@tensorflow-models/coco-ssd
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Object detection model (coco-ssd) in TensorFlow.js
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JavaScript
;
/**
* @license
* Copyright 2019 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
var environment_1 = require("../environment");
var tensor_1 = require("../tensor");
var tensor_util_env_1 = require("../tensor_util_env");
var operation_1 = require("./operation");
/**
* Creates a `tf.Tensor` from an image.
*
* ```js
* const image = new ImageData(1, 1);
* image.data[0] = 100;
* image.data[1] = 150;
* image.data[2] = 200;
* image.data[3] = 255;
*
* tf.browser.fromPixels(image).print();
* ```
*
* @param pixels The input image to construct the tensor from. The
* supported image types are all 4-channel.
* @param numChannels The number of channels of the output tensor. A
* numChannels value less than 4 allows you to ignore channels. Defaults to
* 3 (ignores alpha channel of input image).
*/
/** @doc {heading: 'Browser', namespace: 'browser'} */
function fromPixels_(pixels, numChannels) {
if (numChannels === void 0) { numChannels = 3; }
if (numChannels > 4) {
throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');
}
return environment_1.ENV.engine.fromPixels(pixels, numChannels);
}
/**
* Draws a `tf.Tensor` of pixel values to a byte array or optionally a
* canvas.
*
* When the dtype of the input is 'float32', we assume values in the range
* [0-1]. Otherwise, when input is 'int32', we assume values in the range
* [0-255].
*
* Returns a promise that resolves when the canvas has been drawn to.
*
* @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If
* rank-3, must have depth of 1, 3 or 4. When depth of 1, draws
* grayscale. When depth of 3, we draw with the first three components of
* the depth dimension corresponding to r, g, b and alpha = 1. When depth of
* 4, all four components of the depth dimension correspond to r, g, b, a.
* @param canvas The canvas to draw to.
*/
/** @doc {heading: 'Browser', namespace: 'browser'} */
function toPixels(img, canvas) {
return __awaiter(this, void 0, void 0, function () {
var $img, _a, height, width, depth, minTensor, maxTensor, min, max, data, multiplier, bytes, i, r, g, b, a, j, ctx, imageData;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
$img = tensor_util_env_1.convertToTensor(img, 'img', 'toPixels');
if (!(img instanceof tensor_1.Tensor)) {
// Assume int32 if user passed a native array.
$img = $img.toInt();
}
if ($img.rank !== 2 && $img.rank !== 3) {
throw new Error("toPixels only supports rank 2 or 3 tensors, got rank " + $img.rank + ".");
}
_a = $img.shape.slice(0, 2), height = _a[0], width = _a[1];
depth = $img.rank === 2 ? 1 : $img.shape[2];
if (depth > 4 || depth === 2) {
throw new Error("toPixels only supports depth of size " +
("1, 3 or 4 but got " + depth));
}
minTensor = $img.min();
maxTensor = $img.max();
return [4 /*yield*/, minTensor.data()];
case 1:
min = (_b.sent())[0];
return [4 /*yield*/, maxTensor.data()];
case 2:
max = (_b.sent())[0];
minTensor.dispose();
maxTensor.dispose();
if ($img.dtype === 'float32') {
if (min < 0 || max > 1) {
throw new Error("Tensor values for a float32 Tensor must be in the " +
("range [0 - 1] but got range [" + min + " - " + max + "]."));
}
}
else if ($img.dtype === 'int32') {
if (min < 0 || max > 255) {
throw new Error("Tensor values for a int32 Tensor must be in the " +
("range [0 - 255] but got range [" + min + " - " + max + "]."));
}
}
else {
throw new Error("Unsupported type for toPixels: " + $img.dtype + "." +
" Please use float32 or int32 tensors.");
}
return [4 /*yield*/, $img.data()];
case 3:
data = _b.sent();
multiplier = $img.dtype === 'float32' ? 255 : 1;
bytes = new Uint8ClampedArray(width * height * 4);
for (i = 0; i < height * width; ++i) {
r = void 0, g = void 0, b = void 0, a = void 0;
if (depth === 1) {
r = data[i] * multiplier;
g = data[i] * multiplier;
b = data[i] * multiplier;
a = 255;
}
else if (depth === 3) {
r = data[i * 3] * multiplier;
g = data[i * 3 + 1] * multiplier;
b = data[i * 3 + 2] * multiplier;
a = 255;
}
else if (depth === 4) {
r = data[i * 4] * multiplier;
g = data[i * 4 + 1] * multiplier;
b = data[i * 4 + 2] * multiplier;
a = data[i * 4 + 3] * multiplier;
}
j = i * 4;
bytes[j + 0] = Math.round(r);
bytes[j + 1] = Math.round(g);
bytes[j + 2] = Math.round(b);
bytes[j + 3] = Math.round(a);
}
if (canvas != null) {
canvas.width = width;
canvas.height = height;
ctx = canvas.getContext('2d');
imageData = new ImageData(bytes, width, height);
ctx.putImageData(imageData, 0, 0);
}
if ($img !== img) {
$img.dispose();
}
return [2 /*return*/, bytes];
}
});
});
}
exports.toPixels = toPixels;
exports.fromPixels = operation_1.op({ fromPixels_: fromPixels_ });
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