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Commit 16c95d9e by Javier Ledo Vázquez
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Update to tensorflowjs 1.0.1

parent 21ce7136
Showing with 189 additions and 299 deletions
build/commonjs/mtcnn/Mtcnn.js
...@@ -39,7 +39,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -39,7 +39,7 @@ var Mtcnn = /** @class */ (function (_super) {
stats = {}; stats = {};
tsTotal = Date.now(); tsTotal = Date.now();
imgTensor = tf.tidy(function () { imgTensor = tf.tidy(function () {
return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(tf.fromPixels(inputCanvas)).toFloat()); return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat());
}); });
onReturn = function (results) { onReturn = function (results) {
// dispose tensors on return // dispose tensors on return
......
build/commonjs/mtcnn/Mtcnn.js.map
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\ No newline at end of file \ No newline at end of file
build/commonjs/mtcnn/stage1.js
...@@ -20,15 +20,15 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold ...@@ -20,15 +20,15 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
var indices = []; var indices = [];
for (var y = 0; y < scoresTensor.shape[0]; y++) { for (var y = 0; y < scoresTensor.shape[0]; y++) {
for (var x = 0; x < scoresTensor.shape[1]; x++) { for (var x = 0; x < scoresTensor.shape[1]; x++) {
if (scoresTensor.get(y, x) >= scoreThreshold) { if (scoresTensor.arraySync()[y][x] >= scoreThreshold) {
indices.push(new tfjs_image_recognition_base_1.Point(x, y)); indices.push(new tfjs_image_recognition_base_1.Point(x, y));
} }
} }
} }
var boundingBoxes = indices.map(function (idx) { var boundingBoxes = indices.map(function (idx) {
var cell = new tfjs_image_recognition_base_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale)); var cell = new tfjs_image_recognition_base_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale));
var score = scoresTensor.get(idx.y, idx.x); var score = scoresTensor.arraySync()[idx.y][idx.x];
var region = new MtcnnBox_1.MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3)); var region = new MtcnnBox_1.MtcnnBox(regionsTensor.arraySync()[idx.y][idx.x][0], regionsTensor.arraySync()[idx.y][idx.x][1], regionsTensor.arraySync()[idx.y][idx.x][2], regionsTensor.arraySync()[idx.y][idx.x][3]);
return { return {
cell: cell, cell: cell,
score: score, score: score,
......
build/commonjs/mtcnn/stage1.js.map
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\ No newline at end of file \ No newline at end of file
build/commonjs/mtcnn/stage2.js
...@@ -48,7 +48,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) { ...@@ -48,7 +48,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7); indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
stats.stage2_nms = Date.now() - ts; stats.stage2_nms = Date.now() - ts;
regions_1 = indicesNms.map(function (idx) { regions_1 = indicesNms.map(function (idx) {
return new MtcnnBox_1.MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3)); return new MtcnnBox_1.MtcnnBox(rnetOuts[indices[idx]].regions.arraySync()[0][0], rnetOuts[indices[idx]].regions.arraySync()[0][1], rnetOuts[indices[idx]].regions.arraySync()[0][2], rnetOuts[indices[idx]].regions.arraySync()[0][3]);
}); });
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; }); finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); }); finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
......
build/commonjs/mtcnn/stage2.js.map
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\ No newline at end of file \ No newline at end of file
build/commonjs/mtcnn/stage3.js
...@@ -39,7 +39,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) { ...@@ -39,7 +39,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
var idx = _a.idx; var idx = _a.idx;
return idx; return idx;
}); });
filteredRegions = indices.map(function (idx) { return new MtcnnBox_1.MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); }); filteredRegions = indices.map(function (idx) { return new MtcnnBox_1.MtcnnBox(onetOuts[idx].regions.arraySync()[0][0], onetOuts[idx].regions.arraySync()[0][1], onetOuts[idx].regions.arraySync()[0][2], onetOuts[idx].regions.arraySync()[0][3]); });
filteredBoxes = indices filteredBoxes = indices
.map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); }); .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
filteredScores = indices.map(function (idx) { return scores[idx]; }); filteredScores = indices.map(function (idx) { return scores[idx]; });
...@@ -54,7 +54,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) { ...@@ -54,7 +54,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; }); finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
points = indicesNms.map(function (idx, i) { points = indicesNms.map(function (idx, i) {
return Array(5).fill(0).map(function (_, ptIdx) { return Array(5).fill(0).map(function (_, ptIdx) {
return new tfjs_image_recognition_base_1.Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top)); return new tfjs_image_recognition_base_1.Point(((onetOuts[idx].points.arraySync()[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.arraySync()[0][ptIdx + 5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
}); });
}); });
} }
......
