Unified crop node (#1996)

* Unified crop node

* Fixed crop node

* Crop to Content

backend/src/packages/chaiNNer_standard/image_dimension/crop/crop.py

@@ -0,0 +1,114 @@
+from __future__ import annotations
+
+from enum import Enum
+
+import numpy as np
+
+from nodes.groups import if_enum_group
+from nodes.properties.inputs import EnumInput, ImageInput, NumberInput
+from nodes.properties.outputs import ImageOutput
+from nodes.utils.utils import get_h_w_c
+
+from .. import crop_group
+
+
+class CropMode(Enum):
+    BORDER = 0
+    EDGES = 1
+    OFFSETS = 2
+
+
+@crop_group.register(
+    schema_id="chainner:image:crop",
+    name="Crop",
+    description="Crop an image.",
+    icon="MdCrop",
+    inputs=[
+        ImageInput(),
+        EnumInput(CropMode, default=CropMode.BORDER).with_id(1),
+        if_enum_group(1, CropMode.BORDER)(
+            NumberInput("Amount", unit="px").with_id(2),
+        ),
+        if_enum_group(1, (CropMode.EDGES, CropMode.OFFSETS))(
+            NumberInput("Left", unit="px").with_id(4),
+            NumberInput("Top", unit="px").with_id(3),
+        ),
+        if_enum_group(1, CropMode.EDGES)(
+            NumberInput("Right", unit="px").with_id(6),
+            NumberInput("Bottom", unit="px").with_id(5),
+        ),
+        if_enum_group(1, CropMode.OFFSETS)(
+            NumberInput("Width", unit="px", minimum=1, default=1).with_id(8),
+            NumberInput("Height", unit="px", minimum=1, default=1).with_id(7),
+        ),
+    ],
+    outputs=[
+        ImageOutput(
+            image_type="""
+                let i = Input0;
+
+                let amount = Input2;
+                let top = Input3;
+                let left = Input4;
+                let bottom = Input5;
+                let right = Input6;
+                let height = Input7;
+                let width = Input8;
+
+                let size = match Input1 {
+                    CropMode::Border => Image {
+                        width: (i.width - amount * 2) & int(1..),
+                        height: (i.height - amount * 2) & int(1..),
+                    },
+                    CropMode::Edges => Image {
+                        width: (i.width - (left + right)) & int(1..),
+                        height: (i.height - (top + bottom)) & int(1..),
+                    },
+                    CropMode::Offsets => Image {
+                        width: min(width, i.width - left) & int(1..),
+                        height: min(height, i.height - top) & int(1..),
+                    },
+                };
+
+                size & Image { channels: i.channels }
+            """,
+            assume_normalized=True,
+        ).with_never_reason(
+            "The cropped area would result in an image with no width or no height."
+        )
+    ],
+)
+def crop_node(
+    img: np.ndarray,
+    mode: CropMode,
+    amount: int,
+    top: int,
+    left: int,
+    bottom: int,
+    right: int,
+    height: int,
+    width: int,
+) -> np.ndarray:
+    h, w, _ = get_h_w_c(img)
+
+    if mode == CropMode.BORDER:
+        if amount == 0:
+            return img
+
+        assert 2 * amount < h, "Cropped area would result in an image with no height"
+        assert 2 * amount < w, "Cropped area would result in an image with no width"
+
+        return img[amount : h - amount, amount : w - amount]
+    elif mode == CropMode.EDGES:
+        if top == bottom == left == right == 0:
+            return img
+
+        assert top + bottom < h, "Cropped area would result in an image with no height"
+        assert left + right < w, "Cropped area would result in an image with no width"
+
+        return img[top : h - bottom, left : w - right]
+    elif mode == CropMode.OFFSETS:
+        assert top < h, "Cropped area would result in an image with no height"
+        assert left < w, "Cropped area would result in an image with no width"
+
+        return img[top : top + height, left : left + width]

backend/src/packages/chaiNNer_standard/image_dimension/crop/crop_content.py → backend/src/packages/chaiNNer_standard/image_dimension/crop/crop_to_content.py

@@ -11,7 +11,7 @@
 
 @crop_group.register(
     schema_id="chainner:image:crop_content",
-    name="Crop (Content)",
+    name="Crop to Content",
     description=(
         "Crop an image to the boundaries of the visible image content, "
         "removing borders at or below the given opacity threshold."
@@ -43,7 +43,7 @@
         )
     ],
 )
-def crop_content_node(img: np.ndarray, thresh_val: float) -> np.ndarray:
+def crop_to_content_node(img: np.ndarray, thresh_val: float) -> np.ndarray:
     c = get_h_w_c(img)[2]
     if c < 4:
         return img

src/common/migrations.ts

@@ -1219,6 +1219,62 @@ const saveImageWebPLossless: ModernMigration = (data) => {
     return data;
 };
 
+const unifiedCrop: ModernMigration = (data) => {
+    const map = new Map<string, string>();
+    const changeInputId = (nodeId: string, from: number, to: number) => {
+        map.set(`${nodeId}-${from}`, `${nodeId}-${to}`);
+    };
+
+    data.nodes.forEach((node) => {
+        if (node.data.schemaId === 'chainner:image:crop_border') {
+            node.data.schemaId = 'chainner:image:crop' as SchemaId;
+            node.data.inputData = {
+                1: 0,
+                2: node.data.inputData[1],
+            };
+            changeInputId(node.id, 1, 2);
+        }
+
+        if (node.data.schemaId === 'chainner:image:crop_edges') {
+            node.data.schemaId = 'chainner:image:crop' as SchemaId;
+            node.data.inputData = {
+                1: 1,
+                3: node.data.inputData[1],
+                4: node.data.inputData[2],
+                5: node.data.inputData[4],
+                6: node.data.inputData[3],
+            };
+            changeInputId(node.id, 1, 3);
+            changeInputId(node.id, 2, 4);
+            changeInputId(node.id, 3, 6);
+            changeInputId(node.id, 4, 5);
+        }
+
+        if (node.data.schemaId === 'chainner:image:crop_offsets') {
+            node.data.schemaId = 'chainner:image:crop' as SchemaId;
+            node.data.inputData = {
+                1: 2,
+                3: node.data.inputData[1],
+                4: node.data.inputData[2],
+                7: node.data.inputData[3],
+                8: node.data.inputData[4],
+            };
+            changeInputId(node.id, 1, 3);
+            changeInputId(node.id, 2, 4);
+            changeInputId(node.id, 3, 7);
+            changeInputId(node.id, 4, 8);
+        }
+    });
+
+    data.edges.forEach((e) => {
+        if (e.targetHandle) {
+            e.targetHandle = map.get(e.targetHandle) ?? e.targetHandle;
+        }
+    });
+
+    return data;
+};
+
 // ==============
 
 const versionToMigration = (version: string) => {
@@ -1268,6 +1324,7 @@ const migrations = [
     emptyStringInput,
     surfaceBlurRadius,
     saveImageWebPLossless,
+    unifiedCrop,
 ];
 
 export const currentMigration = migrations.length;