tarot

fastAPI_tarot.py

@@ -6,6 +6,8 @@ import numpy as np
 import cv2
 from typing import Optional
 from contextlib import asynccontextmanager
+import dashscope
+from dashscope import MultiModalConversation
 
 import torch
 import matplotlib
@@ -31,6 +33,10 @@ os.makedirs(RESULT_IMAGE_DIR, exist_ok=True)
 VALID_API_KEY = "123quant-speed"
 API_KEY_HEADER_NAME = "X-API-Key"
 
+# Dashscope 配置 (Qwen-VL)
+dashscope.api_key = 'sk-ce2404f55f744a1987d5ece61c6bac58'
+QWEN_MODEL = 'qwen-vl-max'
+
 # 定义 Header 认证
 api_key_header = APIKeyHeader(name=API_KEY_HEADER_NAME, auto_error=False)
 
@@ -264,6 +270,82 @@ def generate_and_save_result(image: Image.Image, inference_state, output_dir: st
     plt.close()
     return filename
 
+def recognize_card_with_qwen(image_path: str) -> dict:
+    """
+    调用 Qwen-VL 识别塔罗牌
+    """
+    try:
+        # 确保路径是绝对路径并加上 file:// 前缀
+        abs_path = os.path.abspath(image_path)
+        file_url = f"file://{abs_path}"
+        
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"image": file_url},
+                    {"text": "这是一张塔罗牌。请识别它的名字（中文），并判断它是正位还是逆位。请以JSON格式返回，包含 'name' 和 'position' 两个字段。例如：{'name': '愚者', 'position': '正位'}。不要包含Markdown代码块标记。"}
+                ]
+            }
+        ]
+        
+        response = MultiModalConversation.call(model=QWEN_MODEL, messages=messages)
+        
+        if response.status_code == 200:
+            content = response.output.choices[0].message.content[0]['text']
+            # 尝试解析简单的 JSON
+            import json
+            try:
+                # 清理可能存在的 markdown 标记
+                clean_content = content.replace("```json", "").replace("```", "").strip()
+                result = json.loads(clean_content)
+                return result
+            except:
+                return {"raw_response": content}
+        else:
+            return {"error": f"API Error: {response.code} - {response.message}"}
+            
+    except Exception as e:
+        return {"error": f"识别失败: {str(e)}"}
+
+def recognize_spread_with_qwen(image_path: str) -> dict:
+    """
+    调用 Qwen-VL 识别塔罗牌牌阵
+    """
+    try:
+        # 确保路径是绝对路径并加上 file:// 前缀
+        abs_path = os.path.abspath(image_path)
+        file_url = f"file://{abs_path}"
+        
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"image": file_url},
+                    {"text": "这是一张包含多张塔罗牌的图片。请根据牌的排列方式识别这是什么牌阵（例如：圣三角、凯尔特十字、三张牌等）。如果看不出明显的正规牌阵，请返回“不是正规牌阵”。请以JSON格式返回，包含 'spread_name' 和 'description' 两个字段。例如：{'spread_name': '圣三角', 'description': '常见的时间流占卜法'}。不要包含Markdown代码块标记。"}
+                ]
+            }
+        ]
+        
+        response = MultiModalConversation.call(model=QWEN_MODEL, messages=messages)
+        
+        if response.status_code == 200:
+            content = response.output.choices[0].message.content[0]['text']
+            # 尝试解析简单的 JSON
+            import json
+            try:
+                # 清理可能存在的 markdown 标记
+                clean_content = content.replace("```json", "").replace("```", "").strip()
+                result = json.loads(clean_content)
+                return result
+            except:
+                return {"raw_response": content, "spread_name": "Unknown"}
+        else:
+            return {"error": f"API Error: {response.code} - {response.message}"}
+            
+    except Exception as e:
+        return {"error": f"牌阵识别失败: {str(e)}"}
+
 # ------------------- API 接口 (强制依赖验证) -------------------
 @app.post("/segment", dependencies=[Depends(verify_api_key)])
 async def segment(
@@ -398,6 +480,118 @@ async def segment_tarot(
         "scores": scores.tolist() if torch.is_tensor(scores) else scores
     })
 
