Enhance image before ai processing

cloud/app.py

@@ -17,6 +17,7 @@ from APIs.mqtt import send_command
 from APIs.webex import WebexManager
 from APIs.shodan import ShodanAuditor
 from APIs.twilio import send_alert_sms
+from cloud.image_enhancer import enhance_image_for_ai
 from microwaveCookPlanner import MicrowaveCookPlanner
 import safety_checker
 from jobs import job_server_cve_audit, job_client_ip_audit, job_request_telemetry
@@ -153,7 +154,7 @@ start_scheduler_once()
 # ---------------------------------------------------------
 # Authentication Middleware
 # ---------------------------------------------------------
-EXEMPT_ROUTES = {'hello_world', 'oauth_callback', 'odata_metadata', 'debug_telemetryrequest', 'trigger_job_manually'}
+EXEMPT_ROUTES = {'hello_world', 'oauth_callback', 'odata_metadata', 'debug_telemetryrequest', 'trigger_job_manually', 'debug_unsafe_dish', 'debug_safe_dish'}
 
 @app.before_request
 def authenticate_request():
@@ -246,11 +247,13 @@ async def cooking_params():
             f.write(base64.b64decode(camera_image_b64))
         data["camera_image"] = filepath
         
+        enhanced_filepath = enhance_image_for_ai(filepath)
+
         # Concurrent AI Execution
-        safety_task = asyncio.to_thread(safety_checker.check_dish_safety, filepath)
+        safety_task = asyncio.to_thread(safety_checker.check_dish_safety, enhanced_filepath)
         planner_task = asyncio.to_thread(
             microwave_cook_planner.generate_plan,
-            image_path=filepath,
+            image_path=enhanced_filepath,
             height_cm=height_cm,
             initial_temp_c=initial_temp_c,
             microwave_wattage=microwave_wattage,
@@ -585,6 +588,24 @@ def trigger_job_manually(job_id):
             "message": f"Job execution failed: {str(e)}"
         }), 500
         
+@app.route("/debug/dishsafety/not_safe", methods=["GET"])
+def debug_unsafe_dish():
+    """Debug endpoint to simulate an unsafe dish scenario."""
+    # Simulated unsafe dish data
+    unsafe_dish = "storage/dishPhotos/dish_78827473286c4f67855f3fbd0ccb9bb4.jpg"
+    
+    # Call the cooking_params endpoint logic directly
+    return safety_checker.check_dish_safety(unsafe_dish)
+
+@app.route("/debug/dishsafety/safe", methods=["GET"])
+def debug_safe_dish():
+    """Debug endpoint to simulate a safe dish scenario."""
+    # Simulated safe dish data
+    unsafe_dish = "storage/dishPhotos/dish_972251744d034b32bb5134ff9d0f071c.jpg"
+    
+    # Call the cooking_params endpoint logic directly
+    return safety_checker.check_dish_safety(unsafe_dish)
+
 # ---------------------------------------------------------
 # OData Metadata Definition
 # ---------------------------------------------------------

cloud/image_enhancer.py

@@ -0,0 +1,23 @@
+import cv2
+
+def enhance_image_for_ai(input_path: str) -> str:
+    """Enhances dark areas and contrast using CLAHE without blowing out bright areas."""
+    img = cv2.imread(input_path)
+    if img is None:
+        return input_path
+
+    # Convert to LAB color space to modify luminance channel only
+    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+    l, a, b = cv2.split(lab)
+
+    # Apply CLAHE to Lightness channel
+    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+    cl = clahe.apply(l)
+
+    # Merge channels and convert back to BGR
+    limg = cv2.merge((cl, a, b))
+    enhanced = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
+
+    enhanced_path = input_path.replace(".jpg", "_enhanced.jpg")
+    cv2.imwrite(enhanced_path, enhanced)
+    return enhanced_path

cloud/safety_checker.py

@@ -2,6 +2,7 @@ import json
 from pydantic import BaseModel, Field
 from APIs.aichat import generate
 import shared.config as config
+from PIL import Image, ImageEnhance
 
 class UtensilMaterialCheck(BaseModel):
     object_name: str = Field(
@@ -28,6 +29,21 @@ class DishSafetyResult(BaseModel):
         description="If is_safe is False, set to 'REMOVE METAL UTENSIL OR FOIL BEFORE MICROWAVING'. Otherwise empty ''."
     )
 
+def preprocess_dish_image(image_path: str) -> str:
+    """Brightens dark areas and sharpens food texture for small vision models."""
+    img = Image.open(image_path)
+    
+    # 1. Boost brightness slightly
+    enhancer = ImageEnhance.Brightness(img)
+    img = enhancer.enhance(1.4)
+    
+    # 2. Boost contrast to separate food shapes from dark shadows
+    enhancer = ImageEnhance.Contrast(img)
+    img = enhancer.enhance(1.3)
+    
+    processed_path = "/tmp/processed_dish.jpg"
+    img.save(processed_path, quality=85)
+    return processed_path
 
 def check_dish_safety(image_path: str) -> dict:
     prompt = (