mirror of
https://github.com/snapotter-hq/SnapOtter.git
synced 2026-08-03 07:46:42 +02:00
Group 245 flat integration tests and 25 loose unit tests into
discoverable subdirectories per spec section 6:
integration/tools/{image,video,audio,document,data}/ (156 files)
integration/platform/ (64 files)
integration/generated/ (14 files)
integration/security/ (10 files)
unit/security/ (8 files, new subdir)
unit/api/ (7 files moved in)
unit/web/ (3 files moved in)
unit/shared/ (6 files moved in)
unit/image-engine/ (1 file moved in)
All moves via git mv (history preserved). Relative imports repaired
for both depth levels (platform/generated/security = +1, tools/ = +2):
static from-imports, dynamic import() calls, vi.mock() paths,
import.meta.dirname joins, and __dirname joins.
Vitest discovery unchanged (no test.include in config, recursive glob
matches subdirs, shard-by-hash unaffected). test-server.ts and
tool-route-drift.test.ts stay at integration root. fixtures/ untouched.
Parity gate: 13189 passing test names before = 13189 after (0 dropped).
425 lines
15 KiB
TypeScript
425 lines
15 KiB
TypeScript
import { analyzeImage, applyCorrections, scaleCorrections } from "@snapotter/image-engine";
|
|
import sharp from "sharp";
|
|
import { describe, expect, it } from "vitest";
|
|
import { fixtures, readFixture } from "../../fixtures/index.js";
|
|
|
|
const PNG_200x150 = readFixture(fixtures.image.base.png200);
|
|
|
|
describe("analyzeImage", () => {
|
|
it("returns scores, corrections, issues, and suggestedMode", async () => {
|
|
const result = await analyzeImage(PNG_200x150);
|
|
expect(result.scores).toBeDefined();
|
|
expect(result.corrections).toBeDefined();
|
|
expect(result.issues).toBeInstanceOf(Array);
|
|
expect(result.suggestedMode).toBeDefined();
|
|
|
|
for (const key of Object.keys(result.scores) as (keyof typeof result.scores)[]) {
|
|
expect(result.scores[key]).toBeGreaterThanOrEqual(0);
|
|
expect(result.scores[key]).toBeLessThanOrEqual(100);
|
|
}
|
|
});
|
|
|
|
it("detects underexposure on a dark image", async () => {
|
|
const darkBuffer = await sharp({
|
|
create: { width: 100, height: 100, channels: 3, background: { r: 20, g: 20, b: 20 } },
|
|
})
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(darkBuffer);
|
|
expect(result.scores.exposure).toBeLessThan(30);
|
|
expect(result.issues).toContain("underexposed");
|
|
expect(result.corrections.brightness).toBeGreaterThan(0);
|
|
});
|
|
|
|
it("detects overexposure on a bright image", async () => {
|
|
const brightBuffer = await sharp({
|
|
create: { width: 100, height: 100, channels: 3, background: { r: 240, g: 240, b: 240 } },
|
|
})
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(brightBuffer);
|
|
expect(result.scores.exposure).toBeGreaterThan(70);
|
|
expect(result.issues).toContain("overexposed");
|
|
expect(result.corrections.brightness).toBeLessThan(0);
|
|
});
|
|
|
|
it("detects low contrast on a flat image", async () => {
|
|
const flatBuffer = await sharp({
|
|
create: { width: 100, height: 100, channels: 3, background: { r: 128, g: 128, b: 128 } },
|
|
})
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(flatBuffer);
|
|
expect(result.scores.contrast).toBeLessThan(40);
