fix: generators unit tests

Author: piercus

.travis.yml

@@ -4,6 +4,8 @@ node_js:
 install:
   - npm install -g codecov nyc doxdox
   - npm install
+  - mkdir tmp
+
 cache:
   directories:
    - node_modules/opencv-build

lib/augmenters/additive-gaussian-noise.js

@@ -4,7 +4,7 @@ const AdditiveAugmenter = require('./additive');
 
 /**
 * Adds noise sampled from a gaussian distribution
-* 
+*
 * Warning : This function is slow, use `ia.additiveNoise` with tensorflowjs for fast noise generation
 *
 * @param {Object | Number | Hasard} sigma options, if number, this is sigma

lib/augmenters/draw-boxes.js

@@ -3,7 +3,7 @@ const AbstractAugmenter = require('./abstract');
 
 /**
 * Draw boxes in the image
-* 
+*
 * @param {Object} opts options, if array, or number, considered as the color
 * @param {ArrayColor} [opts.color] the RGB color to draw
 * @example

lib/generators/truncated-normal-noise.js

@@ -4,7 +4,7 @@ const GaussianNoiseGenerator = require('./gaussian-noise');
 const getMatrixesPerChannelFixed = ({perChannel, nImages, width, height, channels, isHasardScale, means, stds, scales, backend}) => {
 	return backend.backendLib.tidy(() => {
 		if (nImages === 0) {
-			return backend.zeros([nImages, height, width, channels]);
+			return backend.backendLib.zeros([nImages, height, width, channels]);
 		}
 
 		const shape = isHasardScale ?
@@ -145,8 +145,10 @@ class TruncatedNormalNoiseGenerator extends GaussianNoiseGenerator {
 					width: o.width,
 					height: o.height,
 					channels: o.channels
-				}).unstack();
+				});
+
 				const notPerChannelsMatrixesUnstacked = notPerChannelsMatrixes.unstack();
+
 				this.backend.dispose(notPerChannelsMatrixes);
 
 				const tensorsReordered = imageProps.map(im => {

package.json

@@ -70,7 +70,7 @@
   },
   "dependencies": {
     "debug": "^4.1.1",
-    "hasard": "^1.3.1",
+    "hasard": "^1.4.0",
     "jimp": "^0.6.0"
   }
 }

test/benchmark/benchmark.js

@@ -23,7 +23,7 @@ const formatBenchmark = require('./format-benchmark');
 
 const gridSide = 5;
 
-const batchSize = gridSide*gridSide;
+const batchSize = gridSide * gridSide;
 
 const filenames = new Array(batchSize).fill(path.join(__dirname, '../data', 'opencv4nodejs', 'lenna.png'));
 

test/generators/gaussian-noise.js

@@ -3,18 +3,21 @@ const test = require('ava');
 const GaussianNoise = require('../../lib/generators/gaussian-noise');
 const macroGenerator = require('../macros/generator');
 
-const width = 59;
-const height = 47;
+const wdth = 59;
+const hght = 47;
 const channels = 3;
 const mean = 128;
 const sigma = 50;
 
 test('gaussian-noise', macroGenerator, GaussianNoise, {
-	debugOutput: path.join(__dirname, '../..', 'tmp/gaussian-noise.png'),
-	expectImg: (t, img) => {
-		t.is(img.cols, width);
-		t.is(img.rows, height);
-		const sum = img.getDataAsArray()
+	debugOutput: [path.join(__dirname, '../..', 'tmp/gaussian-noise.png')],
+	expectImg: (t, {images}, backend) => {
+		const metadatas = backend.getMetadata(images);
+		const [{width, height}] = metadatas;
+		t.is(width, wdth);
+		t.is(height, hght);
+		const sum = backend.imageToArray(images)
+			.reduce((a, b) => a.concat(b))
 			.reduce((a, b) => a.concat(b))
 			.reduce((a, b) => a.concat(b))
 			.reduce((a, b) => a + b);
@@ -23,8 +26,8 @@ test('gaussian-noise', macroGenerator, GaussianNoise, {
 			Math.abs((sum / (width * height * channels)) - mean) < 100 / Math.sqrt(width * height * channels)
 		);
 	},
-	width,
-	height,
+	width: wdth,
+	height: hght,
 	channels,
 	options: {
 		mean,

test/generators/truncated-normal-noise.js

@@ -17,10 +17,11 @@ test('truncated-normal noise', macroGenerator, TruncatedNormalNoise, {
 	// 	path.join(__dirname, '../..', 'tmp/truncated-normal-noise3.png')
 	// ],
 	expectImg: (t, {images}, backend) => {
-		const {width, height, nImages} = backend.getMetadata(images);
+		const metadatas = backend.getMetadata(images);
+		const [{width, height}] = metadatas;
 		t.is(width, wdth);
 		t.is(height, hght);
-		t.is(nImages, imageNumber);
+		t.is(metadatas.length, imageNumber);
 		const flatten = a => {
 			return a.reduce((flat, i) => {
 				if (Array.isArray(i)) {
@@ -34,9 +35,9 @@ test('truncated-normal noise', macroGenerator, TruncatedNormalNoise, {
 		const flat = flatten(backend.imageToArray(images));
 
 		const sum = flat.reduce((a, b) => a + b, 0);
-		const l2 = (width * height * channels * nImages);
+		const l2 = (width * height * channels * imageNumber);
 		const average = (sum / l2);
-		const tolerance = 1000 / Math.sqrt(width * height * channels * nImages);
+		const tolerance = 1000 / Math.sqrt(width * height * channels * imageNumber);
 		t.true(
 			Math.abs(average - mean) < tolerance
 		);
@@ -66,10 +67,11 @@ test('truncated-normal noise with hasard', macroGenerator, TruncatedNormalNoise,
 	// 	path.join(__dirname, '../..', 'tmp/truncated-normal-noise9.png')
 	// ],
 	expectImg: (t, {images}, backend) => {
-		const {width, height, nImages} = backend.getMetadata(images);
+		const metadatas = backend.getMetadata(images);
+		const [{width, height}] = metadatas;
 		t.is(width, wdth);
 		t.is(height, hght);
-		t.is(nImages, imageNumber);
+		t.is(metadatas.length, imageNumber);
 	},
 	width: wdth,
 	height: hght,

test/macros/augmenter.js

@@ -15,8 +15,8 @@ module.exports = function (t, Cstr, {
 	backendLibs = [
 		require('opencv4nodejs'),
 		require('@tensorflow/tfjs-node')
-		//,
-		//require('@tensorflow/tfjs-node-gpu')
+		// ,
+		// require('@tensorflow/tfjs-node-gpu')
 	]
 }) {
 	return PromiseBlue.map(backendLibs, backendLib => {

test/macros/generator.js

@@ -11,7 +11,7 @@ module.exports = function (t, Cstr, {
 	channels,
 	options,
 	backendLibs = [
-		require('@tensorflow/tfjs-node-gpu'),
+		require('@tensorflow/tfjs-node'),
 		require('opencv4nodejs')
 	]
 }) {