dotenvの問題の解決

LLM_package.py

@@ -122,11 +122,11 @@ class ObjectDetector:
     """
     Detects the danger level of the image.
     """
-    def detect_danger_level(self, image_path):
+    def detect_auto(self, image_path):
         analysis_prompt = f"""
             画像の個人情報漏洩リスクを分析し、厳密にJSON形式で返答してください。なおこの時、資料があれば、資料を参考にしてください：
                 {{
-                    "risk_level": "high|medium|low",
+                    "risk_level":0~100,
                     "risk_reason": "リスクの具体的理由",
                     "objects_to_remove": ["消去すべきオブジェクトリスト(英語で、例: 'face', 'license_plate')"]
                 }}
@@ -137,4 +137,22 @@ class ObjectDetector:
         
         response = self.model.parse(self.model.get_response(image_path, analysis_prompt))
         print(f"Response: {response}")
-        return response
+        return response
+    def detect_danger_level(self, image_path):
+        analysis_prompt = f"""
+            画像の個人情報漏洩リスクを分析し、厳密にJSON形式で返答してください。なおこの時、資料があれば、資料を参考にしてください：
+                {{
+                    "risk_level": 0~100,
+                }}
+                <資料>
+                {self.text if self.text else "なし"}
+                </資料>
+                """
+        response = self.model.parse(self.model.get_response(image_path, analysis_prompt))
+
+        print(f"Response: {response}")
+        try:
+            risk_level = int(response.get('risk_level', 0))
+        except ValueError:
+            risk_level = 0
+        return risk_level

app.py

@@ -259,6 +259,58 @@ def llm_to_process_image_simple(risk_level, image_path, point1, point2, threshol
     debug_image_pil.save(save_dir + debug_image_path)
     return save_dir + debug_image_path
 
+
+
+def llm_to_process_image_simple_auto(risk_level, image_path, point1, point2, thresholds=None):
+    print(risk_level, image_path, point1, point2, thresholds)
+    print('point1,point2', point1, point2)
+    GEMINI_API_KEY=os.getenv('GEMINI_API_KEY')
+    # 画像処理のロジックをここに追加
+    Objectdetector = ObjectDetector(API_KEY=GEMINI_API_KEY)
+    debug_image_path='/test_llm.jpg'
+    response=Objectdetector.detect_auto(image_path)
+    Objectdetector.prompt_objects=response["objects_to_remove"]
+    # 画像の読み込みとRGB変換
+    
+    image = cv2.imread(image_path)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) 
+    mask_llm = np.zeros(image.shape[:2], dtype=np.uint8)
+    llm_results = Objectdetector.detect_objects(image_path)
+    for result in llm_results:
+            bbox=result['box_2d']
+            x1, y1 = int(bbox[1]* image.shape[1]), int(bbox[0]* image.shape[0])
+            x2, y2 = int(bbox[3]* image.shape[1]), int(bbox[2]* image.shape[0])
+            mask_llm[y1:y2, x1:x2] = 255  # テキスト領域をマスク
+    p1_x, p1_y = int(point1[0] * image.shape[1]), int(point1[1] * image.shape[0])
+    p2_x, p2_y = int(point2[0] * image.shape[1]), int(point2[1] * image.shape[0])
+    x_min, y_min = max(0, min(p1_x, p2_x)), max(0, min(p1_y, p2_y))
+    x_max, y_max = min(image.shape[1], max(p1_x, p2_x)), min(image.shape[0], max(p1_y, p2_y))
+    mask_llm[y_min:y_max, x_min:x_max] = 0  # 範囲を黒に設定
+    save_dir = "./saved_images"
+    os.makedirs(save_dir, exist_ok=True)
+    debug_image_pil = Image.fromarray(mask_llm)
+    debug_image_pil.save(save_dir + debug_image_path)
+    return save_dir + debug_image_path,response
+
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+
 
 
 
@@ -642,10 +694,6 @@ async def create_mask_and_inpaint_simple_lama(image: UploadFile = File(...), ris
     return FileResponse(output_path)
 
