Adjust to results of internal pool regarding naming conventions

Author: PawelPeczek-Roboflow

docs/workflows/execution_engine_changelog.md

@@ -4,34 +4,37 @@ Below you can find the changelog for Execution Engine.
 
 ## Execution Engine `v1.2.0` | inference `v0.23.0`
 
-* [`video_metadata` kind](/workflows/kinds/video_metadata/) got deprecated - **we advise not using this kind for 
-building blocks from now on.** As an alternative approach, [`image` kind](/workflows/kinds/image/) internal and
-external representation got extended with the same metadata that 
-[`video_metadata` kind](/workflows/kinds/video_metadata/) has, which can be provided optionally - making this 
-change **non-breaking** for all old blocks, yet **making some old blocks producing images** incompatible with 
-video processing blocks that will come in the future. Old blocks may also continue using 
-[`video_metadata` kind](/workflows/kinds/video_metadata/), as it is left for now and will be removed in `v2.0.0`.
+* The [`video_metadata` kind](/workflows/kinds/video_metadata/) has been deprecated, and we **strongly recommend discontinuing its use for building 
+blocks moving forward**. As an alternative, the [`image` kind](/workflows/kinds/image/) has been extended to support the same metadata as 
+[`video_metadata` kind](/workflows/kinds/video_metadata/), which can now be provided optionally. This update is 
+**non-breaking** for existing blocks, but **some older blocks** that produce images **may become incompatible** with 
+**future** video processing blocks.
 
 ??? warning "Potential blocks incompatibility"
 
-    As it was mentioned, adding `video_metadata` to `image` kind as optional set of metadata may create a friction 
-    between already existing blocks that output `image` kind and future video processing blocks relying on 
-    `video_metadata` that will be created. The reason is the following - since we are able to provide
-    default value for `video_metadata` in `image`, without explicitly copying them from the input image in the block, 
-    non-default metadata are lost and downstream blocks relying on the metadata may not work 100% as they should. 
-    We adjusted all existing `roboflow_core` blocks to follow, but all blocks created prior this change in external 
-    repositories may produce miss-behaviong Workflows when the output images are used by video-processing blocks.    
+    As previously mentioned, adding `video_metadata` as an optional field to the internal representation of 
+    [`image` kind](/workflows/kinds/image/) (`WorkflowImageData` class) 
+    may introduce some friction between existing blocks that output the [`image` kind](/workflows/kinds/image/) and 
+    future video processing blocks that rely on `video_metadata` being part of `image` representation. 
+    
+    The issue arises because, while we can provide **default** values for `video_metadata` in `image` without 
+    explicitly copying them from the input, any non-default metadata that was added upstream may be lost. 
+    This can lead to downstream blocks that depend on the `video_metadata` not functioning as expected.
 
-* As a result of the above, [`image` kind](/workflows/kinds/image/) got updated internal representation - there is 
-new property `video_metadata` which can be optionally set in constructor, if not - default value with reasonable 
-defaults will be provided. Additionally, to make it easier to manipulate metadata in blocks, `update_image(...)` and
-`build_crop(...)` methods were added - see updates in 
-[`WoorkflowImageData` usage guide](/workflows/internal_data_types/#workflowimagedata)
+    We've updated all existing `roboflow_core` blocks to account for this, but blocks created before this change in
+    external repositories may cause issues in workflows where their output images are used by video processing blocks.
 
 
-!!! warning "Breaking change planned - `v2.0.0`"
+* While the deprecated [`video_metadata` kind](/workflows/kinds/video_metadata/) is still available for use, it will be fully removed in 
+Execution Engine version `v2.0.0`.
+
+!!! warning "Breaking change planned - Execution Engine `v2.0.0`"
 
     [`video_metadata` kind](/workflows/kinds/video_metadata/) got deprecated and will be removed in `v2.0.0`
 
 
-    
+* As a result of the changes mentioned above, the internal representation of the [`image` kind](/workflows/kinds/image/) has been updated to 
+include a new `video_metadata` property. This property can be optionally set in the constructor; if not provided, 
+a default value with reasonable defaults will be used. To simplify metadata manipulation within blocks, we have 
+introduced two new class methods: `WorkflowImageData.copy_and_replace(...)` and `WorkflowImageData.create_crop(...)`. 
+For more details, refer to the updated [`WoorkflowImageData` usage guide](/workflows/internal_data_types/#workflowimagedata).

docs/workflows/internal_data_types.md

@@ -157,7 +157,11 @@ def transform_image(image: WorkflowImageData) -> WorkflowImageData:
     # data lineage (the predecessor-successor relation for images).
     # Lineage is not preserved for cropping and merging images (without common predecessor)
     # - below you may find implementation tips.
-    return image.update_image(image=transformed_image)  
+    return WorkflowImageData.copy_and_replace(
+        origin_image_data=image,
+        numpy_image=transformed_image,
+    )
+
 
 def some_transformation(image: np.ndarray) -> np.ndarray:
     ...
@@ -188,7 +192,8 @@ def some_transformation(image: np.ndarray) -> np.ndarray:
             if not cropped_image.size:
                 # discarding empty crops
                 continue
-            result_crop = image.build_crop(
+            result_crop = WorkflowImageData.create_crop(
+                origin_image_data=image, 
                 crop_identifier=crop_id,
                 cropped_image=cropped_image,
                 offset_x=x_min,
@@ -201,7 +206,7 @@ def some_transformation(image: np.ndarray) -> np.ndarray:
     In some cases you may want to preserve `video_metadata`. Example of such situation is when 
     your block produces crops based on fixed coordinates (like video single footage with multiple fixed Regions of 
     Interest to be applied individual trackers) - then you want result crops to be processed in context of video,
-    as if they were produced by separate cameras. To adjust behaviour of `build_crop(...)` method, simply add 
+    as if they were produced by separate cameras. To adjust behaviour of `create_crop(...)` method, simply add 
     `preserve_video_metadata=True`:
 
