Upload 9 files

.gitignore

@@ -0,0 +1,15 @@
+*.iml
+.gradle
+/local.properties
+/.idea/caches
+/.idea/libraries
+/.idea/modules.xml
+/.idea/workspace.xml
+/.idea/navEditor.xml
+/.idea/assetWizardSettings.xml
+.DS_Store
+/build
+/captures
+.externalNativeBuild
+.cxx
+local.properties

CSD_MT_eval.py

@@ -0,0 +1,238 @@
+import os
+import torch
+import cv2
+import os.path as osp
+import numpy as np
+from PIL import Image
+from CSD_MT.options import Options
+from CSD_MT.model import CSD_MT
+from faceutils.face_parsing.model import BiSeNet
+import torchvision.transforms as transforms
+import faceutils as futils
+
+import warnings
+warnings.filterwarnings("ignore", category=FutureWarning, module="torch")
+
+# load face_parsing model
+n_classes = 19
+face_paseing_model = BiSeNet(n_classes=n_classes)
+save_pth = osp.join('faceutils/face_parsing/res/cp', '79999_iter.pth')
+face_paseing_model.load_state_dict(torch.load(save_pth,map_location='cpu'))
+face_paseing_model.eval()
+
+# load makeup transfer model
+parser = Options()
+opts = parser.parse()
+makeup_model = CSD_MT(opts)
+ep0, total_it = makeup_model.resume('CSD_MT/weights/CSD_MT.pth')
+makeup_model.eval()
+
+def crop_image(image):
+    up_ratio = 0.2 / 0.85  # delta_size / face_size
+    down_ratio = 0.15 / 0.85  # delta_size / face_size
+    width_ratio = 0.2 / 0.85  # delta_size / face_size
+
+    image = Image.fromarray(image)
+    face = futils.dlib.detect(image)
+
+    if not face:
+        raise ValueError("No face !")
+
+    face_on_image = face[0]
+
+    image, face, crop_face = futils.dlib.crop(image, face_on_image, up_ratio, down_ratio, width_ratio)
+    np_image = np.array(image)
+    return np_image
+
+def get_face_parsing(x):
+    to_tensor = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+    ])
+    with torch.no_grad():
+        img = Image.fromarray(x)
+        image = img.resize((512, 512), Image.BILINEAR)
+        img = to_tensor(image)
+        img = torch.unsqueeze(img, 0)
+        out = face_paseing_model(img)[0]
+        parsing = out.squeeze(0).cpu().numpy().argmax(0)
+    return parsing
+
+
+def split_parse(opts,parse):
+    h, w = parse.shape
+    result = np.zeros([h, w, opts.semantic_dim])
+    result[:, :, 0][np.where(parse == 0)] = 1
+    result[:, :, 0][np.where(parse == 16)] = 1
+    result[:, :, 0][np.where(parse == 17)] = 1
+    result[:, :, 0][np.where(parse == 18)] = 1
+    result[:, :, 0][np.where(parse == 9)] = 1
+    result[:, :, 1][np.where(parse == 1)] = 1
+    result[:, :, 2][np.where(parse == 2)] = 1
+    result[:, :, 2][np.where(parse == 3)] = 1
+    result[:, :, 3][np.where(parse == 4)] = 1
+    result[:, :, 3][np.where(parse == 5)] = 1
+    result[:, :, 1][np.where(parse == 6)] = 1
+    result[:, :, 4][np.where(parse == 7)] = 1
+    result[:, :, 4][np.where(parse == 8)] = 1
+    result[:, :, 5][np.where(parse == 10)] = 1
+    result[:, :, 6][np.where(parse == 11)] = 1
+    result[:, :, 7][np.where(parse == 12)] = 1
+    result[:, :, 8][np.where(parse == 13)] = 1
+    result[:, :, 9][np.where(parse == 14)] = 1
+    result[:, :, 9][np.where(parse == 15)] = 1
+    result = np.array(result)
+    return result
