Update src/ai_processor.py

src/ai_processor.py

@@ -845,6 +845,75 @@ class AIProcessor:
                 box_pts = None
                 segmentation_empty = True
 
+            # --- Skin tone estimation using ITA (Individual Typology Angle) ---
+            try:
+                # Convert ROI to LAB for skin tone measurement
+                # cv2.cvtColor returns LAB with L in [0,255], a,b in [0,255]; we convert to L* and b*
+                lab_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2LAB)
+                L_chan = lab_roi[:, :, 0] / 2.55  # scale to 0–100
+                b_chan = lab_roi[:, :, 2].astype(np.float32) - 128.0
+                # Define skin pixels as non-wound region or entire ROI if mask is empty
+                if mask01.any():
+                    skin_mask = (mask01 == 0)
+                else:
+                    skin_mask = np.ones_like(mask01, dtype=bool)
+                L_vals = L_chan[skin_mask]
+                b_vals = b_chan[skin_mask]
+                # Safeguard against empty arrays
+                if L_vals.size == 0 or b_vals.size == 0:
+                    mean_L = float(np.mean(L_chan))
+                    mean_b = float(np.mean(b_chan))
+                else:
+                    mean_L = float(np.mean(L_vals))
+                    mean_b = float(np.mean(b_vals))
+                # Compute ITA in degrees; use arctan2 to handle mean_b=0 gracefully
+                ita_deg = float(np.degrees(np.arctan2((mean_L - 50.0), mean_b))) if mean_b != 0 else 0.0
+                # Map ITA to Fitzpatrick skin tone categories (Del Bino ranges)
+                if ita_deg > 55:
+                    skin_tone_label = "Type I (Very Light)"
+                elif 41 < ita_deg <= 55:
+                    skin_tone_label = "Type II (Light)"
+                elif 28 < ita_deg <= 41:
+                    skin_tone_label = "Type III (Intermediate)"
+                elif 10 < ita_deg <= 28:
+                    skin_tone_label = "Type IV (Tan)"
+                elif -30 < ita_deg <= 10:
+                    skin_tone_label = "Type V (Brown)"
+                else:
+                    skin_tone_label = "Type VI (Dark)"
+            except Exception as e:
+                logging.warning(f"Skin tone estimation failed: {e}")
+                ita_deg = 0.0
+                skin_tone_label = "Unknown"
+
+            # --- Tissue classification (granulation, slough, necrotic) ---
+            try:
+                tissue_type = "Unknown"
+                if mask01.any():
+                    hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
+                    mask_bool = mask01.astype(bool)
+                    # Compute mean hue and value on wound region
+                    h_vals = hsv_roi[:, :, 0][mask_bool]
+                    s_vals = hsv_roi[:, :, 1][mask_bool]
+                    v_vals = hsv_roi[:, :, 2][mask_bool]
+                    # Safeguard against empty arrays
+                    if h_vals.size > 0 and v_vals.size > 0:
+                        mean_h = float(np.mean(h_vals))
+                        mean_v = float(np.mean(v_vals))
+                        # Necrotic (dark) if value is low
+                        if mean_v < 50:
+                            tissue_type = "Necrotic"
+                        # Slough (yellow) if hue between ~10 and 30 on OpenCV scale (0–179)
+                        elif 10 <= mean_h <= 30:
+                            tissue_type = "Slough"
+                        else:
+                            tissue_type = "Granulation"
+                    else:
+                        tissue_type = "Unknown"
+            except Exception as e:
+                logging.warning(f"Tissue classification failed: {e}")
+                tissue_type = "Unknown"
+
             # --- Save visualizations ---
             original_path = os.path.join(out_dir, f"original_{ts}.png")
             cv2.imwrite(original_path, image_cv)
@@ -923,6 +992,10 @@ class AIProcessor:
                 "segmentation_empty": segmentation_empty,
                 "segmentation_debug": seg_debug,
                 "original_image_path": original_path,
+                # Additional AI insights
+                "skin_tone_label": skin_tone_label,
+                "ita_degrees": round(float(ita_deg), 2),
+                "tissue_type": tissue_type,
             }
         except Exception as e:
             logging.error(f"Visual analysis failed: {e}", exc_info=True)