Implementada versión mejorada de check_image_quality y validate_image_dimensions para cálculo dinámico y preciso de brillo y contraste

services/image_service.py

@@ -158,7 +158,6 @@ class ImageService:
     def validate_image_dimensions(self, img: np.ndarray) -> Dict[str, Any]:
         """
         Validates image dimensions for facial processing.
-        Part of Step 3: Validation of dimensions and size.
         
         This method analyzes the image dimensions to ensure they meet
         the requirements for effective facial detection and analysis.
@@ -170,12 +169,14 @@ class ImageService:
         Returns:
             Dict with validation results including:
             - valid: True if dimensions are acceptable, False otherwise
+            - is_optimal: True if dimensions are optimal for processing
             - width, height: Image dimensions
             - warnings: List of warnings if dimensions are not optimal
             - messages: List of informational messages
         """
         result = {
             "valid": True,
+            "is_optimal": False,  # Default to false, set to true if dimensions are optimal
             "width": 0,
             "height": 0,
             "aspect_ratio": 0,
@@ -209,6 +210,7 @@ class ImageService:
         if self.min_width <= width < self.optimal_width or self.min_height <= height < self.optimal_height:
             result["messages"].append(f"Acceptable but not optimal dimensions. Ideal: {self.optimal_width}x{self.optimal_height}")
         elif width >= self.optimal_width and height >= self.optimal_height:
+            result["is_optimal"] = True  # Set to true as dimensions are optimal
             result["messages"].append(f"Optimal dimensions for facial analysis: {width}x{height}")
         
         # Verify aspect ratio (very elongated can be problematic)
@@ -243,7 +245,8 @@ class ImageService:
             "brightness": 0.0,
             "contrast": 0.0,
             "warnings": [],
-            "suggestions": []
+            "suggestions": [],
+            "recommendations": []  # For compatibility
         }
         
         if img is None:
@@ -251,7 +254,7 @@ class ImageService:
             return result
         
         # Convert to grayscale for some metrics
-        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if len(img.shape) == 3 else img
+        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) if len(img.shape) == 3 else img
         
         # Evaluate sharpness (using Laplacian variance)
         laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
@@ -271,23 +274,63 @@ class ImageService:
         contrast_score = min(1.0, result["contrast"] * 3)  # higher contrast is better
         
         # Total quality (0-1)
-        result["quality_score"] = (sharpness_score * 0.5 + brightness_score * 0.25 + contrast_score * 0.25)
+        quality_value = (sharpness_score * 0.5 + brightness_score * 0.25 + contrast_score * 0.25)
+        result["quality_score"] = quality_value
+        
+        # Add backwards compatibility: convert to 0-100 scale and add label
+        result["score"] = int(quality_value * 100)  # For compatibility with previous code
+        
+        # Add a text label for the quality
+        if result["score"] >= 80:
+            result["label"] = "Excellent"
+        elif result["score"] >= 60:
+            result["label"] = "Good"
+        elif result["score"] >= 40:
+            result["label"] = "Fair"
+        elif result["score"] >= 20:
+            result["label"] = "Poor"
+        else:
+            result["label"] = "Very Poor"
+            
+        # Add sharpness label for UI
+        if sharpness_score >= 0.8:
+            result["sharpness_label"] = "Excellent"
+        elif sharpness_score >= 0.6:
+            result["sharpness_label"] = "Good"
+        elif sharpness_score >= 0.4:
+            result["sharpness_label"] = "Fair"
+        elif sharpness_score >= 0.2:
+            result["sharpness_label"] = "Poor"
+        else:
+            result["sharpness_label"] = "Very Poor"
         
         # Generate warnings and suggestions based on metrics
         if result["is_blurry"]:
             result["warnings"].append("The image appears to be blurry.")
-            result["suggestions"].append("Use a sharper image to improve facial detection accuracy.")
+            suggestion = "Use a sharper image to improve facial detection accuracy."
+            result["suggestions"].append(suggestion)
+            result["recommendations"].append(suggestion)  # For compatibility
         
         if result["brightness"] < 0.2:
             result["warnings"].append("The image is too dark.")
-            result["suggestions"].append("Try using a better illuminated image.")
+            suggestion = "Try using a better illuminated image."
+            result["suggestions"].append(suggestion)
+            result["recommendations"].append(suggestion)  # For compatibility
         elif result["brightness"] > 0.8:
             result["warnings"].append("The image is too bright.")
-            result["suggestions"].append("Try using an image with more balanced lighting.")
+            suggestion = "Try using an image with more balanced lighting."
+            result["suggestions"].append(suggestion)
+            result["recommendations"].append(suggestion)  # For compatibility
         
         if result["contrast"] < 0.1:
             result["warnings"].append("The image has very low contrast.")
-            result["suggestions"].append("An image with higher contrast will improve facial feature detection.")
+            suggestion = "An image with higher contrast will improve facial feature detection."
+            result["suggestions"].append(suggestion)
+            result["recommendations"].append(suggestion)  # For compatibility
+        
+        # Convert brightness and contrast to percentage for UI display
+        result["brightness"] = float(result["brightness"]) * 100
+        result["contrast"] = float(result["contrast"]) * 100
         
         return result