def saveParameters(fileDir):
# Model name 1 mean dataset`s folder 1.
model_name = '1'
detection_model = objectDetector.load_model(model_name)
# File is directory
files = utility.get_filenames(fileDir)
fileNames = []
domColors = []
wallColors = []
floorColors = []
for f in files:
if "." not in f:
continue
print("Now proceeding ", f, " [ ", files.index(f), " ]")
coord, str_tag, number_tag, score = objectDetector.inference(
detection_model, f)
# Save file name make.
save_file_name = utility.add_name(f, "_od", extension="bin")
dirs = save_file_name.split("/")
save_image_name = ""
for d in dirs[0:-1]:
save_image_name += d + "/"
save_image_name += f.split("/")[-1].split(".")[0] + "/"
utility.make_dir(save_image_name)
rect_files = []
additional_infor = []
for i in range(len(str_tag)):
additional_infor.append(-1)
rect_image = image_processing.get_rect_image(
f, int(coord[i][0]), int(coord[i][1]), int(coord[i][2]),
int(coord[i][3]))
rect_image_name = save_image_name + f.split("/")[-1]
rect_image_name = utility.add_name(rect_image_name, "_" + str(i))
rect_files.append(rect_image_name)
utility.save_image(rect_image, rect_image_name)
dom_color = image_processing.get_dominant_color(f)
n_color = utility.get_remarkable_color_n(dom_color, MAX_COLOR_LENGTH)
fileNames.append(os.path.basename(f))
domColors.append(n_color)
wallColors.append([])
floorColors.append([])
utility.save_result([
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
], save_file_name)
utility.save_result([files, domColors, wallColors, floorColors],
config.RESEARCH_BASE_FILE)
def getPartChangedImage(inputFile,
outputFile,
str_tag,
coord,
rect_files,
selectedPreferenceImage,
i,
j,
ratio=(0.5, 0.5)):
partChangedOutFile = utility.add_name(outputFile,
"_changed_" + str(i) + str(j))
original_image = utility.read_image(inputFile)
resized_coord = utility.change_arrcoords(coord, ratio=ratio)
recommand_furniture = []
changed_log = []
for k in range(len(str_tag)):
if (str_tag[k] == "sofa" or str_tag[k] == "chair"):
inpaintingRandomValue = random.randint(0, 9)
furniture_file = rect_files[k]
# 만약 userinput 이 있다면, 그것을 대신 사용.
if utility.is_exist(utility.get_userinput_bin(furniture_file)):
furniture_data_file = utility.get_userinput_bin(furniture_file)
else:
furniture_data_file = utility.get_bin(furniture_file)
styled_furniture, change_color = styleTransfer(
furniture_file,
furniture_data_file,
selectedPreferenceImage,
inpaintingRandomValue,
ratio=ratio)
original_image = image_processing.add_up_image_to(original_image, styled_furniture, \
int(resized_coord[k][0]), int(resized_coord[k][1]), int(resized_coord[k][2]), int(resized_coord[k][3]))
rec_furn = getRecommandFurnitureForImage(selectedPreferenceImage,
str_tag[k])
if len(rec_furn) < 3:
utility.logging(selectedPreferenceImage)
utility.logging(str(rec_furn))
recommand_furniture.append(["", "", ""])
else:
recommand_furniture.append(random.sample(rec_furn, 3))
changed_log.append([resized_coord[k], change_color])
out_res_file = utility.add_name(partChangedOutFile,
"_result",
extension=".bin")
utility.save_result([changed_log, recommand_furniture], out_res_file)
utility.save_image(original_image, partChangedOutFile)
return partChangedOutFile, out_res_file
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 changeWallFloor(inputFile,
outputFile,
wall_divided,
wall_total,
wall_number,
i,
preferWallColor,
preferFloorColor,
ratio=(0.5, 0.5)):
wfOutputFile = utility.add_name(outputFile, "_wfColor" + str(i))
styler.change_dest_color(inputFile, wfOutputFile, preferWallColor[i], \
wall_divided, wall_total, [wall_total[wall_number.index(segmentation.WALL_CLASS)][0]],\
touch_hint=wall_number.index(segmentation.WALL_CLASS), ratio=ratio)
styler.change_dest_color(wfOutputFile, wfOutputFile, preferFloorColor[i], \
wall_divided, wall_total, [wall_total[wall_number.index(segmentation.FLOOR_CLASS)][0]],\
touch_hint=wall_number.index(segmentation.FLOOR_CLASS))
return wfOutputFile
def saveParameter(fileName, detection_model):
coord, str_tag, number_tag, score = objectDetector.inference(
detection_model, fileName)
