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https://github.com/snapotter-hq/SnapOtter.git
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SnapOtterandGitHub
6a0768b39d
AI sidecar failures reached Sentry as 'Error: Error': the scrubber type-onlys plain Errors and the tool wrappers threw them from result.error. The bridge now exports toSidecarError(), wrapping the sidecar reason in a SafeError (memory-allocation text classifies as operational, the rest as bug); all 14 wrappers use it, plus the dispatcher crash/stdin/spawn rejection paths and parseStdoutJson. toBgRemovalError from #535 delegates to the shared helper. On the web side, DOMExceptions report their specific name via err.name, so the NATIVE_ERRORS allowlist dropped the whole family's browser-authored messages. It now carries the full WebIDL DOMException name table; messages still pass through url/path redaction. Bridge-mocking test files switched to importOriginal passthrough mocks.
1486 lines
52 KiB
TypeScript
1486 lines
52 KiB
TypeScript
import { beforeEach, describe, expect, it, vi } from "vitest";
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// ---------------------------------------------------------------------------
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// Mock all dependencies BEFORE importing tool modules.
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//
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// vi.mock factories are hoisted to the top of the file, so they CANNOT
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// reference variables declared at module scope. Every mock must be fully
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// self-contained inside the factory function. We use vi.hoisted() to
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// create shared mock references that are safe to use in both the factories
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// and the test bodies.
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// ---------------------------------------------------------------------------
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const {
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mockRunPythonWithProgress,
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mockParseStdoutJson,
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mockIsGpuAvailable,
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mockSharp,
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mockWriteFile,
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mockReadFile,
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mockUnlink,
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mockRm,
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mockExecFile,
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mockRunOcrRuntime,
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mockRunTesseract,
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} = vi.hoisted(() => {
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const mockRunPythonWithProgress = vi.fn();
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const mockParseStdoutJson = vi.fn();
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const mockIsGpuAvailable = vi.fn().mockReturnValue(false);
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function createSharpChain(meta?: Record<string, unknown>) {
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const chain: Record<string, ReturnType<typeof vi.fn>> = {};
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chain.png = vi.fn().mockReturnValue(chain);
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chain.jpeg = vi.fn().mockReturnValue(chain);
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chain.resize = vi.fn().mockReturnValue(chain);
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chain.toBuffer = vi.fn().mockResolvedValue(Buffer.from("mock-png"));
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chain.toFile = vi.fn().mockResolvedValue({});
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chain.metadata = vi.fn().mockResolvedValue({
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width: 800,
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height: 600,
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format: "png",
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...meta,
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});
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return chain;
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}
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const mockSharp = Object.assign(vi.fn().mockReturnValue(createSharpChain()), {
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_createChain: createSharpChain,
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});
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return {
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mockRunPythonWithProgress,
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mockParseStdoutJson,
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mockIsGpuAvailable,
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mockSharp,
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mockWriteFile: vi.fn().mockResolvedValue(undefined),
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mockReadFile: vi.fn().mockResolvedValue(Buffer.from("output-buffer")),
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mockUnlink: vi.fn().mockResolvedValue(undefined),
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mockRm: vi.fn().mockResolvedValue(undefined),
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mockExecFile: vi.fn(),
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mockRunOcrRuntime: vi.fn(),
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mockRunTesseract: vi.fn(),
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};
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});
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vi.mock("../../../packages/ai/src/bridge.js", async (importOriginal) => ({
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...(await importOriginal<typeof import("../../../packages/ai/src/bridge.js")>()),
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runPythonWithProgress: mockRunPythonWithProgress,
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parseStdoutJson: mockParseStdoutJson,
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isGpuAvailable: mockIsGpuAvailable,
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}));
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vi.mock("../../../packages/ai/src/ocr-runtime-dispatcher.js", () => ({
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runOcrRuntime: mockRunOcrRuntime,
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}));
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vi.mock("../../../packages/ai/src/tesseract.js", async (importOriginal) => {
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const actual = await importOriginal<typeof import("../../../packages/ai/src/tesseract.js")>();
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return {
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...actual,
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runAdaptiveTesseract: mockRunTesseract,
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runTesseract: mockRunTesseract,
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};
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});
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vi.mock("sharp", () => ({ default: mockSharp }));
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vi.mock("node:fs/promises", () => ({
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writeFile: mockWriteFile,
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readFile: mockReadFile,
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unlink: mockUnlink,
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rm: mockRm,
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}));
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vi.mock("node:child_process", () => ({
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execFile: mockExecFile,
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spawn: vi.fn(),
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}));
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vi.mock("node:util", () => ({
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promisify: () => mockExecFile,
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}));
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// ---------------------------------------------------------------------------
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// Import tool modules (after mocks are in place)
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// ---------------------------------------------------------------------------
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import { removeBackground } from "../../../packages/ai/src/background-removal.js";
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import { colorize } from "../../../packages/ai/src/colorization.js";
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import { blurFaces, detectFaces } from "../../../packages/ai/src/face-detection.js";
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import { enhanceFaces } from "../../../packages/ai/src/face-enhancement.js";
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import { detectFaceLandmarks } from "../../../packages/ai/src/face-landmarks.js";
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import { inpaint } from "../../../packages/ai/src/inpainting.js";
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import { noiseRemoval } from "../../../packages/ai/src/noise-removal.js";
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import { extractText } from "../../../packages/ai/src/ocr.js";
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import { removeRedEye } from "../../../packages/ai/src/red-eye-removal.js";
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import { restorePhoto } from "../../../packages/ai/src/restoration.js";
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import { upscale } from "../../../packages/ai/src/upscaling.js";
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// ---------------------------------------------------------------------------
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// Helper
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// ---------------------------------------------------------------------------
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function createSharpChain(meta?: Record<string, unknown>) {
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return mockSharp._createChain(meta);
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}
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// ---------------------------------------------------------------------------
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// Shared setup
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// ---------------------------------------------------------------------------
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const INPUT_BUFFER = Buffer.from("test-input");
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const OUTPUT_DIR = "/tmp/test-output";
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beforeEach(() => {
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vi.clearAllMocks();
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mockSharp.mockReturnValue(createSharpChain());
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mockReadFile.mockResolvedValue(Buffer.from("output-buffer"));
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mockWriteFile.mockResolvedValue(undefined);
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mockRunPythonWithProgress.mockResolvedValue({ stdout: "", stderr: "" });
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mockRunTesseract.mockResolvedValue({
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text: "Sample OCR text",
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engine: "tesseract",
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device: "cpu",
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provider: "native",
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});
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mockRunOcrRuntime.mockResolvedValue({
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result: {
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success: true,
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text: "Accurate OCR text",
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engine: "rapidocr-onnx",
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requestedQuality: "best",
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actualQuality: "best",
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device: "cpu",
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provider: "CPUExecutionProvider",
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degraded: false,
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warnings: [],
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},
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stderr: "",
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runtime: {
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generation: "test",
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artifactVersion: "2.1.0",
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target: "linux-amd64-cpu-py312",
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providers: ["CPUExecutionProvider"],
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models: {},
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},
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});
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mockParseStdoutJson.mockReturnValue({ success: true });
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mockIsGpuAvailable.mockReturnValue(false);
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});
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// ═══════════════════════════════════════════════════════════════════════════
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// removeBackground
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// ═══════════════════════════════════════════════════════════════════════════
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describe("removeBackground", () => {
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it("calls runPythonWithProgress with remove_bg.py", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true });
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR);
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expect(mockRunPythonWithProgress).toHaveBeenCalledTimes(1);
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const [script] = mockRunPythonWithProgress.mock.calls[0];
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expect(script).toBe("remove_bg.py");
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});
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it("passes options as JSON in args", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true });
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR, { model: "birefnet" });
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const [, args] = mockRunPythonWithProgress.mock.calls[0];
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const optsArg = JSON.parse(args[2]);
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expect(optsArg.model).toBe("birefnet");
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});
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it("writes input as PNG before processing", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true });
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR);
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expect(mockWriteFile).toHaveBeenCalled();
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const writtenBuffer = mockWriteFile.mock.calls[0][1];
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expect(Buffer.isBuffer(writtenBuffer)).toBe(true);
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});
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it("returns the output file buffer", async () => {
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const expected = Buffer.from("mask-output");
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mockReadFile.mockResolvedValue(expected);
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mockParseStdoutJson.mockReturnValue({ success: true });
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const result = await removeBackground(INPUT_BUFFER, OUTPUT_DIR);
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expect(result).toBe(expected);
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});
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it("throws when Python reports failure", async () => {
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mockParseStdoutJson.mockReturnValue({
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success: false,
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error: "No model available",
