@icecreamcake/autojs6-dts
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TypeScript Declarations for AutoJs6
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TypeScript
// Type definitions for AutoJs6 internal module images
//
// Definitions by: SuperMonster003 <https://github.com/SuperMonster003>
// TypeScript Version: 4.3.5
//
// Last modified: Oct 21, 2021
/// <reference path="../index.d.ts" />
/**
* @Source %AutoJs6Assets%/modules/__images__.js
*/
declare namespace Internal {
import ColorInt = Colors.ColorInt;
import DetectionAlgorithm = Colors.DetectionAlgorithm;
import ConversionCodes = Colors.ConversionCodes;
interface Images {
();
/**
* @see org.autojs.autojs.runtime.api.Images.captureScreen
*/
captureScreen(): ImageWrapper;
/**
* @see org.autojs.autojs.runtime.api.Images.captureScreen
*/
captureScreen(path: string): boolean;
/**
* @see org.autojs.autojs.runtime.api.Images.read
*/
read(path: string): ImageWrapper;
/**
* @see org.autojs.autojs.runtime.api.Images.copy
*/
copy(image: ImageWrapper): ImageWrapper;
/**
* @see org.autojs.autojs.runtime.api.Images.load
*/
load(src: string): ImageWrapper;
/**
* @see org.autojs.autojs.runtime.api.Images.clip
*/
clip(image: ImageWrapper, x: number, y: number, w: number, h: number): ImageWrapper;
clip(image: ImageWrapper, region: OmniRegion): ImageWrapper;
/**
* @see org.autojs.autojs.runtime.api.Images.pixel
*/
pixel(image: ImageWrapper, x: number, y: number): ColorInt;
/**
* Applies an adaptive threshold to an array.
* zh-CN: 自适应阈值化
* @param src - source 8-bit single-channel image.
* zh-CN: 输入图像, 8 位单通道浮点类型
* @param maxValue - non-zero value assigned to the pixels for which the condition is satisfied.
* zh-CN: 预设满足条件的最大值
* @param adaptiveMethod - adaptive thresholding algorithm to use (without "ADAPTIVE_THRESH_" prefix).
* zh-CN: 指定自适应阈值算法 (参数不包含 "ADAPTIVE_THRESH_" 前缀)
* @param thresholdType - thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV (without "THRESH_" prefix).
* zh-CN: 指定阈值类型, 二进制阈值或反二进制阈值 (参数不包含 "THRESH_" 前缀)
* @param blockSize - size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
* zh-CN: 表示邻域块大小, 用来计算区域阈值, 一般选 3, 5, 7 等
* @param C - constant subtracted from the mean or weighted mean. Normally, it is positive but may be zero or negative as well.
* zh-CN: 参数 C 表示与算法有关的参数, 它是一个从均值或加权均值提取的常数, 可以是负数
* @example
* images.requestScreenCapture(false);
* images.adaptiveThreshold(
* images.grayscale(images.captureScreen()), 255, "GAUSSIAN_C", "BINARY_INV", 9, 6
* );
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* images.adaptiveThreshold = function (src, maxValue, adaptiveMethod, thresholdType, blockSize, C) {
* initIfNeeded();
* let mat = new Mat();
* adaptiveMethod = Imgproc["ADAPTIVE_THRESH_" + adaptiveMethod];
* thresholdType = Imgproc["THRESH_" + thresholdType];
* Imgproc.adaptiveThreshold(src.mat, mat, maxValue, adaptiveMethod, thresholdType, blockSize, C);
* return images.matToImage(mat);
* };
* @see org.opencv.imgproc.Imgproc
* @see org.autojs.autojs.core.opencv.Mat
* @see https://blog.csdn.net/sinat_36264666/article/details/77586964
* @see https://blog.csdn.net/abcvincent/article/details/78822191
*/
adaptiveThreshold(
src: ImageWrapper, maxValue: number, adaptiveMethod: Images.AdaptiveThresholdTypes,
thresholdType: 'BINARY' | 'BINARY_INV', blockSize: number, C: number,
): ImageWrapper;
/**
* Blurs an image using the normalized box filter.
