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