翻译

fastAPI_tarot.py

@@ -127,7 +127,48 @@ app.openapi = custom_openapi
 
 app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
 
+import re
+
 # ------------------- 辅助函数 -------------------
+def is_english(text: str) -> bool:
+    """
+    简单判断是否为英文 prompt。
+    如果有中文字符则认为不是英文。
+    """
+    for char in text:
+        if '\u4e00' <= char <= '\u9fff':
+            return False
+    return True
+
+def translate_to_sam3_prompt(text: str) -> str:
+    """
+    使用大模型将非英文提示词翻译为适合 SAM3 的英文提示词
+    """
+    print(f"正在翻译提示词: {text}")
+    try:
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"text": f"请将以下描述翻译成简洁、精准的英文，用于图像分割模型(SAM)的提示词。直接返回英文，不要包含任何解释或其他文字。\n\n输入: {text}"}
+                ]
+            }
+        ]
+        response = MultiModalConversation.call(model=QWEN_MODEL, messages=messages)
+        
+        if response.status_code == 200:
+            translated_text = response.output.choices[0].message.content[0]['text'].strip()
+            # 去除可能的 markdown 标记或引号
+            translated_text = translated_text.replace('"', '').replace("'", "").strip()
+            print(f"翻译结果: {translated_text}")
+            return translated_text
+        else:
+            print(f"翻译失败: {response.code} - {response.message}")
+            return text # Fallback to original
+    except Exception as e:
+        print(f"翻译异常: {e}")
+        return text
+
 def order_points(pts):
     """
     对四个坐标点进行排序：左上，右上，右下，左下
@@ -178,10 +219,10 @@ def load_image_from_url(url: str) -> Image.Image:
     except Exception as e:
         raise HTTPException(status_code=400, detail=f"无法下载图片: {str(e)}")
 
-def crop_and_save_objects(image: Image.Image, masks, boxes, output_dir: str = RESULT_IMAGE_DIR, is_tarot: bool = True) -> list[dict]:
+def crop_and_save_objects(image: Image.Image, masks, boxes, output_dir: str = RESULT_IMAGE_DIR, is_tarot: bool = True, cutout: bool = False) -> list[dict]:
     """
-    根据 mask 和 box 进行透视矫正并裁剪出独立的对象图片 (保留透明背景)
-    返回包含文件名和元数据的列表
+    根据 mask 和 box 进行处理并保存独立的对象图片
+    :param cutout: 如果为 True，则进行轮廓抠图（透明背景）；否则进行透视矫正（主要用于卡片）
     """
     saved_objects = []
     # Convert image to numpy array (RGB)
@@ -194,73 +235,125 @@ def crop_and_save_objects(image: Image.Image, masks, boxes, output_dir: str = RE
         else:
             mask_np = mask.squeeze()
 
-        # Ensure mask is uint8 binary for OpenCV
+        # Handle box conversion
+        if isinstance(box, torch.Tensor):
+            box_np = box.cpu().numpy()
+        else:
+            box_np = np.array(box)
+            
+        # Ensure mask is uint8 binary for OpenCV/Pillow
         if mask_np.dtype == bool:
             mask_uint8 = (mask_np * 255).astype(np.uint8)
         else:
             mask_uint8 = (mask_np > 0.5).astype(np.uint8) * 255
 
-        # Find contours
-        contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        if not contours:
-            continue
+        if cutout:
+            # --- 轮廓抠图模式 (透明背景) ---
+            # 1. 准备 RGBA 原图
+            if image.mode != "RGBA":
+                img_rgba = image.convert("RGBA")
+            else:
+                img_rgba = image.copy()
             
-        # Get largest contour
-        c = max(contours, key=cv2.contourArea)
+            # 2. 准备 Alpha Mask
+            # mask_uint8 已经是 0-255，可以直接作为 Alpha 通道的基础
+            # 将 Mask 缩放到和图片一样大 (一般是一样的，但以防万一)
+            if mask_uint8.shape != img_rgba.size[::-1]: # size is (w, h), shape is (h, w)
+                 # 如果尺寸不一致可能需要 resize，这里假设尺寸一致
+                 pass
 