+
+
+def local_masks(opts,split_parse):
+    h, w, c = split_parse.shape
+    all_mask = np.zeros([h, w])
+    all_mask[np.where(split_parse[:, :, 0] == 0)] = 1
+    all_mask[np.where(split_parse[:, :, 3] == 1)] = 0
+    all_mask[np.where(split_parse[:, :, 6] == 1)] = 0
+    all_mask = np.expand_dims(all_mask, axis=2)  # Expansion of the dimension
+    all_mask = np.concatenate((all_mask, all_mask, all_mask), axis=2)
+    return all_mask
+
+
+
+def load_data_from_image(non_makeup_img, makeup_img,opts):
+    non_makeup_img=crop_image(non_makeup_img)
+    makeup_img = crop_image(makeup_img)
+    non_makeup_img=cv2.resize(non_makeup_img,(opts.resize_size,opts.resize_size))
+    makeup_img = cv2.resize(makeup_img, (opts.resize_size, opts.resize_size))
+    non_makeup_parse = get_face_parsing(non_makeup_img)
+    non_makeup_parse = cv2.resize(non_makeup_parse, (opts.resize_size, opts.resize_size),interpolation=cv2.INTER_NEAREST)
+    makeup_parse = get_face_parsing(makeup_img)
+    makeup_parse = cv2.resize(makeup_parse, (opts.resize_size, opts.resize_size),interpolation=cv2.INTER_NEAREST)
+
+    non_makeup_split_parse = split_parse(opts,non_makeup_parse)
+    makeup_split_parse = split_parse(opts,makeup_parse)
+
+    non_makeup_all_mask = local_masks(opts,
+        non_makeup_split_parse)
+    makeup_all_mask = local_masks(opts,
+        makeup_split_parse)
+
+    non_makeup_img = non_makeup_img / 127.5 - 1
+    non_makeup_img = np.transpose(non_makeup_img, (2, 0, 1))
+    non_makeup_split_parse = np.transpose(non_makeup_split_parse, (2, 0, 1))
+
+    makeup_img = makeup_img / 127.5 - 1
+    makeup_img = np.transpose(makeup_img, (2, 0, 1))
+    makeup_split_parse = np.transpose(makeup_split_parse, (2, 0, 1))
+
+    non_makeup_img=torch.from_numpy(non_makeup_img).type(torch.FloatTensor)
+    non_makeup_img = torch.unsqueeze(non_makeup_img, 0)
+    non_makeup_split_parse = torch.from_numpy(non_makeup_split_parse).type(torch.FloatTensor)
+    non_makeup_split_parse = torch.unsqueeze(non_makeup_split_parse, 0)
+    non_makeup_all_mask = np.transpose(non_makeup_all_mask, (2, 0, 1))
+
+    makeup_img = torch.from_numpy(makeup_img).type(torch.FloatTensor)
+    makeup_img = torch.unsqueeze(makeup_img, 0)
+    makeup_split_parse = torch.from_numpy(makeup_split_parse).type(torch.FloatTensor)
+    makeup_split_parse = torch.unsqueeze(makeup_split_parse, 0)
+    makeup_all_mask = np.transpose(makeup_all_mask, (2, 0, 1))
+
+    data = {'non_makeup_color_img': non_makeup_img,
+            'non_makeup_split_parse':non_makeup_split_parse,
+            'non_makeup_all_mask': torch.unsqueeze(torch.from_numpy(non_makeup_all_mask).type(torch.FloatTensor), 0),
+
+            'makeup_color_img': makeup_img,
+            'makeup_split_parse': makeup_split_parse,
+            'makeup_all_mask': torch.unsqueeze(torch.from_numpy(makeup_all_mask).type(torch.FloatTensor), 0)
+            }
+    return data
+
+
+def extract_eye_mask(parsing, expansion=20, upward_bias=20, side_bias=20):
+    # 눈 영역 마스크 생성
+    
+    eye_mask = np.zeros_like(parsing, dtype=np.uint8)
+    eye_mask[np.where(parsing == 4)] = 1  # 왼쪽 눈