* zh-CN: 均值滤波: 使用标准化的盒式过滤器平滑图像
* @param src - input image; it can have any number of channels,
* which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
* zh-CN: 源图像: 通道数不限, 但位深应为 CV_8U / CV_16U / CV_16S / CV_32F 或 CV_64F
* @param ksize - blurring kernel size.
* zh-CN: 滤波模板尺寸: 用于平滑操作的模板大小
* @param [anchor=[-1, -1]] - anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
* zh-CN: 锚点: 默认值为 Point(-1, -1), 表示锚点在模板中心
* @param [type="DEFAULT"] - border mode used to extrapolate pixels outside of the image (without "BORDER_" prefix).
* BORDER_WRAP is not supported.
* zh-CN: 边界模式: 指定处理边界像素时如何确定图像范围外像素的取值 (参数不包含 "BORDER_" 前缀) , 不支持 BORDER_WRAP
* @example
* images.requestScreenCapture(false);
* let dst = images.blur(images.captureScreen(), [3, 3]);
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.blur = function (src, ksize, anchor, type) {
* initIfNeeded();
* let mat = new Mat();
* ksize = newSize(ksize);
* type = Core["BORDER_" + (type || "DEFAULT")];
* if (anchor === undefined) {
* Imgproc.blur(src.mat, mat, ksize);
* } else {
* Imgproc.blur(src.mat, mat, ksize, new Point(anchor[0], anchor[1]), type);
* }
* return images.matToImage(mat);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function newSize(size) {
* if (!Array.isArray(size)) {
* size = [size, size];
* }
* if (size.length === 1) {
* size = [size[0], size[0]];
* }
* return new Size(size[0], size[1]);
* }
* @see org.autojs.autojs.core.opencv.Mat
* @see org.opencv.imgproc.Imgproc
* @see org.opencv.core.Core
* @see org.opencv.core.Size
* @see org.opencv.core.Point
* @see https://docs.opencv.org/3.4/javadoc/org/opencv/imgproc/Imgproc.html#blur(org.opencv.core.Mat,org.opencv.core.Mat,org.opencv.core.Size,org.opencv.core.Point,int)
* @see https://blog.csdn.net/keith_bb/article/details/54382426
*/
blur(
src: ImageWrapper, ksize: Side | [Width, Height?],
anchor?: [X, Y], type?: Images.BorderTypes,
): ImageWrapper;
/**
* Concatenate (combine) two images vertically and horizontally.
* This method does not change the existing images, but instead returns a new concatenated image.
* zh-CN: 连接两个图像. 若两图像连接处的尺寸不同, 则尺寸较小的图像将适当居中 (未验证 by SuperMonster003 as of Jul 25, 2020)
* @param imgA - source image 1
* @param imgB - source image 2
* @param [direction="RIGHT"] - direction of concatenation
* @example
* images.requestScreenCapture(false);
* let dst = images.concat(images.captureScreen(), images.read(files.path("./test.png")), "BOTTOM");
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.concat = function (imgA, imgB, direction) {
* initIfNeeded();
* direction = direction || "right";
* return javaImages.concat(imgA, imgB, android.view.Gravity[direction.toUpperCase()]);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* @see org.autojs.autojs.runtime.api.Images.concat
*/
concat(
imgA: ImageWrapper, imgB: ImageWrapper,
direction?: 'LEFT' | 'RIGHT' | 'TOP' | 'BOTTOM',
): ImageWrapper;
/**
* Converts an image from one color space to another.
* zh-CN: 颜色空间转换
* @param src - input image: 8-bit unsigned, 16-bit unsigned (CV_16UC...), or single-precision floating-point.
* zh-CN: 输入图像, 支持 8 位无符号整形, 16 位无符号整形或单精度浮点数
* @param code - color space conversion code (without "COLOR_" prefix).
* zh-CN: 颜色空间转换标识符 (参数不包含 "COLOR_" 前缀)
* @param dstCn - number of channels in the destination image;
* if the parameter is 0, the number of the channels is derived automatically from src and code.
