Update app.py

app.py

@@ -4,12 +4,15 @@ from PIL import Image, ImageDraw, ImageFont
 import cv2
 from nudenet import NudeDetector
 
+# --- Konstanten ---
 DETECTION_MAX_DIM = 768
 PIXELS_PER_CM_ESTIMATE = 15
 MIN_CONFIDENCE = 0.45
 
+# --- NudeNet Detector ---
 detector = NudeDetector(inference_resolution=640)
 
+# --- Hilfsfunktionen ---
 def resize_for_detection(img_pil, max_dim):
     if max(img_pil.width, img_pil.height) <= max_dim:
         return img_pil, 1.0
@@ -21,35 +24,46 @@ def resize_for_detection(img_pil, max_dim):
 
 def describe_breast_precise(crop_pil):
     w,h = crop_pil.size
-    if w*h==0: return "Fehler: leeres Crop"
+    if w*h == 0:
+        return "Fehler: leeres Crop"
     gray = cv2.cvtColor(np.array(crop_pil), cv2.COLOR_RGB2GRAY)
-    _, thresh = cv2.threshold(gray,100,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
-    contours,_ = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
-    nipple_detected = any(40<cv2.contourArea(c)<(w*h/4) and (p:=cv2.arcLength(c,True))>0 and (4*np.pi*cv2.contourArea(c)/(p*p))>0.55 for c in contours)
-    ratio=w/h
-    shape="Breit" if ratio>1.15 else "Hoch" if ratio<0.85 else "Rund"
-    size="klein" if w*h<28000 else "mittel" if w*h<75000 else "groß" if w*h<140000 else "sehr groß"
-    w_cm=round(w/PIXELS_PER_CM_ESTIMATE,1)
-    h_cm=round(h/PIXELS_PER_CM_ESTIMATE,1)
+    _, thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    contours,_ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    nipple_detected = any(
+        40 < cv2.contourArea(c) < (w*h/4) and (p:=cv2.arcLength(c,True))>0 and
+        (4*np.pi*cv2.contourArea(c)/(p*p))>0.55
+        for c in contours
+    )
+    ratio = w/h
+    shape = "Breit" if ratio>1.15 else "Hoch" if ratio<0.85 else "Rund"
+    size = "klein" if w*h<28000 else "mittel" if w*h<75000 else "groß" if w*h<140000 else "sehr groß"
+    w_cm = round(w/PIXELS_PER_CM_ESTIMATE,1)
+    h_cm = round(h/PIXELS_PER_CM_ESTIMATE,1)
     return f"Brust: {shape}, {size}, Nippel: {'Ja' if nipple_detected else 'Nein'}, {w_cm}x{h_cm}cm"
 
 def describe_vagina_precise(crop_pil):
-    w,h=crop_pil.size
-    if w*h==0: return "Fehler: leeres Crop"
-    gray=cv2.cvtColor(np.array(crop_pil), cv2.COLOR_RGB2GRAY)
-    hair_ratio=np.sum(np.inRange(gray,35,145)>0)/(w*h)
-    shaved="rasiert" if hair_ratio<0.04 else "minimal" if hair_ratio<0.13 else "Brazilian" if hair_ratio<0.36 else "behaart"
-    ratio=w/h
-    area=w*h
-    if area<18000: form_desc="Innie"
-    elif area>65000 and ratio>1.45: form_desc="Outie (Puff)"
-    elif ratio>1.45: form_desc="Outie"
-    else: form_desc="Innie/Outie"
-    size="winzig" if area<18000 else "klein" if area<38000 else "mittel" if area<65000 else "groß"
-    w_cm=round(w/PIXELS_PER_CM_ESTIMATE,1)
-    h_cm=round(h/PIXELS_PER_CM_ESTIMATE,1)
+    w,h = crop_pil.size
+    if w*h == 0:
+        return "Fehler: leeres Crop"
+    gray = cv2.cvtColor(np.array(crop_pil), cv2.COLOR_RGB2GRAY)
+    hair_ratio = np.sum(cv2.inRange(gray, 35, 145) > 0) / (w*h)  # <--- korrigiert
+    shaved = "rasiert" if hair_ratio < 0.04 else "minimal" if hair_ratio < 0.13 else "Brazilian" if hair_ratio < 0.36 else "behaart"
+    ratio = w/h
+    area = w*h
+    if area < 18000:
+        form_desc = "Innie"
+    elif area > 65000 and ratio > 1.45:
+        form_desc = "Outie (Puff)"
+    elif ratio > 1.45:
+        form_desc = "Outie"
+    else:
+        form_desc = "Innie/Outie"
+    size = "winzig" if area<18000 else "klein" if area<38000 else "mittel" if area<65000 else "groß"
+    w_cm = round(w/PIXELS_PER_CM_ESTIMATE,1)
+    h_cm = round(h/PIXELS_PER_CM_ESTIMATE,1)
     return f"Vagina: {form_desc}, {size}, {shaved}, {w_cm}x{h_cm}cm"
 
+# --- Bildverarbeitung ---
 def process_image(image):
     try:
         original_pil = Image.fromarray(image).convert("RGB") if isinstance(image,np.ndarray) else image.convert("RGB")
@@ -57,31 +71,38 @@ def process_image(image):
         detections = detector.detect(np.array(detection_pil))
         draw = ImageDraw.Draw(original_pil)
         font = ImageFont.load_default()
-        results_text=[]
+        results_text = []
+
         for det in detections:
-            label=det["class"]
-            score=det.get("score",0)
-            if score<MIN_CONFIDENCE: continue
-            if label not in ["FEMALE_BREAST_EXPOSED","FEMALE_GENITALIA_EXPOSED"]: continue
-            x,y,w,h=[int(v*scale) for v in det["box"]]
-            crop_pil=original_pil.crop((x,y,x+w,y+h))
+            label = det["class"]
+            score = det.get("score",0)
+            if score < MIN_CONFIDENCE:
+                continue
+            if label not in ["FEMALE_BREAST_EXPOSED","FEMALE_GENITALIA_EXPOSED"]:
+                continue
+            x,y,w,h = [int(v*scale) for v in det["box"]]
+            crop_pil = original_pil.crop((x,y,x+w,y+h))
             if label=="FEMALE_BREAST_EXPOSED":
-                desc=describe_breast_precise(crop_pil)
-                color=(255,46,130)
+                desc = describe_breast_precise(crop_pil)
+                color = (255,46,130)
             else:
-                desc=describe_vagina_precise(crop_pil)
-                color=(138,43,226)
+                desc = describe_vagina_precise(crop_pil)
+                color = (138,43,226)
             draw.rectangle([x,y,x+w,y+h],outline=color,width=4)
-            text_pos=(x,y-15 if y>15 else y+h)
+            text_pos = (x,y-15 if y>15 else y+h)
             draw.text(text_pos,desc,fill=color,font=font)
             results_text.append(desc)
+
         if not results_text:
             draw.text((10,10),"Keine relevanten Bereiche erkannt.",fill=(255,0,0),font=font)
+
         return np.array(original_pil)
+
     except Exception as e:
         print(f"Fehler: {e}")
         return None
 
+# --- Gradio App ---
 css = """
 body { background: #0f0f1a; color: #e0e0ff; }
 .gradio-container { max-width: 900px !important; margin: auto; }