def getFurnitureShape(inputFile, inputDataFile, outputFile):
'''
입력받은 inputFile과 그 분석 파일 inputDataFile을 통해 grayscale 및 segmentation 된 데이터를 만든다.
만든 데이터는 outputFile로 ( Grayscale 사진 ) Output.
'''
if utility.is_exist(inputDataFile):
[divided_class, _, class_total, _] = utility.load_result(inputDataFile)
else:
segment(inputFile, None, inputDataFile)
[divided_class, _, class_total, _] = utility.load_result(inputDataFile)
gray_image = image_processing.to_gray_scale(inputFile)
utility.print_image(gray_image)
utility.save_image(gray_image, outputFile)
def colorTransferToColor(inputFile, inputDataFile, outputFileName, destColor,
srcColor):
'''
입력받은 inputFile의 정해진 부분( srcColor와 비슷한 부분 )의 색을 destColor로 변경한다.
'''
if utility.is_exist(inputDataFile):
[divided_class, class_number, class_total, class_border] = \
utility.load_result(inputDataFile)
class_count = []
for ct in class_total:
class_count.append(len(ct))
else:
divided_class, class_number, class_total, class_border, class_count, class_length, class_color, _, _, _ = \
segmentation.get_divided_class(inputFile)
class_color = image_processing.get_class_color(
utility.read_image(inputFile), class_total, class_count)
destArea = styler.get_similar_color_area(
divided_class, class_number, class_total, class_color, srcColor,
240) # Simmilar Color threshold to 200.
part_change_image = styler.change_area_color(inputFile, outputFileName,
destColor, divided_class,
destArea)
utility.save_image(part_change_image, outputFileName)
def readResultData(outputFile):
'''
Object Detection 한 output file을 읽어서 사용 가능한 형태로 return.
'''
[coord, str_tag, number_tag, score,
filenames] = utility.load_result(outputFile)
return coord, str_tag
def getRecommandFurnitureForImage(selectedPreferenceImage, str_tag):
[basePreferenceFiles,
recommandFile] = utility.load_result(config.RECOMMAND_BASE_FILE)
temp = recommandFile[basePreferenceFiles.index(
os.path.basename(selectedPreferenceImage))]
retRecomandFile = []
recType = "sofa" if str_tag == "sofa" or str_tag == "chair" else "table"
for t in temp:
if t not in retRecomandFile and recType in t:
retRecomandFile.append(t)
return retRecomandFile
def colorTransferToCoord(inputFile, inputDataFile, outputFileName, destColor,
destCoordList):
'''
입력받은 inputFile의 정해진 부분( destCoordList )의 색을 destColor로 변경한다.
'''
if utility.is_exist(inputDataFile):
[divided_class, _, class_total, _] = utility.load_result(inputDataFile)
else:
divided_class, _, class_total, _, _, _, _, _, _, _ = \
segmentation.get_divided_class(inputFile)
styler.change_dest_color(inputFile, outputFileName, destColor,
divided_class, class_total, destCoordList)
def re_segmentation(fileDir, resegIndex):
fileNames = utility.get_filenames(fileDir)
for fIndex in range(len(fileNames)):
f = fileNames[fIndex]
odf = utility.get_od_bin(f)
[
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
] = utility.load_result(odf)
for i in resegIndex[fIndex]:
rect_data_file = utility.get_bin(rect_files[i])
print(rect_data_file, " will be re-generated. : ", str_tag[i])
segment(rect_files[i], utility.add_name(rect_files[i], "_divided"),
rect_data_file)
def readParameter(fileDir):
# File is directory
files = utility.get_filenames(fileDir)
bin_files = []
for f in files:
if ".bin" in f:
bin_files.append(f)
total_parameter = []
total_files = []
for bf in bin_files:
[_, str_tag, _, _, _, _, n_color] = utility.load_result(bf)
parameter = normParameter(str_tag, n_color)
image_file = bf[:-7] + ".jpg"
total_parameter.append(parameter)
total_files.append(image_file)
return total_parameter, total_files
def colorTransferWithImage(inputFile, inputDataFile, outputFileName,
destImage):
'''
입력받은 inputFile의 색을 destImage와 비슷하게 변경해서 outputFileName에 저장한다.
Segmentation이 된다면 자른 부분만 변경.
