Support external FunCaptcha ONNX fallback

tests/test_funcaptcha.py

@@ -69,18 +69,21 @@ class _FakeSessionOptions:
 
 
 class _FakeInput:
-    def __init__(self, name):
+    def __init__(self, name, shape=None):
         self.name = name
+        self.shape = shape
 
 
 class _FakeSession:
     def __init__(self, path, *args, **kwargs):
         self.path = path
+        self.last_feed_dict = None
 
     def get_inputs(self):
         return [_FakeInput("candidate"), _FakeInput("reference")]
 
     def run(self, output_names, feed_dict):
+        self.last_feed_dict = feed_dict
         batch_size = next(iter(feed_dict.values())).shape[0]
         logits = np.full((batch_size, 1), 0.1, dtype=np.float32)
         if batch_size >= 3:
@@ -93,6 +96,29 @@ class _FakeOrt:
     InferenceSession = _FakeSession
 
 
+class _Batch1FakeSession(_FakeSession):
+    def __init__(self, path, *args, **kwargs):
+        super().__init__(path, *args, **kwargs)
+        self.run_calls = 0
+
+    def get_inputs(self):
+        shape = [1, 3, 48, 48]
+        return [_FakeInput("candidate", shape=shape), _FakeInput("reference", shape=shape)]
+
+    def run(self, output_names, feed_dict):
+        self.run_calls += 1
+        candidate = feed_dict["candidate"]
+        reference = feed_dict["reference"]
+        assert candidate.shape == (1, 3, 48, 48)
+        assert reference.shape == (1, 3, 48, 48)
+        return super().run(output_names, feed_dict)
+
+
+class _Batch1FakeOrt:
+    SessionOptions = _FakeSessionOptions
+    InferenceSession = _Batch1FakeSession
+
+
 class TestFunCaptchaPipeline:
     def test_pipeline_returns_best_object_index(self, tmp_path, monkeypatch):
         model_path = tmp_path / "funcaptcha_rollball_animals.onnx"
@@ -102,6 +128,7 @@ class TestFunCaptchaPipeline:
             {
                 "model_name": "funcaptcha_rollball_animals",
                 "task": "funcaptcha_siamese",
+                "preprocess": "rgb_centered",
                 "question": "4_3d_rollball_animals",
                 "num_candidates": 4,
                 "tile_size": [200, 200],
@@ -121,3 +148,40 @@ class TestFunCaptchaPipeline:
         assert result["objects"] == [2]
         assert result["result"] == "2"
         assert len(result["scores"]) == 4
+        assert pipeline.preprocess_mode == "rgb_centered"
+
+    def test_pipeline_uses_external_model_env_without_metadata(self, tmp_path, monkeypatch):
+        external_model = tmp_path / "external_rollball.onnx"
+        external_model.touch()
+        monkeypatch.setenv("FUNCAPTCHA_ROLLBALL_MODEL_PATH", str(external_model))
+        monkeypatch.setattr(fun_module, "_try_import_ort", lambda: _FakeOrt)
+
+        image = Image.new("RGB", (800, 400), color=(128, 128, 128))
+        sample_path = tmp_path / "0_demo.png"
+        image.save(sample_path)
+
+        empty_models_dir = tmp_path / "missing_models"
+        pipeline = FunCaptchaRollballPipeline(models_dir=empty_models_dir)
+        result = pipeline.solve(sample_path)
+
+        assert result["objects"] == [2]
+        assert pipeline.model_path == external_model
+        assert pipeline.preprocess_mode == "rgb_255"
+        candidate = pipeline.session.last_feed_dict["candidate"]
+        assert candidate.shape == (4, 3, 48, 48)
+        assert candidate[0, 0, 0, 0] == pytest.approx(128 / 255.0, abs=1e-6)
+
+    def test_pipeline_handles_external_fixed_batch_model(self, tmp_path, monkeypatch):
+        external_model = tmp_path / "external_rollball.onnx"
+        external_model.touch()
+        monkeypatch.setenv("FUNCAPTCHA_ROLLBALL_MODEL_PATH", str(external_model))
+        monkeypatch.setattr(fun_module, "_try_import_ort", lambda: _Batch1FakeOrt)
+
+        sample_path = tmp_path / "0_demo.png"
+        _build_rollball_image(sample_path, answer_idx=0)
+
+        pipeline = FunCaptchaRollballPipeline(models_dir=tmp_path / "missing_models")
+        result = pipeline.solve(sample_path)
+
+        assert result["objects"] == [0]
+        assert pipeline.session.run_calls == 4