|
|
expect(result.corrections.contrast).toBeGreaterThan(0);
|
|
});
|
|
|
|
it("handles grayscale images without white balance issues", async () => {
|
|
const grayBuffer = await sharp({
|
|
create: { width: 100, height: 100, channels: 3, background: { r: 128, g: 128, b: 128 } },
|
|
})
|
|
.grayscale()
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(grayBuffer);
|
|
expect(result.scores.whiteBalance).toBe(50);
|
|
// Grayscale PNG from .grayscale() retains 3 channels with zero spread,
|
|
// so saturation formula yields channelSpread * 1.2 + 20 = 20
|
|
expect(result.scores.saturation).toBe(20);
|
|
});
|
|
|
|
it("suggests low-light mode for very dark images", async () => {
|
|
const darkBuffer = await sharp({
|
|
create: { width: 100, height: 100, channels: 3, background: { r: 15, g: 15, b: 15 } },
|
|
})
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(darkBuffer);
|
|
expect(result.suggestedMode).toBe("low-light");
|
|
});
|
|
|
|
it("produces contrast score near 50 for a typical well-exposed image", async () => {
|
|
// A gradient image has stdev ~60, which should score ~50
|
|
const gradientBuffer = await sharp(
|
|
Buffer.from(
|
|
Array.from({ length: 100 * 100 * 3 }, (_, i) => Math.floor(((i % 300) * 255) / 300)),
|
|
),
|
|
{ raw: { width: 100, height: 100, channels: 3 } },
|
|
)
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(gradientBuffer);
|
|
expect(result.scores.contrast).toBeGreaterThanOrEqual(35);
|
|
expect(result.scores.contrast).toBeLessThanOrEqual(65);
|
|
});
|
|
|
|
it("produces near-zero corrections for well-exposed images (dead zone)", async () => {
|
|
// Wide spread centered at 128 gives stdev ~60 (contrast ~50) and mean ~128 (exposure ~50).
|
|
// Both scores land inside the [40,60] dead zone, so corrections should be zero.
|
|
const midGrayBuffer = await sharp(
|
|
Buffer.from(Array.from({ length: 100 * 100 * 3 }, (_, i) => 24 + ((i * 97) % 208))),
|
|
{ raw: { width: 100, height: 100, channels: 3 } },
|
|
)
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(midGrayBuffer);
|
|
// Corrections should be zero or near-zero in the dead zone
|
|
expect(Math.abs(result.corrections.brightness)).toBeLessThanOrEqual(5);
|
|
expect(Math.abs(result.corrections.contrast)).toBeLessThanOrEqual(5);
|
|
});
|
|
});
|
|
|
|
describe("scaleCorrections", () => {
|
|
it("scales corrections by intensity 50 (1x) without change", () => {
|
|
const base = {
|
|
brightness: 20,
|
|
contrast: 10,
|
|
temperature: 5,
|
|
saturation: 15,
|
|
sharpness: 30,
|
|
denoise: 3,
|
|
};
|
|
const scaled = scaleCorrections(base, "auto", 50);
|
|
expect(scaled.brightness).toBe(20);
|
|
expect(scaled.contrast).toBe(10);
|
|
});
|
|
|
|
it("scales corrections to zero at intensity 0", () => {
|
|
const base = {
|
|
brightness: 20,
|
|
contrast: 10,
|
|
temperature: 5,
|
|
saturation: 15,
|
|
sharpness: 30,
|
|
denoise: 3,
|
|
};
|
|
const scaled = scaleCorrections(base, "auto", 0);
|
|
expect(scaled.brightness).toBe(0);
|
|
expect(scaled.contrast).toBe(0);
|
|
expect(scaled.sharpness).toBe(0);
|
|
});
|
|
|
|
it("applies preset multipliers for portrait mode", () => {
|
|
const base = {
|
|
brightness: 20,