 
-#下のendpointは特定領域をマスクしないタイプのもの
-
-
-#下記はDeepFillv2
 
 
 
@@ -775,7 +823,11 @@ async def classify_image(file: UploadFile = File(...)):
     new_image_cluster = classify_new_image(new_image_vector, sums_data, loaded_vectors, loaded_object_names)
 
     return {"danger":dangerarray[int(new_image_cluster + 1)]}#バグったらここを＋にして
-    
+
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+
 
 @app.post("/create-mask-and-inpaint-simple-lama-special")
 async def create_mask_and_inpaint_simple_lama(
@@ -875,8 +927,34 @@ async def mosaic_face(file: UploadFile = File(...)):
 
 
 
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+
+#こっからLLM
+
+
+
+# LLMを使用して画像を分類するエンドポイント
+@app.post("/classify-image-llm/")
+async def classify_image_llm(file: UploadFile = File(...)):
+    image_path = "./temp_image.jpg"
+    # アップロードされた画像を保存
+    with open(image_path, "wb") as buffer:
+        buffer.write(await file.read())
+    danger_level = ObjectDetector().detect_danger_level(image_path)
+    return {"danger":danger_level}
+
+
+
+
+
 @app.post("/create-mask-and-inpaint-sum-llm-simple")
-async def create_mask_sum(image: UploadFile = File(...), risk_level: int = Form(...),
+async def create_mask_sum2(image: UploadFile = File(...), risk_level: int = Form(...),
     x1: float = Form(...),
     y1: float = Form(...),
     x2: float = Form(...),
@@ -900,8 +978,50 @@ async def create_mask_sum(image: UploadFile = File(...), risk_level: int = Form(
 
     return FileResponse(output_path)
 
+
+#三浦と相談
+
+@app.post("/create-mask-and-inpaint-sum-llm-auto")
+async def create_mask_sum_auto(image: UploadFile = File(...), risk_level: int = Form(...),x1: float = Form(...),
+    y1: float = Form(...),
+    x2: float = Form(...),
+    y2: float = Form(...)):
+    default_x = 0.001
+    default_y = 0.001
+
+
+    point1 = [default_x if math.isnan(x1) else x1, default_y if math.isnan(y1) else y1]
+
+    point2 = [default_x if math.isnan(x2) else x2, default_y if math.isnan(y2) else y2]
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    # 一意な識別子を生成
+    unique_id = uuid.uuid4().hex
+    input_path = save_image(image.file, f"./input_{timestamp}_{unique_id}.jpg")
+    mask_path,response = llm_to_process_image_simple_auto(risk_level, input_path, point1, point2,thresholds=thresholds)
+    output_path = f"./output_simple_lama_{timestamp}_{unique_id}.jpg"
+    print('point1,point2',point1,point2)#消去したくない範囲のこと
+    # OpenCVでインペイント
+    inpaint_image_with_mask1(input_path, mask_path, output_path)
+
+    return FileResponse(output_path), response
+
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 # カスケードファイルの読み込み (顔検出)
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+#face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
 
 def apply_mosaic(image, x, y, w, h, mosaic_level=15):
     """ 指定範囲にモザイク処理を適用 """
@@ -910,7 +1030,7 @@ def apply_mosaic(image, x, y, w, h, mosaic_level=15):
     face = cv2.resize(face, (w, h), interpolation=cv2.INTER_NEAREST)
     image[y:y+h, x:x+w] = face
     return image
-
+'''
 @app.post("/mosaic_face")
 async def mosaic_face(file: UploadFile = File(...)):
     # 画像ファイルを読み込み
@@ -934,7 +1054,7 @@ async def mosaic_face(file: UploadFile = File(...)):
     # 一時ファイルをレスポンスとして返す
     return FileResponse(path=temp_file_path, media_type="image/jpeg", filename="mosaic_image.jpg")
 
-
+'''
 
 
 

requirements.txt

@@ -73,5 +73,5 @@ zipp==3.20.2
 supervision
 onnxruntime
 google-genai
-dotenv 
+python-dotenv
 moondream