build/commonjs/mtcnn/stage3.js.map
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\ No newline at end of file \ No newline at end of file
build/commonjs/ssdMobilenetv1/SsdMobilenetv1.js
...@@ -73,12 +73,12 @@ var SsdMobilenetv1 = /** @class */ (function (_super) { ...@@ -73,12 +73,12 @@ var SsdMobilenetv1 = /** @class */ (function (_super) {
results = indices results = indices
.map(function (idx) { .map(function (idx) {
var _a = [ var _a = [
Math.max(0, boxes.get(idx, 0)), Math.max(0, boxes.arraySync()[idx][0]),
Math.min(1.0, boxes.get(idx, 2)) Math.min(1.0, boxes.arraySync()[idx][2])
].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1]; ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
var _b = [ var _b = [
Math.max(0, boxes.get(idx, 1)), Math.max(0, boxes.arraySync()[idx][1]),
Math.min(1.0, boxes.get(idx, 3)) Math.min(1.0, boxes.arraySync()[idx][3])
].map(function (val) { return val * padX; }), left = _b[0], right = _b[1]; ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), { return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0), height: netInput.getInputHeight(0),
......
build/commonjs/ssdMobilenetv1/nonMaxSuppression.js
...@@ -32,14 +32,14 @@ function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThre ...@@ -32,14 +32,14 @@ function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThre
} }
exports.nonMaxSuppression = nonMaxSuppression; exports.nonMaxSuppression = nonMaxSuppression;
function IOU(boxes, i, j) { function IOU(boxes, i, j) {
var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2)); var yminI = Math.min(boxes.arraySync()[i][0], boxes.arraySync()[i][2]);
var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3)); var xminI = Math.min(boxes.arraySync()[i][1], boxes.arraySync()[i][3]);
var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2)); var ymaxI = Math.max(boxes.arraySync()[i][0], boxes.arraySync()[i][2]);
var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3)); var xmaxI = Math.max(boxes.arraySync()[i][1], boxes.arraySync()[i][3]);
var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2)); var yminJ = Math.min(boxes.arraySync()[j][0], boxes.arraySync()[j][2]);
var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3)); var xminJ = Math.min(boxes.arraySync()[j][1], boxes.arraySync()[j][3]);
var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2)); var ymaxJ = Math.max(boxes.arraySync()[j][0], boxes.arraySync()[j][2]);
var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3)); var xmaxJ = Math.max(boxes.arraySync()[j][1], boxes.arraySync()[j][3]);
var areaI = (ymaxI - yminI) * (xmaxI - xminI); var areaI = (ymaxI - yminI) * (xmaxI - xminI);
var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
if (areaI <= 0 || areaJ <= 0) { if (areaI <= 0 || areaJ <= 0) {
......
build/commonjs/ssdMobilenetv1/nonMaxSuppression.js.map
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\ No newline at end of file \ No newline at end of file
build/es6/mtcnn/Mtcnn.js
...@@ -37,7 +37,7 @@ var Mtcnn = /** @class */ (function (_super) { ...@@ -37,7 +37,7 @@ var Mtcnn = /** @class */ (function (_super) {
stats = {}; stats = {};
tsTotal = Date.now(); tsTotal = Date.now();
imgTensor = tf.tidy(function () { imgTensor = tf.tidy(function () {
return bgrToRgbTensor(tf.expandDims(tf.fromPixels(inputCanvas)).toFloat()); return bgrToRgbTensor(tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat());
}); });
onReturn = function (results) { onReturn = function (results) {
// dispose tensors on return // dispose tensors on return
......
build/es6/mtcnn/Mtcnn.js.map
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\ No newline at end of file \ No newline at end of file
build/es6/mtcnn/stage1.js
...@@ -18,15 +18,15 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold ...@@ -18,15 +18,15 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
var indices = []; var indices = [];
for (var y = 0; y < scoresTensor.shape[0]; y++) { for (var y = 0; y < scoresTensor.shape[0]; y++) {
for (var x = 0; x < scoresTensor.shape[1]; x++) { for (var x = 0; x < scoresTensor.shape[1]; x++) {
if (scoresTensor.get(y, x) >= scoreThreshold) { if (scoresTensor.arraySync()[y][x] >= scoreThreshold) {
indices.push(new Point(x, y)); indices.push(new Point(x, y));
} }
} }
} }
var boundingBoxes = indices.map(function (idx) { var boundingBoxes = indices.map(function (idx) {
var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale)); var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale));
var score = scoresTensor.get(idx.y, idx.x); var score = scoresTensor.arraySync()[idx.y][idx.x];
var region = new MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3)); var region = new MtcnnBox(regionsTensor.arraySync()[idx.y][idx.x][0], regionsTensor.arraySync()[idx.y][idx.x][1], regionsTensor.arraySync()[idx.y][idx.x][2], regionsTensor.arraySync()[idx.y][idx.x][3]);
return { return {
cell: cell, cell: cell,
score: score, score: score,
......
