Add image perspective editing and related tools

- Implemented the Image Perspective tool for editing image corners in Blender.
- Created a new operator `EditImagePerspective` to handle the perspective transformation.
- Added a preview feature for the perspective adjustment.
- Introduced the `ImagePolylineTool` for editing image masks with polylines.
- Developed backend support for Real-ESRGAN model inference, including ONNX integration.
- Added functionality for image masking and background removal.
- Created unit tests for the Real-ESRGAN ONNX model to ensure correct preprocessing and inference.

tests/test_worker.py

@@ -115,6 +115,7 @@ class WorkerTest(unittest.TestCase):
         cls.ben2 = load_server_module("models.ben2")
         cls.da3_model = load_server_module("models.da3")
         cls.moge2_model = load_server_module("models.moge2")
+        cls.realesrgan_model = load_server_module("models.realesrgan")
         cls.surface_outputs = load_server_module("outputs.surface")
         cls.debug_da3_outputs = load_server_module("outputs.debug_da3")
         cls.debug_moge2_outputs = load_server_module("outputs.debug_moge2")
@@ -124,6 +125,9 @@ class WorkerTest(unittest.TestCase):
         cls.debug_da3_job = load_server_module("jobs.debug_da3")
         cls.debug_moge2_job = load_server_module("jobs.debug_moge2")
         cls.debug_ben2_job = load_server_module("jobs.debug_ben2")
+        cls.debug_realesrgan_job = load_server_module(
+            "jobs.debug_realesrgan"
+        )
         cls.frames_job = load_server_module("jobs.frames")
         cls.foreground_job = load_server_module("jobs.foreground")
         cls.download = load_server_module("model_download")
@@ -608,13 +612,24 @@ class WorkerTest(unittest.TestCase):
                     "cpu",
                     preserve_input_alpha=True,
                 )
+                raw_result, _load_ms, _inference_ms = self.ben2.infer_foreground(
+                    input_path,
+                    "model",
+                    "cpu",
+                )
 
         inference_pixels = received[0]
         result_pixels = np.asarray(result.convert("RGBA"), dtype=np.uint8)
         self.assertEqual(tuple(inference_pixels[0, 0]), (0, 0, 0))
         self.assertEqual(tuple(inference_pixels[0, 1]), (0, 128, 0))
+        self.assertEqual(tuple(received[1][0, 0]), (0, 0, 0))
+        self.assertEqual(tuple(received[1][0, 1]), (0, 128, 0))
         self.assertEqual(tuple(result_pixels[0, 0]), (255, 0, 0, 0))
         self.assertEqual(tuple(result_pixels[0, 1]), (0, 255, 0, 64))
+        self.assertEqual(
+            tuple(np.asarray(raw_result.convert("RGBA"))[0, 0]),
+            (10, 20, 30, 128),
+        )
 
     def test_ben2_background_removal_processes_each_frame(self):
         inference_options = []
@@ -874,6 +889,39 @@ class WorkerTest(unittest.TestCase):
                 (context.directory / "foreground.png").read_bytes(),
                 b"foreground",
             )
+
+    def test_realesrgan_debug_saves_each_direct_api_prediction(self):
+        from PIL import Image
+
+        with tempfile.TemporaryDirectory() as directory:
+            root = Path(directory)
+            frames = root / "frames"
+            frames.mkdir()
+            (frames / "a.png").touch()
+            (frames / "b.png").touch()
+            context = FakeJobContext(root / "result", "debug-realesrgan")
+            with patch.object(
+                self.debug_realesrgan_job,
+                "infer_one",
+                return_value=Image.new("RGB", (8, 8), (10, 20, 30)),
+            ) as infer_one:
+                self.debug_realesrgan_job.run(
+                    context,
+                    {
+                        "input": str(frames),
+                        "model": "REALESRGAN_GENERAL_X4V3",
+                        "video_frames": 2,
+                        "device": "cpu",
+                    },
+                )
+
+            self.assertEqual(infer_one.call_count, 2)
+            predictions = sorted(context.directory.glob("prediction.*.png"))
+            self.assertEqual(len(predictions), 2)
+            with Image.open(predictions[0]) as prediction:
+                self.assertEqual(prediction.size, (8, 8))
+                self.assertEqual(prediction.mode, "RGB")
+
     def test_da3_removes_background_after_depth_using_original_input(self):
         order = []
         inference_inputs = []