initial commit

Signed-off-by: Igor Davidyuk <igor.davidyuk@intel.com>

merge python version

geti_sdk/benchmarking/benchmarker.py

@@ -33,12 +33,12 @@
     Video,
 )
 from geti_sdk.deployment import Deployment
+from geti_sdk.prediction_visualization.visualizer import Visualizer
 from geti_sdk.rest_clients import ImageClient, ModelClient, TrainingClient, VideoClient
 from geti_sdk.rest_clients.prediction_client import PredictionClient
 from geti_sdk.utils.plot_helpers import (
     concat_prediction_results,
     pad_image_and_put_caption,
-    show_image_with_annotation_scene,
 )
 
 from .utils import get_system_info, load_benchmark_media, suppress_log_output
@@ -859,6 +859,8 @@ def compare_predictions(
             with open(throughput_benchmark_results, "r") as results_file:
                 throughput_benchmark_results = list(csv.DictReader(results_file))
 
+        visusalizer = Visualizer()
+
         # Performe inferece
         with logging_redirect_tqdm(tqdm_class=tqdm):
             results: List[List[np.ndarray]] = []
@@ -890,9 +892,7 @@ def compare_predictions(
                             f"failed. Inference failed with error: `{e}`"
                         )
                 if success:
-                    image_with_prediction = show_image_with_annotation_scene(
-                        image, prediction, show_results=False
-                    )
+                    image_with_prediction = visusalizer.draw(image, prediction)
                     image_with_prediction = cv2.cvtColor(
                         image_with_prediction, cv2.COLOR_BGR2RGB
                     )
@@ -953,8 +953,8 @@ def compare_predictions(
         if include_online_prediction_for_active_model:
             logging.info("Predicting on the platform using the active model")
             online_prediction_result = self._predict_using_active_model(image)
-            image_with_prediction = show_image_with_annotation_scene(
-                image, online_prediction_result["prediction"], show_results=False
+            image_with_prediction = visusalizer.draw(
+                image, online_prediction_result["prediction"]
             )
             image_with_prediction = cv2.cvtColor(
                 image_with_prediction, cv2.COLOR_BGR2RGB

geti_sdk/deployment/resources/OVMS_README.md

@@ -62,8 +62,10 @@ image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 predictions = deployment.infer(image=image)
 