build/es6/mtcnn/stage1.js.map
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\ No newline at end of file \ No newline at end of file
build/es6/mtcnn/stage2.js
...@@ -46,7 +46,7 @@ export function stage2(img, inputBoxes, scoreThreshold, params, stats) { ...@@ -46,7 +46,7 @@ export function stage2(img, inputBoxes, scoreThreshold, params, stats) {
indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7); indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
stats.stage2_nms = Date.now() - ts; stats.stage2_nms = Date.now() - ts;
regions_1 = indicesNms.map(function (idx) { regions_1 = indicesNms.map(function (idx) {
return new MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3)); return new MtcnnBox(rnetOuts[indices[idx]].regions.arraySync()[0][0], rnetOuts[indices[idx]].regions.arraySync()[0][1], rnetOuts[indices[idx]].regions.arraySync()[0][2], rnetOuts[indices[idx]].regions.arraySync()[0][3]);
}); });
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; }); finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); }); finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
......
build/es6/mtcnn/stage2.js.map
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\ No newline at end of file \ No newline at end of file
build/es6/mtcnn/stage3.js
...@@ -37,7 +37,7 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) { ...@@ -37,7 +37,7 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
var idx = _a.idx; var idx = _a.idx;
return idx; return idx;
}); });
filteredRegions = indices.map(function (idx) { return new MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); }); filteredRegions = indices.map(function (idx) { return new MtcnnBox(onetOuts[idx].regions.arraySync()[0][0], onetOuts[idx].regions.arraySync()[0][1], onetOuts[idx].regions.arraySync()[0][2], onetOuts[idx].regions.arraySync()[0][3]); });
filteredBoxes = indices filteredBoxes = indices
.map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); }); .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
filteredScores = indices.map(function (idx) { return scores[idx]; }); filteredScores = indices.map(function (idx) { return scores[idx]; });
...@@ -52,7 +52,7 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) { ...@@ -52,7 +52,7 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; }); finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
points = indicesNms.map(function (idx, i) { points = indicesNms.map(function (idx, i) {
return Array(5).fill(0).map(function (_, ptIdx) { return Array(5).fill(0).map(function (_, ptIdx) {
return new Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top)); return new Point(((onetOuts[idx].points.arraySync()[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.arraySync()[0][ptIdx + 5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
}); });
}); });
} }
......
build/es6/mtcnn/stage3.js.map
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\ No newline at end of file \ No newline at end of file
build/es6/ssdMobilenetv1/SsdMobilenetv1.js
...@@ -71,12 +71,12 @@ var SsdMobilenetv1 = /** @class */ (function (_super) { ...@@ -71,12 +71,12 @@ var SsdMobilenetv1 = /** @class */ (function (_super) {
results = indices results = indices
.map(function (idx) { .map(function (idx) {
var _a = [ var _a = [
Math.max(0, boxes.get(idx, 0)), Math.max(0, boxes.arraySync()[idx][0]),
Math.min(1.0, boxes.get(idx, 2)) Math.min(1.0, boxes.arraySync()[idx][2])
].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1]; ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
var _b = [ var _b = [
Math.max(0, boxes.get(idx, 1)), Math.max(0, boxes.arraySync()[idx][1]),
Math.min(1.0, boxes.get(idx, 3)) Math.min(1.0, boxes.arraySync()[idx][3])
].map(function (val) { return val * padX; }), left = _b[0], right = _b[1]; ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), { return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0), height: netInput.getInputHeight(0),
......
build/es6/ssdMobilenetv1/nonMaxSuppression.js
...@@ -29,14 +29,14 @@ export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, sc ...@@ -29,14 +29,14 @@ export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, sc
return selected; return selected;
} }
function IOU(boxes, i, j) { function IOU(boxes, i, j) {
var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2)); var yminI = Math.min(boxes.arraySync()[i][0], boxes.arraySync()[i][2]);
var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3)); var xminI = Math.min(boxes.arraySync()[i][1], boxes.arraySync()[i][3]);
var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2)); var ymaxI = Math.max(boxes.arraySync()[i][0], boxes.arraySync()[i][2]);
var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3)); var xmaxI = Math.max(boxes.arraySync()[i][1], boxes.arraySync()[i][3]);
var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2)); var yminJ = Math.min(boxes.arraySync()[j][0], boxes.arraySync()[j][2]);
var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3)); var xminJ = Math.min(boxes.arraySync()[j][1], boxes.arraySync()[j][3]);
var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2)); var ymaxJ = Math.max(boxes.arraySync()[j][0], boxes.arraySync()[j][2]);
var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3)); var xmaxJ = Math.max(boxes.arraySync()[j][1], boxes.arraySync()[j][3]);
var areaI = (ymaxI - yminI) * (xmaxI - xminI); var areaI = (ymaxI - yminI) * (xmaxI - xminI);
var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
if (areaI <= 0 || areaJ <= 0) { if (areaI <= 0 || areaJ <= 0) {
......