+    eye_mask[np.where(parsing == 5)] = 1  # 오른쪽 눈
+
+    # 기본 확장
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (expansion, expansion))
+    expanded_mask = cv2.dilate(eye_mask, kernel, iterations=1)
+
+    upward_mask = np.zeros_like(expanded_mask)
+    upward_mask[:-upward_bias, :] = expanded_mask[upward_bias:, :]
+
+    left_mask = np.zeros_like(expanded_mask)
+    left_mask[:, :-side_bias] = expanded_mask[:, side_bias:]
+
+    right_mask = np.zeros_like(expanded_mask)
+    right_mask[:, side_bias:] = expanded_mask[:, :-side_bias]
+
+    final_mask = np.clip(expanded_mask + upward_mask + left_mask + right_mask, 0, 1)
+
+    return final_mask
+
+def extract_eyebrow_mask(parsing):
+    # 눈썹 마스크 생성
+
+    eyebrow_mask = np.zeros_like(parsing, dtype=np.uint8)
+    eyebrow_mask[np.where(parsing == 2)] = 1  # 왼쪽 눈썹
+    eyebrow_mask[np.where(parsing == 3)] = 1  # 오른쪽 눈썹
+    return eyebrow_mask
+
+def extract_lips_mask(parsing):
+    # 입술 마스크 생성
+
+    lips_mask = np.zeros_like(parsing, dtype=np.uint8)
+    lips_mask[np.where(parsing == 12)] = 1  # 윗입술
+    lips_mask[np.where(parsing == 13)] = 1  # 아랫입술
+    return lips_mask
+
+
+def makeup_transfer256(non_makeup_image, makeup_image, alpha_values, regions):
+    """
+    메이크업 전이 함수: 영역별로 다른 alpha 값을 사용하여 특정 영역에 필터 적용.
+    """
+    # 메이크업 전이 수행
+    data = load_data_from_image(non_makeup_image, makeup_image, opts=opts)
+    with torch.no_grad():
+        transfer_tensor = makeup_model.test_pair(data)
+        transfer_img = transfer_tensor[0].cpu().float().numpy()
+        transfer_img = np.transpose((transfer_img / 2 + 0.5) * 255., (1, 2, 0))
+        transfer_img = np.clip(transfer_img, 0, 255).astype(np.uint8)
+
+    # 원본 이미지 크기에 맞게 리사이즈
+    target_size = (non_makeup_image.shape[1], non_makeup_image.shape[0])
+    transfer_img = cv2.resize(transfer_img, target_size, interpolation=cv2.INTER_LINEAR)
+
+    # 얼굴 파싱 및 영역별 마스크 생성
+    non_makeup_parse = get_face_parsing(non_makeup_image)
+    masks = {
+        "eye": extract_eye_mask(non_makeup_parse),
+        "eyebrow": extract_eyebrow_mask(non_makeup_parse),
+        "lip": extract_lips_mask(non_makeup_parse),
+    }
+
+    # 결과 이미지 생성
+    result_image = non_makeup_image.astype(np.float32)
+    transfer_img = transfer_img.astype(np.float32)
+
+    # 선택된 영역에만 메이크업 적용
+    for region in regions:
+        mask = masks.get(region, None)
+        if mask is not None:
+            mask = cv2.resize(mask, target_size, interpolation=cv2.INTER_NEAREST)
+            mask = cv2.GaussianBlur(mask.astype(np.float32), (9, 9), 0)
+            mask = mask / mask.max()
+
+            alpha = alpha_values.get(region, 1)  # 해당 영역의 alpha 값 가져오기
+            for c in range(3):  # RGB 채널별 적용
+                result_image[:, :, c] = result_image[:, :, c] * (1 - alpha * mask) + transfer_img[:, :, c] * (
+                    alpha * mask
+                )
+
+    # 전체 영역에 대한 처리 (regions="all")
+    if "all" in regions:
+        alpha = alpha_values.get("all", 1)
+        for c in range(3):
+            result_image[:, :, c] = result_image[:, :, c] * (1 - alpha) + transfer_img[:, :, c] * alpha
+
+    return result_image.astype(np.uint8)