'''
if utility.is_exist(inputDataFile):
[_, _, class_total, _] = \
utility.load_result(inputDataFile)
class_count = []
for ct in class_total:
class_count.append(len(ct))
else:
_, _, class_total, _, class_count, _, _, _, _, _ = \
segmentation.get_divided_class(inputFile)
_, _, mask_map, (width,
height) = segmentation.get_segmented_image(inputFile)
changed_image = styler.set_color_with_image(inputFile, destImage, mask_map)
utility.save_image(changed_image, outputFileName)
def textureTransferArea(inputFile, inputDataFile, outputFileName, destTexture,
srcColor):
'''
입력받은 inputFile의 정해진 부분( srcColor와 비슷한 색 )의 질감을 destTexture로 변경한다.
'''
if utility.is_exist(inputDataFile):
[divided_class, class_number, class_total, _] = \
utility.load_result(inputDataFile)
class_count = []
for ct in class_total:
class_count.append(len(ct))
else:
divided_class, class_number, class_total, _, class_count, _, class_color, _, _, _ = \
segmentation.get_divided_class(inputFile)
class_color = image_processing.get_class_color(
utility.read_image(inputFile), class_total, class_count)
destArea = styler.get_similar_color_area(
divided_class, class_number, class_total, class_color, srcColor,
240) # Simmilar Color threshold to 200.
styler.change_area_style(inputFile, outputFileName, destTexture, destArea)
def image_color_match(inputImage):
# 이미지와 어울리는 컬러 List의 List를 return.
[fileNames,
colors] = utility.load_result(RESEARCH_BASE_DIR + "/" + COLOR_SYSTEM_FILE)
input_colors = getDominantColor(inputImage)
admitableColors = []
admitableFiles = []
while len(admitableColors) == 0:
admitable = 10
for input_color in input_colors:
res_color, files = color_match(input_color, colors, fileNames,
admitable)
index = 0
for r in res_color:
if r not in admitableColors:
admitableColors.append(r)
admitableFiles.append(files[index])
index += 1
admitable += 10
utility.print_image(utility.color_to_image(input_colors))
return admitableColors, admitableFiles
def getODandSegment(inputFile, od_model):
try:
[coord, str_tag, number_tag, score, rect_files, additional_infor, n_color] = \
utility.load_result(config.RESEARCH_BASE_DIR + "/" + os.path.basename(utility.get_od_bin(inputFile)))
except:
[
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
] = imageClassifier.saveParameter(inputFile, od_model) # Get OD Data
for i in range(len(str_tag)):
if str_tag[i] == "sofa" or str_tag[i] == "chair":
if utility.is_exist(utility.get_userinput_bin(rect_files[i])):
rect_data_file = utility.get_userinput_bin(rect_files[i])
elif utility.is_exist(utility.get_bin(rect_files[i])):
rect_data_file = utility.get_bin(rect_files[i])
else:
rect_data_file = utility.get_bin(rect_files[i])
segment(rect_files[i],
utility.add_name(rect_files[i], "_divided"),
rect_data_file)
return [
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
]
IMAGE_INDEX) + ".jpg"
OUTPUT_FILE = RESEARCH_BASE_DIR + "/" + IMAGE_BASE_NAME + '/' + utility.add_name(
IMAGE_NAME.split("/")[-1], "_divided")
SEG_FILE_NAME = RESEARCH_BASE_DIR + '/' + utility.add_name(
IMAGE_NAME.split("/")[-1], "", extension="bin")
SEG_SAVE_NAME = RESEARCH_BASE_DIR + '/' + utility.add_name(
IMAGE_NAME.split("/")[-1], "_userInput", extension="bin")
# Constant
CHANGE_DIVIED = "Image/example/temp.jpg"
# Init Global Data for classify segmentation
totalClass = [[]]
nowIndex = 0
eraseMode = False
eraseList = []
load_value = utility.load_result(SEG_FILE_NAME)
if len(load_value) == 5:
[divided_class, class_number, class_total, class_border, _] = load_value
else:
[divided_class, class_number, class_total, class_border] = load_value
class MyApp(QWidget):
def __init__(self):
super().__init__()
self.initUI()
def initUI(self):
self.setWindowTitle("Segment GUI Helper")
grid = QGridLayout()
import mlWrapper
import utility
import sys
import random
# To use, 2번째 인자에 리스트들을 담아서 넘겨주면 된다.
if __name__ == "__main__":
[selectedPreferenceImages, wfColorChangeImage, outputFile, str_tag, coord, rect_files, i, j, ratio] = utility.load_result(sys.argv[1])
selectedPreferenceImage = selectedPreferenceImages[random.randint(0, len(selectedPreferenceImages) - 1)]
partChangedOutFile, out_res_file = mlWrapper.getPartChangedImage(wfColorChangeImage[i], outputFile, str_tag, coord, rect_files, selectedPreferenceImage, i, j, ratio=ratio)
print()
print(partChangedOutFile)
print(out_res_file)
def getStyleChangedImage(inputFile,
preferenceImages,
od_model,
baseLight=[255, 255, 255],
changeLight=[178, 220, 240]):
'''
입력 Color는 BGR ( [178, 220, 240] 은 주황불빛 )
preferenceImages 가 4장만 되어도 충분함.
