def upload():
# style_path = request.files['style_file']
# content_path = request.files['image_file']
# best, best_loss = run_style_transfer(
# content_path, style_path, num_iterations=500)
# Image.fromarray(best)
# im = Image.fromarray(best)
# styled = im.save('styled.jpg')
image_file = request.files['image_file']
model = os.path.join(os.path.abspath(''), 'rcnn_model.pkl')
show_objects = load_object(image_file, model)
# contour_outlines = show_selection(raw_input, filename, show_objects)
storage_client = storage.Client(project='amli-245518')
bucket = storage_client.get_bucket(CLOUD_STORAGE_BUCKET)
destination_blob_name = 'test.jpg'
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(style_file.filename)
print("Send file")
return render_template('upload.html')
def upload():
transfer_option = request.form.get('transfer_select')
if transfer_option == 'whole':
style = request.files['style_file']
# style_path = request.files['style_file']
style_name = secure_filename(style.filename)
style_path = os.path.join('static/style_images', style_name)
style.save(style_path)
# url = upload_to_gcloud(style_path)
content = request.files['image_file']
content_name = secure_filename(content.filename)
content_path = os.path.join('static/input_images', content_name)
content.save(content_path)
content_img_name = os.path.basename(content_path)[:-4]
style_img_name = os.path.basename(style_path)[:-4]
# content_path = request.path['image_file']
# print(style_path)
# best, best_loss = run_style_transfer(
# content_path, style_path, num_iterations=1)
# style = "images/style_images/statue_of_liberty_sq.jpg"
# content = "images/content_images/image.jpg"
test = "arbitrary_image_stylization_with_weights \
--checkpoint=arbitrary_style_transfer/model.ckpt \
--output_dir=outputs \
--style_images_paths=" + style_path + "\
--content_images_paths=" + content_path + "\
--image_size=256 \
--content_square_crop=False \
--style_image_size=256 \
--style_square_crop=False \
--logtostderr"
os.system(test)
path = os.path.join(
os.path.abspath('static/out'),
'%s_stylized_%s_%d.jpg' % (content_img_name, style_img_name, 0))
# print(path)
# im=Image.fromarray(best)
# im.save('static/out/styled.jpg')
# styled_file=os.path.join(
# os.path.abspath(''), 'static/out/styled.jpg')
url = upload_to_gcloud_name(
path,
'%s_stylized_%s_%d.jpg' % (content_img_name, style_img_name, 0))
# return render_template('upload.html')
return render_template('upload.html', image_url=url)
elif transfer_option == 'object':
# # Upload style image first
style_path = request.files['style_file']
global STYLE_URL
STYLE_URL = upload_to_gcloud(style_path)
content_path = request.files['image_file']
content_path_copy = request.files['image_file']
global CONTENT_URL
CONTENT_URL = upload_to_gcloud(content_path_copy)
# print(content_path_copy)
# content_path = request.files['image_file']
# content_path_copy = content_path
# print(content_path_copy)
ROOT_DIR = os.path.abspath("../")
MaskRCNN_DIR = os.path.abspath("../Mask_RCNN")
# global CONTENT_URL
# CONTENT_URL = upload_to_gcloud(content_path_copy)
# sys.path.append(os.path.join(MaskRCNN_DIR, "samples/coco/"))
# sys.path.append(MaskRCNN_DIR) # To find local version of the library
MODEL_DIR = os.path.join(MaskRCNN_DIR, "samples/coco/")
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
config = InferenceConfig()
detection_model = modellib.MaskRCNN(mode="inference",
model_dir=MODEL_DIR,
config=config)
detection_model.load_weights(COCO_MODEL_PATH, by_name=True)
global RESULTS
global SHOW_OBJECTS
# content = download_from_gcloud(os.path.basename(CONTENT_URL))
RESULTS, SHOW_OBJECTS = load_object(CONTENT_URL, detection_model)
# load_object(image_file, detection_model)
# config = InferenceConfig()
# model = modellib.MaskRCNN(
# mode="inference", model_dir=MODEL_DIR, config=config)
# model.load_weights(COCO_MODEL_PATH, by_name=True)
# results, show_objects = load_object(image_file)
# RESULTS = results
# SHOW_OBJECTS = show_objects
# contour_outlines = show_selection(raw_input, filename, show_objects)
url = upload_to_gcloud_name(SHOW_OBJECTS, 'all_objects.jpg')
return render_template('object.html', image_url=url)
from google.cloud import storage
import os
from flask import request, send_file
import tempfile
from object_detection import load_object
style_file = os.path.join(os.path.abspath(''), 'stylize.jpg')
model = os.path.join(os.path.abspath(''), 'rcnn_model.pkl')
show_objects = load_object(style_file, model)
# style_file = request.files['stylize.jpg']
# style_file = os.path.abspath('../Flask-STWA/stylize.jpg')
# print(os.path.join(os.path.abspath(''), 'stylize.jpg'))
CLOUD_STORAGE_BUCKET = 'style-input-images-1'
def upload():
style_file = os.path.join(os.path.abspath(''), 'stylize.jpg')
CLOUD_STORAGE_BUCKET = 'style-input-images-1'
storage_client = storage.Client(project='amli-245518')
bucket = storage_client.get_bucket(CLOUD_STORAGE_BUCKET)
destination_blob_name = 'stylize.jpg'
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(style_file)
def download():
CLOUD_STORAGE_BUCKET = 'style-input-images-1'
style_file = 'stylize.jpg'
# style_file = os.path.abspath('../Flask-STWA/stylize.jpg')
def upload():
transfer_option = request.form.get('transfer_select')