* zh-CN: 目标图像的通道数, 该参数为 0 时, 目标图像根据源图像的通道数和具体操作自动决定
* @example
* images.requestScreenCapture(false);
* let dst = images.cvtColor(images.captureScreen(), "BGR2BGRA");
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.cvtColor = function (src, code, dstCn) {
* initIfNeeded();
* let mat = new Mat();
* code = Imgproc["COLOR_" + code];
* if (dstCn === undefined) {
* Imgproc.cvtColor(src.mat, mat, code);
* } else {
* Imgproc.cvtColor(src.mat, mat, code, dstCn);
* }
* return images.matToImage(mat);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* @see org.opencv.imgproc.Imgproc
* @see org.opencv.imgproc.Imgproc.cvtColor
* @see https://docs.opencv.org/3.1.0/d7/d1b/group__imgproc__misc.html#ga4e0972be5de079fed4e3a10e24ef5ef0
*/
cvtColor(src: ImageWrapper, code: ConversionCodes, dstCn?: number): ImageWrapper;
/**
* @param img
* @param color
* @param x
* @param y
* @param [threshold=4]
* @param [algorithm="diff"]
* @example
* images.requestScreenCapture(false);
* let like = idMatches(/.*like_icon/).findOnce();
* if (like) {
* let bounds = like.bounds();
* let [x, y] = [bounds.centerX(), bounds.centerY()];
* if (!images.detectsColor(images.captureScreen(), "#FED9A8", x, y)) {
* like.click();
* }
* }
* @example Source code summary (zh-CN: 源代码摘要)
* const defaultColorThreshold = 4;
* let javaImages = runtime.getImages();
* images.detectsColor = function (img, color, x, y, threshold, algorithm) {
* initIfNeeded();
* color = parseColor(color);
* algorithm = algorithm || "diff";
* threshold = threshold || defaultColorThreshold;
* let colorDetector = getColorDetector(color, algorithm, threshold);
* let pixel = images.pixel(img, x, y);
* return colorDetector.detectsColor(colors.red(pixel), colors.green(pixel), colors.blue(pixel));
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function parseColor(color) {
* if (typeof (color) === 'string') {
* color = colors.parseColor(color);
* }
* return color;
* }
* function getColorDetector(color, algorithm, threshold) {
* switch (algorithm) {
* case "rgb":
* return new org.autojs.autojs.core.image.ColorDetector.RGBDistanceDetector(color, threshold);
* case "equal":
* return new org.autojs.autojs.core.image.ColorDetector.EqualityDetector(color);
* case "diff":
* return new org.autojs.autojs.core.image.ColorDetector.DifferenceDetector(color, threshold);
* case "rgb+":
* return new org.autojs.autojs.core.image.ColorDetector.WeightedRGBDistanceDetector(color, threshold);
* case "hs":
* return new org.autojs.autojs.core.image.ColorDetector.HSDistanceDetector(color, threshold);
* }
* throw new Error("Unknown algorithm: " + algorithm);
* }
*/
detectsColor(
img: ImageWrapper, color: Color$, x: number, y: number,
threshold?: number, algorithm?: DetectionAlgorithm,
): boolean;
/**
* @example
* images.requestScreenCapture(false);
* let capt = images.captureScreen();
* let points_1 = images.findAllPointsForColor(capt, "#FF0000", {threshold: 51});
* let points_2 = images.findAllPointsForColor(capt, "#FF0000", {similarity: 0.8}); // same as above
* let points_3 = images.findAllPointsForColor(capt, "#00FF00", {
* region: [400, 500, 300, 200],
* threshold: 4,
* });
* if (points_3.length) {
* points_3.forEach((point_3) => {
* console.log(point_3.x + ", " + point_3.y);
* });
* }
* @example Source code summary (zh-CN: 源代码摘要)