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});
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await expect(removeBackground(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("No model available");
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});
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it("provides fallback error message when error field is empty", async () => {
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mockParseStdoutJson.mockReturnValue({ success: false });
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await expect(removeBackground(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow(
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"Background removal failed",
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);
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});
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it("passes onProgress callback through to bridge", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true });
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const onProgress = vi.fn();
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR, {}, onProgress);
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const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
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expect(opts.onProgress).toBe(onProgress);
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});
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it("cleans up temp files in finally block", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true });
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR);
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// unlink called for input and output paths
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expect(mockUnlink).toHaveBeenCalledTimes(2);
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});
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it("cleans up temp files even on failure", async () => {
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mockRunPythonWithProgress.mockRejectedValue(new Error("crash"));
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await expect(removeBackground(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("crash");
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expect(mockUnlink).toHaveBeenCalledTimes(2);
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});
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it("retries with u2net fallback on OOM error", async () => {
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// First call fails with OOM, second succeeds
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mockRunPythonWithProgress
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.mockRejectedValueOnce(new Error("Process killed (out of memory)"))
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.mockResolvedValueOnce({ stdout: "", stderr: "" });
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mockParseStdoutJson.mockReturnValue({ success: true });
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR, { model: "birefnet" });
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expect(mockRunPythonWithProgress).toHaveBeenCalledTimes(2);
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// Second call should use u2net
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const secondArgs = mockRunPythonWithProgress.mock.calls[1][1];
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const secondOpts = JSON.parse(secondArgs[2]);
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expect(secondOpts.model).toBe("u2net");
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});
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it("does not retry OOM if already using u2net", async () => {
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mockRunPythonWithProgress.mockRejectedValue(new Error("Process killed (out of memory)"));
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await expect(removeBackground(INPUT_BUFFER, OUTPUT_DIR, { model: "u2net" })).rejects.toThrow(
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"out of memory",
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);
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expect(mockRunPythonWithProgress).toHaveBeenCalledTimes(1);
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});
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it("calculates timeout based on megapixels", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true });
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR);
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const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
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expect(opts.timeout).toBeGreaterThan(0);
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});
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it("uses longer base timeout for birefnet model", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true });
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR, { model: "birefnet-large" });
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const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
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// birefnet gets 600000 base timeout
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expect(opts.timeout).toBeGreaterThanOrEqual(600000);
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});
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it("downscales large images and upscales mask back", async () => {
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// Simulate a 4000x3000 image (larger than MAX_REMBG_PX=2048)
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const largeChain = createSharpChain({ width: 4000, height: 3000 });
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mockSharp.mockReturnValue(largeChain);
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mockParseStdoutJson.mockReturnValue({ success: true });
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await removeBackground(INPUT_BUFFER, OUTPUT_DIR);
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// resize should have been called for downscaling
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expect(largeChain.resize).toHaveBeenCalled();
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});
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});
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// ═══════════════════════════════════════════════════════════════════════════
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// colorize
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// ═══════════════════════════════════════════════════════════════════════════
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describe("colorize", () => {
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it("calls runPythonWithProgress with colorize.py", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true, width: 800, height: 600 });
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await colorize(INPUT_BUFFER, OUTPUT_DIR);
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const [script] = mockRunPythonWithProgress.mock.calls[0];
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expect(script).toBe("colorize.py");
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});
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it("passes options as JSON in args", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true, width: 800, height: 600 });
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await colorize(INPUT_BUFFER, OUTPUT_DIR, { intensity: 0.8, model: "eccv16" });
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const [, args] = mockRunPythonWithProgress.mock.calls[0];
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const optsArg = JSON.parse(args[2]);
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expect(optsArg.intensity).toBe(0.8);
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expect(optsArg.model).toBe("eccv16");
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});
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it("returns structured result with buffer, dimensions, and method", async () => {
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mockParseStdoutJson.mockReturnValue({
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success: true,
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width: 800,
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height: 600,
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method: "eccv16",
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});
