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app_3.py

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+import gradio as gr
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import cv2
+import os
+from nudenet import NudeDetector
+import concurrent.futures
+
+# --- Konstanten ---
+DETECTION_MAX_DIM = 768
+PIXELS_PER_CM_ESTIMATE = 15
+MIN_CONFIDENCE = 0.45
+
+# Initialisiere den Detektor
+detector = NudeDetector(inference_resolution=640)
+
+def resize_for_detection(img_pil, max_dim):
+    if max(img_pil.width, img_pil.height) <= max_dim:
+        return img_pil, 1.0
+    ratio = max_dim / max(img_pil.width, img_pil.height)
+    new_size = (int(img_pil.width * ratio), int(img_pil.height * ratio))
+    resized = img_pil.resize(new_size, Image.Resampling.LANCZOS)
+    scale = 1 / ratio
+    return resized, scale
+
+def describe_breast_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)
+    _, 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(cv2.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)
+    return f"Vagina: {form_desc}, {size}, {shaved}, {w_cm}x{h_cm}cm"
+
+def process_image(image_path):
+    try:
+        original_pil = Image.open(image_path).convert("RGB")
+        detection_pil, scale = resize_for_detection(original_pil, DETECTION_MAX_DIM)
+        detections = detector.detect(np.array(detection_pil))
+        
+        draw = ImageDraw.Draw(original_pil)
+        # Versuche eine Schriftart zu laden, sonst Standard
+        try:
+            font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 20)
+        except:
+            font = ImageFont.load_default()
+
+        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))
+            
+            if label == "FEMALE_BREAST_EXPOSED":
+                desc = describe_breast_precise(crop_pil)
+                color = (255, 46, 130) # Pink
+            else:
+                desc = describe_vagina_precise(crop_pil)
+                color = (138, 43, 226) # Purple
+            
+            # Zeichne Box
+            draw.rectangle([x, y, x + w, y + h], outline=color, width=4)
+            # Zeichne Text-Hintergrund
+            text_pos = (x, y - 25 if y > 25 else y + h)
+            draw.text(text_pos, desc, fill=color, font=font)
+            results_text.append(desc)
+
+        if not results_text:
+            # Wenn nichts gefunden wurde, gib das Originalbild mit Hinweis zurück
+            draw.text((10, 10), "Keine relevanten Bereiche erkannt.", fill=(255, 0, 0), font=font)
+            return original_pil
+
+        return original_pil
+    except Exception as e:
+        print(f"Fehler: {e}")
+        return None
+
+def analyze_all(files):
+    if not files:
+        return None
+    
+    processed_images = []
+    for f in files:
+        res = process_image(f.name)
+        if res:
+            processed_images.append(res)
+    
+    return processed_images
+
+custom_css = """
+body { background: #0f0f1a; color: #e0e0ff; }
+.gradio-container { max-width: 1000px !important; margin: auto; }
+h1 { color: #ff2e82; text-align: center; }
+"""
+
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="pink")) as demo:
+    gr.Markdown("# 👙 Automatischer Nackt-Analyzer")
+    gr.Markdown("Lade Bilder hoch für eine automatische Analyse von Brüsten und Vagina. Die Ergebnisse werden direkt im Bild angezeigt.")
+    
+    with gr.Row():
+        input_files = gr.File(file_count="multiple", label="Bilder hochladen")
+    
+    with gr.Row():
+        output_gallery = gr.Gallery(label="Analyse-Ergebnisse", columns=2, height="auto", show_download_button=True)
+    
+    # Automatischer Trigger bei Upload
+    input_files.change(fn=analyze_all, inputs=input_files, outputs=output_gallery)
+
+if __name__ == "__main__":
+    demo.launch()

requirements_3.txt

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+gradio>=6.9.0
+nudenet>=3.4.2
+opencv-python-headless>=4.11.0
+pillow>=11.1.0
+numpy>=2.2.0
+onnxruntime>=1.20.0