Upload app.py

app.py

@@ -1,7 +1,7 @@
 """
 Cephalometric Landmark Detection API
 HRNet-W32 - 19 Landmarks Cefalométricos
-Versión Final con Visualización Mejorada
+Versión 1.1 - Con Indicadores de Confianza
 """
 
 import os
@@ -67,6 +67,18 @@ def get_color_for_landmark(idx):
             return group_data["color"]
     return (255, 255, 255)
 
+def get_confidence_indicator(confidence):
+    """Retorna color e indicador según nivel de confianza"""
+    # Normalizar confianza (los heatmaps pueden tener valores > 1)
+    conf_pct = min(confidence * 100, 100)
+    
+    if conf_pct >= 80:
+        return (80, 255, 80), "✓", conf_pct    # Verde - Alta
+    elif conf_pct >= 50:
+        return (255, 255, 80), "~", conf_pct   # Amarillo - Media
+    else:
+        return (255, 80, 80), "!", conf_pct    # Rojo - Baja
+
 # ============================================================================
 # ARQUITECTURA HRNET-W32
 # ============================================================================
@@ -365,7 +377,7 @@ def detect_landmarks(image):
             "abbrev": LANDMARK_ABBREV[i],
             "x": round(float(preds[0, i, 0] * scale_x), 1),
             "y": round(float(preds[0, i, 1] * scale_y), 1),
-            "confidence": round(float(maxvals[0, i]), 2)
+            "confidence": round(float(maxvals[0, i]), 4)
         })
     
     return landmarks
@@ -392,8 +404,21 @@ def draw_landmarks_clean(image, landmarks, show_labels=True):
         x, y = lm['x'], lm['y']
         color = get_color_for_landmark(lm['id'])
         
-        # Círculo con borde negro
-        draw.ellipse([x-radius-1, y-radius-1, x+radius+1, y+radius+1], fill=(0, 0, 0))
+        # Obtener info de confianza
+        conf_color, conf_indicator, conf_pct = get_confidence_indicator(lm['confidence'])
+        
+        # Círculo con borde según confianza
+        # Borde más grueso y de color diferente para baja confianza
+        if conf_pct < 50:
+            # Borde rojo grueso para baja confianza
+            draw.ellipse([x-radius-3, y-radius-3, x+radius+3, y+radius+3], fill=(255, 80, 80))
+        elif conf_pct < 80:
+            # Borde amarillo para confianza media
+            draw.ellipse([x-radius-2, y-radius-2, x+radius+2, y+radius+2], fill=(255, 255, 80))
+        else:
+            # Borde negro normal para alta confianza
+            draw.ellipse([x-radius-1, y-radius-1, x+radius+1, y+radius+1], fill=(0, 0, 0))
+        
         draw.ellipse([x-radius, y-radius, x+radius, y+radius], fill=color)
     
     if show_labels:
@@ -469,18 +494,20 @@ def find_label_position(x, y, text, existing_positions, font, draw, img_size):
     return (x + 12, y - 5)
 
 def create_legend_image(landmarks):
-    """Crea imagen con leyenda de landmarks"""
-    width = 280
-    height = 520
+    """Crea imagen con leyenda de landmarks incluyendo confianza"""
+    width = 320
+    height = 580
     legend = Image.new('RGB', (width, height), (30, 30, 30))
     draw = ImageDraw.Draw(legend)
     
     try:
         font_title = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 14)
         font_text = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 11)
+        font_small = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 9)
     except:
         font_title = ImageFont.load_default()
         font_text = font_title
+        font_small = font_title
     
     y_pos = 10
     draw.text((10, y_pos), "LANDMARKS DETECTADOS", fill=(255, 255, 255), font=font_title)
@@ -495,15 +522,42 @@ def create_legend_image(landmarks):
         
         for idx in group_data["indices"]:
             lm = landmarks[idx]
-            # Círculo de color
+            conf = lm.get('confidence', 0)
+            conf_color, conf_indicator, conf_pct = get_confidence_indicator(conf)
+            
+            # Círculo de color del grupo
             draw.ellipse([15, y_pos+2, 23, y_pos+10], fill=group_data["color"])
-            # Texto
-            text = f"{lm['abbrev']}: ({lm['x']:.0f}, {lm['y']:.0f})"
-            draw.text((30, y_pos), text, fill=(220, 220, 220), font=font_text)
+            
+            # Texto con coordenadas
+            coord_text = f"{lm['abbrev']}: ({lm['x']:.0f}, {lm['y']:.0f})"
+            draw.text((30, y_pos), coord_text, fill=(220, 220, 220), font=font_text)
+            
+            # Indicador de confianza con color
+            conf_text = f"{conf_pct:.0f}%"
+            # Posicionar a la derecha
+            conf_x = width - 60
+            draw.text((conf_x, y_pos), conf_text, fill=conf_color, font=font_text)
+            
+            # Indicador visual
+            indicator_x = width - 20
+            draw.text((indicator_x, y_pos), conf_indicator, fill=conf_color, font=font_text)
+            
             y_pos += 16
         