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const result = await colorize(INPUT_BUFFER, OUTPUT_DIR);
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expect(result.width).toBe(800);
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expect(result.height).toBe(600);
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expect(result.method).toBe("eccv16");
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expect(Buffer.isBuffer(result.buffer)).toBe(true);
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});
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it("defaults method to 'unknown' when not provided", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
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const result = await colorize(INPUT_BUFFER, OUTPUT_DIR);
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expect(result.method).toBe("unknown");
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});
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it("throws on failure", async () => {
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mockParseStdoutJson.mockReturnValue({ success: false, error: "Model missing" });
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await expect(colorize(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("Model missing");
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});
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it("uses output_path from result when available", async () => {
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mockParseStdoutJson.mockReturnValue({
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success: true,
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width: 100,
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height: 100,
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output_path: "/custom/path.png",
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});
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await colorize(INPUT_BUFFER, OUTPUT_DIR);
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expect(mockReadFile).toHaveBeenCalledWith("/custom/path.png");
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});
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it("forwards onProgress callback", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
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const onProgress = vi.fn();
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await colorize(INPUT_BUFFER, OUTPUT_DIR, {}, onProgress);
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const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
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expect(opts.onProgress).toBe(onProgress);
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});
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});
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// ═══════════════════════════════════════════════════════════════════════════
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// blurFaces
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// ═══════════════════════════════════════════════════════════════════════════
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describe("blurFaces", () => {
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it("calls runPythonWithProgress with detect_faces.py", async () => {
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mockParseStdoutJson.mockReturnValue({
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success: true,
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facesDetected: 2,
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faces: [
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{ x: 10, y: 20, w: 50, h: 50 },
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{ x: 100, y: 200, w: 60, h: 60 },
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],
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});
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await blurFaces(INPUT_BUFFER, OUTPUT_DIR);
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const [script] = mockRunPythonWithProgress.mock.calls[0];
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expect(script).toBe("detect_faces.py");
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});
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it("passes blur options in args", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 0, faces: [] });
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await blurFaces(INPUT_BUFFER, OUTPUT_DIR, { blurRadius: 30, sensitivity: 0.5 });
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const [, args] = mockRunPythonWithProgress.mock.calls[0];
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const optsArg = JSON.parse(args[2]);
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expect(optsArg.blurRadius).toBe(30);
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expect(optsArg.sensitivity).toBe(0.5);
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});
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it("returns buffer, facesDetected, and faces array", async () => {
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const faces = [{ x: 10, y: 20, w: 50, h: 50 }];
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mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 1, faces });
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const result = await blurFaces(INPUT_BUFFER, OUTPUT_DIR);
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expect(result.facesDetected).toBe(1);
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expect(result.faces).toEqual(faces);
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expect(Buffer.isBuffer(result.buffer)).toBe(true);
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});
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it("defaults faces to empty array when absent", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 0 });
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const result = await blurFaces(INPUT_BUFFER, OUTPUT_DIR);
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expect(result.faces).toEqual([]);
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});
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it("throws on failure", async () => {
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mockParseStdoutJson.mockReturnValue({ success: false, error: "No face detector" });
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await expect(blurFaces(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("No face detector");
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});
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});
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// ═══════════════════════════════════════════════════════════════════════════
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// detectFaces (detect-only mode)
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// ═══════════════════════════════════════════════════════════════════════════
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describe("detectFaces", () => {
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it("passes detectOnly: true in options", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 0, faces: [] });
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await detectFaces(INPUT_BUFFER);
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const [, args] = mockRunPythonWithProgress.mock.calls[0];
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const optsArg = JSON.parse(args[2]);
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expect(optsArg.detectOnly).toBe(true);
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});
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it("passes 'unused' as outputPath arg", async () => {
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mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 0, faces: [] });
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await detectFaces(INPUT_BUFFER);
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const [, args] = mockRunPythonWithProgress.mock.calls[0];
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expect(args[1]).toBe("unused");
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});
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it("returns facesDetected and faces without a buffer", async () => {
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const faces = [{ x: 5, y: 10, w: 30, h: 30 }];
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mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 1, faces });
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|
|
const result = await detectFaces(INPUT_BUFFER);
