feat: 添加以图搜图功能（CLIP ONNX + pgvector）+ Dify会话修复 + 版本升至V3.30

inventory-backend/app/utils/ai_vision.py

@@ -0,0 +1,132 @@
+# -*- coding: utf-8 -*-
+"""
+AI Vision 模块 - CLIP Vision Encoder ONNX 推理
+"""
+
+import os
+import numpy as np
+from PIL import Image
+import onnxruntime as ort
+
+# ============================================================================
+# 全局模型单例（项目启动时加载一次）
+# ============================================================================
+
+MODEL_PATH = os.path.join(os.path.dirname(__file__), '..', '..', 'models', 'clip_vision.onnx')
+
+# 加载选项：CPU 推理，禁用依赖库的启动开销
+_session_options = ort.SessionOptions()
+_session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+ort_session: ort.InferenceSession = None
+
+
+def load_clip_model():
+    """启动时调用：全局加载 CLIP Vision 模型"""
+    global ort_session
+    if ort_session is not None:
+        return ort_session
+
+    if not os.path.exists(MODEL_PATH):
+        raise FileNotFoundError(f"CLIP Vision 模型未找到: {MODEL_PATH}")
+
+    ort_session = ort.InferenceSession(MODEL_PATH, sess_options=_session_options, providers=['CPUExecutionProvider'])
+    print(f"✅ [AI Vision] CLIP 模型加载成功: {MODEL_PATH}")
+    return ort_session
+
+
+# ============================================================================
+# CLIP 预处理常量
+# ============================================================================
+
+# ImageNet 标准归一化（CLIP 官方）
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+
+# 模型输入尺寸
+INPUT_SIZE = 224
+
+
+def _center_crop_and_resize(image: Image.Image) -> Image.Image:
+    """
+    CLIP 官方预处理：中心裁剪抗干扰
+    - 将图片最短边缩放到 224
+    - 从正中间切取 224x224 区域
+    """
+    w, h = image.size
+
+    # 计算缩放后的目标尺寸
+    if w < h:
+        new_w = INPUT_SIZE
+        new_h = int(h * INPUT_SIZE / w)
+    else:
+        new_h = INPUT_SIZE
+        new_w = int(w * INPUT_SIZE / h)
+
+    # 缩放
+    image = image.resize((new_w, new_h), Image.BILINEAR)
+
+    # 中心裁剪
+    left = (new_w - INPUT_SIZE) // 2
+    top = (new_h - INPUT_SIZE) // 2
+    right = left + INPUT_SIZE
+    bottom = top + INPUT_SIZE
+
+    return image.crop((left, top, right, bottom))
+
+
+def _normalize(image_np: np.ndarray) -> np.ndarray:
+    """
+    对 224x224x3 图像进行 CLIP 标准归一化
+    image_np: shape (H, W, C), dtype uint8, 值域 [0, 255]
+    返回: shape (C, H, W), dtype float32, 值域 [0, 1]
+    """
+    # HWC -> CHW
+    image_np = image_np.transpose(2, 0, 1).astype(np.float32) / 255.0
+
+    # 归一化
+    for i, (mean, std) in enumerate(zip(IMAGENET_MEAN, IMAGENET_STD)):
+        image_np[i] = (image_np[i] - mean) / std
+
+    return image_np
+
+
+# ============================================================================
+# 主函数：提取图像 embedding
+# ============================================================================
+
+def get_image_embedding(image_path: str) -> list:
+    """
+    提取图像的 512 维 CLIP embedding 向量
+
+    参数:
+        image_path: 图像文件路径（支持本地路径或 URL）
+
+    返回:
+        list: 512 维浮点向量
+    """
+    if ort_session is None:
+        load_clip_model()
+
+    # 加载图像
+    try:
+        image = Image.open(image_path).convert('RGB')
+    except Exception as e:
+        raise ValueError(f"图像加载失败: {image_path}, 错误: {e}")
+
+    # 中心裁剪
+    image = _center_crop_and_resize(image)
+
+    # 归一化
+    input_data = _normalize(np.array(image))
+
+    # 添加 batch 维度: (C, H, W) -> (1, C, H, W)
+    input_data = np.expand_dims(input_data, axis=0)
+
+    # 推理
+    outputs = ort_session.run(None, {'images': input_data.astype(np.float32)})
+
+    # 输出通常是 (1, 512) 的向量，取第一项并展平为 list
+    embedding = outputs[0][0].tolist()
+
+    return embedding