README.md

@@ -0,0 +1,16 @@
+### MakeupSimulator
+
+Hi there,
+This is a project for 'make-up simulator'😎😎
+You could make up your face images in simulator!!
+
+
+# Our Features
+
+1. Make up transfer
+2. Two types of Color control
+  1) filter-based
+  2) user-based
+3. Enhance image equaity
+4. Recommend make-up products in real
+

build.gradle.kts

@@ -0,0 +1,5 @@
+plugins {
+    id("com.android.application") version "8.1.1" apply false
+    id("org.jetbrains.kotlin.android") version "1.9.0" apply false
+}
+

gradio_makeup_transfer.py

@@ -0,0 +1,88 @@
+import os
+import torch
+import cv2
+import time
+import os.path as osp
+import numpy as np
+from PIL import Image
+import torchvision.transforms as transforms
+import gradio as gr
+import CSD_MT_eval
+
+def get_makeup_transfer_results256(non_makeup_img, makeup_img, alpha_eye, alpha_eyebrow, alpha_lip, alpha_all, regions):
+
+    # Alpha 값을 영역별로 매핑
+    alpha_values = {
+        "eye": alpha_eye,
+        "eyebrow": alpha_eyebrow,
+        "lip": alpha_lip,
+        "all": alpha_all,
+    }
+
+    # 메이크업 전이 수행
+    transfer_img = CSD_MT_eval.makeup_transfer256(non_makeup_img, makeup_img, alpha_values, regions)
+    return transfer_img
+
+
+
+example = {}
+non_makeup_dir = 'examples/non_makeup'
+makeup_dir = 'examples/makeup'
+non_makeup_list = [os.path.join(non_makeup_dir, file) for file in os.listdir(non_makeup_dir)]
+non_makeup_list.sort()
+makeup_list = [os.path.join(makeup_dir, file) for file in os.listdir(makeup_dir)]
+makeup_list.sort()
+
+# Gradio 인터페이스 정의
+with gr.Blocks() as demo:
+    with gr.Group():
+        with gr.Tab("CSD-MT"):
+            with gr.Row():
+                with gr.Column():
+                    # Non-makeup 및 Makeup 이미지 업로드
+                    non_makeup = gr.Image(source='upload', elem_id="image_upload", type="numpy",
+                                          label="Non-makeup Image")
+                    gr.Examples(non_makeup_list, inputs=[non_makeup], label="Examples - Non-makeup Image",
+                                examples_per_page=6)
+
+                    makeup = gr.Image(source='upload', elem_id="image_upload", type="numpy",
+                                      label="Makeup Image")
+                    gr.Examples(makeup_list, inputs=[makeup], label="Examples - Makeup Image", examples_per_page=6)
+
+                with gr.Column():
+                    image_out = gr.Image(label="Output", type="numpy", elem_id="output-img").style(height=550)
+
+                    # 영역별 투명도 슬라이더
+                    alpha_eye = gr.Slider(0, 1, value=1, step=0.1, label="Eye Makeup Transparency (Alpha)")
+                    alpha_eyebrow = gr.Slider(0, 1, value=1, step=0.1, label="Eyebrow Makeup Transparency (Alpha)")
+                    alpha_lip = gr.Slider(0, 1, value=1, step=0.1, label="Lip Makeup Transparency (Alpha)")
+                    alpha_all = gr.Slider(0, 1, value=1, step=0.1, label="Overall Makeup Transparency (Alpha)")
+
+                    # 체크박스 그룹 (영역 선택)
+                    region_selector = gr.CheckboxGroup(
+                        ["eye", "eyebrow", "lip", "all"],
+                        label="Select Makeup Regions",
+                        value=["all"],
+                    )
+
+                    with gr.Row().style(mobile_collapse=False, equal_height=True):
+                        btn = gr.Button("Apply Makeup! (CSD-MT)").style()
+
+            # Click result
+            btn.click(
+                fn=get_makeup_transfer_results256,
+                inputs=[
+                    non_makeup,
+                    makeup,
+                    alpha_eye,
+                    alpha_eyebrow,
+                    alpha_lip,
+                    alpha_all,
+                    region_selector,
+                ],
+                outputs=image_out,
+            )
+
+
+demo.launch()
+

gradle.properties

@@ -0,0 +1,21 @@
+# Project-wide Gradle settings.
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx2048m -Dfile.encoding=UTF-8
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. For more details, visit
+# https://developer.android.com/r/tools/gradle-multi-project-decoupled-projects
+# org.gradle.parallel=true
+# AndroidX package structure to make it clearer which packages are bundled with the
+# Android operating system, and which are packaged with your app's APK
+# https://developer.android.com/topic/libraries/support-library/androidx-rn
+android.useAndroidX=true
+# Enables namespacing of each library's R class so that its R class includes only the
+# resources declared in the library itself and none from the library's dependencies,
+# thereby reducing the size of the R class for that library
+android.nonTransitiveRClass=true

gradlew

@@ -0,0 +1,185 @@
+#!/usr/bin/env sh
+
+#
+# Copyright 2015 the original author or authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn () {
+    echo "$*"
+}
+
+die () {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+nonstop=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+  NONSTOP* )
+    nonstop=true
+    ;;
+esac
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin or MSYS, switch paths to Windows format before running java
+if [ "$cygwin" = "true" -o "$msys" = "true" ] ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=`expr $i + 1`
+    done
+    case $i in
+        0) set -- ;;
+        1) set -- "$args0" ;;
+        2) set -- "$args0" "$args1" ;;
+        3) set -- "$args0" "$args1" "$args2" ;;
+        4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Escape application args
+save () {
+    for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
+    echo " "
+}
+APP_ARGS=`save "$@"`
+
+# Collect all arguments for the java command, following the shell quoting and substitution rules
+eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
+
+exec "$JAVACMD" "$@"

gradlew.bat

@@ -0,0 +1,89 @@
+@rem
+@rem Copyright 2015 the original author or authors.
+@rem
+@rem Licensed under the Apache License, Version 2.0 (the "License");
+@rem you may not use this file except in compliance with the License.
+@rem You may obtain a copy of the License at
+@rem
+@rem      https://www.apache.org/licenses/LICENSE-2.0
+@rem
+@rem Unless required by applicable law or agreed to in writing, software
+@rem distributed under the License is distributed on an "AS IS" BASIS,
+@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+@rem See the License for the specific language governing permissions and
+@rem limitations under the License.
+@rem
+
+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Resolve any "." and ".." in APP_HOME to make it shorter.
+for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto execute
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto execute
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %*
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

settings.gradle.kts

@@ -0,0 +1,17 @@
+pluginManagement {
+    repositories {
+        google()
+        mavenCentral()
+        gradlePluginPortal()
+    }
+}
+
+dependencyResolutionManagement {
+    repositories {
+        google()
+        mavenCentral()
+    }
+}
+
+rootProject.name = "Makeup" // 프로젝트 이름
+include(":app") // 모듈 이름