智能推荐：淘宝AI设计

import numpy as npfrom dataclasses import dataclass, fieldfrom typing import List, Dict, Tuple, Optionalfrom enum import Enumimport math class BlendMode(Enum):    NORMAL = "normal"    MULTIPLY = "multiply"    SCREEN = "screen"    OVERLAY = "overlay" @dataclassclass ImageLayer:    name: str    width: int    height: int    data: np.ndarray    opacity: float = 1.0    blend_mode: BlendMode = BlendMode.NORMAL @dataclassclass SegmentationMask:    mask: np.ndarray    confidence: float = 0.0 @dataclassclass SceneConfig:    scene_name: str    lighting_direction: Tuple[float, float]    color_temperature: int    shadow_enabled: bool = True    perspective_match: bool = True class SmartCutout:    def __init__(self):        self._refinement_iterations = 3     def segment(self, image: np.ndarray) -> SegmentationMask:        h, w = image.shape[:2]        mask = np.zeros((h, w), dtype=np.float32)        cy, cx = h // 2, w // 2        for y in range(h):            for x in range(w):                dist = math.sqrt((y - cy)**2 + (x - cx)**2)                max_dist = math.sqrt(cy**2 + cx**2) * 0.6                if dist < max_dist:                    mask[y, x] = 1.0 - 0.3 * (dist / max_dist)        return SegmentationMask(mask=mask, confidence=0.92)     def refine_edges(self, image: np.ndarray, mask: SegmentationMask, iterations: int = 3) -> SegmentationMask:        refined = mask.mask.copy()        h, w = refined.shape        for _ in range(iterations):            temp = refined.copy()            for y in range(1, h - 1):                for x in range(1, w - 1):                    if 0.1 < refined[y, x] < 0.9:                        neighbors = [refined[y-1, x], refined[y+1, x], refined[y, x-1], refined[y, x+1]]                        temp[y, x] = np.mean(neighbors)            refined = temp        new_conf = min(mask.confidence + 0.05 * iterations, 0.99)        return SegmentationMask(mask=refined, confidence=new_conf)     def extract(self, image: np.ndarray) -> np.ndarray:        mask = self.segment(image)        refined = self.refine_edges(image, mask)        h, w = image.shape[:2]        rgba = np.zeros((h, w, 4), dtype=np.uint8)        rgba[:, :, :3] = image[:, :, :3] if image.shape[2] >= 3 else image        rgba[:, :, 3] = (refined.mask * 255).astype(np.uint8)        return rgba class StyleTransfer:    def __init__(self):        self._supported_scenes = ["beach", "urban", "home", "studio", "nature"]     def harmonize_lighting(self, fg: np.ndarray, bg: np.ndarray, scene_config: SceneConfig) -> np.ndarray:        result = fg.copy().astype(np.float32)        temp_factor = scene_config.color_temperature / 5500.0        result[:, :, 0] = np.clip(result[:, :, 0] * (1.0 + 0.1 * temp_factor), 0, 255)        result[:, :, 2] = np.clip(result[:, :, 2] * (1.0 - 0.05 * temp_factor), 0, 255)        return result.astype(np.uint8)     def composite(self, fg_rgba: np.ndarray, bg: np.ndarray, position: Tuple[int, int], scene_config: SceneConfig) -> np.ndarray:        result = bg.copy()        x, y = position        fh, fw = fg_rgba.shape[:2]        alpha = fg_rgba[:, :, 3:4].astype(np.float32) / 255.0        fg_rgb = self.harmonize_lighting(fg_rgba[:, :, :3], bg, scene_config).astype(np.float32)        roi = result[y:y+fh, x:x+fw].astype(np.float32)        blended = fg_rgb * alpha + roi * (1 - alpha)        result[y:y+fh, x:x+fw] = blended.astype(np.uint8)        return result class BatchLayoutEngine:    def __init__(self):        self.templates: Dict[str, dict] = {            "standard": {"cols": 1, "modules": ["hero_image", "selling_points", "specs", "scene"]},            "grid": {"cols": 2, "modules": ["hero_image", "features_grid", "specs", "reviews"]},            "premium": {"cols": 1, "modules": ["hero_image", "brand_story", "details", "specs", "scene"]}        }     def generate_copy(self, product_attrs: dict) -> List[str]:        name = product_attrs.get("name", "商品")        material = product_attrs.get("material", "优质材料")        feature = product_attrs.get("feature", "精湛工艺")        copies = [            f"{name} — {feature}，品质之选",            f"精选{material}，触感舒适，经久耐用",            f"匠心设计，每一处细节都经过精心打磨",            f"时尚外观与实用功能的完美结合"        ]        return copies     def generate_detail_page(self, product_attrs: dict, images: List[np.ndarray], template_name: str = "standard") -> dict:        template = self.templates.get(template_name, self.templates["standard"])        copies = self.generate_copy(product_attrs)        page = {"product_name": product_attrs.get("name", ""), "template": template_name, "layout_columns": template["cols"], "modules": [], "copies": copies, "image_count": len(images)}        for module_name in template["modules"]:            module = {"type": module_name, "content": copies[0] if copies else "", "images": []}            page["modules"].append(module)        return page     def batch_generate(self, products: List[dict], template_name: str = "standard") -> List[dict]:        results = []        for idx, product in enumerate(products):            images = product.get("images", [])            page = self.generate_detail_page(product, images, template_name)            results.append(page)        return results def run_demo():    print("=== 淘宝AI设计工具流程演示 ===")    cutout = SmartCutout()    style_engine = StyleTransfer()    layout_engine = BatchLayoutEngine()    print("\n[步骤1] 智能抠图...")    mock_image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)    rgba_result = cutout.extract(mock_image)    print(f"  输入图像: {mock_image.shape}")    print(f"  输出RGBA: {rgba_result.shape}")    print(f"  前景像素数: {np.sum(rgba_result[:, :, 3] > 128)}")    print("\n[步骤2] 场景合成...")    bg_scene = np.random.randint(180, 240, (200, 200, 3), dtype=np.uint8)    scene_cfg = SceneConfig(scene_name="beach", lighting_direction=(45.0, 30.0), color_temperature=5800)    composite_result = style_engine.composite(rgba_result, bg_scene, (50, 50), scene_cfg)    print(f"  背景场景: {bg_scene.shape}")    print(f"  合成结果: {composite_result.shape}")    print(f"  场景: {scene_cfg.scene_name}")    print("\n[步骤3] 批量详情页生成...")    products = [        {"name": "夏季清凉T恤", "material": "纯棉面料", "feature": "透气舒适", "images": [mock_image]},        {"name": "商务休闲衬衫", "material": "棉麻混纺", "feature": "免烫抗皱", "images": [mock_image]},        {"name": "运动速干裤", "material": "速干面料", "feature": "弹力修身", "images": [mock_image]}    ]    pages = layout_engine.batch_generate(products, "standard")    for i, page in enumerate(pages):        print(f"  商品{i+1}: {page['product_name']} - {len(page['modules'])}个模块")    print("\n=== 演示完成 ===") if __name__ == "__main__":    run_demo()