+@app.post("/recognize_tarot", dependencies=[Depends(verify_api_key)])
+async def recognize_tarot(
+    request: Request,
+    file: Optional[UploadFile] = File(None),
+    image_url: Optional[str] = Form(None),
+    expected_count: int = Form(3)
+):
+    """
+    塔罗牌全流程接口: 分割 + 矫正 + 识别
+    1. 检测是否包含指定数量的塔罗牌 (SAM3)
+    2. 分割并透视矫正
+    3. 调用 Qwen-VL 识别每张牌的名称和正逆位
+    """
+    if not file and not image_url:
+        raise HTTPException(status_code=400, detail="必须提供 file (图片文件) 或 image_url (图片链接)")
+
+    try:
+        if file:
+            image = Image.open(file.file).convert("RGB")
+        elif image_url:
+            image = load_image_from_url(image_url)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"图片解析失败: {str(e)}")
+
+    processor = request.app.state.processor
+
+    try:
+        inference_state = processor.set_image(image)
+        # 固定 Prompt 检测塔罗牌
+        output = processor.set_text_prompt(state=inference_state, prompt="tarot card")
+        masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"模型推理错误: {str(e)}")
+
+    # 核心逻辑：判断数量
+    detected_count = len(masks)
+    
+    # 创建本次请求的独立文件夹
+    request_id = f"{int(time.time())}_{uuid.uuid4().hex[:8]}"
+    output_dir = os.path.join(RESULT_IMAGE_DIR, request_id)
+    os.makedirs(output_dir, exist_ok=True)
+    
+    # 保存整体效果图 (无论是成功还是失败，都先保存一张主图)
+    try:
+        main_filename = generate_and_save_result(image, inference_state, output_dir=output_dir)
+        main_file_path = os.path.join(output_dir, main_filename)
+        main_file_url = str(request.url_for("static", path=f"results/{request_id}/{main_filename}"))
+    except:
+        main_filename = None
+        main_file_path = None
+        main_file_url = None
+
+    # Step 0: 牌阵识别 (在判断数量之前或之后都可以，这里放在前面作为全局判断)
+    spread_info = {"spread_name": "Unknown"}
+    if main_file_path:
+        # 使用带有mask绘制的主图或者原始图？
+        # 使用原始图可能更好，不受mask遮挡干扰，但是main_filename是带mask的。
+        # 我们这里暂时用原始图保存一份临时文件给Qwen看
+        temp_raw_path = os.path.join(output_dir, "raw_for_spread.jpg")
+        image.save(temp_raw_path)
+        spread_info = recognize_spread_with_qwen(temp_raw_path)
+    
+    # 如果识别结果明确说是“不是正规牌阵”，是否要继续？
+    # 用户需求：“如果没有正确的牌阵则返回‘不是正规牌阵’”
+    # 我们将其放在返回结果中，由客户端决定是否展示警告
+    
+    if detected_count != expected_count:
+        return JSONResponse(
+            status_code=400, 
+            content={
+                "status": "failed",
+                "message": f"检测到 {detected_count} 个目标，需要严格的 {expected_count} 张塔罗牌。请调整拍摄角度或背景。",
+                "detected_count": detected_count,
+                "spread_info": spread_info,
+                "debug_image_url": str(main_file_url) if main_file_url else None
+            }
+        )
+
+    # 数量正确，执行抠图 + 矫正
+    try:
+        saved_objects = crop_and_save_objects(image, masks, boxes, output_dir=output_dir)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"抠图处理错误: {str(e)}")
+
+    # 遍历每张卡片进行识别
+    tarot_cards = []
+    for obj in saved_objects:
+        fname = obj["filename"]
+        file_path = os.path.join(output_dir, fname)
+        
+        # 调用 Qwen-VL 识别
+        # 注意：这里会串行调用，速度可能较慢。
+        recognition_res = recognize_card_with_qwen(file_path)
+        
+        file_url = str(request.url_for("static", path=f"results/{request_id}/{fname}"))
+        tarot_cards.append({
+            "url": file_url,
+            "is_rotated": obj["is_rotated_by_algorithm"],
+            "orientation_status": "corrected_to_portrait" if obj["is_rotated_by_algorithm"] else "original_portrait",
+            "recognition": recognition_res,
+            "note": obj["note"]
+        })
+
+    return JSONResponse(content={
+        "status": "success",
+        "message": f"成功识别并分割 {expected_count} 张塔罗牌 (含Qwen识别结果)",
+        "spread_info": spread_info,
+        "tarot_cards": tarot_cards,
+        "full_visualization": main_file_url,
+        "scores": scores.tolist() if torch.is_tensor(scores) else scores
+    })
+
 if __name__ == "__main__":
     import uvicorn
     # 注意：如果你的文件名不是 fastAPI_tarot.py，请修改下面第一个参数

requirement.txt

@@ -1,3 +1,4 @@
 uvicorn
 python-multipart
-fastapi
+fastapi
+dashscope