* const defaultColorThreshold = 4;
* let javaImages = runtime.getImages();
* let colorFinder = javaImages.colorFinder;
* images.findAllPointsForColor = function (img, color, options) {
* initIfNeeded();
* color = parseColor(color);
* options = options || {};
* let threshold;
* if (options.similarity) {
* threshold = parseInt(255 * (1 - options.similarity));
* } else {
* threshold = options.threshold || defaultColorThreshold;
* }
* if (options.region) {
* return toPointArray(colorFinder.findAllPointsForColor(img, color, threshold, buildRegion(options.region, img)));
* } else {
* return toPointArray(colorFinder.findAllPointsForColor(img, color, threshold, null));
* }
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function parseColor(color) {
* if (typeof (color) === 'string') {
* color = colors.parseColor(color);
* }
* return color;
* }
* function buildRegion(region, img) {
* if (region === undefined) {
* region = [];
* }
* let x = region[0] === undefined ? 0 : region[0];
* let y = region[1] === undefined ? 0 : region[1];
* let width = region[2] === undefined ? img.getWidth() - x : region[2];
* let height = region[3] === undefined ? (img.getHeight() - y) : region[3];
* let r = new org.opencv.core.Rect(x, y, width, height);
* if (x < 0 || y < 0 || x + width > img.width || y + height > img.height) {
* throw new Error('out of region: region = [' + [x, y, width, height] + "], image.size = [' + [img.width, img.height] + ']");
* }
* return r;
* }
* function toPointArray(points) {
* let arr = [];
* for (let i = 0; i < points.length; i++) {
* arr.push(points[i]);
* }
* return arr;
* }
* @see findPointsByColor
*/
findAllPointsForColor(img: ImageWrapper, color: Color$, options?: {
similarity?: number,
threshold?: number,
region?: OmniRegion,
}): OpenCV.Points;
/**
* @param grayImg - 8-bit, single-channel, grayscale input image.
* zh-CN: 源图像: 单通道 8 位灰度图像
* @param [options={}]
* @param [options.region]
* @param [options.dp=1] - Inverse ratio of the accumulator resolution to the image resolution.
* For example, if dp=1 , the accumulator has the same resolution as the input image.
* If dp=2 , the accumulator has half as big width and height.
* zh-CN: 图像分辨率与累加器 (参数空间) 分辨率的比值 (圆弧圆心的累计分辨率).
* dp=1, 累加器 (参数空间) 与图像像素空间的分辨率相同.
* dp=2, 累加器 (参数空间) 的分辨率为像素空间的一半 (即累加器分辨率受影响更小)
* @param [options.minDst=grayImg.height/8] - Minimum distance between the centers of the detected circles.
* If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one.
* If it is too large, some circles may be missed.
* zh-CN: 圆心之间最小距离. 若距离太小会产生很多相交的圆. 若距离太大则会漏掉正确的圆
* @param [options.param1=100] - First method-specific parameter.
* It is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
* zh-CN: 用于 Canny 的边缘阀值上限. (下限为上限的一半)
* @param [options.param2=100] - Second method-specific parameter.
* It is the accumulator threshold for the circle centers at the detection stage.
* The smaller it is, the more false circles may be detected.
* Circles, corresponding to the larger accumulator values, will be returned first.
* zh-CN: 累加器阈值 (基于圆心的最小投票数). 阈值越小, 检测到的圆越多
* @param [options.minRadius=0] - Minimum circle radius.
* zh-CN: 最小圆半径
* @param [options.maxRadius=0] - Maximum circle radius.
* If <= 0, uses the maximum image dimension.
* If < 0, returns centers without finding the radius.
* zh-CN: 最大圆半径
* @example
* images.requestScreenCapture(false);
* let capt = images.captureScreen();
* let results = images.findCircles(images.grayscale(capt), {
* region: [120, 144, 720 - 120 * 2, 1280 - 144 * 2],
* dp: 1,
* minDst: 720 * 0.091,
* param1: 15,
* param2: 15,
* minRadius: 720 * 0.06,
* maxRadius: 720 * 0.078,
* }).map((o) => {
* o.radius = +o.radius.toFixed(2);
* return o;
* });
* console.log(results);
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.findCircles = function (grayImg, options) {
* initIfNeeded();
* options = options || {};
* let mat = options.region === undefined ? grayImg.mat : new Mat(grayImg.mat, buildRegion(options.region, grayImg));
* let resultMat = new Mat();
* let dp = options.dp === undefined ? 1 : options.dp;
* let minDst = options.minDst === undefined ? grayImg.height / 8 : options.minDst;
* let param1 = options.param1 === undefined ? 100 : options.param1;
* let param2 = options.param2 === undefined ? 100 : options.param2;
* let minRadius = options.minRadius === undefined ? 0 : options.minRadius;
* let maxRadius = options.maxRadius === undefined ? 0 : options.maxRadius;
* Imgproc.HoughCircles(mat, resultMat, Imgproc.CV_HOUGH_GRADIENT, dp, minDst, param1, param2, minRadius, maxRadius);
* let result = [];
* for (let i = 0; i < resultMat.rows(); i++) {
* for (let j = 0; j < resultMat.cols(); j++) {
* let d = resultMat.get(i, j);
* result.push({x: d[0], y: d[1], radius: d[2]});
* }
* }
* if (options.region !== undefined) {
* mat.release();
* }
* resultMat.release();
* return result;
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function buildRegion(region, img) {
* if (region === undefined) {
* region = [];
* }
* let x = region[0] === undefined ? 0 : region[0];
* let y = region[1] === undefined ? 0 : region[1];
* let width = region[2] === undefined ? img.getWidth() - x : region[2];
* let height = region[3] === undefined ? (img.getHeight() - y) : region[3];
* let r = new org.opencv.core.Rect(x, y, width, height);
* if (x < 0 || y < 0 || x + width > img.width || y + height > img.height) {
* throw new Error('out of region: region = [' + [x, y, width, height] + "], image.size = [' + [img.width, img.height] + ']");
* }
* return r;
* }
* @see org.opencv.imgproc.Imgproc.HoughCircles
* @see org.autojs.autojs.core.opencv.Mat
* @see org.opencv.imgproc.Imgproc
* @see https://docs.opencv.org/3.4/javadoc/org/opencv/imgproc/Imgproc.html#HoughCircles(org.opencv.core.Mat,org.opencv.core.Mat,int,double,double,double,double,int,int)
*/
findCircles(grayImg: ImageWrapper, options?: Images.Circles.Options): Images.Circles.Result;
/**
* @example
* images.requestScreenCapture(false);
* let capt = images.captureScreen();
* let point_1 = images.findColor(capt, "#FF0000", {threshold: 51});
* let point_2 = images.findColor(capt, "#FF0000", {similarity: 0.8}); // same as above
* let point_3 = images.findColor(capt, "#00FF00", {
* region: [400, 500, 300, 200],
* threshold: 4,
* });
* if (point_3) {
* toastLog(point_3.x + ", " + point_3.y);
* }
* @example Source code summary (zh-CN: 源代码摘要)
* const defaultColorThreshold = 4;
* let javaImages = runtime.getImages();
* let colorFinder = javaImages.colorFinder;
* images.findColor = function (img, color, options) {
* initIfNeeded();
* color = parseColor(color);
* options = options || {};
* let threshold;
* if (options.similarity) {
* threshold = parseInt(255 * (1 - options.similarity));
* } else {
* threshold = options.threshold || defaultColorThreshold;
* }
* if (options.region) {
* return colorFinder.findColor(img, color, threshold, buildRegion(options.region, img));
* } else {
* return colorFinder.findColor(img, color, threshold, null);
* }
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function parseColor(color) {
* if (typeof (color) === 'string') {
* color = colors.parseColor(color);
* }
* return color;
* }
* function buildRegion(region, img) {
* if (region === undefined) {
* region = [];
* }
* let x = region[0] === undefined ? 0 : region[0];
* let y = region[1] === undefined ? 0 : region[1];
* let width = region[2] === undefined ? img.getWidth() - x : region[2];
* let height = region[3] === undefined ? (img.getHeight() - y) : region[3];
* let r = new org.opencv.core.Rect(x, y, width, height);
* if (x < 0 || y < 0 || x + width > img.width || y + height > img.height) {