'''
if len(preferenceImages) <= 2:
preferenceImages = preferenceImages + preferenceImages
print(preferenceImages)
inputBaseFile, preferenceBaseFile = utility.file_basify(
inputFile, preferenceImages)
now = time.time()
detection_model = pspnet_50_ADE_20K()
outputFile = utility.get_add_dir(inputFile, "temp")
# Object Detect & Segmentation
[coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color] = getODandSegment(inputBaseFile, od_model)
(imgHeight, imgWidth, _) = utility.read_image(inputFile).shape
if imgWidth > destSize[0] and imgHeight > destSize[1]:
ratio = (destSize[0] / imgWidth, destSize[1] / imgHeight)
else:
ratio = (1, 1)
print("Loading Finished")
temp = time.time()
print("Loading Time : ", temp - now)
# Wall Detection with input image.
wall_divided = segmentation.detect_wall_floor(inputFile, detection_model)
wall_divided = utility.resize_2darr(wall_divided, ratio=ratio)
wall_total, wall_number = matrix_processing.divided_class_into_class_total(
wall_divided)
print("Wall Divided.")
# Get preference image`s data.
preferWallColor = []
preferFloorColor = []
selectedPreferenceImages = []
[files, domColors, wallColors, floorColors] = utility.load_result(
config.RESEARCH_BASE_FILE
) # Each files` dom color, wall color, floor color will be saved.
baseNameFiles = [os.path.basename(files[f]) for f in range(len(files))]
print("Wall Color start.")
indx = list(range(0, len(preferenceBaseFile)))
random.shuffle(indx)
# Select 2 color of above to preferWallColor and preferFloorColor
for i in range(MAX_WALL_IMAGE):
ind = indx[i]
preferImage = preferenceBaseFile[ind]
loadIndex = baseNameFiles.index(os.path.basename(
preferImage)) # We do only compare with base name.
preferWallColor.append(wallColors[loadIndex])
preferFloorColor.append(floorColors[loadIndex])
selectedPreferenceImages.append(files[loadIndex])
print("Wall Colored Selected.")
# Change wall & floor
wfColorChangeImage = []
for i in range(MAX_WALL_IMAGE):
wfOutputFile = changeWallFloor(inputFile,
outputFile,
wall_divided,
wall_total,
wall_number,
i,
preferWallColor,
preferFloorColor,
ratio=ratio)
wfColorChangeImage.append(wfOutputFile)
print("Wall Color Changed")
temp = time.time()
print("Wall Coloring Time : ", temp - now)
# Change Object ( Table and Chair )
partChangedFiles = []
procs = []
recommandFurnitureList = []
changeFurnitureLocation = []
changeFurnitureColor = []
for i in range(MAX_WALL_IMAGE):
for j in range(MAX_PART_CHANGE_IMAGE):
# 넘겨줄 인자를 저장하고, Thread를 실행시켜서 속도 향상.
argvFile = utility.add_name(
config.SUBPROCESS_ARGV,
"_" + str(MAX_PART_CHANGE_IMAGE * i + j))
utility.save_result([
selectedPreferenceImages, wfColorChangeImage, outputFile,
str_tag, coord, rect_files, i, j, ratio
], argvFile)
# Subprocess need to calculate with given ratio.
proc = subprocess.Popen(
['python', 'getPartChangedImage.py', argvFile],
shell=True,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
encoding="cp949")
procs.append(proc)
for i in range(len(procs)):
out = procs[i].communicate()[0]
out = str(out).split("\n")
tout = []
for i in range(len(out)):
if len(out[i]) > 0:
tout.append(out[i])
[changed_log, recommand_furniture] = utility.load_result(tout[-1])
partChangedFiles.append(tout[-2])
recommandFurnitureList.append(recommand_furniture)
for i in range(len(changed_log)):
changeFurnitureLocation.append(changed_log[i][0])
changeFurnitureColor.append(changed_log[i][1])
print("Part Changed Finished")