# Save file name make.
save_file_name = config.RESEARCH_BASE_DIR + "/" + os.path.basename(
utility.get_od_bin(fileName))
dirs = save_file_name.split("/")
save_image_name = ""
for d in dirs[0:-1]:
save_image_name += d + "/"
save_image_name += fileName.split("/")[-1].split(".")[0] + "/"
utility.make_dir(save_image_name)
rect_files = []
additional_infor = []
for i in range(len(str_tag)):
additional_infor.append(-1)
rect_image = image_processing.get_rect_image(fileName,
int(coord[i][0]),
int(coord[i][1]),
int(coord[i][2]),
int(coord[i][3]))
rect_image_name = save_image_name + fileName.split("/")[-1]
rect_image_name = utility.add_name(rect_image_name, "_" + str(i))
rect_files.append(rect_image_name)
utility.save_image(rect_image, rect_image_name)
dom_color = image_processing.get_dominant_color(fileName)
n_color = utility.get_remarkable_color_n(dom_color, MAX_COLOR_LENGTH)
utility.save_result([
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
], save_file_name)
return [
coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color
]
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
]
import matrix_processing
import image_processing
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import Qt
import config
# File Name global variale
# 608, 580, 543, 333*
RESEARCH_BASE_DIR = config.RESEARCH_BASE_DIR
IMAGE_BASE_NAME = "interior (704)"
IMAGE_INDEX = 0
IMAGE_NAME = "C:/MLDATA/" + IMAGE_BASE_NAME + "/" + IMAGE_BASE_NAME + "_" + str(
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
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 getStyleChangedImage_past(inputFile, preferenceImages, tempdata="temp"):
'''
inputFile에 대한 preferenceImages 를 출력.
print 함수로 각 변환한 사진의 이름을 출력하고, 마지막에 몇 장을 줄것인지 출력한다.
1. Object Detect 결과로 나온 가구들을 Segmentation 화 한다.
2. 사용자가 좋아한다고 고른 인테리어의 가구 + 사용자가 좋아할것 같은 단독 가구의 색 / 재질 / Segment 를 가져온다.
3. 원래의 인테리어의 가구들에 적절하게 배치한다.
3-1. 원래의 인테리어 가구의 재질과 색을 변경한다. ( 모든 sofa, chair 에 대해서 ) -> 40%
3-2. 원래의 인테리어 가구를 사용자가 좋아할만한 가구로 변경한다. ( 모든 sofa, chair에 대해서 color filter 적용한걸로 ) -> 40%
3-3. 원래의 인테리어에서 색상 filter만 입혀준다. ( 위의 0.2 부분 )
'''
if "\\" in inputFile:
dirs = inputFile.split("\\")
inputFile = ""
for d in dirs[:-1]:
inputFile += d + "/"
inputFile += dirs[-1]
outputFile = utility.get_add_dir(inputFile, tempdata)
# fav_furniture_list = "Image/InteriorImage/test_furniture/sofa"
# fav_furniture_list = utility.get_filenames(fav_furniture_list)
# 기존 Data 출력.
base_name = inputFile.split("/")[-1].split("Z")[-1]
researched_files = utility.get_only_jpg_files(
"C:/workspace/IOU-Backend/util/IOU-ML/Image/InteriorImage/test")
checked_file = ""
for rf in researched_files:
if base_name in rf:
checked_file = rf
[coord, str_tag, number_tag, score, rect_files, additional_infor,
n_color] = utility.get_od_data(checked_file)
'''
segment_data = []
for f in rect_files:
segment_data.append(utility.get_segment_data(f))
fav_furniture_seg_data = []
for f in fav_furniture_list:
fav_furniture_seg_data.append(utility.get_segment_data(f))
'''
returnImageList = []
for i in range(MAX_OUT_IMAGE):
now_index = random.randint(0, len(preferenceImages) - 1)
saveOutputFile = utility.add_name(outputFile, "_" + str(i))
if i < MAX_OUT_IMAGE * 0.2:
original_image = utility.read_image(inputFile)
decrese_ratio = (1.0, 1.0)
if original_image.shape[0] * original_image.shape[1] > 1200 * 960:
decrese_ratio = (0.3, 0.3)
changed_image = styler.set_color_with_image(
inputFile, preferenceImages[now_index], mask_map=None)
utility.save_image(changed_image, saveOutputFile)
elif i < MAX_OUT_IMAGE * 1.0:
original_image = utility.read_image(inputFile)
