"""Check if image files exist"""
for path in [args.content_image, args.style_image]:
if path is None or not os.path.isfile(path):
print("Image file {} does not exist.".format(path))
exit(1)
content_image = load_image(args.content_image)
style_image = load_image(args.style_image)
# use existing if available
if (load_segmentation):
print("Load segmentation from files.")
content_segmentation_image = cv2.imread(content_segmentation_filename)
style_segmentation_image = cv2.imread(style_segmentation_filename)
content_segmentation_masks = extract_segmentation_masks(content_segmentation_image)
style_segmentation_masks = extract_segmentation_masks(style_segmentation_image)
# otherwise compute it
else:
print("Create segmentation.")
content_segmentation, style_segmentation = compute_segmentation(args.content_image, args.style_image)
cv2.imwrite(change_filename(args.seg_dir, args.content_image, '_seg_raw', '.png'), content_segmentation)
cv2.imwrite(change_filename(args.seg_dir, args.style_image, '_seg_raw', '.png'), style_segmentation)
content_segmentation_masks, style_segmentation_masks = merge_segments(content_segmentation, style_segmentation,
args.semantic_thresh, args.similarity_metric)
cv2.imwrite(change_filename(args.seg_dir, args.content_image, '_seg', '.png'),
reduce_dict(content_segmentation_masks, content_image))
from operator import itemgetter
import networkx as nx
import nltk
import numpy as np
import tensorflow as tf
from os.path import join
from components.PSPNet.model import load_color_label_dict
from components.path import WEIGHTS_DIR
content_segmentation, style_segmentation = compute_segmentation(
"/home/skhan22/PoeticStyleTransfer/data2/summer_lake/2108832-1366x768-[DesktopNexus.com].jpg",
"/home/skhan22/PoeticStyleTransfer/data2/summer_lake/2504-1271617411m0fL.jpg"
)
print(content_segmentation.shape)
print(style_segmentation.shape)
# load color - label mapping
color_label_dict = load_color_label_dict()
label_color_dict = {
label: color
for color, labels in color_label_dict.items() for label in labels
}
colors = color_label_dict.keys()
# Extract the boolean mask for every color
content_masks = extract_segmentation_masks(content_segmentation, colors)
style_masks = extract_segmentation_masks(style_segmentation, colors)