diff --git a/fastAPI_tarot.py b/fastAPI_tarot.py index 564cd81..9bfb740 100644 --- a/fastAPI_tarot.py +++ b/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 diff --git a/requirement.txt b/requirement.txt index 9250748..e020c21 100644 --- a/requirement.txt +++ b/requirement.txt @@ -1,3 +1,4 @@ uvicorn python-multipart -fastapi \ No newline at 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