new image with selected style
string = list_of_contents.split(";base64,")[-1]
image = drc.b64_to_pil(string)
image_path = f'images/input/{filename}'
image.save(image_path)
print('image')
stylized_image = stylize_image(image_path, style, style_weight)
print('stylized_image')
b64_stylized_image = drc.pil_to_b64(Image.open(stylized_image))
print('b64_stylized_image')
# CALL MODEL
return [
html.Img(
id="image",
src=drc.HTML_IMG_SRC_PARAMETERS +
drc.pil_to_b64(Image.open('test_save_image.png'))),
html.Div(id="div-storage",
children=json.dumps(storage),
style={"display": "none"}),
html.Div(
id="div-style-text",
children=[
f'Style : {style} with weight {style_weight} applied'
])
]
else:
return [
html.Img(id="image",
src=drc.HTML_IMG_SRC_PARAMETERS +
drc.pil_to_b64(Image.open('test_save_image.png'))),
html.Div(id="div-storage",
children=json.dumps(storage),
style={"display": "none"})
]
def update_image(contents, input_1):
if contents != None:
string = input_1.split(';base64,')[-1]
im_pil = drc.b64_to_pil(string)
im_pil = im_pil.resize((128, 128), Image.ANTIALIAS)
if im_pil.mode != "RGB":
im_pil = im_pil.convert("RGB")
# Convert image to numpy array
img_array = np.asarray(im_pil)
img_array = (img_array - 127.5) / 127.5
img2 = img_array
#img2 = np.zeros((img_array.shape[0],img_array.shape[1],3))
#img2[:,:,0] = img_array
#img2[:,:,1] = img_array
#img2[:,:,2] = img_array
# Create payload
payload = {'instances': [img2.tolist()]}
# Predict image
#res = requests.post('http://localhost:8080/v1/models/color_sketch_model:predict', json=payload)
res = requests.post(
'http://color-sketch_model-development_1:8080/v1/models/color_sketch_model:predict',
json=payload)
res = res.json()
res = res['predictions'][0]
res = ((np.array(res) + 1) / 2.0) * 255.0
# Encode prediction
pred_pil = Image.fromarray(np.uint8(res))
pred_pil = pred_pil.resize((300, 300), Image.ANTIALIAS)
enc_pred = drc.pil_to_b64(pred_pil)
return 'data:image/png;base64, ' + enc_pred
def update_image(content, input_1):
if content != None:
string = input_1.split(';base64,')[-1]
im_pil = drc.b64_to_pil(string)
im_pil = im_pil.resize((300, 300), Image.ANTIALIAS)
enc_img = drc.pil_to_b64(im_pil)
return 'data:image/png;base64, ' + enc_img
import dash_html_components as html
import json
import plotly.graph_objs as go
import dash_reusable_components as drc
from PIL import Image, ImageFilter, ImageDraw, ImageEnhance
BUCKET_NAME = 'bucketeer-dash-image-processing'
# [filename, image_signature, action_stack]
STORAGE_PLACEHOLDER = json.dumps({
'filename': None,
'image_signature': None,
'action_stack': []
})
IMAGE_STRING_PLACEHOLDER = drc.pil_to_b64(
Image.open('images/default.jpg').copy(), enc_format='jpeg')
GRAPH_PLACEHOLDER = dcc.Graph(id='interactive-image', style={'height': '80vh'})
# Maps process name to the Image filter corresponding to that process
FILTERS_DICT = {
'blur': ImageFilter.BLUR,
'contour': ImageFilter.CONTOUR,
'detail': ImageFilter.DETAIL,
'edge_enhance': ImageFilter.EDGE_ENHANCE,
'edge_enhance_more': ImageFilter.EDGE_ENHANCE_MORE,
'emboss': ImageFilter.EMBOSS,
'find_edges': ImageFilter.FIND_EDGES,
'sharpen': ImageFilter.SHARPEN,
'smooth': ImageFilter.SMOOTH,
'smooth_more': ImageFilter.SMOOTH_MORE
# drc = importlib.import_module("apps.dash-iamge-processing.dash_reusable_components")
#
APP_PATH = str(pathlib.Path(__file__).parent.resolve())
# [filename, image_signature, action_stack]
STORAGE_PLACEHOLDER = json.dumps({
"filename": None,
"image_signature": None,
"action_stack": []
})
IMAGE_STRING_PLACEHOLDER = drc.pil_to_b64(
Image.open(os.path.join(APP_PATH, os.path.join("images",
"default.jpg"))).copy(),
enc_format="jpeg",
)
GRAPH_PLACEHOLDER = dcc.Graph(
id="interactive-image",
figure={
"data": [],
"layout": {
"autosize":
True,
"paper_bgcolor":
"#272a31",
"plot_bgcolor":
"#272a31",
"margin":
from PIL import Image, ImageFilter, ImageDraw, ImageEnhance
#
APP_PATH = str(pathlib.Path(__file__).parent.resolve())
# [filename, image_signature, action_stack]
STORAGE_PLACEHOLDER = json.dumps({
"filename": None,
"image_signature": None,
"action_stack": []
})
IMAGE_STRING_PLACEHOLDER = drc.pil_to_b64(
Image.open(
os.path.join(APP_PATH, os.path.join("input/test/autistic",
"001.jpg"))).copy(),
enc_format="jpeg",
)
GRAPH_PLACEHOLDER = dcc.Graph(
id="interactive-image",
figure={
"data": [],
"layout": {
"autosize":
True,
"paper_bgcolor":
"#272a31",
"plot_bgcolor":
"#272a31",
"margin":
