Initial public release

tests/support/image_objects.py

@@ -0,0 +1,108 @@
+import bpy
+import numpy as np
+
+
+from anyimage.common import image as image_data
+from anyimage.common.selection import SelectionMask
+from anyimage.operators.cutout_tool import geometry as cutout_geometry
+from anyimage.operators.cutout_tool import object as cutout_object
+from tests.support.color_image import color_image as prepared_color_image
+
+
+def _inputs(group):
+    return [
+        item.name
+        for item in group.interface.items_tree
+        if item.item_type == "SOCKET" and item.in_out == "INPUT"
+    ]
+
+
+def _group_node(group, node_tree):
+    return next(
+        node
+        for node in node_tree.nodes
+        if node.bl_idname == "ShaderNodeGroup" and node.node_tree == group
+    )
+
+
+def _create_cutout_shape(
+    source,
+    shape,
+    edge_length,
+    *,
+    bounds=None,
+    depth_image=None,
+    depth_metadata=None,
+    selection_values=None,
+    gesture="LASSO",
+):
+    if bounds is None:
+        bounds = (0, 0, *tuple(int(value) for value in source.data.size))
+    left, top, right, bottom = bounds
+    if selection_values is None:
+        selection_values = np.ones((bottom - top, right - left), dtype=np.float32)
+    selection_mask = SelectionMask(selection_values, bounds)
+    source_rgba = image_data.image_rgba(source.data)
+    color_image = prepared_color_image(
+        source.data,
+        selection_mask.bounds,
+        source_rgba,
+    )
+    base_shape = cutout_geometry.build_base_shape(
+        bpy.context,
+        source,
+        edge_length,
+        selection_mask.values,
+        selection_mask.bounds,
+    )
+    return cutout_object.create_shape_object(
+        bpy.context,
+        source,
+        shape,
+        selection_mask.values,
+        selection_mask.bounds,
+        base_shape,
+        depth_image=depth_image,
+        depth_metadata=depth_metadata,
+        gesture=gesture,
+        color_image=color_image,
+    )
+
+
+def _depth_image(points, alpha, name="Depth"):
+    points = np.asarray(points, dtype=np.float32)
+    alpha = np.asarray(alpha, dtype=np.float32)
+    height, width = alpha.shape
+    pixels = np.zeros((height, width, 4), dtype=np.float32)
+    pixels[..., :3] = points
+    pixels[..., 3] = alpha
+    image = bpy.data.images.new(
+        name,
+        width=width,
+        height=height,
+        alpha=True,
+        float_buffer=True,
+    )
+    image.colorspace_settings.name = "Non-Color"
+    image.alpha_mode = "CHANNEL_PACKED"
+    image.pixels.foreach_set(np.flipud(pixels).ravel())
+    image.pack()
+    return image
+
+
+def _depth_metadata(intrinsics, image_size):
+    return {
+        "intrinsics": tuple(tuple(float(value) for value in row) for row in intrinsics),
+        "image_size": tuple(int(value) for value in image_size),
+    }
+
+
+def _evaluated_positions(obj):
+    evaluated = obj.evaluated_get(bpy.context.evaluated_depsgraph_get())
+    result = evaluated.to_mesh()
+    try:
+        return np.asarray([tuple(vertex.co) for vertex in result.vertices])
+    finally:
+        evaluated.to_mesh_clear()
+
+