使用Python轻松解析PSD文件的终极指南

git clone https://gitcode.com/gh_mirrors/ps/psd-tools
cd psd-tools
pip install -e .

# PSD文件的基本组成
class PSDStructure:
    def __init__(self):
        self.header = None           # 文件头信息
        self.color_mode = None       # 颜色模式数据
        self.resources = None        # 图像资源
        self.layer_info = None        # 图层和蒙版信息
        self.image_data = None       # 最终的图像数据

def categorize_layers_smartly(psd_file):
    """智能分类PSD中的各种图层类型"""
    categories = {
        'text': [],      # 文字图层
        'shape': [],     # 形状图层
        'smart': [],     # 智能对象
        'adjustment': [] # 调整图层
    }
    
    for layer in psd_file.descendants():
        if layer.kind == 'type':
            categories['text'].append(layer)
        elif layer.kind == 'shape': 
            categories['shape'].append(layer)
        elif layer.kind == 'smart_object':
            categories['smart'].append(layer)
        elif hasattr(layer, 'adjustment'):
            categories['adjustment'].append(layer)
    
    return categories

def process_psd_safely(file_path, batch_size=5):
    """安全处理大型PSD文件，避免内存问题"""
    psd = PSDImage.open(file_path)
    
    # 按批次处理图层
    for i in range(0, len(psd.layers), batch_size):
        batch = psd.layers[i:i+batch_size]
        process_batch(batch)
        
        # 及时释放内存
        del batch

def analyze_complex_masks(psd):
    """分析复杂蒙版结构"""
    mask_layers = []
    
    for layer in psd:
        if hasattr(layer, 'mask'):
            mask_data = {
                'name': layer.name,
                'bounds': layer.mask.bounds,
                'enabled': layer.mask.enabled
            }
            mask_layers.append(mask_data)
    
    return mask_layers

from PIL import Image, ImageFilter

def enhance_extracted_layers(psd):
    """对提取的图层进行图像增强"""
    enhanced_results = []
    
    for layer in psd:
        if layer.visible:
            base_image = layer.composite()
            # 应用锐化滤镜
            sharpened = base_image.filter(ImageFilter.SHARPEN)
            enhanced_results.append((layer.name, sharpened))
    
    return enhanced_results