     ```{ .py linenums="1" hl_lines="11"}
@@ -210,7 +215,8 @@ def some_transformation(image: np.ndarray) -> np.ndarray:
         crops: List[Tuple[str, int, int, int, int]],
     ) -> List[WorkflowImageData]:
         # [...]
-        result_crop = image.build_crop(
+        result_crop = WorkflowImageData.create_crop(
+            origin_image_data=image, 
             crop_identifier=crop_id,
             cropped_image=cropped_image,
             offset_x=x_min,

inference/core/workflows/core_steps/classical_cv/camera_focus/v1.py

@@ -83,8 +83,10 @@ def get_manifest(cls) -> Type[CameraFocusManifest]:
     def run(self, image: WorkflowImageData, *args, **kwargs) -> BlockResult:
         # Calculate the Brenner measure
         brenner_image, brenner_value = calculate_brenner_measure(image.numpy_image)
-        output = image.update_image(image=brenner_image)
-
+        output = WorkflowImageData.copy_and_replace(
+            origin_image_data=image,
+            numpy_image=brenner_image,
+        )
         return {
             OUTPUT_IMAGE_KEY: output,
             "focus_measure": brenner_value,

inference/core/workflows/core_steps/classical_cv/contours/v1.py

@@ -107,9 +107,9 @@ def run(
         contour_image, contours, hierarchy = find_and_draw_contours(
             image.numpy_image, thickness=line_thickness
         )
-
-        output = image.update_image(image=contour_image)
-
+        output = WorkflowImageData.copy_and_replace(
+            origin_image_data=image, numpy_image=contour_image
+        )
         return {
             OUTPUT_IMAGE_KEY: output,
             "contours": contours,

inference/core/workflows/core_steps/classical_cv/convert_grayscale/v1.py

@@ -79,5 +79,7 @@ def run(
     ) -> BlockResult:
         # Convert the image to grayscale
         gray = cv2.cvtColor(image.numpy_image, cv2.COLOR_BGR2GRAY)
-        output = image.update_image(image=gray)
+        output = WorkflowImageData.copy_and_replace(
+            origin_image_data=image, numpy_image=gray
+        )
         return {OUTPUT_IMAGE_KEY: output}

inference/core/workflows/core_steps/classical_cv/image_blur/v1.py

@@ -101,7 +101,10 @@ def run(
     ) -> BlockResult:
         # Apply blur to the image
         blurred_image = apply_blur(image.numpy_image, blur_type, kernel_size)
-        output = image.update_image(image=blurred_image)
+        output = WorkflowImageData.copy_and_replace(
+            origin_image_data=image,
+            numpy_image=blurred_image,
+        )
         return {OUTPUT_IMAGE_KEY: output}
 
 

inference/core/workflows/core_steps/classical_cv/image_preprocessing/v1.py

@@ -160,7 +160,9 @@ def run(
         else:
             raise ValueError(f"Invalid task type: {task_type}")
 
-        output_image = image.update_image(image=response_image)
+        output_image = WorkflowImageData.copy_and_replace(
+            origin_image_data=image, numpy_image=response_image
+        )
         return {"image": output_image}
 
 

inference/core/workflows/core_steps/classical_cv/sift/v1.py

@@ -87,7 +87,10 @@ def get_manifest(cls) -> Type[SIFTDetectionManifest]:
 
     def run(self, image: WorkflowImageData, *args, **kwargs) -> BlockResult:
         img_with_kp, keypoints, descriptors = apply_sift(image.numpy_image)
-        output_image = image.update_image(image=img_with_kp)
+        output_image = WorkflowImageData.copy_and_replace(
+            origin_image_data=image,
+            numpy_image=img_with_kp,
+        )
         return {
             OUTPUT_IMAGE_KEY: output_image,
             "keypoints": keypoints,

inference/core/workflows/core_steps/classical_cv/sift_comparison/v2.py

@@ -201,10 +201,16 @@ def run(
         images_match = good_matches_count >= good_matches_threshold
 
         if visualization_1 is not None:
-            visualization_1 = input_1.update_image(image=visualization_1)
+            visualization_1 = WorkflowImageData.copy_and_replace(
+                origin_image_data=input_1,
+                numpy_image=visualization_1,
+            )
 
         if visualization_2 is not None:
-            visualization_2 = input_2.update_image(image=visualization_2)
+            visualization_2 = WorkflowImageData.copy_and_replace(
+                origin_image_data=input_2,
+                numpy_image=visualization_2,
+            )
 
         visualization_matches = None
         if visualize and image_1 is not None and image_2 is not None:
@@ -229,7 +235,10 @@ def run(
                 **draw_params,
             )
 
-            visualization_matches = input_1.update_image(image=visualization_matches)
+            visualization_matches = WorkflowImageData.copy_and_replace(
+                origin_image_data=input_1,
+                numpy_image=visualization_matches,
+            )
 
         return {
             "good_matches_count": good_matches_count,

inference/core/workflows/core_steps/classical_cv/threshold/v1.py

@@ -117,7 +117,10 @@ def run(
         thresholded_image = apply_thresholding(
             image.numpy_image, threshold_type, thresh_value, max_value
         )
-        output = image.update_image(image=thresholded_image)
+        output = WorkflowImageData.copy_and_replace(
+            origin_image_data=image,
+            numpy_image=thresholded_image,
+        )
         return {OUTPUT_IMAGE_KEY: output}