build/es6/ssdMobilenetv1/nonMaxSuppression.js.map
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dist/face-api.js
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dist/face-api.js.map
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dist/face-api.min.js
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package-lock.json
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...@@ -211,17 +123,17 @@ ...@@ -211,17 +123,17 @@
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...@@ -567,7 +479,7 @@ ...@@ -567,7 +479,7 @@
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"dev": true "dev": true
} }
...@@ -3429,12 +3341,6 @@ ...@@ -3429,12 +3341,6 @@
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} }
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...@@ -3735,7 +3641,7 @@ ...@@ -3735,7 +3641,7 @@
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...@@ -4841,7 +4718,7 @@ ...@@ -4841,7 +4718,7 @@
}, },
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}, },
"semver": { "semver": {
...@@ -5369,6 +5246,19 @@ ...@@ -5369,6 +5246,19 @@
"requires": { "requires": {
"@tensorflow/tfjs-core": "0.14.2", "@tensorflow/tfjs-core": "0.14.2",
"tslib": "^1.9.3" "tslib": "^1.9.3"
},
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"@types/webgl2": "0.0.4",
"seedrandom": "2.4.3"
}
}
} }
}, },
"through2": { "through2": {
......
package.json
...@@ -35,12 +35,12 @@ ...@@ -35,12 +35,12 @@
"author": "justadudewhohacks", "author": "justadudewhohacks",
"license": "MIT", "license": "MIT",
"dependencies": { "dependencies": {
"@tensorflow/tfjs-core": "0.14.2", "@tensorflow/tfjs-core": "1.0.1",
"tfjs-image-recognition-base": "^0.4.1", "tfjs-image-recognition-base": "^0.4.1",
"tslib": "^1.9.3" "tslib": "^1.9.3"
}, },
"devDependencies": { "devDependencies": {
"@tensorflow/tfjs-node": "^0.2.3", "@tensorflow/tfjs-node": "^1.0.1",
"@types/jasmine": "^3.3.8", "@types/jasmine": "^3.3.8",
"@types/node": "^10.12.18", "@types/node": "^10.12.18",
"canvas": "2.0.1", "canvas": "2.0.1",
......
src/mtcnn/Mtcnn.ts
...@@ -45,7 +45,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> { ...@@ -45,7 +45,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
const imgTensor = tf.tidy(() => const imgTensor = tf.tidy(() =>
bgrToRgbTensor( bgrToRgbTensor(
tf.expandDims(tf.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D
) )
) )
......
src/mtcnn/stage1.ts
...@@ -30,7 +30,7 @@ function extractBoundingBoxes( ...@@ -30,7 +30,7 @@ function extractBoundingBoxes(
const indices: Point[] = [] const indices: Point[] = []
for (let y = 0; y < scoresTensor.shape[0]; y++) { for (let y = 0; y < scoresTensor.shape[0]; y++) {
for (let x = 0; x < scoresTensor.shape[1]; x++) { for (let x = 0; x < scoresTensor.shape[1]; x++) {
if (scoresTensor.get(y, x) >= scoreThreshold) { if (scoresTensor.arraySync()[y][x] >= scoreThreshold) {
indices.push(new Point(x, y)) indices.push(new Point(x, y))
} }
} }
...@@ -44,13 +44,13 @@ function extractBoundingBoxes( ...@@ -44,13 +44,13 @@ function extractBoundingBoxes(
Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale) Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale)
) )
const score = scoresTensor.get(idx.y, idx.x) const score = scoresTensor.arraySync()[idx.y][idx.x]
const region = new MtcnnBox( const region = new MtcnnBox(
regionsTensor.get(idx.y, idx.x, 0), regionsTensor.arraySync()[idx.y][idx.x][0],
regionsTensor.get(idx.y, idx.x, 1), regionsTensor.arraySync()[idx.y][idx.x][1],
regionsTensor.get(idx.y, idx.x, 2), regionsTensor.arraySync()[idx.y][idx.x][2],
regionsTensor.get(idx.y, idx.x, 3) regionsTensor.arraySync()[idx.y][idx.x][3]
) )
return { return {
......
src/mtcnn/stage2.ts
...@@ -56,10 +56,10 @@ export async function stage2( ...@@ -56,10 +56,10 @@ export async function stage2(
const regions = indicesNms.map(idx => const regions = indicesNms.map(idx =>
new MtcnnBox( new MtcnnBox(
rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.arraySync()[0][0],
rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.arraySync()[0][1],
rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.arraySync()[0][2],
rnetOuts[indices[idx]].regions.get(0, 3) rnetOuts[indices[idx]].regions.arraySync()[0][3]
) )
) )
......