-        # Approximate contour to polygon
-        peri = cv2.arcLength(c, True)
-        approx = cv2.approxPolyDP(c, 0.04 * peri, True)
+            mask_img = Image.fromarray(mask_uint8, mode='L')
+            
+            # 3. 将 Mask 应用到 Alpha 通道
+            # 创建一个新的空白透明图
+            cutout_img = Image.new("RGBA", img_rgba.size, (0, 0, 0, 0))
+            # 将原图粘贴上去，使用 mask 作为 mask
+            cutout_img.paste(image.convert("RGB"), (0, 0), mask=mask_img)
+            
+            # 4. Crop to Box
+            # box is [x1, y1, x2, y2]
+            x1, y1, x2, y2 = map(int, box_np)
+            # 边界检查
+            w, h = cutout_img.size
+            x1 = max(0, x1); y1 = max(0, y1)
+            x2 = min(w, x2); y2 = min(h, y2)
+            
+            # 避免无效 crop
+            if x2 > x1 and y2 > y1:
+                final_img = cutout_img.crop((x1, y1, x2, y2))
+            else:
+                final_img = cutout_img # Fallback
+            
+            # Save
+            prefix = "cutout"
+            is_rotated = False # 抠图模式下不进行自动旋转
+            
+            filename = f"{prefix}_{uuid.uuid4().hex}_{i}.png"
+            save_path = os.path.join(output_dir, filename)
+            final_img.save(save_path)
+            
+            saved_objects.append({
+                "filename": filename,
+                "is_rotated_by_algorithm": is_rotated,
+                "note": "Mask cutout applied. Background removed."
+            })
 
-        # If we have 4 points, use them. If not, fallback to minAreaRect
-        if len(approx) == 4:
-            pts = approx.reshape(4, 2)
         else:
-            rect = cv2.minAreaRect(c)
-            pts = cv2.boxPoints(rect)
+            # --- 透视矫正模式 (原有逻辑) ---
+            # Find contours
+            contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            if not contours:
+                continue
                 
-        # Apply perspective transform
-        # 注意：这里我们只变换RGB部分，Alpha通道需要额外处理或者直接应用同样的变换
-        # 为了简单，我们直接对原图（假设不带Alpha）进行变换
-        # 如果需要保留背景透明，需要先将原图转为RGBA，再做变换
+            # Get largest contour
+            c = max(contours, key=cv2.contourArea)
             
-        # Check if original image has Alpha
-        if img_arr.shape[2] == 4:
-            warped = four_point_transform(img_arr, pts)
-        else:
-            # Add alpha channel from mask? 
-            # 透视变换后的矩形本身就是去掉了背景的，所以不需要额外的Mask Alpha
-            # 但是为了保持一致性，我们可以给变换后的图加一个全不透明的Alpha，或者保留RGB
-            warped = four_point_transform(img_arr, pts)
+            # Approximate contour to polygon
+            peri = cv2.arcLength(c, True)
+            approx = cv2.approxPolyDP(c, 0.04 * peri, True)
             
-        # Check orientation (Portrait vs Landscape)
-        h, w = warped.shape[:2]
-        is_rotated = False
+            # If we have 4 points, use them. If not, fallback to minAreaRect
+            if len(approx) == 4:
+                pts = approx.reshape(4, 2)
+            else:
+                rect = cv2.minAreaRect(c)
+                pts = cv2.boxPoints(rect)
                 
-        # Enforce Portrait for Tarot cards (Standard 7x12 cm ratio approx)
-        if is_tarot and w > h:
-            # Rotate 90 degrees clockwise
-            warped = cv2.rotate(warped, cv2.ROTATE_90_CLOCKWISE)
-            is_rotated = True
+            # Apply perspective transform
+            # Check if original image has Alpha
+            if img_arr.shape[2] == 4:
+                warped = four_point_transform(img_arr, pts)
+            else:
+                warped = four_point_transform(img_arr, pts)
             
-        # Convert back to PIL
-        pil_warped = Image.fromarray(warped)
+            # Check orientation (Portrait vs Landscape)
+            h, w = warped.shape[:2]
+            is_rotated = False
             
-        # Save
-        prefix = "tarot" if is_tarot else "segment"
-        filename = f"{prefix}_{uuid.uuid4().hex}_{i}.png" 
-        save_path = os.path.join(output_dir, filename)
-        pil_warped.save(save_path)
+            # Enforce Portrait for Tarot cards (Standard 7x12 cm ratio approx)
+            if is_tarot and w > h:
+                # Rotate 90 degrees clockwise
+                warped = cv2.rotate(warped, cv2.ROTATE_90_CLOCKWISE)
+                is_rotated = True
                 
-        # 正逆位判断逻辑 (基于几何只能做到这一步，无法区分上下颠倒)
-        # 这里我们假设长边垂直为正位，如果做了旋转则标记
-        # 真正的正逆位需要OCR或图像识别
+            # Convert back to PIL
+            pil_warped = Image.fromarray(warped)
             