# Add some plant.
# partChangedFiles = print() # Image number will not be changed.
temp = time.time()
print("Part Changing Time : ", temp - now)
lightList = []
# Change Light
for i in range(MAX_OUT_IMAGE):
print("Now Proceed : ", i)
files = utility.add_name(partChangedFiles[i], "_lighter")
if random.randint(1, MAX_OUT_IMAGE) > 4:
changed_file = styler.get_light_change(partChangedFiles[i],
baseLight, changeLight)
lightList.append(changeLight)
else:
changed_file = styler.get_light_change(partChangedFiles[i],
baseLight, baseLight)
lightList.append(baseLight)
utility.save_image(changed_file, files)
partChangedFiles[i] = files
# partChangedFiles 가 결국 바뀐 파일들
temp = time.time()
print("Total Time : ", temp - now)
changeLog = makeChangeInfor(preferWallColor, preferFloorColor, [preferenceImages[indx[0]], preferenceImages[indx[1]]], partChangedFiles, lightList, changeFurnitureLocation, changeFurnitureColor, \
recommandFurnitureList, [])
resultDictionary = utility.save_log_dictionary(inputFile, partChangedFiles,
changeLog)
utility.logging(str(resultDictionary))
with open(FILE_OUTQUEUE, 'a') as f:
f.write(str(resultDictionary) + "\n")
def styleTransfer(inputFile,
inputDataFile,
destFile,
inpaintingRandomValue,
ratio=(1.0, 1.0)):
'''
입력받은 inputFile의 색과 질감을 destFile의 색과 질감으로 임의로 변형해준다.
'''
if utility.is_exist(inputDataFile):
loadData = utility.load_result(inputDataFile)
if len(loadData) == 5:
# Newer Version of segmentation.
[divided_class, class_number, class_total, _,
largest_mask] = loadData
else:
[divided_class, class_number, class_total, _] = loadData
largest_mask = None
class_count = []
for ct in class_total:
class_count.append(len(ct))
else:
divided_class, class_number, class_total, _, class_count, _, class_color, _, _, _ = \
segmentation.get_divided_class(inputFile)
# Init Variables. - TODO : Change this part with largest mask.
# largest_mask, _, _, (width, height) = segmentation.get_segmented_image(inputFile)
# class_color = image_processing.get_class_color(utility.read_image(inputFile), class_total, class_count)
img = utility.read_image(inputFile)
(height, width, _) = img.shape
file_extension = "." + inputFile.split(".")[1]
file_base_name = inputFile.split(".")[0]
resized_class_total = utility.changed_coords2d(class_total, ratio=ratio)
# 중복 제거
temp_class_total = resized_class_total
resized_class_total = []
for tc in temp_class_total:
if tc not in resized_class_total:
resized_class_total.append(tc)
input_sample = [
resized_class_total[i][0] for i in range(len(resized_class_total))
]
if len(input_sample) < MAX_CHANGE_COLOR:
input_sample *= int(MAX_CHANGE_COLOR // len(input_sample)) + 1
dest_color = image_processing.get_dominant_color(destFile, clusters=8)
next_file_name = file_base_name + "_" + str(0) + file_extension
now_input_sample = random.sample(input_sample, MAX_CHANGE_COLOR)
now_dest_color = random.sample(dest_color, MAX_CHANGE_COLOR)
part_change_image = utility.read_image(inputFile)
part_change_image = utility.resize_image(part_change_image, ratio=ratio)
randomValue = inpaintingRandomValue
if randomValue < -1:
# Image Inpainting
masking_coord = []
for ct in resized_class_total:
masking_coord += ct
tempFile = utility.add_name(next_file_name, "_temp")
tempFile = config.RESEARCH_BASE_DIR + "/temp/" + tempFile.split(
"/")[-1]
utility.logging("Image Inpainting Starting." + str(randomValue))
utility.save_image(
utility.make_whitemask_image(part_change_image, masking_coord),
tempFile)
change_image = image_processing.inpainting(part_change_image, tempFile)
part_change_image = image_processing.add_up_image(
part_change_image, change_image, masking_coord, width, height)
now_dest_color = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
else:
utility.logging("Image Inpainting Do not proceed. : " +
str(randomValue))
# If not earse, recoloring.
for j in range(MAX_CHANGE_COLOR):
change_image = styler.change_dest_color(inputFile, next_file_name, now_dest_color[j], divided_class, resized_class_total,\
[now_input_sample[j]], save_flag=False, ratio=ratio)
part_change_image = image_processing.add_up_image(
part_change_image, change_image,
resized_class_total[input_sample.index(now_input_sample[j])],
width, height)
return part_change_image, now_dest_color