src/mtcnn/stage3.ts
...@@ -40,10 +40,10 @@ export async function stage3( ...@@ -40,10 +40,10 @@ export async function stage3(
.map(({ idx }) => idx) .map(({ idx }) => idx)
const filteredRegions = indices.map(idx => new MtcnnBox( const filteredRegions = indices.map(idx => new MtcnnBox(
onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.arraySync()[0][0],
onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.arraySync()[0][1],
onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.arraySync()[0][2],
onetOuts[idx].regions.get(0, 3) onetOuts[idx].regions.arraySync()[0][3]
)) ))
const filteredBoxes = indices const filteredBoxes = indices
.map((idx, i) => inputBoxes[idx].calibrate(filteredRegions[i])) .map((idx, i) => inputBoxes[idx].calibrate(filteredRegions[i]))
...@@ -69,8 +69,8 @@ export async function stage3( ...@@ -69,8 +69,8 @@ export async function stage3(
points = indicesNms.map((idx, i) => points = indicesNms.map((idx, i) =>
Array(5).fill(0).map((_, ptIdx) => Array(5).fill(0).map((_, ptIdx) =>
new Point( new Point(
((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left) , ((onetOuts[idx].points.arraySync()[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left) ,
((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top) ((onetOuts[idx].points.arraySync()[0][ptIdx+5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top)
) )
) )
) )
......
src/ssdMobilenetv1/SsdMobilenetv1.ts
...@@ -88,12 +88,12 @@ export class SsdMobilenetv1 extends NeuralNetwork<NetParams> { ...@@ -88,12 +88,12 @@ export class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
const results = indices const results = indices
.map(idx => { .map(idx => {
const [top, bottom] = [ const [top, bottom] = [
Math.max(0, boxes.get(idx, 0)), Math.max(0, boxes.arraySync()[idx][0]),
Math.min(1.0, boxes.get(idx, 2)) Math.min(1.0, boxes.arraySync()[idx][2])
].map(val => val * padY) ].map(val => val * padY)
const [left, right] = [ const [left, right] = [
Math.max(0, boxes.get(idx, 1)), Math.max(0, boxes.arraySync()[idx][1]),
Math.min(1.0, boxes.get(idx, 3)) Math.min(1.0, boxes.arraySync()[idx][3])
].map(val => val * padX) ].map(val => val * padX)
return new FaceDetection( return new FaceDetection(
scoresData[idx], scoresData[idx],
......
src/ssdMobilenetv1/nonMaxSuppression.ts
...@@ -49,14 +49,14 @@ export function nonMaxSuppression( ...@@ -49,14 +49,14 @@ export function nonMaxSuppression(
} }
function IOU(boxes: tf.Tensor2D, i: number, j: number) { function IOU(boxes: tf.Tensor2D, i: number, j: number) {
const yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2)) const yminI = Math.min(boxes.arraySync()[i][0], boxes.arraySync()[i][2])
const xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3)) const xminI = Math.min(boxes.arraySync()[i][1], boxes.arraySync()[i][3])
const ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2)) const ymaxI = Math.max(boxes.arraySync()[i][0], boxes.arraySync()[i][2])
const xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3)) const xmaxI = Math.max(boxes.arraySync()[i][1], boxes.arraySync()[i][3])
const yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2)) const yminJ = Math.min(boxes.arraySync()[j][0], boxes.arraySync()[j][2])
const xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3)) const xminJ = Math.min(boxes.arraySync()[j][1], boxes.arraySync()[j][3])
const ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2)) const ymaxJ = Math.max(boxes.arraySync()[j][0], boxes.arraySync()[j][2])
const xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3)) const xmaxJ = Math.max(boxes.arraySync()[j][1], boxes.arraySync()[j][3])
const areaI = (ymaxI - yminI) * (xmaxI - xminI) const areaI = (ymaxI - yminI) * (xmaxI - xminI)
const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ) const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)
if (areaI <= 0 || areaJ <= 0) { if (areaI <= 0 || areaJ <= 0) {
......
test/tests/dom/extractFaceTensors.test.ts
...@@ -6,7 +6,7 @@ describe('extractFaceTensors', () => { ...@@ -6,7 +6,7 @@ describe('extractFaceTensors', () => {
let imgTensor: tf.Tensor3D let imgTensor: tf.Tensor3D
beforeAll(async () => { beforeAll(async () => {
imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/face1.png'))) imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/face1.png')))
}) })
describe('extracts tensors', () => { describe('extracts tensors', () => {
......