-        saved_objects.append({
-            "filename": filename,
-            "is_rotated_by_algorithm": is_rotated,
-            "note": "Geometric correction applied. True upright/reversed requires content analysis."
-        })
+            # Save
+            prefix = "tarot" if is_tarot else "segment"
+            filename = f"{prefix}_{uuid.uuid4().hex}_{i}.png" 
+            save_path = os.path.join(output_dir, filename)
+            pil_warped.save(save_path)
+            
+            # 正逆位判断逻辑 (基于几何只能做到这一步，无法区分上下颠倒)
+            # 这里我们假设长边垂直为正位，如果做了旋转则标记
+            # 真正的正逆位需要OCR或图像识别
+            
+            saved_objects.append({
+                "filename": filename,
+                "is_rotated_by_algorithm": is_rotated,
+                "note": "Geometric correction applied. True upright/reversed requires content analysis."
+            })
         
     return saved_objects
 
@@ -405,11 +498,28 @@ async def segment(
     prompt: str = Form(...),
     file: Optional[UploadFile] = File(None),
     image_url: Optional[str] = Form(None),
-    save_segment_images: bool = Form(False)
+    save_segment_images: bool = Form(False),
+    cutout: bool = Form(False)
 ):
     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)")
 
+    # ------------------- Prompt 翻译/优化 -------------------
+    final_prompt = prompt
+    if not is_english(prompt):
+        # 使用 Qwen 进行翻译
+        try:
+            # 构建消息请求翻译
+            # 注意：translate_to_sam3_prompt 是一个阻塞调用，可能会增加耗时
+            # 但对于分割任务来说是可以接受的
+            translated = translate_to_sam3_prompt(prompt)
+            if translated:
+                final_prompt = translated
+        except Exception as e:
+            print(f"Prompt翻译失败，使用原始Prompt: {e}")
+            
+    print(f"最终使用的 Prompt: {final_prompt}")
+
     try:
         if file:
             image = Image.open(file.file).convert("RGB")
@@ -422,7 +532,7 @@ async def segment(
 
     try:
         inference_state = processor.set_image(image)
-        output = processor.set_text_prompt(state=inference_state, prompt=prompt)
+        output = processor.set_text_prompt(state=inference_state, prompt=final_prompt)
         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)}")
@@ -442,21 +552,26 @@ async def segment(
             output_dir = os.path.join(RESULT_IMAGE_DIR, request_id)
             os.makedirs(output_dir, exist_ok=True)
             
-            saved_objects = crop_and_save_objects(image, masks, boxes, output_dir=output_dir, is_tarot=False)
+            # 传递 cutout 参数
+            saved_objects = crop_and_save_objects(
+                image, 
+                masks, 
+                boxes, 
+                output_dir=output_dir, 
+                is_tarot=False, 
+                cutout=cutout
+            )
             
             for obj in saved_objects:
                 fname = obj["filename"]
                 seg_url = str(request.url_for("static", path=f"results/{request_id}/{fname}"))
                 saved_segments_info.append({
                     "url": seg_url,
-                    "filename": fname
+                    "filename": fname,
+                    "note": obj.get("note", "")
                 })
         except Exception as e:
-             # Log error but don't fail the whole request if segmentation saving fails?
-             # Or fail it? Let's fail it to be safe or include error in response.
-             # Given simple requirement, I'll let it fail or just print.
              print(f"Error saving segments: {e}")
-             # We can optionally raise HTTPException here too.
              raise HTTPException(status_code=500, detail=f"保存分割图片失败: {str(e)}")
 
     response_content = {
@@ -691,6 +806,18 @@ async def segment_face(
     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)")
 
+    # ------------------- Prompt 翻译/优化 -------------------
+    final_prompt = prompt
+    if not is_english(prompt):
+        try:
+            translated = translate_to_sam3_prompt(prompt)
+            if translated:
+                final_prompt = translated
+        except Exception as e:
+            print(f"Prompt翻译失败，使用原始Prompt: {e}")
+            
+    print(f"Face Segment 最终使用的 Prompt: {final_prompt}")
+
     # 1. 加载图片
     try:
         if file:
@@ -709,7 +836,7 @@ async def segment_face(
         result = human_analysis_service.process_face_segmentation_and_analysis(
             processor=processor,
             image=image,
-            prompt=prompt,
+            prompt=final_prompt,
             output_base_dir=RESULT_IMAGE_DIR
         )
     except Exception as e: