* added BATCH TO LIST node

* `VECTOR` node(s) default step changed to 0.1

README.md

@@ -138,6 +138,10 @@ Nodes that have been migrated:
 
 [Migrated to Jovi_GLSL](https://github.com/Amorano/Jovi_GLSL)
 
+**2025/07/27** @2.1.19:
+* added `BATCH TO LIST` node
+* `VECTOR` node(s) default step changed to 0.1
+
 **2025/07/13** @2.1.18:
 * allow numpy>=1.25.0
 

core/create.py

@@ -202,6 +202,7 @@ Create n-sided polygons. These shapes can be customized by adjusting parameters
                 rgb = (gaussian(rgb, sigma=blur, channel_axis=2) * 255).astype(np.uint8)
                 mask = (gaussian(mask, sigma=blur, channel_axis=2) * 255).astype(np.uint8)
 
+            mask = (mask * (color[3] / 255.)).astype(np.uint8)
             back = list(matte[:3]) + [255]
             canvas = np.full((height, width, 4), back, dtype=rgb.dtype)
             rgba = image_blend(canvas, rgb, mask)

core/utility/batch.py

@@ -240,6 +240,29 @@ Processes a batch of data based on the selected mode. Merge, pick, slice, random
 
         return (data, [size],)
 
+class BatchToList(CozyBaseNode):
+    NAME = "BATCH TO LIST (JOV)"
+    NAME_PRETTY = "BATCH TO LIST (JOV)"
+    CATEGORY = JOV_CATEGORY
+    RETURN_TYPES = (COZY_TYPE_ANY, )
+    RETURN_NAMES = ("LIST", )
+    DESCRIPTION = """
+Convert a batch of values into a pure python list of values.
+"""
+    @classmethod
+    def INPUT_TYPES(cls) -> InputType:
+        d = super().INPUT_TYPES()
+        return deep_merge(d, {
+            "optional": {
+                Lexicon.BATCH: (COZY_TYPE_ANY, {}),
+            }
+        })
+
+    def run(self, **kw) -> tuple[list[Any]]:
+        batch = parse_param(kw, Lexicon.BATCH, EnumConvertType.LIST, [])
+        batch = [f[0] for f in batch]
+        return (batch,)
+
 class QueueBaseNode(CozyBaseNode):
     CATEGORY = JOV_CATEGORY
     RETURN_TYPES = (COZY_TYPE_ANY, COZY_TYPE_ANY, "STRING", "INT", "INT", "BOOLEAN")

node_list.json

@@ -10,6 +10,7 @@
     "ADJUST: SHARPEN (JOV)": "Sharpen the pixels of an image",
     "AKASHIC (JOV) \ud83d\udcd3": "Visualize data",
     "ARRAY (JOV) \ud83d\udcda": "Processes a batch of data based on the selected mode",
+    "BATCH TO LIST (JOV)": "Convert a batch of values into a pure python list of values",
     "BIT SPLIT (JOV) \u2b44": "Split an input into separate bits",
     "BLEND (JOV) \u2697\ufe0f": "Combine two input images using various blending modes, such as normal, screen, multiply, overlay, etc",
     "COLOR BLIND (JOV) \ud83d\udc41\u200d\ud83d\udde8": "Simulate color blindness effects on images",

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "jovimetrix"
 description = "Animation via tick. Parameter manipulation with wave generator. Unary and Binary math support. Value convert int/float/bool, VectorN and Image, Mask types. Shape mask generator. Stack images, do channel ops, split, merge and randomize arrays and batches. Load images & video from anywhere. Dynamic bus routing. Save output anywhere! Flatten, crop, transform; check colorblindness or linear interpolate values."
-version = "2.1.18"
+version = "2.1.19"
 license = { file = "LICENSE" }
 readme = "README.md"
 authors = [{ name = "Alexander G. Morano", email = "amorano@gmail.com" }]

web/widget_vector.js

@@ -62,7 +62,7 @@ const VectorWidget = (app, inputName, options, initial) => {
 
     widget.convert = parseFloat;
     widget.options.precision = widget.options?.precision || 2;
-    widget.options.step = widget.options?.step || 0.01;
+    widget.options.step = widget.options?.step || 0.1;
     widget.options.round = 1 / 10 ** widget.options.step;
 
     if (widget.options?.rgb || widget.options?.int || false) {