test/tests/faceExpressionNet/faceExpressionNet.test.ts
...@@ -57,7 +57,7 @@ describe('faceExpressionNet', () => { ...@@ -57,7 +57,7 @@ describe('faceExpressionNet', () => {
}) })
it('computes face landmarks for batch of tf.Tensor3D', async () => { it('computes face landmarks for batch of tf.Tensor3D', async () => {
const inputs = [imgElAngry, imgElSurprised].map(el => tf.fromPixels(createCanvasFromMedia(el))) const inputs = [imgElAngry, imgElSurprised].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][] const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][]
expect(Array.isArray(results)).toBe(true) expect(Array.isArray(results)).toBe(true)
...@@ -80,7 +80,7 @@ describe('faceExpressionNet', () => { ...@@ -80,7 +80,7 @@ describe('faceExpressionNet', () => {
}) })
it('computes face landmarks for batch of mixed inputs', async () => { it('computes face landmarks for batch of mixed inputs', async () => {
const inputs = [imgElAngry, tf.fromPixels(createCanvasFromMedia(imgElSurprised))] const inputs = [imgElAngry, tf.browser.fromPixels(createCanvasFromMedia(imgElSurprised))]
const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][] const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][]
expect(Array.isArray(results)).toBe(true) expect(Array.isArray(results)).toBe(true)
...@@ -125,7 +125,7 @@ describe('faceExpressionNet', () => { ...@@ -125,7 +125,7 @@ describe('faceExpressionNet', () => {
}) })
it('single tf.Tensor3D', async () => { it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgElAngry)) const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgElAngry))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor)) const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor))
...@@ -136,7 +136,7 @@ describe('faceExpressionNet', () => { ...@@ -136,7 +136,7 @@ describe('faceExpressionNet', () => {
}) })
it('multiple tf.Tensor3Ds', async () => { it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.fromPixels(createCanvasFromMedia(el))) const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors)) const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors))
...@@ -147,7 +147,7 @@ describe('faceExpressionNet', () => { ...@@ -147,7 +147,7 @@ describe('faceExpressionNet', () => {
}) })
it('single batch size 1 tf.Tensor4Ds', async () => { it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor)) const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor))
...@@ -159,7 +159,7 @@ describe('faceExpressionNet', () => { ...@@ -159,7 +159,7 @@ describe('faceExpressionNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => { it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgElAngry, imgElAngry, imgElAngry] const tensors = [imgElAngry, imgElAngry, imgElAngry]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[] .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors)) const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors))
...@@ -186,7 +186,7 @@ describe('faceExpressionNet', () => { ...@@ -186,7 +186,7 @@ describe('faceExpressionNet', () => {
}) })
it('single tf.Tensor3D', async () => { it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgElAngry)) const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgElAngry))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceExpressionNet.predictExpressions(tensor) await faceExpressionNet.predictExpressions(tensor)
...@@ -196,7 +196,7 @@ describe('faceExpressionNet', () => { ...@@ -196,7 +196,7 @@ describe('faceExpressionNet', () => {
}) })
it('multiple tf.Tensor3Ds', async () => { it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.fromPixels(createCanvasFromMedia(el))) const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
...@@ -207,7 +207,7 @@ describe('faceExpressionNet', () => { ...@@ -207,7 +207,7 @@ describe('faceExpressionNet', () => {
}) })
it('single batch size 1 tf.Tensor4Ds', async () => { it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceExpressionNet.predictExpressions(tensor) await faceExpressionNet.predictExpressions(tensor)
...@@ -218,7 +218,7 @@ describe('faceExpressionNet', () => { ...@@ -218,7 +218,7 @@ describe('faceExpressionNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => { it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgElAngry, imgElAngry, imgElAngry] const tensors = [imgElAngry, imgElAngry, imgElAngry]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[] .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceExpressionNet.predictExpressions(tensors) await faceExpressionNet.predictExpressions(tensors)
......
test/tests/faceLandmarkNet/faceLandmark68Net.test.ts
...@@ -92,7 +92,7 @@ describe('faceLandmark68Net', () => { ...@@ -92,7 +92,7 @@ describe('faceLandmark68Net', () => {
}) })
it('computes face landmarks for batch of tf.Tensor3D', async () => { it('computes face landmarks for batch of tf.Tensor3D', async () => {
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(createCanvasFromMedia(el))) const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
const faceLandmarkPositions = [ const faceLandmarkPositions = [
faceLandmarkPositions1, faceLandmarkPositions1,
...@@ -117,7 +117,7 @@ describe('faceLandmark68Net', () => { ...@@ -117,7 +117,7 @@ describe('faceLandmark68Net', () => {
}) })
it('computes face landmarks for batch of mixed inputs', async () => { it('computes face landmarks for batch of mixed inputs', async () => {
const inputs = [imgEl1, tf.fromPixels(createCanvasFromMedia(imgEl2)), tf.fromPixels(createCanvasFromMedia(imgElRect))] const inputs = [imgEl1, tf.browser.fromPixels(createCanvasFromMedia(imgEl2)), tf.browser.fromPixels(createCanvasFromMedia(imgElRect))]
const faceLandmarkPositions = [ const faceLandmarkPositions = [
faceLandmarkPositions1, faceLandmarkPositions1,
...@@ -164,7 +164,7 @@ describe('faceLandmark68Net', () => { ...@@ -164,7 +164,7 @@ describe('faceLandmark68Net', () => {
}) })
it('single tf.Tensor3D', async () => { it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1)) const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const netInput = new NetInput([tensor]) const netInput = new NetInput([tensor])
...@@ -176,7 +176,7 @@ describe('faceLandmark68Net', () => { ...@@ -176,7 +176,7 @@ describe('faceLandmark68Net', () => {
}) })
it('multiple tf.Tensor3Ds', async () => { it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el))) const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const netInput = new NetInput(tensors) const netInput = new NetInput(tensors)
