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 segment(inputFile, outputFile, outputDataFile, total=True):
divided_class, class_number, class_total, class_border, _, _, class_color, largest_mask, width, height = \
segmentation.get_divided_class(inputFile, total=total)
utility.save_result(
[divided_class, class_number, class_total, class_border, largest_mask],
outputDataFile)
dc_image = utility.divided_class_into_image(divided_class, class_number,
class_color, width, height,
class_number)
if not outputFile == None:
utility.save_image(dc_image, outputFile)
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 get_color_system(directory):
'''
directory 내부에 있는 모든 Color system의 목록을 조사한다. Remarkable Color로 조사한다.
조사한 결과는 pickle을 통해 directory에 저장해둔다.
'''
fileNames = utility.get_filenames(directory)
colors = []
baseName = []
for f in fileNames:
print(f, " now doing .. ", str(fileNames.index(f)))
baseName.append(utility.get_base_name(f))
colors.append(getDominantColor(f))
utility.save_result([baseName, colors],
RESEARCH_BASE_DIR + "/" + COLOR_SYSTEM_FILE)
def segment(inputFile, outputFile, outputDataFile, total=False):
'''
입력받은 파일을 Segmentation 해서 output한다.
Output 한 결과는 조각난 사진 모음.
'''
divided_class, class_number, class_total, class_border, _, _, class_color, largest_mask, width, height = \
segmentation.get_divided_class(inputFile, total=total)
utility.save_result(
[divided_class, class_number, class_total, class_border, largest_mask],
outputDataFile)
dc_image = utility.divided_class_into_image(divided_class, class_number,
class_color, width, height,
class_number)
if not outputFile == None:
utility.save_image(dc_image, outputFile)
return divided_class, class_number, class_total, class_border
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 saveData(self):
global nowIndex # 현재 추가하고 있는 index
global divided_class # Class Number map
global class_number # Class Number 의 종류
global class_total # 각 Class들의 total Coords
global class_border # Class border.
img = cv2.imread(IMAGE_NAME)
(height, width, _) = img.shape
class_total, class_number, divided_class = mergeGroup(
class_total, class_number, divided_class, nowIndex)
utility.save_result(
[divided_class, class_number, class_total, class_border],
SEG_SAVE_NAME)
class_count = [len(class_total[i]) for i in range(len(class_total))]
class_color = image_processing.get_class_color(
utility.read_image(IMAGE_NAME), class_total, class_count)
dc_image = utility.divided_class_into_image(divided_class,
class_number, class_color,
width, height,
class_number)
utility.save_image(dc_image, CHANGE_DIVIED)
self.imageLabel.changePixmap(CHANGE_DIVIED)
label_min, label_max, label_median, label_var = get_label_args(label)
plot_label_distribution(label)
# Convert dataframe to array and normalize the data
train_array, label_array, test_array = scale_data(train, test, label)
# Train and predict
# Simple 3-layers neural nets
print "\n[Simple 3-layers neural nets]\n"
NeuralNet1 = load_model_if_exists(NeuralNet1, SIMPLE_3LAYERS_FILENAME)
NeuralNet1.fit(train_array, label_array)
NeuralNet1.save_params_to(SIMPLE_3LAYERS_FILENAME)
submission, prediction, score = predict(NeuralNet1, test_array, IMAGE_SIZE,
label.columns.values)
save_result(submission)
plot_lasagne_learning_curves(NeuralNet1)
plot_images(test, prediction)
# Count running time
endtime = datetime.datetime.now()
print("Simple 3-layers neural nets score: %f, spend %d seconds" %
(score, (endtime - starttime).seconds))
# LeNet5-style convolutional neural nets
print "\n[LeNet5-style convolutional neural nets]\n"
train_array, test_array = reshape_data(train_array, test_array, IMAGE_SIZE)
NeuralNet2 = load_model_if_exists(NeuralNet2, LENET5_CNN_FILENAME)
NeuralNet2.fit(train_array, label_array)
NeuralNet2.save_params_to(LENET5_CNN_FILENAME)
submission, prediction, score = predict(NeuralNet2, test_array, IMAGE_SIZE,
def eval(args):
# parameters from arguments
model_name = args.model
pretrained_model = args.pretrained_model
save_npy = args.save_npy
image_shape = [int(m) for m in args.image_shape.split(",")]
assert model_name in model_list, "{} is not in lists: {}".format(
args.model, model_list)
image = fluid.data(name='image',
shape=[None] + image_shape,
dtype='float32')
group = fluid.data(name='group', shape=[None, 1], dtype='int64')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
seq_id = fluid.data(name='seq_id', shape=[None, 1], dtype='int64')
test_loader = fluid.io.DataLoader.from_generator(
feed_list=[image, group, label, seq_id],
capacity=64,
use_double_buffer=True,
iterable=True)
# model definition
model = models.__dict__[model_name]()
out = model.net(input=image, embedding_size=args.embedding_size)
test_program = fluid.default_main_program().clone(for_test=True)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if pretrained_model:
fluid.load(program=test_program,
model_path=pretrained_model,
executor=exe)
test_loader.set_sample_generator(reader.test(args),
batch_size=args.batch_size,
drop_last=False,
places=place)
fetch_list = [out.name]
f, l, g, s = [], [], [], []
for batch_id, data in enumerate(test_loader()):
t1 = time.time()
[feas] = exe.run(test_program, fetch_list=fetch_list, feed=data)
group = np.asarray(data[0]['group'])
label = np.asarray(data[0]['label'])
seq_id = np.asarray(data[0]['seq_id'])
f.append(feas)
g.append(np.squeeze(group))
l.append(np.squeeze(label))
s.append(np.squeeze(seq_id))
t2 = time.time()
period = t2 - t1
if batch_id % 2 == 0:
print("[%s] testbatch %d, time %2.2f sec" % \
(fmt_time(), batch_id, period))
f = np.vstack(f)
g = np.hstack(g)
l = np.hstack(l)
s = np.hstack(s)
if save_npy == True:
np.save(args.output_path + '_' + model_name, f)
np.save(args.output_path + '_' + 'g', g)
np.save(args.output_path + '_' + 'l', l)
np.save(args.output_path + '_' + 's', s)
res_final, res_score = post_process(f, g, l, s, args.thresh, k=args.top_k)
output_path = args.output_path
if not os.path.exists(output_path):
os.makedirs(output_path)
print("Saving result to {}".format(output_path))
save_result(res_final, res_score, output_path, args.detect_path)
sys.stdout.flush()
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 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)