fix: bbox points should be relative to intial image

Dockerfile

@@ -17,4 +17,4 @@ WORKDIR /opt/nuclio
 
 # Run the tests
 # Nuclio will overwrite this CMD when it is deployed
-CMD pytest .
+CMD pytest . -vv

nuclio_lighting_flash/flash_model_handler.py

@@ -32,7 +32,12 @@ def infer(self, image: Image, threshold: float = 0.0):
         predictions = self.trainer.predict(
             self.model,
             datamodule=datamodule,
-            output=NuclioDetectionLabelsOutput(threshold=threshold, labels=self.labels),
+            output=NuclioDetectionLabelsOutput(
+                threshold=threshold,
+                labels=self.labels,
+                image_width=image.width,
+                image_height=image.height,
+            ),
         )
         if predictions is None:
             return []

nuclio_lighting_flash/nuclio_detection_labels_output.py

@@ -7,29 +7,45 @@
 from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, lazy_import, requires
 from flash.core.utilities.providers import _FIFTYONE
 
+from flash.core.classification import FiftyOneLabelsOutput
+
 
 class NuclioDetectionLabelsOutput(Output):
     """A :class:`.Output` which converts model outputs to Nuclio detection format.
 
     Args:
+        image_width: The size the image (before resizing)
+        image_height: The size the image (before resizing)
         labels: A list of labels, assumed to map the class index to the label for that class.
         threshold: a score threshold to apply to candidate detections.
     """
 
     def __init__(
         self,
+        image_width: int,
+        image_height: int,
         labels: Optional[List[str]] = None,
         threshold: Optional[float] = None,
     ):
         super().__init__()
         self._labels = labels
+        self.image_width = image_width
+        self.image_height = image_height
         self.threshold = threshold
 
     @classmethod
     def from_task(cls, task: Task, **kwargs) -> Output:
         return cls(labels=getattr(task, "labels", None))
 
     def transform(self, sample: Dict[str, Any]) -> List[Dict[str, Any]]:
+        if DataKeys.METADATA not in sample:
+            raise ValueError(
+                "sample requires DataKeys.METADATA to use a FiftyOneDetectionLabelsOutput output."
+            )
+
+        # This is the size the image was resized to, i.e. 1024x1024
+        height, width = sample[DataKeys.METADATA]["size"]
+
         detections = []
 
         preds = sample[DataKeys.PREDS]
@@ -40,11 +56,14 @@ def transform(self, sample: Dict[str, Any]) -> List[Dict[str, Any]]:
             if self.threshold is not None and confidence < self.threshold:
                 continue
 
+            # The image is resized to "width" x "height" and we want the box relative to
+            # the actual image size, "self.image_width", " self.image_height".
+            # This is why we "/ width * self.image_width" etc
             box = [
-                bbox["xmin"],
-                bbox["ymin"],
-                bbox["xmin"] + bbox["width"],
-                bbox["ymin"] + bbox["height"],
+                bbox["xmin"] / width * self.image_width,
+                bbox["ymin"] / height * self.image_height,
+                (bbox["xmin"] + bbox["width"]) / width * self.image_width,
+                (bbox["ymin"] + bbox["height"]) / height * self.image_height,
             ]
 
             label = label.item()

nuclio_lighting_flash/test_main.py

@@ -19,17 +19,22 @@ def test_main():
             {
                 "confidence": 0.9171872138977051,
                 "label": "giraffe",
-                "points": [596.2103271484375, 276.046875, 962.4700927734375, 759.198974609375],
+                "points": [
+                    372.63145446777344,
+                    114.83981323242188,
+                    601.5438079833984,
+                    315.83863592147827,
+                ],
                 "type": "rectangle",
             },
             {
                 "confidence": 0.8744097352027893,
                 "label": "giraffe",
                 "points": [
-                    84.43610382080078,
-                    734.1155395507812,
-                    297.03192138671875,
-                    840.5248413085938,
+                    52.77256488800049,
+                    305.4035350084305,
+                    185.64495086669922,
+                    349.67146718502045,
                 ],
                 "type": "rectangle",
             },