...@@ -188,7 +188,7 @@ describe('faceLandmark68Net', () => { ...@@ -188,7 +188,7 @@ describe('faceLandmark68Net', () => {
}) })
it('single batch size 1 tf.Tensor4Ds', async () => { it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensor)) const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensor))
...@@ -200,7 +200,7 @@ describe('faceLandmark68Net', () => { ...@@ -200,7 +200,7 @@ describe('faceLandmark68Net', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => { it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1] const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[] .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensors)) const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensors))
...@@ -227,7 +227,7 @@ describe('faceLandmark68Net', () => { ...@@ -227,7 +227,7 @@ describe('faceLandmark68Net', () => {
}) })
it('single tf.Tensor3D', async () => { it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1)) const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceLandmark68Net.detectLandmarks(tensor) await faceLandmark68Net.detectLandmarks(tensor)
...@@ -237,7 +237,7 @@ describe('faceLandmark68Net', () => { ...@@ -237,7 +237,7 @@ describe('faceLandmark68Net', () => {
}) })
it('multiple tf.Tensor3Ds', async () => { it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el))) const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
...@@ -248,7 +248,7 @@ describe('faceLandmark68Net', () => { ...@@ -248,7 +248,7 @@ describe('faceLandmark68Net', () => {
}) })
it('single batch size 1 tf.Tensor4Ds', async () => { it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceLandmark68Net.detectLandmarks(tensor) await faceLandmark68Net.detectLandmarks(tensor)
...@@ -259,7 +259,7 @@ describe('faceLandmark68Net', () => { ...@@ -259,7 +259,7 @@ describe('faceLandmark68Net', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => { it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1] const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[] .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceLandmark68Net.detectLandmarks(tensors) await faceLandmark68Net.detectLandmarks(tensors)
......
test/tests/faceLandmarkNet/faceLandmark68TinyNet.test.ts
...@@ -91,7 +91,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -91,7 +91,7 @@ describe('faceLandmark68TinyNet', () => {
}) })
it('computes face landmarks for batch of tf.Tensor3D', async () => { it('computes face landmarks for batch of tf.Tensor3D', async () => {
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(createCanvasFromMedia(el))) const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
const faceLandmarkPositions = [ const faceLandmarkPositions = [
faceLandmarkPositions1, faceLandmarkPositions1,
...@@ -116,7 +116,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -116,7 +116,7 @@ describe('faceLandmark68TinyNet', () => {
}) })
it('computes face landmarks for batch of mixed inputs', async () => { it('computes face landmarks for batch of mixed inputs', async () => {
const inputs = [imgEl1, tf.fromPixels(createCanvasFromMedia(imgEl2)), tf.fromPixels(createCanvasFromMedia(imgElRect))] const inputs = [imgEl1, tf.browser.fromPixels(createCanvasFromMedia(imgEl2)), tf.browser.fromPixels(createCanvasFromMedia(imgElRect))]
const faceLandmarkPositions = [ const faceLandmarkPositions = [
faceLandmarkPositions1, faceLandmarkPositions1,
...@@ -164,7 +164,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -164,7 +164,7 @@ describe('faceLandmark68TinyNet', () => {
}) })
it('single tf.Tensor3D', async () => { it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1)) const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const netInput = new NetInput([tensor]) const netInput = new NetInput([tensor])
...@@ -176,7 +176,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -176,7 +176,7 @@ describe('faceLandmark68TinyNet', () => {
}) })
it('multiple tf.Tensor3Ds', async () => { it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el))) const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const netInput = new NetInput(tensors) const netInput = new NetInput(tensors)
...@@ -188,7 +188,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -188,7 +188,7 @@ describe('faceLandmark68TinyNet', () => {
}) })
it('single batch size 1 tf.Tensor4Ds', async () => { it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensor)) const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensor))
...@@ -200,7 +200,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -200,7 +200,7 @@ describe('faceLandmark68TinyNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => { it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1] const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[] .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensors)) const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensors))
...@@ -227,7 +227,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -227,7 +227,7 @@ describe('faceLandmark68TinyNet', () => {
}) })
it('single tf.Tensor3D', async () => { it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1)) const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensor) await faceLandmark68TinyNet.detectLandmarks(tensor)
...@@ -237,7 +237,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -237,7 +237,7 @@ describe('faceLandmark68TinyNet', () => {
}) })
it('multiple tf.Tensor3Ds', async () => { it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el))) const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
...@@ -248,7 +248,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -248,7 +248,7 @@ describe('faceLandmark68TinyNet', () => {
}) })
it('single batch size 1 tf.Tensor4Ds', async () => { it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensor) await faceLandmark68TinyNet.detectLandmarks(tensor)
...@@ -259,7 +259,7 @@ describe('faceLandmark68TinyNet', () => { ...@@ -259,7 +259,7 @@ describe('faceLandmark68TinyNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => { it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1] const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[] .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensors) await faceLandmark68TinyNet.detectLandmarks(tensors)
......