 # Show inference result
-from geti_sdk.utils import show_image_with_annotation_scene
-show_image_with_annotation_scene(image=image, annotation_scene=predictions);
+from geti_sdk.prediction_visualization.visualizer import Visualizer
+visualizer = Visualizer()
+result_image = visualizer.draw(image=image, annotation_scene=predictions)
+visualizer.show_window(result_image)
 ```
 
 The example uses a sample image, please make sure to replace it with your own.

geti_sdk/post_inference_hooks/actions/file_system_data_collection.py

@@ -22,8 +22,8 @@
 
 from geti_sdk.data_models import Prediction
 from geti_sdk.deployment.inference_hook_interfaces import PostInferenceAction
+from geti_sdk.prediction_visualization.visualizer import Visualizer
 from geti_sdk.rest_converters import PredictionRESTConverter
-from geti_sdk.utils import show_image_with_annotation_scene
 
 
 class FileSystemDataCollection(PostInferenceAction):
@@ -81,6 +81,7 @@ def __init__(
         self.save_predictions = save_predictions
         self.save_scores = save_scores
         self.save_overlays = save_overlays
+        self.visualizer = Visualizer()
 
         self._repr_info_ = (
             f"target_folder=`{target_folder}`, "
@@ -147,12 +148,8 @@ def __call__(
 
             if self.save_overlays:
                 overlay_path = os.path.join(self.overlays_path, filename + ".jpg")
-                show_image_with_annotation_scene(
-                    image=image,
-                    annotation_scene=prediction,
-                    filepath=overlay_path,
-                    show_results=False,
-                )
+                result = self.visualizer.draw(image, prediction)
+                self.visualizer.save_image(result, overlay_path)
         except Exception as e:
             logging.exception(e, stack_info=True, exc_info=True)
 

geti_sdk/prediction_visualization/visualizer.py

@@ -15,12 +15,17 @@
 """The package provides the Visualizer class for models predictions visualization."""
 
 
-from typing import Optional
+from os import PathLike
+from typing import List, Optional, Union
 
 import cv2
 import numpy as np
+from IPython.display import display
+from PIL import Image
 
 from geti_sdk.data_models.annotation_scene import AnnotationScene
+from geti_sdk.data_models.containers.media_list import MediaList
+from geti_sdk.data_models.media import VideoFrame
 from geti_sdk.data_models.predictions import Prediction
 from geti_sdk.prediction_visualization.shape_drawer import ShapeDrawer
 
@@ -44,8 +49,6 @@ def __init__(
         show_confidence: bool = True,
         show_count: bool = False,
         is_one_label: bool = False,
-        delay: Optional[int] = None,
-        output: Optional[str] = None,
     ) -> None:
         """
         Initialize the Visualizer.
@@ -55,19 +58,12 @@ def __init__(
         :param show_confidence: Show confidence on the output image
         :param show_count: Show count of the shapes on the output image
         :param is_one_label: Show only one label on the output image
-        :param delay: Delay time for the output image
-        :param output: Path to save the output image
         """
         self.window_name = "Window" if window_name is None else window_name
         self.shape_drawer = ShapeDrawer(
             show_count, is_one_label, show_labels, show_confidence
         )
 
-        self.delay = delay
-        if delay is None:
-            self.delay = 1
-        self.output = output
-
     def draw(
         self,
         image: np.ndarray,
@@ -140,7 +136,53 @@ def explain_label(
             result = self.draw(result, filtered_prediction, fill_shapes=False)
         return result
 
-    def show(self, image: np.ndarray) -> None:
+    @staticmethod
+    def save_image(image: np.ndarray, output_path: PathLike) -> None:
+        """
+        Save the image to the output path.
+
+        :param image: Image in RGB format to be saved
+        :param output_path: Path to save the image
+        """
+        bgr_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        cv2.imwrite(output_path, bgr_image)
+
+    @staticmethod
+    def save_video(
+        video_frames: MediaList[VideoFrame],
+        annotation_scenes: List[Union[AnnotationScene, Prediction]],
+        output_path: PathLike,
+        fps: float = 1,
+    ) -> None:
+        """
+        Save the video to the output path.
+
+        :param video_frames: List of video frames
+        :param annotation_scenes: List of annotation scenes to be drawn on the video frames
+        :param output_path: Path to save the image
+        """
+        out_writer = cv2.VideoWriter(
+            filename=f"{output_path}",
+            fourcc=cv2.VideoWriter_fourcc("M", "J", "P", "G"),
+            fps=fps,
+            frameSize=(
+                video_frames[0].media_information.width,
+                video_frames[0].media_information.height,
+            ),
+        )
+        for frame, annotation in zip(video_frames, annotation_scenes):
+            out_writer.write(frame)
+
+    @staticmethod
+    def show_in_notebook(image: np.ndarray) -> None:
+        """
+        Show the image in the Jupyter notebook.
+
+        :param image: Image to be shown in RGB format
+        """
+        display(Image.fromarray(image))
+
+    def show_window(self, image: np.ndarray) -> None:
         """
         Show result image.
 
@@ -149,6 +191,6 @@ def show(self, image: np.ndarray) -> None:
         image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
         cv2.imshow(self.window_name, image_bgr)
 
-    def is_quit(self) -> bool:
+    def is_quit(self, delay: int = 1) -> bool:
         """Check user wish to quit."""
-        return ord("q") == cv2.waitKey(self.delay)
+        return ord("q") == cv2.waitKey(delay)

notebooks/003_upload_and_predict_image.ipynb

@@ -207,15 +207,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from geti_sdk.utils import show_image_with_annotation_scene\n",
+    "import cv2\n",
+    "\n",
+    "from geti_sdk.prediction_visualization.visualizer import Visualizer\n",
     "\n",
     "# To visualise the image, we have to retrieve the pixel data from the platform using the `image.get_data` method. The actual pixel data is\n",
     "# downloaded and cached only on the first call to this method\n",
     "image.get_data(geti.session)\n",
     "\n",
-    "show_image_with_annotation_scene(\n",
-    "    image, prediction, show_in_notebook=True, channel_order=\"bgr\"\n",
-    ");"
+    "visualizer = Visualizer()\n",
+    "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "result = visualizer.draw(image_rgb, prediction)\n",
+    "visualizer.show_in_notebook(result)"
    ]
   },
   {
@@ -240,18 +243,10 @@
     "    visualise_output=False,\n",
     "    delete_after_prediction=False,\n",
     ")\n",
-    "show_image_with_annotation_scene(\n",
-    "    quick_image, quick_prediction, show_in_notebook=True, channel_order=\"bgr\"\n",
-    ");"
+    "quick_image_rgb = cv2.cvtColor(quick_image.numpy, cv2.COLOR_BGR2RGB)\n",
+    "quick_result = visualizer.draw(quick_image_rgb, quick_prediction)\n",
+    "visualizer.show_in_notebook(quick_result)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "51090376-c85e-4af3-9ff8-b030934fd095",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -270,7 +265,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

notebooks/005_modify_image.ipynb

@@ -219,8 +219,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from geti_sdk.prediction_visualization.visualizer import Visualizer\n",
     "from geti_sdk.rest_clients import AnnotationClient\n",
-    "from geti_sdk.utils import show_image_with_annotation_scene\n",
     "\n",
     "annotation_client = AnnotationClient(\n",
     "    session=geti.session, workspace_id=geti.workspace_id, project=project\n",
@@ -231,9 +231,11 @@
     "\n",
     "# Inspect the annotation\n",
     "print(annotation.overview)\n",
-    "show_image_with_annotation_scene(\n",
-    "    image, annotation, show_in_notebook=True, channel_order=\"bgr\"\n",
-    ");"
+    "\n",
+    "visualizer = Visualizer()\n",
+    "image_rgb = cv2.cvtColor(image.numpy(), cv2.COLOR_BGR2RGB)\n",
+    "result = visualizer.draw(image_rgb, annotation)\n",
+    "visualizer.show_in_notebook(result)"
    ]
   },
   {
@@ -276,11 +278,8 @@
     ")\n",
     "\n",
     "# Inspect the annotation\n",
-    "show_image_with_annotation_scene(\n",
-    "    grayscale_image.get_data(geti.session),\n",
-    "    grayscale_annotation,\n",
-    "    show_in_notebook=True,\n",
-    ");"
+    "result = visualizer.draw(grayscale_image.get_data(geti.session), grayscale_annotation)\n",
+    "visualizer.show_in_notebook(result)"
    ]
   },
   {
@@ -342,7 +341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,