app.py

import gradio as gr
import cv2
import numpy as np
from PIL import Image
import requests
from io import BytesIO
import torch
import sys
from pathlib import Path
import os
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.general import (check_img_size, non_max_suppression, scale_boxes)
from utils.plots import Annotator, colors
from utils.torch_utils import select_device

# YOLOv9 모델 로드
device = select_device('')
model = DetectMultiBackend('./weights/nsfw_detector_e_rok.pt', device=device, dnn=False, data=None, fp16=False)
stride, names, pt = model.stride, model.names, model.pt
imgsz = check_img_size((640, 640), s=stride)  # check image size

def process_image(image, conf_threshold, iou_threshold, label_mode):
    # 이미지 전처리
    im = torch.from_numpy(image).to(device).permute(2, 0, 1)  # HWC to CHW
    im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
    im /= 255  # 0 - 255 to 0.0 - 1.0
    if len(im.shape) == 3:
        im = im[None]  # expand for batch dim
    
    # 이미지 크기 조정
    im = torch.nn.functional.interpolate(im, size=imgsz, mode='bilinear', align_corners=False)
    
    # 추론
    pred = model(im, augment=False, visualize=False)
    if isinstance(pred, list):
        pred = pred[0]  # 첫 번째 요소 선택 (일반적으로 단일 이미지 추론의 경우)
        
    # NMS
    pred = non_max_suppression(pred, conf_threshold, iou_threshold, None, False, max_det=1000)

    # 결과 처리
    img = image.copy()
    
    harmful_label_list = []
    annotations = []
    
    for i, det in enumerate(pred):  # per image
        if len(det):
            # Rescale boxes from img_size to im0 size
            det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], img.shape).round()
            
            # Write results
            for *xyxy, conf, cls in reversed(det):
                c = int(cls)  # integer class
                if c != 6:
                    harmful_label_list.append(c)
                
                annotation = {
                    'xyxy': xyxy,
                    'conf': conf,
                    'cls': c,
                    'label': f"{names[c]} {conf:.2f}" if label_mode == "Draw Confidence" else f"{names[c]}"
                }
                annotations.append(annotation)
    
    if harmful_label_list:
        gr.Error("Warning, this is a harmful image.")
        # 이미지 전체를 흐리게 처리
        img = cv2.GaussianBlur(img, (125, 125), 0)
    else:
        gr.Info('This is a safe image.')
    
    # Annotator 적용
    annotator = Annotator(img, line_width=3, example=str(names))
    
    for ann in annotations:
        if label_mode == "Draw box":
            annotator.box_label(ann['xyxy'], None, color=colors(ann['cls'], True))
        elif label_mode in ["Draw Label", "Draw Confidence"]:
            annotator.box_label(ann['xyxy'], ann['label'], color=colors(ann['cls'], True))
        elif label_mode == "Censor Predictions":
            cv2.rectangle(img, (int(ann['xyxy'][0]), int(ann['xyxy'][1])), (int(ann['xyxy'][2]), int(ann['xyxy'][3])), (0, 0, 0), -1)

    return annotator.result()

def detect_nsfw(input_image, conf_threshold, iou_threshold, label_mode):
    if isinstance(input_image, str):  # URL input
        response = requests.get(input_image)
        image = Image.open(BytesIO(response.content))
    else:  # File upload
        image = Image.fromarray(input_image)
    
    image = np.array(image)
    if len(image.shape) == 2:  # grayscale
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
    elif image.shape[2] == 4:  # RGBA
        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
    
    # 이미지 크기 조정
    image = cv2.resize(image, imgsz)
    
    processed_image = process_image(image, conf_threshold, iou_threshold, label_mode)
    return processed_image

# Gradio 인터페이스 설정
demo = gr.Interface(
    fn=detect_nsfw,
    inputs=[
        gr.Image(type="numpy", label="Upload an image or enter a URL"),
        gr.Slider(0, 1, value=0.45, label="Confidence Threshold"),
        gr.Slider(0, 1, value=0.45, label="Overlap Threshold"),
        gr.Dropdown(["Draw box", "Draw Label", "Draw Confidence", "Censor Predictions"], label="Label Display Mode", value="Draw box")
    ],
    outputs=gr.Image(type="numpy", label="Processed Image"),
    title="YOLOv9 NSFW Content Detection",
    description="Upload an image or enter a URL to detect NSFW content using YOLOv9."
)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0")