基于Python构建智能图像增强系统

class ImageEnhancer:
    def __init__(self, config):
        self.sr_model = self._load_sr_model(config['sr_path'])
        self.denoise_model = self._load_denoise_model(config['dn_path'])
        self.device = torch.device(config.get('device', 'cuda:0'))
        
    def _load_sr_model(self, path):
        """加载超分模型"""
        model = RRDBNet(3, 3, 64, 23, gc=32)
        model.load_state_dict(torch.load(path))
        return model.to(self.device).eval()
    
    def enhance(self, img, scale=4, denoise_strength=0.5):
        """完整增强流程"""
        with torch.no_grad():
            # 预处理
            lr = self._preprocess(img)
            # 超分辨率
            sr = self.sr_model(lr)
            # 去噪
            denoised = self._adaptive_denoise(sr, denoise_strength)
            # 后处理
            return self._postprocess(denoised)
    
    def batch_enhance(self, input_dir, output_dir):
        """分布式批处理"""
        pass

def adaptive_denoise(image, model, noise_level=None):
    """根据噪声水平动态调整去噪强度"""
    if noise_level is None:
        noise_level = estimate_noise(image)
    
    # 噪声水平映射到模型参数
    if noise_level < 5:
        denoise_weight = 0.3
    elif noise_level < 15:
        denoise_weight = 0.6
    else:
        denoise_weight = 0.9
    
    # 创建噪声图
    noise_map = torch.ones_like(image) * denoise_weight
    
    # 带噪声引导的去噪
    return model(torch.cat([image, noise_map], dim=1))

def frequency_aware_sharpen(img, kernel_size=3, amount=0.8):
    """保持自然感的智能锐化"""
    # 分离高低频
    low_freq = cv2.GaussianBlur(img, (kernel_size,kernel_size), 0)
    high_freq = img.astype(np.float32) - low_freq.astype(np.float32)
    
    # 自适应锐化强度
    sharpened = img + amount * high_freq
    return np.clip(sharpened, 0, 255).astype(np.uint8)

from airflow import DAG
from airflow.operators.python import PythonOperator

default_args = {
    'retries': 3,
    'retry_delay': timedelta(minutes=5)
}

dag = DAG('image_enhancement',
          default_args=default_args,
          schedule_interval='@daily')

def process_batch(**kwargs):
    enhancer = ImageEnhancer()
    enhancer.batch_enhance('/input', '/output')

task = PythonOperator(
    task_id='enhance_images',
    python_callable=process_batch,
    dag=dag)

def build_trt_engine(onnx_path, engine_path):
    """转换ONNX到TensorRT引擎"""
    logger = trt.Logger(trt.Logger.INFO)
    builder = trt.Builder(logger)
    
    network = builder.create_network(
        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
    
    parser = trt.OnnxParser(network, logger)
    with open(onnx_path, 'rb') as model:
        parser.parse(model.read())
    
    config = builder.create_builder_config()
    config.set_flag(trt.BuilderFlag.FP16)
    config.max_workspace_size = 1 << 30
    
    engine = builder.build_engine(network, config)
    with open(engine_path, 'wb') as f:
        f.write(engine.serialize())

class QualityEvaluator:
    def __init__(self):
        self.lpips = LPIPS(net='vgg').eval()
        
    def evaluate(self, original, enhanced):
        """综合质量评估"""
        # 计算传统指标
        psnr = calculate_psnr(original, enhanced)
        ssim = calculate_ssim(original, enhanced)
        
        # 计算感知相似度
        lpips_score = self.lpips(
            preprocess(original), 
            preprocess(enhanced))
        
        # 生成综合评分
        return {
            'psnr': psnr,
            'ssim': ssim,
            'lpips': lpips_score.item(),
            'composite': 0.4*psnr + 0.3*ssim + 0.3*(1-lpips_score)

class EnhancementCache:
    def __init__(self, max_size=100):
        self.cache = {}
        self.lru = []
        self.max_size = max_size
        
    def get(self, img_hash):
        if img_hash in self.cache:
            self.lru.remove(img_hash)
            self.lru.append(img_hash)
            return self.cache[img_hash]
        return None
        
    def put(self, img_hash, result):
        if len(self.cache) >= self.max_size:
            oldest = self.lru.pop(0)
            del self.cache[oldest]
        self.cache[img_hash] = result
        self.lru.append(img_hash)

def dynamic_scaling(image, target_size=512):
    """保持长宽比的智能缩放"""
    h, w = image.shape[:2]
    
    if max(h,w) <= target_size:
        return image
        
    scale = target_size / max(h,w)
    new_size = (int(w*scale), int(h*scale))
    
    return cv2.resize(image, new_size, 
                     interpolation=cv2.INTER_LANCZOS4)

def progressive_enhance(image, model, steps=3):
    """多阶段渐进增强"""
    current = image.copy()
    
    for i in range(1, steps+1):
        # 逐步提高增强强度
        scale = i/steps
        enhanced = model(current, scale=scale)
        
        # 混合原始图像保持自然感
        current = cv2.addWeighted(
            current, 0.7, 
            enhanced, 0.3, 0)
    
    return current

class TestEnhancement(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.test_set = load_test_images()
        cls.enhancer = ImageEnhancer()
        
    def test_quality_improvement(self):
        for img in self.test_set:
            enhanced = self.enhancer.enhance(img)
            psnr = calculate_psnr(img.ground_truth, enhanced)
            self.assertGreater(psnr, 30.0)
    
    def test_artifact_detection(self):
        for img in self.test_set:
            enhanced = self.enhancer.enhance(img)
            artifact_score = detect_artifacts(enhanced)
            self.assertLess(artifact_score, 0.05)

yaml

name: Enhancement CI

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    container:
      image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
    steps:
    - uses: actions/checkout@v2
    - name: Install dependencies
      run: |
        pip install -r requirements.txt
        pip install pytest pytest-cov
    - name: Run tests
      run: |
        pytest --cov=src --cov-report=xml
    - name: Upload coverage
      uses: codecov/codecov-action@v1