|
|
|
|
expect(result.facesDetected).toBe(1);
|
|
expect(result.faces).toEqual(faces);
|
|
expect((result as Record<string, unknown>).buffer).toBeUndefined();
|
|
});
|
|
|
|
it("cleans up temp input file", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 0, faces: [] });
|
|
|
|
await detectFaces(INPUT_BUFFER);
|
|
|
|
expect(mockUnlink).toHaveBeenCalledTimes(1);
|
|
});
|
|
|
|
it("cleans up temp file even on error", async () => {
|
|
mockRunPythonWithProgress.mockRejectedValue(new Error("fail"));
|
|
|
|
await expect(detectFaces(INPUT_BUFFER)).rejects.toThrow("fail");
|
|
expect(mockUnlink).toHaveBeenCalledTimes(1);
|
|
});
|
|
|
|
it("merges sensitivity option", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 0, faces: [] });
|
|
|
|
await detectFaces(INPUT_BUFFER, { sensitivity: 0.3 });
|
|
|
|
const [, args] = mockRunPythonWithProgress.mock.calls[0];
|
|
const optsArg = JSON.parse(args[2]);
|
|
expect(optsArg.sensitivity).toBe(0.3);
|
|
expect(optsArg.detectOnly).toBe(true);
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// enhanceFaces
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("enhanceFaces", () => {
|
|
it("calls enhance_faces.py", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
facesDetected: 1,
|
|
faces: [],
|
|
model: "gfpgan",
|
|
});
|
|
|
|
await enhanceFaces(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
const [script] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(script).toBe("enhance_faces.py");
|
|
});
|
|
|
|
it("passes all options through", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 0, faces: [] });
|
|
|
|
await enhanceFaces(INPUT_BUFFER, OUTPUT_DIR, {
|
|
model: "codeformer",
|
|
strength: 0.7,
|
|
onlyCenterFace: true,
|
|
sensitivity: 0.4,
|
|
});
|
|
|
|
const [, args] = mockRunPythonWithProgress.mock.calls[0];
|
|
const optsArg = JSON.parse(args[2]);
|
|
expect(optsArg.model).toBe("codeformer");
|
|
expect(optsArg.strength).toBe(0.7);
|
|
expect(optsArg.onlyCenterFace).toBe(true);
|
|
});
|
|
|
|
it("returns result with buffer, facesDetected, faces, and model", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
facesDetected: 2,
|
|
faces: [{ x: 1, y: 2, w: 3, h: 4 }],
|
|
model: "codeformer",
|
|
});
|
|
|
|
const result = await enhanceFaces(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.facesDetected).toBe(2);
|
|
expect(result.model).toBe("codeformer");
|
|
expect(result.faces).toHaveLength(1);
|
|
});
|
|
|
|
it("defaults model to 'unknown'", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, facesDetected: 0 });
|
|
|
|
const result = await enhanceFaces(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.model).toBe("unknown");
|
|
});
|
|
|
|
it("throws on failure with specific error", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false, error: "GFPGAN not installed" });
|
|
|
|
await expect(enhanceFaces(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("GFPGAN not installed");
|
|
});
|
|
|
|
it("provides fallback error message", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false });
|
|
|
|
await expect(enhanceFaces(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("Face enhancement failed");
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// detectFaceLandmarks
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("detectFaceLandmarks", () => {
|
|
it("calls face_landmarks.py", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
faceDetected: true,
|
|
landmarks: null,
|
|
});
|
|
|
|
await detectFaceLandmarks(INPUT_BUFFER);
|
|
|
|
const [script] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(script).toBe("face_landmarks.py");
|
|
});
|
|
|
|
it("passes 'unused' as output path and empty JSON options", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, faceDetected: false });
|
|
|
|
await detectFaceLandmarks(INPUT_BUFFER);
|
|
|
|
const [, args] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(args[1]).toBe("unused");
|
|
expect(args[2]).toBe("{}");
|
|
});
|
|
|
|
it("returns landmarks result", async () => {
|
|
const landmarks = {
|
|
leftEye: { x: 100, y: 100 },
|
|
rightEye: { x: 200, y: 100 },
|
|
eyeCenter: { x: 150, y: 100 },
|
|
chin: { x: 150, y: 250 },
|
|
forehead: { x: 150, y: 50 },
|
|
crown: { x: 150, y: 30 },
|
|
nose: { x: 150, y: 150 },
|
|
faceCenterX: 150,
|
|
};
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
faceDetected: true,
|
|
landmarks,
|
|
imageWidth: 800,
|
|
imageHeight: 600,
|
|
});
|
|
|
|
const result = await detectFaceLandmarks(INPUT_BUFFER);
|
|
|
|
expect(result.faceDetected).toBe(true);
|
|
expect(result.landmarks).toEqual(landmarks);
|
|
expect(result.imageWidth).toBe(800);
|
|
expect(result.imageHeight).toBe(600);
|
|
});
|
|
|
|
it("returns null landmarks when no face found", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
faceDetected: false,
|
|
});
|
|
|
|
const result = await detectFaceLandmarks(INPUT_BUFFER);
|
|
|
|
expect(result.faceDetected).toBe(false);
|
|
expect(result.landmarks).toBeNull();
|
|
});
|
|
|
|
it("defaults dimensions to 0 when absent", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, faceDetected: false });
|
|
|
|
const result = await detectFaceLandmarks(INPUT_BUFFER);
|
|
|
|
expect(result.imageWidth).toBe(0);
|
|
expect(result.imageHeight).toBe(0);
|
|
});
|
|
|
|
it("cleans up temp file", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, faceDetected: false });
|
|
|
|
await detectFaceLandmarks(INPUT_BUFFER);
|
|
|
|
expect(mockUnlink).toHaveBeenCalledTimes(1);
|
|
});
|
|
|
|
it("throws on failure", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false, error: "MediaPipe not found" });
|
|
|
|
await expect(detectFaceLandmarks(INPUT_BUFFER)).rejects.toThrow("MediaPipe not found");
|
|
});
|
|
|
|
it("converts input buffer to PNG before writing", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, faceDetected: false });
|
|
|
|
await detectFaceLandmarks(INPUT_BUFFER);
|
|
|
|
expect(mockSharp).toHaveBeenCalledWith(INPUT_BUFFER);
|
|
expect(mockWriteFile).toHaveBeenCalledWith(expect.any(String), Buffer.from("mock-png"));
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// inpaint
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("inpaint", () => {
|
|
const MASK_BUFFER = Buffer.from("mask-data");
|
|
|
|
it("calls inpaint.py with input, mask, and output paths", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true });
|
|
|
|
await inpaint(INPUT_BUFFER, MASK_BUFFER, OUTPUT_DIR);
|
|
|
|
const [script, args] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(script).toBe("inpaint.py");
|
|
expect(args).toHaveLength(3);
|
|
expect(args[0]).toContain("input_inpaint.png");
|
|
expect(args[1]).toContain("mask_inpaint.png");
|
|
expect(args[2]).toContain("output_inpaint.png");
|
|
});
|
|
|
|
it("converts both input and mask to PNG", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true });
|
|
|
|
await inpaint(INPUT_BUFFER, MASK_BUFFER, OUTPUT_DIR);
|
|
|
|
// sharp is called for both input and mask
|
|
expect(mockSharp).toHaveBeenCalledTimes(2);
|
|
});
|
|
|
|
it("writes both input and mask files", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true });
|
|
|
|
await inpaint(INPUT_BUFFER, MASK_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(mockWriteFile).toHaveBeenCalledTimes(2);
|
|
});
|
|
|
|
it("returns the output buffer", async () => {
|
|
const outputBuf = Buffer.from("inpainted");
|
|
mockReadFile.mockResolvedValue(outputBuf);
|
|
mockParseStdoutJson.mockReturnValue({ success: true });
|
|
|
|
const result = await inpaint(INPUT_BUFFER, MASK_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result).toBe(outputBuf);
|
|
});
|
|
|
|
it("throws on failure", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false, error: "LaMa model not found" });
|
|
|
|
await expect(inpaint(INPUT_BUFFER, MASK_BUFFER, OUTPUT_DIR)).rejects.toThrow(
|
|
"LaMa model not found",
|
|
);
|
|
});
|
|
|
|
it("provides fallback error message", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false });
|
|
|
|
await expect(inpaint(INPUT_BUFFER, MASK_BUFFER, OUTPUT_DIR)).rejects.toThrow(
|
|
"Inpainting failed",
|
|
);
|
|
});
|
|
|
|
it("forwards onProgress", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true });
|
|
|
|
const onProgress = vi.fn();
|
|
await inpaint(INPUT_BUFFER, MASK_BUFFER, OUTPUT_DIR, onProgress);
|
|
|
|
const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(opts.onProgress).toBe(onProgress);
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// noiseRemoval
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("noiseRemoval", () => {
|
|
it("calls noise_removal.py", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 800, height: 600 });
|
|
|
|
await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
const [script] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(script).toBe("noise_removal.py");
|
|
});
|
|
|
|
it("passes options as JSON", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 800, height: 600 });
|
|
|
|
await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR, {
|
|
tier: "quality",
|
|
strength: 0.8,
|
|
detailPreservation: 0.5,
|
|
});
|
|
|
|
const [, args] = mockRunPythonWithProgress.mock.calls[0];
|
|
const optsArg = JSON.parse(args[2]);
|
|
expect(optsArg.tier).toBe("quality");
|
|
expect(optsArg.strength).toBe(0.8);
|
|
expect(optsArg.detailPreservation).toBe(0.5);
|
|
});
|
|
|
|
it("returns structured result", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 1920,
|
|
height: 1080,
|
|
format: "png",
|
|
tier: "quality",
|
|
});
|
|
|
|
const result = await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.width).toBe(1920);
|
|
expect(result.height).toBe(1080);
|
|
expect(result.format).toBe("png");
|
|
expect(result.tier).toBe("quality");
|
|
});
|
|
|
|
it("defaults format and tier", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
|
|
|
|
const result = await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.format).toBe("png");
|
|
expect(result.tier).toBe("balanced");
|
|
});
|
|
|
|
it("prefers tier from result over options", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 100,
|
|
height: 100,
|
|
tier: "fast",
|
|
});
|
|
|
|
const result = await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR, { tier: "quality" });
|
|
|
|
expect(result.tier).toBe("fast");
|
|
});
|
|
|
|
it("calculates timeout based on megapixels", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
|
|
|
|
await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(opts.timeout).toBeGreaterThanOrEqual(300_000);
|
|
});
|
|
|
|
it("throws on failure", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false, error: "Denoiser unavailable" });
|
|
|
|
await expect(noiseRemoval(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("Denoiser unavailable");
|
|
});
|
|
|
|
it("uses output_path from result when available", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 100,
|
|
height: 100,
|
|
output_path: "/custom/denoise.png",
|
|
});
|
|
|
|
await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(mockReadFile).toHaveBeenCalledWith("/custom/denoise.png");
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// extractText (OCR)
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("extractText (OCR)", () => {
|
|
it("uses built-in Tesseract for the default Fast tier", async () => {
|
|
await extractText(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(mockRunTesseract).toHaveBeenCalledWith(
|
|
expect.stringContaining("input_ocr.png"),
|
|
expect.objectContaining({ timeoutMs: expect.any(Number) }),
|
|
);
|
|
expect(mockRunPythonWithProgress).not.toHaveBeenCalled();
|
|
});
|
|
|
|
it("passes accurate options to the isolated runtime", async () => {
|
|
await extractText(INPUT_BUFFER, OUTPUT_DIR, { quality: "best", language: "en" });
|
|
|
|
const [, args] = mockRunOcrRuntime.mock.calls[0];
|
|
const optsArg = JSON.parse(args[1]);
|
|
expect(optsArg.quality).toBe("best");
|
|
expect(optsArg.language).toBe("en");
|
|
});
|
|
|
|
it("returns text and truthful engine metadata", async () => {
|
|
const result = await extractText(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.text).toBe("Sample OCR text");
|
|
expect(result.engine).toBe("tesseract");
|
|
expect(result.actualQuality).toBe("fast");
|
|
});
|
|
|
|
it("preserves source resolution", async () => {
|
|
const chain = createSharpChain();
|
|
mockSharp.mockReturnValue(chain);
|
|
|
|
await extractText(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(chain.resize).not.toHaveBeenCalled();
|
|
});
|
|
|
|
it("throws on Fast failure", async () => {
|
|
mockRunTesseract.mockRejectedValueOnce(new Error("Tesseract failed"));
|
|
|
|
await expect(extractText(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("Tesseract failed");
|
|
});
|
|
|
|
it("rejects incomplete accurate metadata without falling back", async () => {
|
|
mockRunOcrRuntime.mockResolvedValueOnce({
|
|
result: { success: true, text: "incomplete" },
|
|
stderr: "",
|
|
runtime: {
|
|
generation: "test",
|
|
artifactVersion: "2.1.0",
|
|
target: "linux-amd64-cpu-py312",
|
|
providers: ["CPUExecutionProvider"],
|
|
models: {},
|
|
},
|
|
});
|
|
|
|
await expect(extractText(INPUT_BUFFER, OUTPUT_DIR, { quality: "best" })).rejects.toThrow(
|
|
"invalid metadata",
|
|
);
|
|
expect(mockRunTesseract).not.toHaveBeenCalled();
|
|
});
|
|
|
|
it("calculates timeout based on megapixels", async () => {
|
|
await extractText(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
const [, opts] = mockRunTesseract.mock.calls[0];
|
|
expect(opts.timeoutMs).toBeGreaterThanOrEqual(600_000);
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// removeRedEye
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("removeRedEye", () => {
|
|
it("calls red_eye_removal.py", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 800, height: 600 });
|
|
|
|
await removeRedEye(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
const [script] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(script).toBe("red_eye_removal.py");
|
|
});
|
|
|
|
it("passes options as JSON", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 800, height: 600 });
|
|
|
|
await removeRedEye(INPUT_BUFFER, OUTPUT_DIR, { sensitivity: 0.6, strength: 0.9 });
|
|
|
|
const [, args] = mockRunPythonWithProgress.mock.calls[0];
|
|
const optsArg = JSON.parse(args[2]);
|
|
expect(optsArg.sensitivity).toBe(0.6);
|
|
expect(optsArg.strength).toBe(0.9);
|
|
});
|
|
|
|
it("returns structured result", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
facesDetected: 2,
|
|
eyesCorrected: 3,
|
|
width: 1920,
|
|
height: 1080,
|
|
format: "png",
|
|
});
|
|
|
|
const result = await removeRedEye(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.facesDetected).toBe(2);
|
|
expect(result.eyesCorrected).toBe(3);
|
|
expect(result.width).toBe(1920);
|
|
expect(result.height).toBe(1080);
|
|
expect(result.format).toBe("png");
|
|
});
|
|
|
|
it("defaults optional fields", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
|
|
|
|
const result = await removeRedEye(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.facesDetected).toBe(0);
|
|
expect(result.eyesCorrected).toBe(0);
|
|
expect(result.format).toBe("png");
|
|
});
|
|
|
|
it("throws on failure", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false, error: "Eye detector failed" });
|
|
|
|
await expect(removeRedEye(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("Eye detector failed");
|
|
});
|
|
|
|
it("provides fallback error message", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false });
|
|
|
|
await expect(removeRedEye(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("Red eye removal failed");
|
|
});
|
|
|
|
it("uses output_path from result when available", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 100,
|
|
height: 100,
|
|
output_path: "/alt/redeye.png",
|
|
});
|
|
|
|
await removeRedEye(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(mockReadFile).toHaveBeenCalledWith("/alt/redeye.png");
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// restorePhoto
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("restorePhoto", () => {
|
|
it("calls restore.py", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 800, height: 600 });
|
|
|
|
await restorePhoto(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
const [script] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(script).toBe("restore.py");
|
|
});
|
|
|
|
it("passes options as JSON", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 800, height: 600 });
|
|
|
|
await restorePhoto(INPUT_BUFFER, OUTPUT_DIR, {
|
|
mode: "heavy",
|
|
scratchRemoval: true,
|
|
faceEnhancement: true,
|
|
fidelity: 0.5,
|
|
denoise: true,
|
|
denoiseStrength: 0.3,
|
|
colorize: true,
|
|
});
|
|
|
|
const [, args] = mockRunPythonWithProgress.mock.calls[0];
|
|
const optsArg = JSON.parse(args[2]);
|
|
expect(optsArg.mode).toBe("heavy");
|
|
expect(optsArg.scratchRemoval).toBe(true);
|
|
expect(optsArg.colorize).toBe(true);
|
|
});
|
|
|
|
it("returns full restoration result", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 2000,
|
|
height: 1500,
|
|
steps: ["denoise", "scratch_removal", "colorize"],
|
|
scratchCoverage: 15.5,
|
|
facesEnhanced: 2,
|
|
isGrayscale: true,
|
|
colorized: true,
|
|
});
|
|
|
|
const result = await restorePhoto(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.width).toBe(2000);
|
|
expect(result.height).toBe(1500);
|
|
expect(result.steps).toEqual(["denoise", "scratch_removal", "colorize"]);
|
|
expect(result.scratchCoverage).toBe(15.5);
|
|
expect(result.facesEnhanced).toBe(2);
|
|
expect(result.isGrayscale).toBe(true);
|
|
expect(result.colorized).toBe(true);
|
|
});
|
|
|
|
it("defaults optional result fields", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
|
|
|
|
const result = await restorePhoto(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.steps).toEqual([]);
|
|
expect(result.scratchCoverage).toBe(0);
|
|
expect(result.facesEnhanced).toBe(0);
|
|
expect(result.isGrayscale).toBe(false);
|
|
expect(result.colorized).toBe(false);
|
|
});
|
|
|
|
it("throws on failure", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false, error: "Restoration model missing" });
|
|
|
|
await expect(restorePhoto(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow(
|
|
"Restoration model missing",
|
|
);
|
|
});
|
|
|
|
it("provides fallback error message", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false });
|
|
|
|
await expect(restorePhoto(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow(
|
|
"Photo restoration failed",
|
|
);
|
|
});
|
|
|
|
it("uses output_path from result when available", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 100,
|
|
height: 100,
|
|
output_path: "/restored/out.png",
|
|
});
|
|
|
|
await restorePhoto(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(mockReadFile).toHaveBeenCalledWith("/restored/out.png");
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// upscale
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("upscale", () => {
|
|
it("calls upscale.py", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 1600,
|
|
height: 1200,
|
|
method: "realesrgan",
|
|
});
|
|
|
|
await upscale(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
const [script] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(script).toBe("upscale.py");
|
|
});
|
|
|
|
it("passes options as JSON", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 3200, height: 2400 });
|
|
|
|
await upscale(INPUT_BUFFER, OUTPUT_DIR, {
|
|
scale: 4,
|
|
model: "realesrgan-x4plus",
|
|
faceEnhance: true,
|
|
denoise: 0.5,
|
|
});
|
|
|
|
const [, args] = mockRunPythonWithProgress.mock.calls[0];
|
|
const optsArg = JSON.parse(args[2]);
|
|
expect(optsArg.scale).toBe(4);
|
|
expect(optsArg.model).toBe("realesrgan-x4plus");
|
|
expect(optsArg.faceEnhance).toBe(true);
|
|
expect(optsArg.denoise).toBe(0.5);
|
|
});
|
|
|
|
it("returns structured result", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 3200,
|
|
height: 2400,
|
|
method: "realesrgan",
|
|
format: "png",
|
|
});
|
|
|
|
const result = await upscale(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.width).toBe(3200);
|
|
expect(result.height).toBe(2400);
|
|
expect(result.method).toBe("realesrgan");
|
|
expect(result.format).toBe("png");
|
|
});
|
|
|
|
it("defaults method and format", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
|
|
|
|
const result = await upscale(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(result.method).toBe("unknown");
|
|
expect(result.format).toBe("png");
|
|
});
|
|
|
|
it("calculates timeout with GPU rate when GPU available", async () => {
|
|
mockIsGpuAvailable.mockReturnValue(true);
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
|
|
|
|
await upscale(INPUT_BUFFER, OUTPUT_DIR, { scale: 2 });
|
|
|
|
const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
|
|
// GPU rate is 30_000 per MP, CPU rate is 180_000
|
|
// With GPU, timeout should be lower than CPU
|
|
expect(opts.timeout).toBeGreaterThanOrEqual(600_000);
|
|
});
|
|
|
|
it("calculates higher timeout for CPU mode", async () => {
|
|
mockIsGpuAvailable.mockReturnValue(false);
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
|
|
|
|
await upscale(INPUT_BUFFER, OUTPUT_DIR, { scale: 4 });
|
|
|
|
const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
|
|
expect(opts.timeout).toBeGreaterThanOrEqual(600_000);
|
|
});
|
|
|
|
it("throws on failure", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false, error: "RealESRGAN OOM" });
|
|
|
|
await expect(upscale(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("RealESRGAN OOM");
|
|
});
|
|
|
|
it("provides fallback error message", async () => {
|
|
mockParseStdoutJson.mockReturnValue({ success: false });
|
|
|
|
await expect(upscale(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("Upscaling failed");
|
|
});
|
|
|
|
it("uses output_path from result when available", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 100,
|
|
height: 100,
|
|
output_path: "/custom/upscaled.webp",
|
|
});
|
|
|
|
await upscale(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
expect(mockReadFile).toHaveBeenCalledWith("/custom/upscaled.webp");
|
|
});
|
|
|
|
it("defaults scale to 2 for timeout calculation", async () => {
|
|
mockIsGpuAvailable.mockReturnValue(false);
|
|
mockParseStdoutJson.mockReturnValue({ success: true, width: 100, height: 100 });
|
|
|
|
await upscale(INPUT_BUFFER, OUTPUT_DIR); // no scale option
|
|
|
|
const [, , opts] = mockRunPythonWithProgress.mock.calls[0];
|
|
// scale defaults to 2, effectiveMp = mp * 4
|
|
expect(opts.timeout).toBeGreaterThan(0);
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// seamCarve (uses caire binary, not Python bridge)
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("seamCarve", () => {
|
|
beforeEach(() => {
|
|
// Mock execFile for findCaire -- the -help call and the actual carve call
|
|
mockExecFile.mockResolvedValue({ stdout: "", stderr: "" });
|
|
mockReadFile.mockResolvedValue(Buffer.from("carved-output"));
|
|
});
|
|
|
|
it("writes input as JPEG", async () => {
|
|
const chain = createSharpChain();
|
|
mockSharp.mockReturnValue(chain);
|
|
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 600 });
|
|
|
|
expect(chain.jpeg).toHaveBeenCalledWith({ quality: 95 });
|
|
});
|
|
|
|
it("passes -width and -height flags", async () => {
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 600, height: 400 });
|
|
|
|
// The actual carve call (second call -- first is -help for findCaire)
|
|
const carveCall = mockExecFile.mock.calls.find(
|
|
(c) => Array.isArray(c[1]) && c[1].includes("-width"),
|
|
);
|
|
expect(carveCall).toBeDefined();
|
|
const args = carveCall?.[1] as string[];
|
|
expect(args).toContain("-width");
|
|
expect(args).toContain("600");
|
|
expect(args).toContain("-height");
|
|
expect(args).toContain("400");
|
|
});
|
|
|
|
it("passes -face flag when protectFaces is true", async () => {
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 600, protectFaces: true });
|
|
|
|
const carveCall = mockExecFile.mock.calls.find(
|
|
(c) => Array.isArray(c[1]) && c[1].includes("-face"),
|
|
);
|
|
expect(carveCall).toBeDefined();
|
|
});
|
|
|
|
it("passes -square flag with shortest dimension", async () => {
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR, { square: true });
|
|
|
|
const carveCall = mockExecFile.mock.calls.find(
|
|
(c) => Array.isArray(c[1]) && c[1].includes("-square"),
|
|
);
|
|
expect(carveCall).toBeDefined();
|
|
const args = carveCall?.[1] as string[];
|
|
// For 800x600 image, shortest = 600
|
|
expect(args).toContain("-width");
|
|
expect(args).toContain("600");
|
|
});
|
|
|
|
it("passes blur and sobel options", async () => {
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR, {
|
|
width: 600,
|
|
blurRadius: 3,
|
|
sobelThreshold: 5,
|
|
});
|
|
|
|
const carveCall = mockExecFile.mock.calls.find(
|
|
(c) => Array.isArray(c[1]) && c[1].includes("-blur"),
|
|
);
|
|
expect(carveCall).toBeDefined();
|
|
const args = carveCall?.[1] as string[];
|
|
expect(args).toContain("-blur");
|
|
expect(args).toContain("3");
|
|
expect(args).toContain("-sobel");
|
|
expect(args).toContain("5");
|
|
});
|
|
|
|
it("returns buffer with dimensions", async () => {
|
|
const outChain = createSharpChain({ width: 600, height: 600 });
|
|
// First call for input, second for output metadata
|
|
let callIdx = 0;
|
|
mockSharp.mockImplementation(() => {
|
|
callIdx++;
|
|
if (callIdx >= 3) return outChain;
|
|
return createSharpChain();
|
|
});
|
|
|
|
const result = await seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 600 });
|
|
|
|
expect(Buffer.isBuffer(result.buffer)).toBe(true);
|
|
expect(typeof result.width).toBe("number");
|
|
expect(typeof result.height).toBe("number");
|
|
});
|
|
|
|
it("cleans up temp files in finally block", async () => {
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 600 });
|
|
|
|
expect(mockRm).toHaveBeenCalledTimes(2);
|
|
});
|
|
|
|
it("cleans up temp files even on error", async () => {
|
|
// findCaire caches the path after the first successful call, so only
|
|
// the actual carve invocation needs to be mocked here.