|
|
contrast: 10,
|
|
temperature: 5,
|
|
saturation: 15,
|
|
sharpness: 30,
|
|
denoise: 3,
|
|
};
|
|
const scaled = scaleCorrections(base, "portrait", 50);
|
|
expect(scaled.brightness).toBe(16);
|
|
expect(scaled.contrast).toBe(7);
|
|
});
|
|
});
|
|
|
|
describe("applyCorrections", () => {
|
|
it("produces a valid output buffer", async () => {
|
|
const corrections = {
|
|
brightness: -20,
|
|
contrast: 10,
|
|
temperature: 0,
|
|
saturation: 10,
|
|
sharpness: 20,
|
|
denoise: 0,
|
|
};
|
|
const image = sharp(PNG_200x150);
|
|
const enhanced = applyCorrections(image, corrections, "auto", 50, {});
|
|
const buffer = await enhanced.toBuffer();
|
|
expect(buffer.length).toBeGreaterThan(0);
|
|
});
|
|
|
|
it("respects toggle overrides", async () => {
|
|
const corrections = {
|
|
brightness: 40,
|
|
contrast: 30,
|
|
temperature: 20,
|
|
saturation: 20,
|
|
sharpness: 30,
|
|
denoise: 3,
|
|
};
|
|
const toggles = {
|
|
exposure: false,
|
|
contrast: false,
|
|
whiteBalance: false,
|
|
saturation: false,
|
|
sharpness: false,
|
|
denoise: false,
|
|
};
|
|
const image = sharp(PNG_200x150);
|
|
const enhanced = applyCorrections(image, corrections, "auto", 50, toggles);
|
|
const enhancedBuf = await enhanced.toBuffer();
|
|
const originalMeta = await sharp(PNG_200x150).metadata();
|
|
const enhancedMeta = await sharp(enhancedBuf).metadata();
|
|
expect(enhancedMeta.width).toBe(originalMeta.width);
|
|
expect(enhancedMeta.height).toBe(originalMeta.height);
|
|
});
|
|
});
|
|
|
|
describe("applyCorrections pipeline (CLAHE + normalise + gamma)", () => {
|
|
it("does not darken a well-exposed image", async () => {
|
|
const analysis = await analyzeImage(PNG_200x150);
|
|
const image = sharp(PNG_200x150);
|
|
const enhanced = applyCorrections(image, analysis.corrections, "auto", 50, {});
|
|
const enhancedBuf = await enhanced.toBuffer();
|
|
|
|
const origStats = await sharp(PNG_200x150).stats();
|
|
const enhStats = await sharp(enhancedBuf).stats();
|
|
|
|
const origLum =
|
|
origStats.channels[0].mean * 0.299 +
|
|
origStats.channels[1].mean * 0.587 +
|
|
origStats.channels[2].mean * 0.114;
|
|
const enhLum =
|
|
enhStats.channels[0].mean * 0.299 +
|
|
enhStats.channels[1].mean * 0.587 +
|
|
enhStats.channels[2].mean * 0.114;
|
|
|
|
// Enhanced image should not be more than 5% darker
|
|
expect(enhLum).toBeGreaterThan(origLum * 0.95);
|
|
});
|
|
|
|
it("brightens a dark image", async () => {
|
|
const darkBuffer = await sharp({
|
|
create: { width: 100, height: 100, channels: 3, background: { r: 30, g: 30, b: 30 } },
|
|
})
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const analysis = await analyzeImage(darkBuffer);
|
|
const enhanced = applyCorrections(sharp(darkBuffer), analysis.corrections, "auto", 50, {});
|
|
const enhancedBuf = await enhanced.toBuffer();
|
|
|
|
const origStats = await sharp(darkBuffer).stats();
|
|
const enhStats = await sharp(enhancedBuf).stats();
|
|
|
|
const origLum =
|
|
origStats.channels[0].mean * 0.299 +
|
|
origStats.channels[1].mean * 0.587 +
|
|
origStats.channels[2].mean * 0.114;
|
|
const enhLum =
|
|
enhStats.channels[0].mean * 0.299 +
|
|
enhStats.channels[1].mean * 0.587 +
|
|
