Real-ESRGAN多场景自适应智能增强系统｜全自动场景识别+一键全能修复｜毕设终极整合大项目

bash
pip install torch torchvision opencv-python numpy pillow realesrgan moviepy scikit-image -i https://pypi.tuna.tsinghua.edu.cn/simple

python
import os
import cv2
import numpy as np
from PIL import Image
from realesrgan import RealESRGANer

# 创建总结果保存文件夹
os.makedirs("total_auto_restore_result", exist_ok=True)

# 初始化超分模型
upscale = 4
model = RealESRGANer(
    scale=upscale,
    model_path="RealESRGAN_x4plus.pth",
    tile=0,
    tile_pad=10,
    pre_pad=0,
    half=False
)

def judge_image_scene(img):
    """
    图像场景智能判别：暗光/逆光/雾霾/压缩/普通
    """
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    mean_bright = np.mean(gray)
    std_noise = np.std(gray)

    # 场景分类规则
    if mean_bright < 60:
        return "dark"       # 暗光夜景
    elif mean_bright > 180:
        return "backlight"  # 逆光过曝
    elif std_noise < 20:
        return "haze"       # 雾霾灰蒙蒙
    elif std_noise > 50:
        return "compress"   # 压缩噪点
    else:
        return "normal"     # 普通模糊

def scene_preprocess(img, scene_type):
    """根据场景自动匹配预处理方案"""
    if scene_type == "dark":
        # 夜景暗光提亮
        lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
        l,a,b = cv2.split(lab)
        clahe = cv2.createCLAHE(clipLimit=2.5, tileGridSize=(8,8))
        l = clahe.apply(l)
        img = cv2.merge((l,a,b))
        return cv2.cvtColor(img, cv2.COLOR_LAB2BGR)

    elif scene_type == "backlight":
        # 逆光光影平衡
        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
        h,s,v = cv2.split(hsv)
        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
        v = clahe.apply(v)
        hsv = cv2.merge((h,s,v))
        return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

    elif scene_type == "haze":
        # 去雾通透增强
        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
        h,s,v = cv2.split(hsv)
        clahe = cv2.createCLAHE(clipLimit=2.2, tileGridSize=(8,8))
        v = clahe.apply(v)
        s = np.clip(s+15,0,255)
        hsv = cv2.merge((h,s,v))
        return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

    elif scene_type == "compress":
        # 压缩伪影平滑
        return cv2.bilateralFilter(img, d=5, sigmaColor=25, sigmaSpace=25)

    else:
        return img

def auto_all_scene_restore(img_path):
    # 读取图像
    origin_bgr = cv2.imread(img_path)
    # 1.智能场景识别
    scene = judge_image_scene(origin_bgr)
    # 2.自适应预处理
    pre_img = scene_preprocess(origin_bgr, scene)
    img_rgb = cv2.cvtColor(pre_img, cv2.COLOR_BGR2RGB)
    # 3.超分高清修复
    output, _ = model.enhance(img_rgb, outscale=upscale)
    # 4.保存结果
    result_img = Image.fromarray(output)
    save_path = os.path.join("total_auto_restore_result","auto_result.jpg")
    result_img.save(save_path)
    # 拼接对比图
    origin_resize = cv2.resize(origin_bgr,(output.shape[1],output.shape[0]))
    contrast = np.hstack((origin_resize,output))
    cv2.imshow("多场景自适应AI智能增强系统",contrast)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    print(f"自动识别场景：{scene}，修复完成！")

if __name__ == "__main__":
    auto_all_scene_restore("test.jpg")