|
|
mockExecFile.mockRejectedValueOnce(new Error("caire crashed"));
|
|
|
|
await expect(seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 600 })).rejects.toThrow(
|
|
"caire crashed",
|
|
);
|
|
expect(mockRm).toHaveBeenCalledTimes(2);
|
|
});
|
|
|
|
it("rejects images larger than 25 MP", async () => {
|
|
// 6000 x 5000 = 30 MP
|
|
mockSharp.mockReturnValue(createSharpChain({ width: 6000, height: 5000 }));
|
|
|
|
await expect(seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 5000 })).rejects.toThrow(
|
|
"too large for content-aware resize",
|
|
);
|
|
});
|
|
|
|
it("pre-resizes when reduction exceeds 75% instead of rejecting", async () => {
|
|
// 800x600, requesting width: 100 => ratio 0.125 < 0.25
|
|
// Should succeed by pre-resizing to bring within 75% limit
|
|
await expect(seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 100 })).resolves.toBeDefined();
|
|
});
|
|
|
|
it("uses original dimensions when width/height not specified", async () => {
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
// Should not throw -- target equals original (800x600)
|
|
const carveCall = mockExecFile.mock.calls.find(
|
|
(c) => Array.isArray(c[1]) && c[1].includes("-in"),
|
|
);
|
|
expect(carveCall).toBeDefined();
|
|
});
|
|
|
|
it("calculates timeout based on megapixels", async () => {
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 600 });
|
|
|
|
const carveCall = mockExecFile.mock.calls.find(
|
|
(c) => Array.isArray(c[1]) && c[1].includes("-in"),
|
|
);
|
|
expect(carveCall).toBeDefined();
|
|
const opts = carveCall?.[2] as { timeout: number };
|
|
expect(opts.timeout).toBeGreaterThanOrEqual(120_000);
|
|
});
|
|
|
|
it("passes -preview=false", async () => {
|
|
await seamCarve(INPUT_BUFFER, OUTPUT_DIR, { width: 600 });
|
|
|
|
const carveCall = mockExecFile.mock.calls.find(
|
|
(c) => Array.isArray(c[1]) && c[1].includes("-preview=false"),
|
|
);
|
|
expect(carveCall).toBeDefined();
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// Cross-cutting: all Python-based tools share common patterns
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
describe("cross-cutting tool patterns", () => {
|
|
it("all Python tools call parseStdoutJson on the result", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 100,
|
|
height: 100,
|
|
text: "",
|
|
facesDetected: 0,
|
|
faces: [],
|
|
faceDetected: false,
|
|
});
|
|
|
|
// Run each tool
|
|
await colorize(INPUT_BUFFER, OUTPUT_DIR);
|
|
await blurFaces(INPUT_BUFFER, OUTPUT_DIR);
|
|
await enhanceFaces(INPUT_BUFFER, OUTPUT_DIR);
|
|
await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR);
|
|
await removeRedEye(INPUT_BUFFER, OUTPUT_DIR);
|
|
await restorePhoto(INPUT_BUFFER, OUTPUT_DIR);
|
|
|
|
// Each tool calls parseStdoutJson exactly once
|
|
expect(mockParseStdoutJson).toHaveBeenCalledTimes(6);
|
|
});
|
|
|
|
it("all Python tools propagate runPythonWithProgress errors", async () => {
|
|
mockRunPythonWithProgress.mockRejectedValue(new Error("Python script timed out"));
|
|
|
|
await expect(colorize(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("timed out");
|
|
await expect(blurFaces(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("timed out");
|
|
await expect(enhanceFaces(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("timed out");
|
|
await expect(noiseRemoval(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("timed out");
|
|
await expect(removeRedEye(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("timed out");
|
|
await expect(restorePhoto(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("timed out");
|
|
await expect(upscale(INPUT_BUFFER, OUTPUT_DIR)).rejects.toThrow("timed out");
|
|
|
|
mockRunOcrRuntime.mockRejectedValueOnce(new Error("OCR runtime timed out"));
|
|
await expect(extractText(INPUT_BUFFER, OUTPUT_DIR, { quality: "balanced" })).rejects.toThrow(
|
|
"timed out",
|
|
);
|
|
});
|
|
|
|
it("all Python tools convert input to PNG via sharp", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 100,
|
|
height: 100,
|
|
text: "",
|
|
facesDetected: 0,
|
|
faces: [],
|
|
});
|
|
|
|
const chain = createSharpChain();
|
|
mockSharp.mockReturnValue(chain);
|
|
|
|
await colorize(INPUT_BUFFER, OUTPUT_DIR);
|
|
expect(chain.png).toHaveBeenCalled();
|
|
|
|
chain.png.mockClear();
|
|
await blurFaces(INPUT_BUFFER, OUTPUT_DIR);
|
|
expect(chain.png).toHaveBeenCalled();
|
|
});
|
|
|
|
it("all Python tools accept default empty options", async () => {
|
|
mockParseStdoutJson.mockReturnValue({
|
|
success: true,
|
|
width: 100,
|
|
height: 100,
|
|
text: "",
|
|
facesDetected: 0,
|
|
faces: [],
|
|
faceDetected: false,
|
|
});
|
|
|
|
// These should not throw due to missing options
|
|
await colorize(INPUT_BUFFER, OUTPUT_DIR);
|
|
await blurFaces(INPUT_BUFFER, OUTPUT_DIR);
|
|
await enhanceFaces(INPUT_BUFFER, OUTPUT_DIR);
|
|
await noiseRemoval(INPUT_BUFFER, OUTPUT_DIR);
|
|
await removeRedEye(INPUT_BUFFER, OUTPUT_DIR);
|
|
await restorePhoto(INPUT_BUFFER, OUTPUT_DIR);
|
|
await upscale(INPUT_BUFFER, OUTPUT_DIR);
|
|
await extractText(INPUT_BUFFER, OUTPUT_DIR);
|
|
await detectFaceLandmarks(INPUT_BUFFER);
|
|
});
|
|
});
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// seamCarve (imported separately since it uses caire, not Python bridge)
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
import { seamCarve } from "../../../packages/ai/src/seam-carving.js";
|