Accept numpy image in batch as base64 encoded string

docs/quickstart/http_inference.md

@@ -106,19 +106,19 @@ You can send a URL with an image, a NumPy array, or a base64-encoded image to an
         img_str = img_str.decode("ascii")
 
         infer_payload = {
-        "model_id": f"{project_id}/{model_version}",
-        "image": {
-            "type": "base64",
-            "value": img_str,
-        },
-        "confidence": confidence,
-        "iou_threshold": iou_thresh,
-        "api_key": api_key,
+            "model_id": f"{project_id}/{model_version}",
+            "image": {
+                "type": "base64",
+                "value": img_str,
+            },
+            "confidence": confidence,
+            "iou_threshold": iou_thresh,
+            "api_key": api_key,
         }
 
         res = requests.post(
-        f"http://localhost:9001/infer/{task}",
-        json=infer_payload,
+            f"http://localhost:9001/infer/{task}",
+            json=infer_payload,
         )
 
         predictions = res.json()
@@ -167,29 +167,29 @@ You can send a URL with an image, a NumPy array, or a base64-encoded image to an
         img_str = img_str.decode("ascii")
 
         infer_payload = {
-        "model_id": f"{project_id}/{model_version}",
-        "image": [
-            {
-                "type": "base64",
-                "value": img_str,
-            },
-            {
-                "type": "base64",
-                "value": img_str,
-            },
-            {
-                "type": "base64",
-                "value": img_str,
-            }
-        ],
-        "confidence": confidence,
-        "iou_threshold": iou_thresh,
-        "api_key": api_key,
+            "model_id": f"{project_id}/{model_version}",
+            "image": [
+                {
+                    "type": "base64",
+                    "value": img_str,
+                },
+                {
+                    "type": "base64",
+                    "value": img_str,
+                },
+                {
+                    "type": "base64",
+                    "value": img_str,
+                }
+            ],
+            "confidence": confidence,
+            "iou_threshold": iou_thresh,
+            "api_key": api_key,
         }
 
         res = requests.post(
-        f"http://localhost:9001/infer/{task}",
-        json=infer_payload,
+            f"http://localhost:9001/infer/{task}",
+            json=infer_payload,
         )
 
         predictions = res.json()
@@ -212,7 +212,60 @@ You can send a URL with an image, a NumPy array, or a base64-encoded image to an
 
     === "Numpy Array"
 
-        Numpy array inputs are currently not supported by V2 routes.
+        Create a new Python file and add the following code:
+
+        ```python
+        import requests
+        import base64
+        from PIL import Image
+        from io import BytesIO
+
+        project_id = "soccer-players-5fuqs"
+        model_version = 1
+        task = "object_detection"
+        confidence = 0.5
+        iou_thresh = 0.5
+        api_key = "YOUR ROBOFLOW API KEY"
+        file_name = "path/to/local/image.jpg"
+
+        image = cv2.imread(file_name)
+        numpy_data = pickle.dumps(image)
+        img_str = base64.b64encode(numpy_data)
+        img_str = img_str.decode("ascii")
+
+        infer_payload = {
+            "model_id": f"{project_id}/{model_version}",
+            "image": {
+                "type": "base64",
+                "value": img_str,
+            },
+            "confidence": confidence,
+            "iou_threshold": iou_thresh,
+            "api_key": api_key,
+        }
+
+        res = requests.post(
+            f"http://localhost:9001/infer/{task}",
+            json=infer_payload,
+        )
+
+        predictions = res.json()
+        print(predictions)
+        ```
+
+        Above, specify:
+
+        1. `project_id`, `model_version`: Your project ID and model version number. [Learn how to retrieve your project ID and model version number](https://docs.roboflow.com/api-reference/workspace-and-project-ids).
+        2. `confidence`: The confidence threshold for predictions. Predictions with a confidence score below this threshold will be filtered out.
+        3. `api_key`: Your Roboflow API key. [Learn how to retrieve your Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).
+        4. `task`: The type of task you want to run. Choose from `object_detection`, `classification`, or `segmentation`.
+        5. `filename`: The path to the image you want to run inference on.
+
+        Then, run the Python script:
+
+        ```
+        python app.py
+        ```
 
 ### V1 Routes
 

inference/core/utils/image_utils.py

@@ -1,3 +1,4 @@
+import binascii
 import os
 import pickle
 import re
@@ -222,11 +223,11 @@ def load_image_from_buffer(
     return result
 
 
-def load_image_from_numpy_str(value: bytes) -> np.ndarray:
+def load_image_from_numpy_str(value: Union[bytes, str]) -> np.ndarray:
     """Loads an image from a numpy array string.
 
     Args:
-        value (str): String representing the numpy array of the image.
+        value (Union[bytes, str]): Base64 string or byte sequence representing the pickled numpy array of the image.
 
     Returns:
         Image.Image: The loaded PIL image.
@@ -235,8 +236,10 @@ def load_image_from_numpy_str(value: bytes) -> np.ndarray:
         InvalidNumpyInput: If the numpy data is invalid.
     """
     try:
+        if isinstance(value, str):
+            value = pybase64.b64decode(value)
         data = pickle.loads(value)
-    except (EOFError, TypeError, pickle.UnpicklingError) as error:
+    except (EOFError, TypeError, pickle.UnpicklingError, binascii.Error) as error:
         raise InvalidNumpyInput(
             f"Could not unpickle image data. Cause: {error}"
         ) from error

tests/inference/unit_tests/conftest.py

@@ -84,6 +84,12 @@ def image_as_pickled_bytes() -> bytes:
     image = np.zeros((128, 128, 3), dtype=np.uint8)
     return pickle.dumps(image)
 
+@fixture(scope="function")
+def image_as_base64_encoded_pickled_bytes() -> bytes:
+    image = np.zeros((128, 128, 3), dtype=np.uint8)
+    bytes = pickle.dumps(image)
+    return base64.b64encode(bytes).decode()
+
 
 @fixture(scope="function")
 def image_as_pickled_bytes_rgba() -> bytes:

tests/inference/unit_tests/core/utils/test_image_utils.py

@@ -357,6 +357,7 @@ def test_load_image_from_encoded_bytes_when_decoding_should_fail() -> None:
         "image_as_png_bytes",
         "image_as_jpeg_bytes",
         "image_as_pickled_bytes",
+        "image_as_base64_encoded_pickled_bytes",
     ],
 )
 def test_attempt_loading_image_from_string_when_parsing_should_be_successful(
@@ -505,6 +506,7 @@ def test_extract_image_payload_and_type_when_value_is_request_and_type_is_not_re
         ("image_as_local_path", ImageType.FILE, True),
         ("image_as_buffer", ImageType.MULTIPART, True),
         ("image_as_pickled_bytes", ImageType.NUMPY, True),
+        ("image_as_base64_encoded_pickled_bytes", ImageType.NUMPY, True),
         ("image_as_pillow", ImageType.PILLOW, False),
     ],
 )
@@ -626,6 +628,7 @@ def test_convert_gray_image_to_bgr_when_2d_input_submitted(
         ("image_as_rgba_buffer", True),  # works due to cv load flags
         ("image_as_gray_buffer", True),
         ("image_as_pickled_bytes", True),
+        ("image_as_base64_encoded_pickled_bytes", True),
         ("image_as_pickled_bytes_gray", True),
         ("image_as_pillow", False),
     ],