# 특정 크기 이상이면 decrease ratio를 조절하여 1/3으로..
decrese_ratio = (1.0, 1.0)
if original_image.shape[0] * original_image.shape[1] > 1200 * 960:
decrese_ratio = (0.3, 0.3)
original_image = cv2.resize(original_image,
None,
fx=decrese_ratio[0],
fy=decrese_ratio[1],
interpolation=cv2.INTER_AREA)
for i in range(len(str_tag)):
if (str_tag[i] == "sofa" or str_tag[i] == "chair"):
styled_furniture = styler.set_color_with_image(
"C:/workspace/IOU-Backend/util/IOU-ML/" +
rect_files[i], preferenceImages[now_index], None,
decrese_ratio)
original_image = image_processing.add_up_image_to(original_image, styled_furniture, \
int(coord[i][0] * decrese_ratio[0]), int(coord[i][1] * decrese_ratio[0]), int(coord[i][2] * decrese_ratio[0]), int(coord[i][3] * decrese_ratio[0]))
utility.save_image(original_image, saveOutputFile)
else:
original_image = utility.read_image(inputFile)
for i in range(len(str_tag)):
if (str_tag[i] == "sofa" or str_tag[i] == "chair"):
stylized_image = styler.set_style(
"C:/workspace/IOU-Backend/util/IOU-ML/" +
rect_files[i], preferenceImages[now_index])
stylized_image = np.array((stylized_image * 255)[0],
np.uint8)
styled_furniture = cv2.cvtColor(stylized_image,
cv2.COLOR_BGR2RGB)
original_image = image_processing.add_up_image_to(
original_image, styled_furniture, int(coord[i][0]),
int(coord[i][1]), int(coord[i][2]), int(coord[i][3]))
utility.save_image(original_image, saveOutputFile)
returnImageList.append(saveOutputFile)
returnImageList.append(MAX_OUT_IMAGE)
return returnImageList
def objectDetect(inputFile, outputFile):
'''
입력받은 inputFile의 가구를 ObjectDetection한 결과를 outputFile에 저장한다. json 형태로 저장한다.
현재는 bin file로만 입출력이 가능.
폴더를 입력하면 outputFile은 무시됨.
'''
if "." not in inputFile:
# File is directory
files = utility.get_filenames(inputFile)
for f in files:
if "." not in f:
continue
coord, str_tag, number_tag, score = objectDetector.inference(
detection_model, f)
# Save file name make.
save_file_name = utility.add_name(f, "_od", extension="bin")
dirs = save_file_name.split("/")
save_image_name = ""
for d in dirs[0:-1]:
save_image_name += d + "/"
save_image_name += f.split("/")[-1].split(".")[0] + "/"
utility.make_dir(save_image_name)
rect_files = []
additional_infor = []
for i in range(len(str_tag)):
additional_infor.append(-1)
rect_image = image_processing.get_rect_image(
f, int(coord[i][0]), int(coord[i][1]), int(coord[i][2]),
int(coord[i][3]))
rect_image_name = save_image_name + f.split("/")[-1]
rect_image_name = utility.add_name(rect_image_name,
"_" + str(i))
rect_files.append(rect_image_name)
utility.save_image(rect_image, rect_image_name)
utility.save_result([
coord, str_tag, number_tag, score, rect_files, additional_infor
], save_file_name)
else:
coord, str_tag, number_tag, score = objectDetector.inference(
detection_model, inputFile)
# Save file name make.
save_file_name = utility.add_name(inputFile, "_od", extension="bin")
dirs = save_file_name.split("/")
save_image_name = ""
for d in dirs[0:-1]:
save_image_name += d + "/"
save_image_name += inputFile.split("/")[-1].split(".")[0] + "/"
utility.make_dir(save_image_name)
rect_files = []
additional_infor = []
for i in range(len(str_tag)):
additional_infor.append(-1)
rect_image = image_processing.get_rect_image(
inputFile, int(coord[i][0]), int(coord[i][1]),
int(coord[i][2]), int(coord[i][3]))
rect_image_name = save_image_name + inputFile.split("/")[-1]
rect_image_name = utility.add_name(rect_image_name, "_" + str(i))
rect_files.append(rect_image_name)
utility.save_image(rect_image, rect_image_name)
utility.save_result(
[coord, str_tag, number_tag, score, rect_files, additional_infor],
outputFile)
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