File size: 4,646 Bytes
b2f0d83 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | import gradio as gr
import numpy as np
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
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) # <--- 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")
detection_pil, scale = resize_for_detection(original_pil, DETECTION_MAX_DIM)
detections = detector.detect(np.array(detection_pil))
draw = ImageDraw.Draw(original_pil)
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)
else:
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)
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; }
h1 { color: #ff2e82; text-align: center; }
"""
with gr.Blocks(css=css) as demo:
gr.Markdown("# 👙 Automatischer Nackt-Analyzer")
gr.Markdown("Lade ein Bild hoch und erhalte direkt das analysierte Bild mit Annotationen.")
input_image = gr.Image(type="numpy", label="Bild hochladen")
output_image = gr.Image(label="Analyse-Ergebnis")
input_image.change(fn=process_image, inputs=input_image, outputs=output_image)
demo.launch() |