Fixed test cases

Ran python -m unittest a lot.

Author: Tyler Odenthal

roboflow/models/inference.py

@@ -34,15 +34,22 @@ def __get_image_params(self, image_path):
         hosted_image = urllib.parse.urlparse(image_path).scheme in ("http", "https")
 
         if hosted_image:
-            return {"image": image_path}, {}
+            image_dims = {"width": "Undefined", "height": "Undefined"}
+            return {"image": image_path}, {}, image_dims
 
         image = Image.open(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}}
+        return (
+            {},
+            {"data": data, "headers": {"Content-Type": data.content_type}},
+            image_dims,
+        )
 
     def predict(self, image_path, prediction_type=None, **kwargs):
         """
@@ -54,7 +61,7 @@ def predict(self, image_path, prediction_type=None, **kwargs):
         :return: PredictionGroup - a group of predictions based on Roboflow JSON response
         :raises Exception: Image path is not valid
         """
-        params, request_kwargs = self.__get_image_params(image_path)
+        params, request_kwargs, image_dims = self.__get_image_params(image_path)
 
         params["api_key"] = self.__api_key
 
@@ -65,5 +72,8 @@ def predict(self, image_path, prediction_type=None, **kwargs):
         response.raise_for_status()
 
         return PredictionGroup.create_prediction_group(
-            response.json(), image_path=image_path, prediction_type=prediction_type
+            response.json(),
+            image_path=image_path,
+            prediction_type=prediction_type,
+            image_dims=image_dims,
         )

roboflow/models/object_detection.py

@@ -163,16 +163,17 @@ def predict(
             elif isinstance(image_path, np.ndarray):
                 # Performing inference on a OpenCV2 frame
                 retval, buffer = cv2.imencode(".jpg", image_path)
+                # Currently cv2.imencode does not properly return shape
                 dimensions = buffer.shape
                 img_str = base64.b64encode(buffer)
-                # print(img_str)
                 img_str = img_str.decode("ascii")
                 resp = requests.post(
                     self.api_url,
                     data=img_str,
                     headers={"Content-Type": "application/x-www-form-urlencoded"},
                 )
-                image_dims = {"width": str(dimensions[0]), "height": str(dimensions[1])}
+                # Replace with dimensions variable once cv2.imencode shape solution is found
+                image_dims = {"width": "0", "height": "0"}
             else:
                 raise ValueError("image_path must be a string or a numpy array.")
         else: