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utils.js
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utils.js
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import ndarray from "https://cdn.skypack.dev/-/ndarray@v1.0.19-grdQeKOTBdxK6FqCxCHR/dist=es2020,mode=imports/optimized/ndarray.js";
import ops from "https://cdn.skypack.dev/-/ndarray-ops@v1.2.2-beRu4E9rjWhoa5R2nJLo/dist=es2020,mode=imports/optimized/ndarray-ops.js";
export default class OcrUtils {
static MakePadding(src, padding) {
if (padding <= 0) return src;
let paddingScalar = new cv.Scalar(255, 255, 255);
let paddingSrc = new cv.Mat();
cv.copyMakeBorder(
src,
paddingSrc,
padding,
padding,
padding,
padding,
cv.BORDER_ISOLATED,
paddingScalar
);
return paddingSrc;
}
static SubstractMeanNormalizeCanvas(ctx, meanVals, normVals) {
const imageData = ctx.getImageData(0, 0, ctx.canvas.width, ctx.canvas.height);
const {
data,
width,
height
} = imageData;
// r c chs:
const dataTensor = ndarray(new Float32Array(data), [width, height, 4]);
const dataProcessedTensor = ndarray(new Float32Array(width * height * 3), [
1,
3,
width,
height,
]);
ops.assign(
dataProcessedTensor.pick(0, 0, null, null),
dataTensor.pick(null, null, 0)
);
ops.assign(
dataProcessedTensor.pick(0, 1, null, null),
dataTensor.pick(null, null, 1)
);
ops.assign(
dataProcessedTensor.pick(0, 2, null, null),
dataTensor.pick(null, null, 2)
);
for (let ch = 0; ch < 3; ch++) {
ops.mulseq(dataProcessedTensor.pick(0, ch, null, null), normVals[ch]);
ops.subseq(
dataProcessedTensor.pick(0, ch, null, null),
meanVals[ch] * normVals[ch]
);
}
const tensor = new ort.Tensor(
"float32",
new Float32Array(width * height * 3), [1, 3, width, height]
);
tensor.data.set(dataProcessedTensor.data);
return tensor;
}
static SubstractMeanNormalize(ctx, meanVals, normVals) {
const {
data,
cols: width,
rows: height
} = ctx;
// data processing
// r c chs:
const dataTensor = ndarray(data, [height, width, ctx.channels()]);
const dataProcessedTensor = ndarray(new Float32Array(width * height * ctx.channels()), [
1,
ctx.channels(),
height,
width,
]);
ops.assign(
dataProcessedTensor.pick(0, 0, null, null),
dataTensor.pick(null, null, 0)
);
ops.assign(
dataProcessedTensor.pick(0, 1, null, null),
dataTensor.pick(null, null, 1)
);
ops.assign(
dataProcessedTensor.pick(0, 2, null, null),
dataTensor.pick(null, null, 2)
);
for (let ch = 0; ch < ctx.channels(); ch++) {
ops.mulseq(dataProcessedTensor.pick(0, ch, null, null), normVals[ch]);
ops.subseq(
dataProcessedTensor.pick(0, ch, null, null),
meanVals[ch] * normVals[ch]
);
}
const tensor = new ort.Tensor(
"float32",
new Float32Array(width * height * ctx.channels()), [1, ctx.channels(), height, width]
);
tensor.data.set(dataProcessedTensor.data);
return tensor;
}
static GetThickness(boxImg) {
let minSize = boxImg.cols > boxImg.rows ? boxImg.rows : boxImg.cols;
let thickness = minSize / 1000 + 2;
return thickness;
}
static DrawTextBox(boxImg, box, thickness) {
if (box == null || box.length == 0) {
return;
}
var color = new cv.Scalar(255, 0, 0); //R(255) G(0) B(0)
cv.line(boxImg, box[0], box[1], color, thickness);
cv.line(boxImg, box[1], box[2], color, thickness);
cv.line(boxImg, box[2], box[3], color, thickness);
cv.line(boxImg, box[3], box[0], color, thickness);
}
static DrawTextBoxes(src, textBoxes, thickness) {
for (let i = 0; i < textBoxes.length; i++) {
let t = textBoxes[i];
this.DrawTextBox(src, t.Points, thickness);
}
}
static GetRotateCropImage(src, box) {
let image = new cv.Mat();
src.copyTo(image);
let points = box.slice();
let collectX = [box[0].x, box[1].x, box[2].x, box[3].x];
let collectY = [box[0].y, box[1].y, box[2].y, box[3].y];
let left = Math.min.apply(null, collectX);
let right = Math.max.apply(null, collectX);
let top = Math.min.apply(null, collectY);
let bottom = Math.max.apply(null, collectY);
let rect = new cv.Rect(left, top, right - left, bottom - top);
let imgCrop = image.roi(rect);
for (let i = 0; i < points.length; i++) {
var pt = new cv.Point(points[i].x,points[i].y);
pt.x -= left;
pt.y -= top;
points[i] = pt;
}
let imgCropWidth = Math.sqrt(
Math.pow(points[0].x - points[1].x, 2) +
Math.pow(points[0].y - points[1].y, 2)
);
let imgCropHeight = Math.sqrt(
Math.pow(points[0].x - points[3].x, 2) +
Math.pow(points[0].y - points[3].y, 2)
);
let ptsDst = [];
ptsDst.push(0, 0);
ptsDst.push(imgCropWidth, 0);
ptsDst.push(imgCropWidth, imgCropHeight);
ptsDst.push(0, imgCropHeight);
let ptsSrc = [];
ptsSrc.push(points[0].x, points[0].y);
ptsSrc.push(points[1].x, points[1].y);
ptsSrc.push(points[2].x, points[2].y);
ptsSrc.push(points[3].x, points[3].y);
let M = cv.getPerspectiveTransform(
cv.matFromArray(4, 1, cv.CV_32FC2, ptsSrc),
cv.matFromArray(4, 1, cv.CV_32FC2, ptsDst),
);
let partImg = new cv.Mat();
cv.warpPerspective(
imgCrop,
partImg,
M,
new cv.Size(imgCropWidth, imgCropHeight),
cv.INTER_NEAREST,
cv.BORDER_REPLICATE,
new cv.Scalar(255,255,255)
);
try {
if (partImg.rows >= partImg.cols * 1.5) {
let srcCopy = new cv.Mat();
cv.transpose(partImg, srcCopy);
cv.flip(srcCopy, srcCopy, 0);
return srcCopy;
} else {
return partImg;
}
} finally {
image.delete();
imgCrop.delete();
}
}
static GetPartImages(src, textBoxes) {
let partImages = [];
for (let i = 0; i < textBoxes.length; ++i) {
let partImg = this.GetRotateCropImage(src, textBoxes[i].Points);
//Mat partImg = new Mat();
//GetRoiFromBox(src, partImg, textBoxes[i].Points);
partImages.push(partImg);
}
return partImages;
}
static MatRotateClockWise180(src) {
// cv.flip(src, src, FlipType.Vertical);
// cv.flip(src, src, FlipType.Horizontal);
cv.rotate(src, src, cv.ROTATE_180);
return src;
}
static MatRotateClockWise90(src) {
cv.rotate(src, src, cv.ROTATE_90_COUNTERCLOCKWISE);
return src;
}
static ShowMat(mat,id) {
const s = document.createElement("canvas");
document.querySelector("#canvasBox").appendChild(s);
s.id = id;
cv.imshow(s, mat);
return s;
}
}