A iflask

AI/aiflask.py

@@ -0,0 +1,3 @@
+import app as a
+
+app = a.app

AI/app/__init__.py

@@ -1,7 +1,9 @@
 from flask import Flask
 
+app = Flask(__name__)
+
+
 def create_app():
-    app = Flask(__name__)
 
     if __name__ == "__main__":
         app.run()

AI/app/controllers/example_controllers.py

@@ -6,13 +6,13 @@
                import_name=__name__,
                url_prefix='/example')
 
-ai = Blueprint(name='ai',
+ai: Blueprint = Blueprint(name='ai',
                import_name=__name__,
                url_prefix='/ai')
 
 
 @ai.route('/base64', methods=['POST'])
-def draw() -> str:
+def draw() -> Response:
     return jsonify(auto_draw.AIbyBase64(json.loads(request.get_json())))
 
 
@@ -26,6 +26,3 @@ def drawByURL() -> str:
     s = request.get_data()
     l = json.loads(s)
     return jsonify(auto_draw.AIbyURL(l))
-
-
-String.format("https://ap-northeast-2.console.aws.amazon.com/s3/object/ai-icons?region=ap-northeast-2&bucketType=general&prefix=svg/{}/{}-outline.png", 이름, 이름)

AI/app/services/LoadImage.py → AI/app/services/ImageLoading.py

@@ -1,5 +1,4 @@
 import io, base64
-import os
 from PIL import Image
 import boto3
 import requests
@@ -18,8 +17,7 @@ def downloadFromS3(bucket, key):
     client = boto3.client('s3',
                           aws_access_key_id=AWS_ACCESS_KEY_ID,
                           aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
-                          region_name=AWS_DEFAULT_REGION
-                          )
+                          region_name=AWS_DEFAULT_REGION)
 
     file_name = 'downLoad.png'  # 다운될 이미지 이름
     # bucket = 'mufi-photo'  # 버켓 주소

AI/app/services/ImagePreprocessing.py

@@ -0,0 +1,30 @@
+from PIL import Image
+import numpy as np
+
+# 이미지를 흑백으로 변환
+def convertMono(img: Image):
+    fn = lambda x: 255 if x > 0 else 0
+    img = img.convert('L').point(fn, mode='1')
+    return img
+
+
+# 이미지 크기를 128*128로 변경 후 이미지를 배열화
+def resizing(img):
+    img = img.resize((128, 128))
+    img = img.convert('RGB')
+    return img
+
+
+# 이미지를 배열화
+def imageToArray(img):
+    img = np.array(img)
+    img = np.expand_dims(img, axis=0)
+    return img
+
+
+# 이미지를 AI에 맞게 전환
+def imageProcessing(img: Image):
+    img = convertMono(img)
+    img = resizing(img)
+    img = imageToArray(img)
+    return img

AI/app/services/auto_draw.py

@@ -1,29 +1,20 @@
 from keras.models import load_model
-import numpy as np
 import operator, os
-from app.services.LoadImage import decodeFromJsonToImage, downloadFromS3, downloadFromURL
+from app.services.ImageLoading import decodeFromJsonToImage, downloadFromS3, downloadFromURL
+from app.services.ImagePreprocessing import imageProcessing
 
 tags = 'airplane,apartment,apple,arm,arrow,axe,bag,baseball,basketball,bath,bed,bench,book,bottle,box,bread,broom,brush,bucket,bulb,cake,calendar,candle,candy,castle,chair,cherry,clock,cloud,coffee,coin,cookie,cup,cylinder,dice,dress,ear,earth,envelope,eraser,eye,finger,flag,flame,flask,flower,gamecontroller,ghost,gift,grape,hammer,heart,hospital,house,industry,injection,ladder,lake,leaf,leg,lightning,meat,megaphone,mic,money,monitor,mouse,mushroom,nail,nose,officebuilding,pants,peanut,pencil,police,pumpkin,rain,ribbon,rocket,ruler,school,shield,shirt,skirt,soccer,speaker,sprout,star,stethoscope,sun,swim,sword,television,tennis,tree,truck,umbrella,vehicle,volleyball,watch,wheel,windmill,zoom'.split(
     ',')
 
 
-# 이미지 크기를 128*128로 변경
-def resizing(image):
-    image = image.resize((128, 128))
-    image = image.convert('RGB')
-    image = np.array(image)
-    image = np.expand_dims(image, axis=0)
-    return image
-
-
-# AI를 이용하여 유사한 이미지 출력
+# AI를 이용하여 유사하다고 예상되는 이미지(이름) 출력
 def getPredict(img):
     saved_model = load_model(os.getcwd() + "/app/services/cnn1.h5")
     pre = saved_model.predict(img)
     return pre
 
 
-# AI 결과를 정확도에 따라 오름차순으로 정렬
+# AI 결과를 정확도에 따라 오름차 순으로 정렬
 def resultByDesc(result):
     x = {}
     for i in range(len(result)):
@@ -34,15 +25,16 @@ def resultByDesc(result):
 def getImageNameList(result):
     names = []
     for k, v in result:
-        print(k)
+        print(k,tags[k])
         names.append(tags[k])
     return names
 
+
 def AI_process(image):
-    resized = resizing(image)
-    pre_result = getPredict(resized)
-    result2 = resultByDesc(pre_result[0])
-    return getImageNameList(result2)
+    imgData = imageProcessing(image)
+    predict = getPredict(imgData)
+    result = resultByDesc(predict[0])
+    return getImageNameList(result)
 
 
 def AIbyBase64(json_data):

AI/dockerfile

@@ -1,14 +1,38 @@
-FROM python:3.8-slim
+# NVIDIA CUDA 베이스 이미지 사용
+FROM nvidia/cuda:11.2.2-cudnn8-runtime-ubuntu20.04
 
-COPY . .
+# 환경 변수 설정
+ENV DEBIAN_FRONTEND=noninteractive
 
-RUN pip3 install flask
-RUN pip3 install tensorflow
-RUN pip3 install pillow
-RUN pip3 install boto3
+# NVIDIA 패키지 리포지토리 추가 및 TensorRT 설치
+RUN apt-get update && apt-get install -y gnupg2
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub && \
+    apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/7fa2af80.pub && \
+    sh -c 'echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list' && \
+    apt-get update && apt-get install -y \
+    tzdata \
+    build-essential \
+    libhdf5-dev \
+    pkg-config \
+    python3 \
+    python3-pip \
+    libnvinfer8 \
+    libnvonnxparsers8 \
+    libnvparsers8 \
+    libnvinfer-plugin8 \
+    python3-libnvinfer-dev \
+    && ln -fs /usr/share/zoneinfo/Asia/Seoul /etc/localtime \
+    && dpkg-reconfigure --frontend noninteractive tzdata
 
-WORKDIR .
+# 작업 디렉토리 설정
+WORKDIR /app
 
-ENV FLASK_APP=app.py
+# 애플리케이션 코드 복사
+COPY . /app
 
-CMD ["python3", "-m", "flask", "run","--host=0.0.0.0"]
+# pip 업그레이드 및 패키지 설치
+RUN pip3 install --upgrade pip
+RUN pip3 install --no-cache-dir tensorflow gunicorn -r requirements.txt
+
+# Gunicorn을 사용하여 Flask 애플리케이션 실행
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:8000", "aiflask:app"]

AI/requirements.txt

@@ -0,0 +1,3 @@
+flask
+pillow
+boto3