Add background-removal unit test

Author: xenova

tests/asset_cache.js

@@ -24,6 +24,7 @@ const TEST_IMAGES = Object.freeze({
   book_cover: BASE_URL + "book-cover.png",
   corgi: BASE_URL + "corgi.jpg",
   man_on_car: BASE_URL + "young-man-standing-and-leaning-on-car.jpg",
+  portrait_of_woman: BASE_URL + "portrait-of-woman_small.jpg",
 });
 
 const TEST_AUDIOS = {

tests/pipelines/test_pipelines_background_removal.js

@@ -0,0 +1,70 @@
+import { pipeline, BackgroundRemovalPipeline, RawImage } from "../../src/transformers.js";
+
+import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
+import { load_cached_image } from "../asset_cache.js";
+
+const PIPELINE_ID = "background-removal";
+
+export default () => {
+  describe("Background Removal", () => {
+    describe("Portrait Segmentation", () => {
+      const model_id = "Xenova/modnet";
+      /** @type {BackgroundRemovalPipeline} */
+      let pipe;
+      beforeAll(async () => {
+        pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
+      }, MAX_MODEL_LOAD_TIME);
+
+      it("should be an instance of BackgroundRemovalPipeline", () => {
+        expect(pipe).toBeInstanceOf(BackgroundRemovalPipeline);
+      });
+
+      it(
+        "single",
+        async () => {
+          const image = await load_cached_image("portrait_of_woman");
+
+          const output = await pipe(image);
+          expect(output).toHaveLength(1);
+          expect(output[0]).toBeInstanceOf(RawImage);
+          expect(output[0].width).toEqual(image.width);
+          expect(output[0].height).toEqual(image.height);
+          expect(output[0].channels).toEqual(4); // With alpha channel
+        },
+        MAX_TEST_EXECUTION_TIME,
+      );
+
+      afterAll(async () => {
+        await pipe.dispose();
+      }, MAX_MODEL_DISPOSE_TIME);
+    });
+
+    describe("Selfie Segmentation", () => {
+      const model_id = "onnx-community/mediapipe_selfie_segmentation";
+      /** @type {BackgroundRemovalPipeline } */
+      let pipe;
+      beforeAll(async () => {
+        pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
+      }, MAX_MODEL_LOAD_TIME);
+
+      it(
+        "single",
+        async () => {
+          const image = await load_cached_image("portrait_of_woman");
+
+          const output = await pipe(image);
+          expect(output).toHaveLength(1);
+          expect(output[0]).toBeInstanceOf(RawImage);
+          expect(output[0].width).toEqual(image.width);
+          expect(output[0].height).toEqual(image.height);
+          expect(output[0].channels).toEqual(4); // With alpha channel
+        },
+        MAX_TEST_EXECUTION_TIME,
+      );
+
+      afterAll(async () => {
+        await pipe.dispose();
+      }, MAX_MODEL_DISPOSE_TIME);
+    });
+  });
+};