enhStats.channels[2].mean * 0.114;
|
|
|
|
expect(enhLum).toBeGreaterThan(origLum * 1.1);
|
|
});
|
|
|
|
it("applies CLAHE at intensity 0 with no visible effect", async () => {
|
|
const image = sharp(PNG_200x150);
|
|
const corrections = {
|
|
brightness: 0,
|
|
contrast: 0,
|
|
temperature: 0,
|
|
saturation: 0,
|
|
sharpness: 0,
|
|
denoise: 0,
|
|
};
|
|
const enhanced = applyCorrections(image, corrections, "auto", 0, {});
|
|
const enhancedBuf = await enhanced.toBuffer();
|
|
|
|
const origStats = await sharp(PNG_200x150).stats();
|
|
const enhStats = await sharp(enhancedBuf).stats();
|
|
const origLum =
|
|
origStats.channels[0].mean * 0.299 +
|
|
origStats.channels[1].mean * 0.587 +
|
|
origStats.channels[2].mean * 0.114;
|
|
const enhLum =
|
|
enhStats.channels[0].mean * 0.299 +
|
|
enhStats.channels[1].mean * 0.587 +
|
|
enhStats.channels[2].mean * 0.114;
|
|
expect(Math.abs(enhLum - origLum)).toBeLessThan(origLum * 0.15);
|
|
});
|
|
|
|
it("does not darken a bright-but-normal image (exposure score ~55-65)", async () => {
|
|
const brightishBuffer = await sharp({
|
|
create: { width: 100, height: 100, channels: 3, background: { r: 160, g: 160, b: 160 } },
|
|
})
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const analysis = await analyzeImage(brightishBuffer);
|
|
expect(analysis.scores.exposure).toBeGreaterThan(55);
|
|
expect(analysis.scores.exposure).toBeLessThan(70);
|
|
|
|
const enhanced = applyCorrections(sharp(brightishBuffer), analysis.corrections, "auto", 50, {});
|
|
const enhancedBuf = await enhanced.toBuffer();
|
|
const enhStats = await sharp(enhancedBuf).stats();
|
|
const origStats = await sharp(brightishBuffer).stats();
|
|
|
|
const origLum =
|
|
origStats.channels[0].mean * 0.299 +
|
|
origStats.channels[1].mean * 0.587 +
|
|
origStats.channels[2].mean * 0.114;
|
|
const enhLum =
|
|
enhStats.channels[0].mean * 0.299 +
|
|
enhStats.channels[1].mean * 0.587 +
|
|
enhStats.channels[2].mean * 0.114;
|
|
|
|
// Must NOT darken by more than 10%
|
|
expect(enhLum).toBeGreaterThan(origLum * 0.9);
|
|
});
|
|
|
|
it("each enhancement mode produces output without crashing", async () => {
|
|
const modes = ["auto", "portrait", "landscape", "low-light", "food", "document"] as const;
|
|
for (const mode of modes) {
|
|
const analysis = await analyzeImage(PNG_200x150);
|
|
const enhanced = applyCorrections(sharp(PNG_200x150), analysis.corrections, mode, 50, {});
|
|
const buf = await enhanced.toBuffer();
|
|
expect(buf.length).toBeGreaterThan(0);
|
|
}
|
|
});
|
|
|
|
it("intensity 100 produces a visibly different output from intensity 0", async () => {
|
|
const analysis = await analyzeImage(PNG_200x150);
|
|
|
|
const low = applyCorrections(sharp(PNG_200x150), analysis.corrections, "auto", 0, {});
|
|
const high = applyCorrections(sharp(PNG_200x150), analysis.corrections, "auto", 100, {});
|
|
|
|
const lowBuf = await low.toBuffer();
|
|
const highBuf = await high.toBuffer();
|
|
|
|
expect(Buffer.compare(lowBuf, highBuf)).not.toBe(0);
|
|
});
|
|
});
|
|
|
|
describe("auto-enhance edge cases", () => {
|
|
it("produces negative saturation correction for over-saturated image (saturation > 60)", async () => {
|
