feat: add outfit generation core

src/core/ai_outfit.py

@@ -0,0 +1,131 @@
+import logging
+import re
+from io import BytesIO
+from pathlib import Path
+
+from PIL import Image, ImageOps
+
+from core.models import OutfitResult, OutfitTask
+from services.ai_image_service import ImageApiClient
+
+logger = logging.getLogger(__name__)
+
+QUALITY_SMALL = 75
+QUALITY_BALANCED = 85
+QUALITY_HIGH = 92
+QUALITY_PRESETS = {
+    "small": QUALITY_SMALL,
+    "balanced": QUALITY_BALANCED,
+    "high": QUALITY_HIGH,
+    "小文件": QUALITY_SMALL,
+    "均衡": QUALITY_BALANCED,
+    "高清": QUALITY_HIGH,
+}
+
+MAX_JPG_BYTES = 2 * 1024 * 1024
+_INVALID_FILENAME_CHARS = re.compile(r'[<>:"/\\|?*\x00-\x1f]')
+
+
+def render_prompt(template, task):
+    """Render an outfit prompt for one task."""
+    return str(template).replace("{title}", task.title).replace("{product_id}", task.product_id)
+
+
+def safe_product_filename(product_id):
+    """Return a filesystem-safe base filename without changing Excel data."""
+    name = _INVALID_FILENAME_CHARS.sub("_", str(product_id))
+    name = name.strip().strip(".")
+    return name or "outfit"
+
+
+def make_outfit_output_path(output_dir, product_id):
+    """Return output_dir/<product_id>.jpg without overwriting existing files."""
+    target_dir = Path(output_dir)
+    stem = safe_product_filename(product_id)
+    candidate = target_dir / (stem + ".jpg")
+    counter = 1
+    while candidate.exists():
+        candidate = target_dir / ("{}_{}.jpg".format(stem, counter))
+        counter += 1
+    return candidate
+
+
+def save_jpg_under_limit(image_bytes, output_path, quality=QUALITY_BALANCED, max_bytes=MAX_JPG_BYTES):
+    """Save image bytes as 1:1 JPG, reducing quality/size until under limit."""
+    output_path = Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with Image.open(BytesIO(image_bytes)) as opened:
+        img = ImageOps.exif_transpose(opened).convert("RGB")
+        img = _crop_square(img)
+
+    quality = _coerce_quality(quality)
+    min_quality = 55
+    while True:
+        _save_jpeg(img, output_path, quality)
+        if output_path.stat().st_size <= max_bytes:
+            return output_path
+        if quality > min_quality:
+            quality = max(min_quality, quality - 7)
+            continue
+        if img.width <= 512 or img.height <= 512:
+            return output_path
+        new_size = max(512, int(img.width * 0.85))
+        img = img.resize((new_size, new_size), Image.LANCZOS)
+        quality = _coerce_quality(quality)
+
+
+def generate_outfit_image(
+    task,
+    prompt_template,
+    output_dir,
+    model_config,
+    quality=QUALITY_BALANCED,
+    resolution="1K",
+    api_client=None,
+):
+    """Generate one outfit image and return OutfitResult. Never raises."""
+    if not isinstance(task, OutfitTask):
+        raise TypeError("task must be OutfitTask")
+
+    try:
+        prompt = render_prompt(prompt_template, task)
+        client = api_client or ImageApiClient(model_config)
+        image_bytes = client.generate(prompt, task.garment_path, resolution=resolution)
+        output_path = make_outfit_output_path(output_dir, task.product_id)
+        save_jpg_under_limit(image_bytes, output_path, quality=quality)
+        logger.info("Generated outfit row %s -> %s", task.row_index, output_path)
+        return OutfitResult(
+            task=task,
+            success=True,
+            output_path=str(output_path),
+            attempts=1,
+        )
+    except Exception as exc:
+        logger.exception("Outfit generation failed for row %s", task.row_index)
+        return OutfitResult(
+            task=task,
+            success=False,
+            error=str(exc),
+            attempts=1,
+        )
+
+
+def _coerce_quality(quality):
+    if isinstance(quality, str):
+        return QUALITY_PRESETS.get(quality, QUALITY_BALANCED)
+    try:
+        return max(1, min(95, int(quality)))
+    except (TypeError, ValueError):
+        return QUALITY_BALANCED
+
+
+def _crop_square(img):
+    width, height = img.size
+    side = min(width, height)
+    left = (width - side) // 2
+    top = (height - side) // 2
+    return img.crop((left, top, left + side, top + side))
+
+
+def _save_jpeg(img, output_path, quality):
+    img.save(str(output_path), format="JPEG", quality=quality, optimize=True)