global STYLE_URL, CONTENT_URL
global RESULTS, SHOW_OBJECTS
global LOCATION
global SELECTION
if transfer_option == 'whole':
style = request.files['style_file']
style_name = secure_filename(style.filename)
style_path = os.path.join('static/style_images', style_name)
style.save(style_path)
content = request.files['image_file']
content_name = secure_filename(content.filename)
content_path = os.path.join('static/input_images', content_name)
content.save(content_path)
content_img_name = os.path.basename(content_path)[:-4]
style_img_name = os.path.basename(style_path)[:-4]
test = "arbitrary_image_stylization_with_weights \
--checkpoint=arbitrary_style_transfer/model.ckpt \
--output_dir=static/out \
--style_images_paths=" + style_path + "\
--content_images_paths=" + content_path + "\
--image_size=512 \
--content_square_crop=False \
--style_image_size=512 \
--style_square_crop=False \
--logtostderr"
os.system(test)
path = 'static/out/' + ('%s_stylized_%s_0.jpg' %
(content_img_name, style_img_name))
return render_template('upload.html', image_url=path)
elif transfer_option == 'object':
# # Upload style image first
SELECTION = 'object'
style = request.files['style_file']
style_name = secure_filename(style.filename)
style_path = os.path.join('static/style_images', style_name)
style.save(style_path)
STYLE_URL = style_path
content = request.files['image_file']
content_name = secure_filename(content.filename)
content_path = os.path.join('static/input_images', content_name)
content.save(content_path)
CONTENT_URL = content_path
RESULTS, SHOW_OBJECTS = load_object(CONTENT_URL, detection_model)
return render_template('object.html', image_url=SHOW_OBJECTS)
elif transfer_option == 'inverse':
SELECTION = 'inverse'
# # Upload style image first
style = request.files['style_file']
style_name = secure_filename(style.filename)
style_path = os.path.join('static/style_images', style_name)
style.save(style_path)
STYLE_URL = style_path
content = request.files['image_file']
content_name = secure_filename(content.filename)
content_path = os.path.join('static/input_images', content_name)
content.save(content_path)
CONTENT_URL = content_path
RESULTS, SHOW_OBJECTS = load_object(CONTENT_URL, detection_model)
return render_template('object.html', image_url=SHOW_OBJECTS)
def upload():
transfer_option = request.form.get('transfer_select')
# Set global variable to access across different pages
global STYLE_URL, CONTENT_URL
global RESULTS, SHOW_OBJECTS
global LOCATION
global SELECTION
# Directly transform the whole image
if transfer_option == 'whole':
style = request.files['style_file']
content = request.files['image_file']
STYLE_URL, CONTENT_URL = upload_style_content_images(style, content)
content_img_name = os.path.basename(CONTENT_URL)[:-4]
style_img_name = os.path.basename(STYLE_URL)[:-4]
# Run 100% style transfer with arbitrary_image_stylization model
out = "arbitrary_image_stylization_with_weights \
--checkpoint=arbitrary_style_transfer/model.ckpt \
--output_dir=static/final \
--style_images_paths=" + STYLE_URL + "\
--content_images_paths=" + CONTENT_URL + "\
--image_size=512 \
--content_square_crop=False \
--style_image_size=512 \
--style_square_crop=False \
--logtostderr"
os.system(out)
path = 'static/final/' + ('%s_stylized_%s_0.jpg' %
(content_img_name, style_img_name))
return render_template('upload.html', image_url=path)
# Transform the whole image with different weights of transfer
elif transfer_option == 'adjust':
style = request.files['style_file']
content = request.files['image_file']
STYLE_URL, CONTENT_URL = upload_style_content_images(style, content)
content_img_name = os.path.basename(CONTENT_URL)[:-4]
style_img_name = os.path.basename(STYLE_URL)[:-4]
# Run different weights of style transfer from 20% to 100%
INTERPOLATION_WEIGHTS = '[0.2,0.4,0.6,0.8,1.0]'
output = "arbitrary_image_stylization_with_weights \
--checkpoint=arbitrary_style_transfer/model.ckpt \
--output_dir=static/final \
--style_images_paths=" + STYLE_URL + "\
--content_images_paths=" + CONTENT_URL + "\
--image_size=512 \
--content_square_crop=False \
--style_image_size=512 \
--style_square_crop=False \
--interpolation_weights=" + INTERPOLATION_WEIGHTS + "\
--logtostderr"
os.system(output)
changed_paths = []
for i in range(5):
changed_paths.append('static/final/' +
('%s_stylized_%s_%d.jpg' %
(content_img_name, style_img_name, i)))
return render_template('wholeOptions.html', image_url=changed_paths)
# Object Detection
elif transfer_option == 'object':
SELECTION = 'object'
style = request.files['style_file']
content = request.files['image_file']
STYLE_URL, CONTENT_URL = upload_style_content_images(style, content)
# Run Object Detection
RESULTS, SHOW_OBJECTS = load_object(CONTENT_URL, detection_model)
return render_template('object.html', image_url=SHOW_OBJECTS)
# Inverse Object Detection
elif transfer_option == 'inverse':
SELECTION = 'inverse'
style = request.files['style_file']
content = request.files['image_file']
STYLE_URL, CONTENT_URL = upload_style_content_images(style, content)
# Run Object Detection
RESULTS, SHOW_OBJECTS = load_object(CONTENT_URL, detection_model)
return render_template('object.html', image_url=SHOW_OBJECTS)