def apply_actions_on_image(session_id, action_stack, filename,
image_signature):
action_stack = deepcopy(action_stack)
# If we have arrived to the original image
if len(action_stack) == 0:
return im_pil0
# Pop out the last action
last_action = action_stack.pop()
# Apply all the previous action_stack, and gets the image PIL
im_pil = apply_actions_on_image(session_id, action_stack, filename,
image_signature)
im_size = im_pil.size
# Apply the rest of the action_stack
operation = last_action['operation']
selectedData = last_action['selectedData']
type = last_action['type']
# Select using Lasso
if selectedData and 'lassoPoints' in selectedData:
selection_mode = 'lasso'
selection_zone = generate_lasso_mask(im_pil, selectedData)
# Select using rectangular box
elif selectedData and 'range' in selectedData:
selection_mode = 'select'
lower, upper = map(int, selectedData['range']['y'])
left, right = map(int, selectedData['range']['x'])
# Adjust height difference
height = im_size[1]
upper = height - upper
lower = height - lower
selection_zone = (left, upper, right, lower)
# Select the whole image
else:
selection_mode = 'select'
selection_zone = (0, 0) + im_size
# Apply the filters
if type == 'filter':
apply_filters(image=im_pil,
zone=selection_zone,
filter=operation,
mode=selection_mode)
elif type == 'enhance':
enhancement = operation['enhancement']
factor = operation['enhancement_factor']
apply_enhancements(image=im_pil,
zone=selection_zone,
enhancement=enhancement,
enhancement_factor=factor,
mode=selection_mode)
return im_pil
def apply_actions_on_image(session_id, action_stack, filename,
image_signature):
action_stack = deepcopy(action_stack)
# If we have arrived to the original image
if len(action_stack) == 0 and LOCAL:
with open("image_string.csv", mode="r") as image_file:
image_reader = csv.DictReader(image_file)
for row in image_reader:
im_pil = drc.b64_to_pil(row["image"])
return im_pil
if len(action_stack) == 0 and not LOCAL:
# Retrieve the url in which the image string is stored inside s3,
# using the session ID
url = s3.generate_presigned_url(ClientMethod="get_object",
Params={
"Bucket": bucket_name,
"Key": session_id
})
# A key replacement is required for URL pre-sign in gcp
url = url.replace("AWSAccessKeyId", "GoogleAccessId")
response = requests.get(url)
if DEBUG:
print("IMAGE STRING LENGTH: " + str(len(response.text)))
im_pil = drc.b64_to_pil(response.text)
return im_pil
# Pop out the last action
last_action = action_stack.pop()
# Apply all the previous action_stack recursively, and gets the image PIL
im_pil = apply_actions_on_image(session_id, action_stack, filename,
image_signature)
im_size = im_pil.size
# Apply the rest of the action_stack
operation = last_action["operation"]
selected_data = last_action["selectedData"]
action_type = last_action["type"]
# Select using Lasso
if selected_data and "lassoPoints" in selected_data:
selection_mode = "lasso"
selection_zone = utils.generate_lasso_mask(im_pil, selected_data)
# Select using rectangular box
elif selected_data and "range" in selected_data:
selection_mode = "select"
lower, upper = map(int, selected_data["range"]["y"])
left, right = map(int, selected_data["range"]["x"])
# Adjust height difference
height = im_size[1]
upper = height - upper
lower = height - lower
selection_zone = (left, upper, right, lower)
# Select the whole image
else:
selection_mode = "select"
selection_zone = (0, 0) + im_size
# Apply the filters
if action_type == "filter":
utils.apply_filters(image=im_pil,
zone=selection_zone,
filter=operation,
mode=selection_mode)
elif action_type == "enhance":
enhancement = operation["enhancement"]
factor = operation["enhancement_factor"]
utils.apply_enhancements(
image=im_pil,
zone=selection_zone,
enhancement=enhancement,
enhancement_factor=factor,
mode=selection_mode,
)
return im_pil
def apply_actions_on_image(session_id,
action_stack,
filename,
image_signature):
action_stack = deepcopy(action_stack)
# If we have arrived to the original image
if len(action_stack) == 0:
# Retrieve the url in which the image string is stored inside s3,
# using the session ID
url = s3.generate_presigned_url(
ClientMethod='get_object',
Params={
'Bucket': bucket_name,
'Key': session_id
}
)
# A key replacement is required for URL pre-sign in gcp
url = url.replace('AWSAccessKeyId', 'GoogleAccessId')
response = requests.get(url)
print(len(response.text))
im_pil = drc.b64_to_pil(response.text)
return im_pil
# Pop out the last action
last_action = action_stack.pop()
# Apply all the previous action_stack, and gets the image PIL
im_pil = apply_actions_on_image(
session_id,
action_stack,
filename,
image_signature
)
im_size = im_pil.size
# Apply the rest of the action_stack
operation = last_action['operation']
selectedData = last_action['selectedData']
type = last_action['type']
