Python实现智能图像背景移除功能

class BackgroundRemover:
    def __init__(self, model_path="unet_weights.h5"):
        self.model = self._load_model(model_path)
        self.preprocessor = ImagePreprocessor()
        self.postprocessor = EdgeRefiner()
        
    def _load_model(self, path):
        """加载预训练模型"""
        custom_objects = {
            'dice_loss': self._dice_loss,
            'iou_score': self._iou_score
        }
        return tf.keras.models.load_model(path, custom_objects=custom_objects)
    
    def process_image(self, image):
        """完整处理流程"""
        preprocessed = self.preprocessor.run(image)
        mask = self.model.predict(preprocessed)
        return self.postprocessor.refine(image, mask)
    
    def batch_process(self, input_dir, output_dir):
        """批量处理实现"""
        pass

def build_enhanced_unet(input_size=(512,512,3)):
    inputs = Input(input_size)
    
    # 编码器部分
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = AttentionGate(conv1)
    
    # 跳跃连接增强
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = ResBlock(pool1, 128)
    
    # 解码器部分
    up4 = Conv2DTranspose(64, (2,2), strides=(2,2))(conv4)
    up4 = concatenate([up4, conv1], axis=3)
    conv7 = Conv2D(64, 3, activation='relu', padding='same')(up4)
    
    # 输出层
    outputs = Conv2D(1, 1, activation='sigmoid')(conv7)
    
    return Model(inputs=[inputs], outputs=[outputs])

def refine_edge(image, mask):
    """基于引导滤波的边缘优化"""
    radius = 15
    eps = 1e-3
    
    # 将mask转换为灰度图
    guide = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    
    # 归一化处理
    mask_norm = mask.astype(np.float32)/255.0
    
    # 应用引导滤波
    refined = cv2.ximgproc.guidedFilter(
        guide=guide,
        src=mask_norm,
        radius=radius,
        eps=eps,
        dDepth=-1
    )
    
    return (refined*255).astype(np.uint8)

def generate_shadow(foreground, mask):
    """生成逼真投影效果"""
    # 计算距离变换
    dist_transform = cv2.distanceTransform(
        mask, cv2.DIST_L2, 5)
    
    # 创建投影基础
    shadow = np.zeros_like(foreground)
    intensity = 0.7 * (1 - dist_transform/np.max(dist_transform))
    
    # 应用光照模型
    for c in range(3):
        shadow[:,:,c] = foreground[:,:,c] * intensity
        
    # 模糊处理
    shadow = cv2.GaussianBlur(shadow, (15,15), 5)
    
    return shadow

from celery import Celery

app = Celery('bg_removal',
             broker='pyamqp://guest@localhost//')

@app.task(bind=True)
def process_single_task(self, image_path):
    try:
        remover = BackgroundRemover()
        result = remover.process_image(image_path)
        return {'status': 'success', 'path': result}
    except Exception as e:
        raise self.retry(exc=e)

def convert_to_tensorrt(model):
    """模型转换到TensorRT"""
    conversion_params = trt.TrtConversionParams(
        precision_mode=trt.TrtPrecisionMode.FP16,
        max_workspace_size=1<<30)
    
    converter = trt.TrtGraphConverter(
        input_saved_model_dir='saved_model',
        conversion_params=conversion_params)
    
    converter.convert()
    converter.save('trt_model')

class QualityEvaluator:
    def __init__(self):
        self.model = load_evaluation_model()
        
    def evaluate(self, original, result):
        """评估抠图质量"""
        # 提取边缘特征
        edge_diff = self._calc_edge_discontinuity(original, result)
        
        # 语义一致性评估
        semantic_score = self.model.predict(
            np.concatenate([original, result], axis=-1))
        
        return {
            'edge_score': edge_diff,
            'semantic_score': semantic_score,
            'overall': 0.6*semantic_score + 0.4*edge_diff
        }

class ImageCache:
    def __init__(self, max_size=100):
        self.lru_cache = OrderedDict()
        self.max_size = max_size
        
    def get(self, key):
        if key not in self.lru_cache:
            return None
            
        value = self.lru_cache.pop(key)
        self.lru_cache[key] = value
        return value
        
    def put(self, key, value):
        if key in self.lru_cache:
            self.lru_cache.pop(key)
        elif len(self.lru_cache) >= self.max_size:
            self.lru_cache.popitem(last=False)
        self.lru_cache[key] = value

def dynamic_batching(image_list, max_batch=8):
    """根据显存动态调整批大小"""
    free_mem = get_gpu_memory()[0]
    required = estimate_memory_usage(512,512,3)
    
    batch_size = min(
        max_batch,
        int(free_mem*0.8/required))
    
    return [image_list[i:i+batch_size] 
            for i in range(0, len(image_list), batch_size)]

class ProgressiveLoader:
    def __init__(self, img_path, tile_size=256):
        self.tiff = TiffFile(img_path)
        self.tiles = self._generate_tiles(tile_size)
        
    def _generate_tiles(self, size):
        """生成分块加载方案"""
        width, height = self.tiff.shape
        return [(x, y, min(size,width-x), min(size,height-y))
                for y in range(0, height, size)
                for x in range(0, width, size)]
                
    def process_tile(self, tile):
        """处理单个分块"""
        x,y,w,h = tile
        data = self.tiff[y:y+h, x:x+w]
        return process_partial(data)

class TestBackgroundRemoval(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.test_images = load_test_dataset()
        cls.model = BackgroundRemover()
        
    def test_edge_precision(self):
        for img in self.test_images:
            result = self.model.process(img)
            edge_acc = calculate_edge_accuracy(
                img.ground_truth, result)
            self.assertGreater(edge_acc, 0.95)
    
    def test_performance(self):
        start = time.time()
        self.model.batch_process(test_dir)
        duration = time.time() - start
        self.assertLess(duration, 60)  # 60秒限制

# .github/workflows/ci.yml
name: CI Pipeline

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
    - name: Install dependencies
      run: |
        pip install -r requirements.txt
        pip install pytest-cov
    - name: Run tests
      run: |
        pytest --cov=./ --cov-report=xml
    - name: Upload coverage
      uses: codecov/codecov-action@v1