[feat]: Router in API with model opennsfw2 and nsfw-detector

Pipfile

@@ -16,6 +16,7 @@ numpy = "*"
 Pillow = "*"
 requests = "*"
 opencv-python = "*"
+opennsfw2= "*"
 
 [requires]
 python_version = "3.8"

apps/pyserver/Dockerfile

@@ -16,6 +16,8 @@ EXPOSE 5000
 FROM base as debug
 RUN pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org ptvsd
 
+ENV PYTHONPATH=/app
+
 WORKDIR /app
 CMD ["python", "-m", "ptvsd", "--host", "0.0.0.0", "--port", "5678", \
     "--wait", "--multiprocess", "-m", \

apps/pyserver/app/api/v1/nstw.py

@@ -0,0 +1,68 @@
+from fastapi import APIRouter
+from fastapi import APIRouter, UploadFile, File
+from nsfw_detector import predict
+from core.use_case.nstw.crud import CRUDNstw
+from PIL import Image
+import io
+
+TENSORFLOW_URL = "http://tensorflow:8501/v1/models/rfcn:predict"
+router = APIRouter()
+
+
+@router.get("/hello")
+def hello():
+    return {"hello": "world"}
+
+
+
+@router.post("/nstw/")
+async def detect_nstw(file: UploadFile = File(...)):
+    """
+        NSTW Model: Detects Explicit or Implicit Pornography in an Image
+        It returns a value from 0.99 to 0.01. Being 0.99 being safe (99% safe)
+        and 0.01 being unsafe (1% safe)
+
+        Typically, the values indicate the following possible outcomes:
+
+        => 0.99 would be an image without any kind of nudity captured
+            =<0.90 person without pants or shirt, or showing some part of the body, but not pornographic.
+            =< 0.80 person without pants or shirt, or showing some part of the body.
+            =< 0.20 person without pants and without shirt
+            =< 0.10 explicit pornographic content detected.
+
+        Acceptable values to proceed: 0.95 - 0.99
+    """
+    try:
+        image_bytes = await file.read()
+        result = CRUDNstw.detect_nsfw(image_bytes)
+        return result
+    except Exception as e:
+        return {"error": str(e)}
+
+
+model = predict.load_model("/app/nsfw_model/nsfw_mobilenet2.224x224.h5")
+
+
+@router.post("/nsfw/")
+async def detect_nsfw_labels(file: UploadFile = File(...)):
+    """
+    NSFW Model: Detects Explicit or Implicit Pornography with a More Detailed Index
+    Faster model, but may be less sensitive to soft porn detection
+
+    Categories detected:
+        drawings: 0.0057229697704315186,
+        "hentai": 0.0005879989475943148,
+        "neutral": 0.3628992736339569,
+        "porn": 0.011622844263911247,
+        sexy: 0.619166910648346
+
+    """
+    try:
+        image_bytes = await file.read()
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        tmp_path = "/tmp/tmp.jpg"
+        image.save(tmp_path)
+        result = predict.classify(model, tmp_path)
+        return result
+    except Exception as e:
+        return {"error": str(e)}

apps/pyserver/app/api/v1/oracle.py

@@ -0,0 +1,52 @@
+from fastapi import APIRouter
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import StreamingResponse
+from starlette.responses import RedirectResponse
+import io
+
+
+from core.use_case.oracle.crud import CRUDOracle
+
+TENSORFLOW_URL = "http://tensorflow:8501/v1/models/rfcn:predict"
+
+router = APIRouter()
+
+
+@router.get("/hello_oracle")
+def hello_oracle():
+    return {"hello": "world"}
+
+
+@router.post("/get_predictions/")
+async def get_predictions(file: UploadFile = File(...)):
+    """
+    Submit a picture to be analyzed by the R-FCN model and retrieve the objects
+    identified on the scene.
+    """
+    image_file = await file.read()
+    r = CRUDOracle.get_predictions(image_file, TENSORFLOW_URL)
+
+    return r
+
+
+@router.post("/get_predicted_image/")
+async def get_predicted_image(file: UploadFile = File(...), detections_limit: int = 20):
+    """
+    Submit a picture to be analyzed by the R-FCN model and get then download the picture
+    with all the objects identified in the scene highlighted.
+
+    - **detections_limit** [int]: Define the limit of objects to be ploted in the returning image.
+    """
+    try:
+        image_file = await file.read()
+        processed_image = CRUDOracle.get_predicted_image(image_file, TENSORFLOW_URL, detections_limit)
+        filename = file.filename.split(".")[0]
+        extension = file.filename.split(".")[-1]
+        filename = "".join([filename, " (processed).", extension])
+
+        return StreamingResponse(io.BytesIO(processed_image),
+                                 headers={'Content-Disposition': 'attachment; filename=' + filename},
+                                 media_type="image/jpg")
+
+    except Exception as e:
+        return {"error": str(e)}

apps/pyserver/app/api/v1/router.py

@@ -0,0 +1,9 @@
+from fastapi import APIRouter, FastAPI
+
+from api.v1 import nstw, oracle
+
+api_router = APIRouter()
+app = FastAPI()
+
+api_router.include_router(nstw.router, tags=["nstw"])
+api_router.include_router(oracle.router, tags=["oracle"])

apps/pyserver/app/core/use_case/nstw/crud.py

@@ -0,0 +1,26 @@
+from PIL import Image
+import io
+import opennsfw2 as n2
+
+
+class CRUDNstw:
+    @staticmethod
+    def detect_nsfw(image_bytes: bytes):
+        # Carrega a imagem a partir dos bytes
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+
+        # Preprocessa a imagem conforme o modelo
+        processed_image = n2.preprocess_image(image, n2.Preprocessing.YAHOO)
+
+        # Cria o modelo
+        model = n2.make_open_nsfw_model()
+
+        # Realiza a predição
+        prediction = model.predict(processed_image[None, ...])[0][0]
+
+        # Define o limiar para considerar como NSFW
+        threshold = 0.9
+        is_nsfw = bool(prediction >= threshold)
+        detail = f"Probabilidade NSFW: {prediction:.2f}"
+
+        return {"nsfw": is_nsfw, "detail": detail}

apps/pyserver/app/core/use_case/oracle/coco-labels-2014_2017.txt

@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+dining table
+toilet
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

apps/pyserver/app/core/use_case/oracle/coco-labels-paper.txt

@@ -0,0 +1,91 @@
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+street sign
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+hat
+backpack
+umbrella
+shoe
+eye glasses
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+plate
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+mirror
+dining table
+window
+desk
+toilet
+door
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+blender
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
+hair brush