tasks.md

@@ -1049,7 +1049,7 @@
 - [x] `core/models.py` 新增 `OutfitTask` / `OutfitResult`（纯 dataclass，Python 3.7 兼容，不依赖 PySide6）
 - [x] `services/excel_service.py`：读行 → `List[OutfitTask]`、写回 D/E/F、占用检测、跳过「完成」/按设置重试「失败」/空字段安全跳过、每行即存
 - [x] `services/ai_image_service.py`：移植旧项目 `ImageApiClient`（多模型、多请求格式 `chat/gemini/images/images_edits`、传图 data-url、递归取图、URL 归一化、字段校验）；注意 PEP585 类型注解改 Python 3.7 写法
-- [ ] `core/ai_outfit.py`：单行生成纯逻辑编排（提示词渲染 + 调用 + 保存 JPG + 产出 `OutfitResult`）
+- [x] `core/ai_outfit.py`：单行生成纯逻辑编排（提示词渲染 + 调用 + 保存 JPG + 产出 `OutfitResult`）
 - [ ] 单测：Excel 读写、提示词渲染、取图、命名去重（API 用 mock）；可在 Python 3.7 运行、不依赖 GUI
 
 ### 19.2 批量编排 — docs/11 §14 阶段 2

tests/test_ai_outfit.py

@@ -0,0 +1,123 @@
+"""Tests for single-row AI outfit generation core."""
+import shutil
+import sys
+import tempfile
+import unittest
+from io import BytesIO
+from pathlib import Path
+
+from PIL import Image
+
+sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
+
+
+class TestAiOutfitCore(unittest.TestCase):
+    def setUp(self):
+        self.tmp = Path(tempfile.mkdtemp())
+
+    def tearDown(self):
+        shutil.rmtree(str(self.tmp), ignore_errors=True)
+
+    def _task(self, product_id="TY001"):
+        from core.models import OutfitTask
+
+        return OutfitTask(
+            row_index=2,
+            title="纯棉短袖",
+            product_id=product_id,
+            garment_path=str(self.tmp / "garment.png"),
+        )
+
+    def _image_bytes(self, size=(640, 480), color=(200, 120, 80)):
+        img = Image.new("RGB", size, color)
+        buffer = BytesIO()
+        img.save(buffer, format="PNG")
+        return buffer.getvalue()
+
+    def test_render_prompt_replaces_placeholders(self):
+        from core.ai_outfit import render_prompt
+
+        prompt = render_prompt("商品 {title} / {product_id}", self._task())
+
+        self.assertEqual(prompt, "商品 纯棉短袖 / TY001")
+
+    def test_safe_product_filename_replaces_invalid_chars(self):
+        from core.ai_outfit import safe_product_filename
+
+        self.assertEqual(safe_product_filename('TY:00/1*?"'), "TY_00_1___")
+
+    def test_make_output_path_avoids_overwrite(self):
+        from core.ai_outfit import make_outfit_output_path
+
+        first = make_outfit_output_path(self.tmp, "TY001")
+        first.write_text("exists")
+
+        second = make_outfit_output_path(self.tmp, "TY001")
+
+        self.assertEqual(second.name, "TY001_1.jpg")
+
+    def test_save_jpg_under_limit_outputs_square_jpg(self):
+        from core.ai_outfit import save_jpg_under_limit
+
+        out = self.tmp / "out.jpg"
+        save_jpg_under_limit(self._image_bytes(size=(640, 480)), out, quality=85, max_bytes=200000)
+
+        self.assertTrue(out.exists())
+        self.assertLessEqual(out.stat().st_size, 200000)
+        with Image.open(str(out)) as img:
+            self.assertEqual(img.format, "JPEG")
+            self.assertEqual(img.size[0], img.size[1])
+
+    def test_generate_outfit_image_success(self):
+        from core.ai_outfit import generate_outfit_image
+
+        client = _FakeClient(self._image_bytes())
+
+        result = generate_outfit_image(
+            self._task(),
+            "为 {title} 生成 {product_id}",
+            self.tmp,
+            model_config={"url": "https://api", "model": "m", "api_key": "k"},
+            api_client=client,
+        )
+
+        self.assertTrue(result.success, result.error)
+        self.assertTrue(Path(result.output_path).exists())
+        self.assertEqual(client.prompt, "为 纯棉短袖 生成 TY001")
+        self.assertTrue(client.image_path.endswith("garment.png"))
+
+    def test_generate_outfit_image_failure(self):
+        from core.ai_outfit import generate_outfit_image
+
+        with self.assertLogs("core.ai_outfit", level="ERROR"):
+            result = generate_outfit_image(
+                self._task(),
+                "prompt",
+                self.tmp,
+                model_config={},
+                api_client=_FailingClient(),
+            )
+
+        self.assertFalse(result.success)
+        self.assertIn("boom", result.error)
+
+
+class _FakeClient:
+    def __init__(self, image_bytes):
+        self._image_bytes = image_bytes
+        self.prompt = None
+        self.image_path = None
+
+    def generate(self, prompt, image_path, resolution="1K"):
+        self.prompt = prompt
+        self.image_path = str(image_path)
+        return self._image_bytes
+
+
+class _FailingClient:
+    def generate(self, prompt, image_path, resolution="1K"):
+        raise RuntimeError("boom")
+
+
+if __name__ == "__main__":
+    unittest.main()