fix: allow model.predict to handle numpy array inputs

roboflow/models/inference.py

@@ -62,14 +62,26 @@ def __get_image_params(self, image_path):
         Get parameters about an image (i.e. dimensions) for use in an inference request.
 
         Args:
-            image_path (str): path to the image you'd like to perform prediction on
+            image_path (Union[str, np.ndarray]): path to image or numpy array
 
         Returns:
             Tuple containing a dict of querystring params and a dict of requests kwargs
 
         Raises:
             Exception: Image path is not valid
         """
+        import numpy as np
+
+        if isinstance(image_path, np.ndarray):
+            # Convert numpy array to PIL Image
+            image = Image.fromarray(image_path)
+            dimensions = image.size
+            image_dims = {"width": str(dimensions[0]), "height": str(dimensions[1])}
+            buffered = io.BytesIO()
+            image.save(buffered, quality=90, format="JPEG")
+            data = MultipartEncoder(fields={"file": ("imageToUpload", buffered.getvalue(), "image/jpeg")})
+            return {}, {"data": data, "headers": {"Content-Type": data.content_type}}, image_dims
+
         validate_image_path(image_path)
 
         hosted_image = urllib.parse.urlparse(image_path).scheme in ("http", "https")

tests/models/test_instance_segmentation.py

@@ -142,3 +142,23 @@ def test_predict_with_non_200_response_raises_http_error(self):
 
         with self.assertRaises(HTTPError):
             instance.predict(image_path)
+
+    @responses.activate
+    def test_predict_with_numpy_array(self):
+        # Create a simple numpy array image
+        import numpy as np
+
+        image_array = np.zeros((100, 100, 3), dtype=np.uint8)  # Create a black image
+        image_array[30:70, 30:70] = 255  # Add a white square
+
+        instance = InstanceSegmentationModel(self.api_key, self.version_id)
+
+        responses.add(responses.POST, self.api_url, json=MOCK_RESPONSE)
+        group = instance.predict(image_array)
+        self.assertIsInstance(group, PredictionGroup)
+
+        request = responses.calls[0].request
+        self.assertEqual(request.method, "POST")
+        self.assertRegex(request.url, rf"^{self.api_url}")
+        self.assertDictEqual(request.params, self._default_params)
+        self.assertIsNotNone(request.body)