# Select using Lasso
if selectedData and 'lassoPoints' in selectedData:
selection_mode = 'lasso'
selection_zone = generate_lasso_mask(im_pil, selectedData)
# Select using rectangular box
elif selectedData and 'range' in selectedData:
selection_mode = 'select'
lower, upper = map(int, selectedData['range']['y'])
left, right = map(int, selectedData['range']['x'])
# Adjust height difference
height = im_size[1]
upper = height - upper
lower = height - lower
selection_zone = (left, upper, right, lower)
# Select the whole image
else:
selection_mode = 'select'
selection_zone = (0, 0) + im_size
# Apply the filters
if type == 'filter':
apply_filters(
image=im_pil,
zone=selection_zone,
filter=operation,
mode=selection_mode
)
elif type == 'enhance':
enhancement = operation['enhancement']
factor = operation['enhancement_factor']
apply_enhancements(
image=im_pil,
zone=selection_zone,
enhancement=enhancement,
enhancement_factor=factor,
mode=selection_mode
)
return im_pil
def apply_actions_on_image(session_id, action_stack, filename,
image_signature):
action_stack = deepcopy(action_stack)
# If we have arrived to the original image
if len(action_stack) == 0 and LOCAL:
with open("image_string.csv", mode="r") as image_file:
image_reader = csv.DictReader(image_file)
for row in image_reader:
im_pil = drc.b64_to_pil(row["image"])
return im_pil
if len(action_stack) == 0 and not LOCAL:
pass
# Pop out the last action
last_action = action_stack.pop()
# Apply all the previous action_stack recursively, and gets the image PIL
im_pil = apply_actions_on_image(session_id, action_stack, filename,
image_signature)
im_size = im_pil.size
# Apply the rest of the action_stack
operation = last_action["operation"]
selected_data = last_action["selectedData"]
action_type = last_action["type"]
# Select using Lasso
if selected_data and "lassoPoints" in selected_data:
selection_mode = "lasso"
selection_zone = utils.generate_lasso_mask(im_pil, selected_data)
# Select using rectangular box
elif selected_data and "range" in selected_data:
selection_mode = "select"
lower, upper = map(int, selected_data["range"]["y"])
left, right = map(int, selected_data["range"]["x"])
# Adjust height difference
height = im_size[1]
upper = height - upper
lower = height - lower
selection_zone = (left, upper, right, lower)
# Select the whole image
else:
selection_mode = "select"
selection_zone = (0, 0) + im_size
# Apply the filters
if action_type == "filter":
utils.apply_filters(image=im_pil,
zone=selection_zone,
filter=operation,
mode=selection_mode)
elif action_type == "enhance":
enhancement = operation["enhancement"]
factor = operation["enhancement_factor"]
utils.apply_enhancements(
image=im_pil,
zone=selection_zone,
enhancement=enhancement,
enhancement_factor=factor,
mode=selection_mode,
)
return im_pil
def update_graph_interactive_image(content, n_clicks, figure, selectedData,
filters, enhance, enhancement_factor,
new_filename, dragmode, enc_format,
storage):
t1 = time.time()
# Retrieve metadata stored in the storage
filename = storage
# If the file has changed (when a file is uploaded)
if new_filename and new_filename != filename:
if DEBUG:
print(filename, "replaced by", new_filename)
string = content.split(';base64,')[-1]
im_pil = drc.b64_to_pil(string)
# If the file HAS NOT changed (which means an operation was applied)
else:
# Retrieve the image stored inside the figure
enc_str = figure['layout']['images'][0]['source'].split(';base64,')[-1]
# Creates the PIL Image object from the b64 png encoding
im_pil = drc.b64_to_pil(string=enc_str)
im_size = im_pil.size
# Select using Lasso
if selectedData and 'lassoPoints' in selectedData:
selection_mode = 'lasso'
selection_zone = generate_lasso_mask(im_pil, selectedData)
# Select using rectangular box
elif selectedData and 'range' in selectedData:
selection_mode = 'select'
lower, upper = map(int, selectedData['range']['y'])
left, right = map(int, selectedData['range']['x'])
# Adjust height difference
height = im_size[1]
upper = height - upper
lower = height - lower
selection_zone = (left, upper, right, lower)
# Select the whole image
else:
selection_mode = 'select'
selection_zone = (0, 0) + im_size
# If the filter dropdown was chosen, apply the filter selected by the user
if filters:
apply_filters(image=im_pil,
zone=selection_zone,
filter=filters,
mode=selection_mode)
if enhance:
apply_enhancements(image=im_pil,
zone=selection_zone,
enhancement=enhance,
enhancement_factor=enhancement_factor,
mode=selection_mode)
t2 = time.time()
if DEBUG:
print(f"Updated Image Storage in {t2-t1:.3f} sec")
return [
drc.InteractiveImagePIL(image_id='interactive-image',
image=im_pil,
enc_format=enc_format,
display_mode='fixed',
dragmode=dragmode,
verbose=DEBUG),
html.Div(id='div-filename-image',
children=new_filename,
style={'display': 'none'})
]