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from xml.dom import minidom
import cv2
import numpy as np
import math
from annotation_builder import AnnotationBuilder as AnnBuild
from pycocotools.coco import COCO
from shapely import geometry
from datetime import datetime
import os
import sys
import random
from from_root import from_root
from rcnn_model.preprocessing.cleaning_single_image import preprocess_image
from rcnn_model.utils.floorplan_vectorizer_utils import get_image_size, draw_from_coco
### After running, its split with https://github.com/akarazniewicz/cocosplit
### This may or may not be temporary
### Main functionality ###
scale_factor = .5
dataset_root = str(from_root("dataset"))+"/"
def main():
extract_all_cubicasa_anns(True)
def extract_all_cubicasa_anns(export_image=False):
#initialize annotation builder
ann_builder = AnnBuild()
ann_builder.set_info("converted from cubicasa 5k SVG file","cubicasa 5k","https://github.com/cubicasa/cubicasa5k",datetime(2019,5,24))
ann_builder.add_license("Creative Commons Attribution-NonCommercial 4.0 International License", "http://creativecommons.org/licenses/by-nc/4.0/")
#iterate through cubicasa files
for name in os.listdir(str(from_root(dataset_root+"cubicasa_data/"))):
process_cubicasa_image(ann_builder, name)
#save data
print("SAVING TO annotations/cubicasa_coco.json")
ann_builder.save_file(str(from_root(dataset_root+"annotations/cubicasa_coco.json")))
if(export_image):
save_validation_images(str(from_root(dataset_root+"annotations/cubicasa_coco.json")))
def process_cubicasa_image(ann_builder, name):
#load and preprocess image
print("\nprocessing "+name)
source_img_path = str(from_root(dataset_root+"cubicasa_data/"+name+"/F1_scaled.png"))
processed_img_path = str(from_root(dataset_root+"preprocessed/casa"+name+".png"))
apply_preprocessing(source_img_path, processed_img_path)
#load svg
source_svg_path = str(from_root(dataset_root+"cubicasa_data/"+name+"/model.svg"))
print("from "+source_svg_path)
print("image in "+processed_img_path)
#extract data from svg
try:
width, height = get_image_size(processed_img_path)
ann_builder = process_cubicasa(ann_builder, source_svg_path, processed_img_path, width, height)
except:
print("ERROR while extracting "+name)
print(sys.exc_info())
def find_svg(path, name):
for file in os.listdir(path):
found_name = file.startswith(name+"_gt_")
if(found_name):
found_svg = file.endswith(".svg")
if(found_svg):
return path+file
def process_cubicasa(ann_builder, sourve_svg_path, source_img_path, width, height):
#Get points
doc = minidom.parse(sourve_svg_path)
walls = extract_casa_elements_with_id("Wall",doc)
windows = extract_casa_elements_with_id("Window",doc)
doors = extract_casa_elements_with_id("Door",doc)
doc.unlink()
#export to JSON and potentially imges for visual confirmation that the process works
ann_builder = export_to_builder_casa(ann_builder,source_img_path,width,height,walls,doors,windows)
return ann_builder
### Coco Formatting/Export ###
def export_to_builder_casa(ann_builder,source_img,width,height,walls,doors,windows):
#initialization
id = ann_builder.add_image(source_img, width, height)
#walls
wall_polygons = get_features_from_ann_set(walls)
door_polygons = get_features_from_ann_set(doors)
window_polygons = get_features_from_ann_set(windows)
features = wall_polygons + door_polygons + window_polygons
rooms = create_rooms_from_features(features, width, height)
for poly in rooms.geoms:
ann_builder.add_annotation(id, 2, poly)
return ann_builder
def get_features_from_ann_set(set, coco = None, image_id = 0, category_id = 0):
polygons = []
for points in set:
poly = geometry.Polygon([[p[0], p[1]] for p in points])
if(coco is not None):
coco.add_annotation(image_id, category_id, poly)
polygons.append(poly)
return polygons
def create_rooms_from_features(features, width, height):
room_polygons = geometry.Polygon([(0,0),
(width,0),
(width,height),
(0,height)
])
for poly in features:
room_polygons = room_polygons.difference(poly,3)
return geometry.MultiPolygon(room_polygons.geoms[1:]) #this eliminates the exterior from the rooms
def apply_preprocessing(source_path, processed_path):
img = cv2.imread(source_path)
small_img = cv2.resize(img, (0,0), fx=scale_factor, fy=scale_factor)
cv2.imwrite(processed_path,small_img)
processed_img = preprocess_image(processed_path)
#small = cv2.resize(processed, (0,0), fx=scale_factor, fy=scale_factor)
cv2.imwrite(processed_path,processed_img)
print(get_image_size(source_path))
print(get_image_size(processed_path))
### SVG element extraction ###
def get_casa_size(doc):
path = doc.getElementsByTagName('svg')[0]
return int(float(path.getAttribute('width'))), int(float(path.getAttribute('height')))
def extract_casa_elements_with_id(id, doc):
elements = []
for path in doc.getElementsByTagName('g'):
#iterates through everything and finds items labelled as walls
if(id in path.getAttribute('id')):
#luckily, the first attribute after all of these is a polygon containing a list of coordinate points
string = path.firstChild.getAttribute('points')
points = points_string_to_int_points(string)
elements.append(points)
return elements
### Helper Functions ###
def quadrilateral_to_line(points):
base_point = [0,0]
points.sort(key=lambda p: check_distance(base_point,p))
base_point = points[0]
points.sort(key=lambda p: check_distance(base_point,p))
return np.array([get_midpoint(points[0], points[1]), get_midpoint(points[2], points[3])])
def check_distance(point_A, point_B):
return math.sqrt(((point_A[0]-point_B[0]) ** 2) + ((point_A[1]-point_B[1]) ** 2))
def get_midpoint(point_A, point_B):
return np.array([round((point_A[0]+point_B[0])/2), round((point_A[1]+point_B[1])/2)])
def points_string_to_int_points(string):
return [[int(round(float(pi)*scale_factor)) for pi in p.split(",")] for p in string.split()]
### Validation Images ###
def save_validation_images(filepath):
count = 0
result = COCO(filepath)
for id in random.sample(result.getImgIds(), 15):
print("IMAGE "+str(result.imgs[id]))
validation_path = str(from_root(dataset_root+"validation_images/casa_"+str(count)))
draw_from_coco(id, result, validation_path)
count+=1
main() |