test/tests/faceRecognitionNet/faceRecognitionNet.test.ts
...@@ -60,7 +60,7 @@ describe('faceRecognitionNet', () => { ...@@ -60,7 +60,7 @@ describe('faceRecognitionNet', () => {
}) })
it('computes face descriptors for batch of tf.Tensor3D', async () => { it('computes face descriptors for batch of tf.Tensor3D', async () => {
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(el)) const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(el))
const faceDescriptors = [ const faceDescriptors = [
faceDescriptor1, faceDescriptor1,
...@@ -77,7 +77,7 @@ describe('faceRecognitionNet', () => { ...@@ -77,7 +77,7 @@ describe('faceRecognitionNet', () => {
}) })
it('computes face descriptors for batch of mixed inputs', async () => { it('computes face descriptors for batch of mixed inputs', async () => {
const inputs = [imgEl1, tf.fromPixels(imgEl2), tf.fromPixels(imgElRect)] const inputs = [imgEl1, tf.browser.fromPixels(imgEl2), tf.browser.fromPixels(imgElRect)]
const faceDescriptors = [ const faceDescriptors = [
faceDescriptor1, faceDescriptor1,
...@@ -116,7 +116,7 @@ describe('faceRecognitionNet', () => { ...@@ -116,7 +116,7 @@ describe('faceRecognitionNet', () => {
}) })
it('single tf.Tensor3D', async () => { it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl1) const tensor = tf.browser.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const netInput = new NetInput([tensor]) const netInput = new NetInput([tensor])
...@@ -128,7 +128,7 @@ describe('faceRecognitionNet', () => { ...@@ -128,7 +128,7 @@ describe('faceRecognitionNet', () => {
}) })
it('multiple tf.Tensor3Ds', async () => { it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el)) const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(el))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const netInput = new NetInput(tensors) const netInput = new NetInput(tensors)
...@@ -140,7 +140,7 @@ describe('faceRecognitionNet', () => { ...@@ -140,7 +140,7 @@ describe('faceRecognitionNet', () => {
}) })
it('single batch size 1 tf.Tensor4Ds', async () => { it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D const tensor = tf.tidy(() => tf.browser.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensor)) const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensor))
...@@ -152,7 +152,7 @@ describe('faceRecognitionNet', () => { ...@@ -152,7 +152,7 @@ describe('faceRecognitionNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => { it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1] const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[] .map(el => tf.tidy(() => tf.browser.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensors)) const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensors))
...@@ -179,7 +179,7 @@ describe('faceRecognitionNet', () => { ...@@ -179,7 +179,7 @@ describe('faceRecognitionNet', () => {
}) })
it('single tf.Tensor3D', async () => { it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl1) const tensor = tf.browser.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceRecognitionNet.computeFaceDescriptor(tensor) await faceRecognitionNet.computeFaceDescriptor(tensor)
...@@ -189,7 +189,7 @@ describe('faceRecognitionNet', () => { ...@@ -189,7 +189,7 @@ describe('faceRecognitionNet', () => {
}) })
it('multiple tf.Tensor3Ds', async () => { it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el)) const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(el))
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
...@@ -200,7 +200,7 @@ describe('faceRecognitionNet', () => { ...@@ -200,7 +200,7 @@ describe('faceRecognitionNet', () => {
}) })
it('single batch size 1 tf.Tensor4Ds', async () => { it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D const tensor = tf.tidy(() => tf.browser.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceRecognitionNet.computeFaceDescriptor(tensor) await faceRecognitionNet.computeFaceDescriptor(tensor)
...@@ -211,7 +211,7 @@ describe('faceRecognitionNet', () => { ...@@ -211,7 +211,7 @@ describe('faceRecognitionNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => { it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1] const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[] .map(el => tf.tidy(() => tf.browser.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => { await expectAllTensorsReleased(async () => {
await faceRecognitionNet.computeFaceDescriptor(tensors) await faceRecognitionNet.computeFaceDescriptor(tensors)
......
test/tests/ssdMobilenetv1/ssdMobilenetv1.node.test.ts
...@@ -15,7 +15,7 @@ describe('ssdMobilenetv1 - node', () => { ...@@ -15,7 +15,7 @@ describe('ssdMobilenetv1 - node', () => {
const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61] const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61]
beforeAll(async () => { beforeAll(async () => {
imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg'))) imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedSsdBoxes) expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedSsdBoxes)
}) })
......
test/tests/tinyFaceDetector/tinyFaceDetector.node.test.ts
...@@ -15,7 +15,7 @@ describe('tinyFaceDetector - node', () => { ...@@ -15,7 +15,7 @@ describe('tinyFaceDetector - node', () => {
const expectedScores = [0.7, 0.82, 0.93, 0.86, 0.79, 0.84] const expectedScores = [0.7, 0.82, 0.93, 0.86, 0.79, 0.84]
beforeAll(async () => { beforeAll(async () => {
imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg'))) imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedTinyFaceDetectorBoxes) expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedTinyFaceDetectorBoxes)
}) })
......
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