         y_pos += 8
     
+    # Leyenda de confianza al final
+    y_pos += 5
+    draw.line([(10, y_pos), (width-10, y_pos)], fill=(100, 100, 100), width=1)
+    y_pos += 8
+    draw.text((10, y_pos), "Confianza:", fill=(200, 200, 200), font=font_small)
+    y_pos += 14
+    draw.text((15, y_pos), "✓ ≥80%  Alta", fill=(80, 255, 80), font=font_small)
+    y_pos += 12
+    draw.text((15, y_pos), "~ 50-79%  Media", fill=(255, 255, 80), font=font_small)
+    y_pos += 12
+    draw.text((15, y_pos), "! <50%  Baja (revisar)", fill=(255, 80, 80), font=font_small)
+    
     return legend
 
 def combine_images(main_image, legend):
@@ -537,14 +591,27 @@ def process_image(image, show_labels):
         legend = create_legend_image(landmarks)
         combined = combine_images(annotated, legend)
         
-        # JSON output
+        # Calcular estadísticas de confianza
+        confidences = [lm['confidence'] for lm in landmarks]
+        avg_conf = np.mean(confidences) * 100
+        min_conf = np.min(confidences) * 100
+        low_conf_count = sum(1 for c in confidences if c * 100 < 50)
+        
+        # JSON output con confianza
         result = {
             "num_landmarks": len(landmarks),
+            "confidence_stats": {
+                "average": round(avg_conf, 1),
+                "minimum": round(min_conf, 1),
+                "low_confidence_count": low_conf_count
+            },
             "landmarks": [{
                 "id": lm["id"],
                 "name": lm["name"],
+                "abbrev": lm["abbrev"],
                 "x": lm["x"],
-                "y": lm["y"]
+                "y": lm["y"],
+                "confidence": round(lm["confidence"] * 100, 1)
             } for lm in landmarks]
         }
         
@@ -555,15 +622,18 @@ def process_image(image, show_labels):
         return None, f"Error: {e}\n{traceback.format_exc()}"
 
 print("=" * 50)
-print("Cephalometric Landmark Detection v1.0")
+print("Cephalometric Landmark Detection v1.1")
+print("Con indicadores de confianza")
 print("=" * 50)
 load_model()
 
-with gr.Blocks(title="Cephalometric Landmark Detection", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="Cephalometric Landmark Detection") as demo:
     gr.Markdown("""
     # 🦷 Detección de Landmarks Cefalométricos
     
     Detección automática de **19 puntos anatómicos** en radiografías cefalométricas laterales usando HRNet-W32.
+    
+    **Nuevo:** Indicadores de confianza para cada punto detectado.
     """)
     
     with gr.Row():
@@ -576,7 +646,7 @@ with gr.Blocks(title="Cephalometric Landmark Detection", theme=gr.themes.Soft())
             output_image = gr.Image(label="📍 Resultado con Leyenda", height=500)
     
     with gr.Accordion("📋 Datos JSON", open=False):
-        output_json = gr.Code(label="Coordenadas", language="json", lines=10)
+        output_json = gr.Code(label="Coordenadas y Confianza", language="json", lines=15)
     
     gr.Markdown("""
     ---
@@ -589,6 +659,13 @@ with gr.Blocks(title="Cephalometric Landmark Detection", theme=gr.themes.Soft())
     | 🟣 | Mandibular | Point B, Pogonion, Menton, Gnathion, Gonion |
     | 🟡 | Dental | Upper Incisor (U1), Lower Incisor (L1) |
     | 🩵 | Tejido Blando | Upper Lip, Lower Lip, Subnasale, Soft Tissue Pog |
+    
+    ### 🎯 Indicadores de Confianza
+    | Indicador | Significado |
+    |-----------|-------------|
+    | ✓ (verde) | Alta confianza (≥80%) - Punto bien detectado |
+    | ~ (amarillo) | Confianza media (50-79%) - Verificar posición |
+    | ! (rojo) | Baja confianza (<50%) - Requiere revisión manual |
     """)
     
     detect_btn.click(fn=process_image, inputs=[input_image, show_labels], outputs=[output_image, output_json])