|
// Create a highly saturated image (pure bright colors with extreme channel spread)
|
|
const saturatedBuf = await sharp({
|
|
create: { width: 100, height: 100, channels: 3, background: { r: 255, g: 0, b: 0 } },
|
|
})
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(saturatedBuf);
|
|
// A pure red image has extreme channel spread, so saturation score should be > 60
|
|
expect(result.scores.saturation).toBeGreaterThan(60);
|
|
// The correction should be negative (desaturate)
|
|
expect(result.corrections.saturation).toBeLessThan(0);
|
|
});
|
|
|
|
it("applies denoise with kernel 5 when denoise adjustment is >= 4", async () => {
|
|
// To get adj >= 4, we need corrections.denoise * presets.denoise * scale >= 4
|
|
// With low-light preset (denoise: 2.0), intensity 100 (scale = 2.0):
|
|
// adj = denoise * 2.0 * 2.0 = denoise * 4.0
|
|
// For denoise = 5: adj = 20 >= 4, so kernel = 5
|
|
const corrections = {
|
|
brightness: 0,
|
|
contrast: 0,
|
|
temperature: 0,
|
|
saturation: 0,
|
|
sharpness: 0,
|
|
denoise: 5,
|
|
};
|
|
const image = sharp(PNG_200x150);
|
|
const enhanced = applyCorrections(image, corrections, "low-light", 100, {});
|
|
const buf = await enhanced.toBuffer();
|
|
expect(buf.length).toBeGreaterThan(0);
|
|
});
|
|
|
|
it("applies denoise with kernel 3 when denoise adjustment is >= 2 but < 4", async () => {
|
|
// With auto preset (denoise: 1.0), intensity 50 (scale = 1.0):
|
|
// adj = denoise * 1.0 * 1.0 = denoise
|
|
// For denoise = 3: adj = 3 (>= 2 but < 4), so kernel = 3
|
|
const corrections = {
|
|
brightness: 0,
|
|
contrast: 0,
|
|
temperature: 0,
|
|
saturation: 0,
|
|
sharpness: 0,
|
|
denoise: 3,
|
|
};
|
|
const image = sharp(PNG_200x150);
|
|
const enhanced = applyCorrections(image, corrections, "auto", 50, {});
|
|
const buf = await enhanced.toBuffer();
|
|
expect(buf.length).toBeGreaterThan(0);
|
|
});
|
|
|
|
it("suggests document mode for high-contrast low-saturation image", async () => {
|
|
// Create a high-contrast black & white image
|
|
const bwBuf = await sharp(
|
|
Buffer.from(
|
|
Array.from({ length: 100 * 100 * 3 }, (_, i) => {
|
|
const row = Math.floor(i / 300);
|
|
return row % 2 === 0 ? 255 : 0;
|
|
}),
|
|
),
|
|
{ raw: { width: 100, height: 100, channels: 3 } },
|
|
)
|
|
.png()
|
|
.toBuffer();
|
|
|
|
const result = await analyzeImage(bwBuf);
|
|
// High contrast + low saturation should suggest document mode
|
|
if (result.scores.contrast > 60 && result.scores.saturation < 30) {
|
|
expect(result.suggestedMode).toBe("document");
|
|
}
|
|
});
|
|
|
|
it("scaleCorrections with landscape mode applies correct multipliers", () => {
|
|
const base = {
|
|
brightness: 10,
|
|
contrast: 10,
|
|
temperature: 10,
|
|
saturation: 10,
|
|
sharpness: 10,
|
|
denoise: 10,
|
|
};
|
|
const scaled = scaleCorrections(base, "landscape", 50);
|
|
// Landscape preset: brightness=1.0, contrast=1.3, saturation=1.4, sharpness=1.5
|
|
expect(scaled.brightness).toBe(10); // 10 * 1.0 * 1.0 = 10
|
|
expect(scaled.contrast).toBe(13); // 10 * 1.3 * 1.0 = 13
|
|
expect(scaled.saturation).toBe(14); // 10 * 1.4 * 1.0 = 14
|
|
expect(scaled.sharpness).toBe(15); // 10 * 1.5 * 1.0 = 15
|
|
});
|
|
});
|