* throw new Error('out of region: region = [' + [x, y, width, height] + "], image.size = [' + [img.width, img.height] + ']");
* }
* return r;
* }
*/
findColor(img: ImageWrapper, color: Color$, options?: {
similarity?: number,
threshold?: number,
region?: OmniRegion,
}): OpenCV.Point | null;
/**
* @param img
* @param color
* @param [x=0]
* @param [y=0]
* @param [width=img.getWidth()-x]
* @param [height=img.getHeight()-y]
* @example
* images.requestScreenCapture(false);
* let capt = images.captureScreen();
* let point = images.findColorEquals(capt, "#FFFFFF");
* if (point) {
* toastLog(point.x + ", " + point.y);
* }
* @example Source code summary (zh-CN: 源代码摘要)
* images.findColorEquals = function (img, color, x, y, width, height) {
* return findColor(img, color, {
* region: [x, y, width, height],
* threshold: 0
* });
* };
* @see findColor
*/
findColorEquals(img: ImageWrapper, color: Color$, x?: X, y?: Y, width?: Width, height?: Height): OpenCV.Point | null;
/**
* @param img
* @param color
* @param [x=0]
* @param [y=0]
* @param [width=img.getWidth()-x]
* @param [height=img.getHeight()-y]
* @param [threshold=4]
* @example
* images.requestScreenCapture(false);
* let capt = images.captureScreen();
* let point = images.findColorInRegion(capt, "#FFFFFF");
* if (point) {
* toastLog(point.x + ", " + point.y);
* }
* @example Source code summary (zh-CN: 源代码摘要)
* images.findColorInRegion = function (img, color, x, y, width, height, threshold) {
* return findColor(img, color, {
* region: [x, y, width, height],
* threshold: threshold
* });
* };
* @see findColor
*/
findColorInRegion(img: ImageWrapper, color: Color$, x?: X, y?: Y, width?: Width, height?: Height, threshold?: number): OpenCV.Point | null;
/**
* @param img
* @param template
* @param [options={}]
* @param [options.threshold=0.9]
* @param [options.weakThreshold=0.6]
* @param [options.level=-1]
* @param [options.region]
* @example
* images.requestScreenCapture(false);
* let capt = images.captureScreen();
* let img = images.read("/sdcard/capture.png");
* let templ = images.read("/sdcard/template.png");
* let point = images.findImage(img, templ);
* if (point) {
* console.log(point.x + ", " + point.y);
* }
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.findImage = function (img, template, options) {
* initIfNeeded();
* options = options || {};
* let threshold = options.threshold || 0.9;
* let maxLevel = -1;
* if (typeof (options.level) === 'number') {
* maxLevel = options.level;
* }
* let weakThreshold = options.weakThreshold || 0.6;
* if (options.region) {
* return javaImages.findImage(img, template, weakThreshold, threshold, buildRegion(options.region, img), maxLevel);
* } else {
* return javaImages.findImage(img, template, weakThreshold, threshold, null, maxLevel);
* }
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function buildRegion(region, img) {
* if (region === undefined) {
* region = [];
* }
* let x = region[0] === undefined ? 0 : region[0];
* let y = region[1] === undefined ? 0 : region[1];
* let width = region[2] === undefined ? img.getWidth() - x : region[2];
* let height = region[3] === undefined ? (img.getHeight() - y) : region[3];
* let r = new org.opencv.core.Rect(x, y, width, height);
* if (x < 0 || y < 0 || x + width > img.width || y + height > img.height) {
* throw new Error('out of region: region = [' + [x, y, width, height] + "], image.size = [' + [img.width, img.height] + ']");
* }
* return r;
* }
* @see org.autojs.autojs.runtime.api.Images.findImage
*/
findImage(img: ImageWrapper, template: ImageWrapper, options?: {
threshold?: number,
weakThreshold?: number,
level?: number,
region?: OmniRegion,
}): OpenCV.Point | null;
/**
* @param image
* @param template
* @param [x=0]
* @param [y=0]
* @param [width=img.getWidth()-x]
* @param [height=img.getHeight()-y]
* @param [threshold=4]
* @example Source code summary (zh-CN: 源代码摘要)
* images.findImageInRegion = function (img, template, x, y, width, height, threshold) {
* return images.findImage(img, template, {
* region: [x, y, width, height],
* threshold: threshold
* });
* };
* @see findImage
*/
findImageInRegion(
image: ImageWrapper, template: ImageWrapper, x?: X, y?: Y,
width?: Width, height?: Height, threshold?: number,
): OpenCV.Point | null;
/**
* @example
* images.requestScreenCapture(false);
* let capt = images.captureScreen();
* let point = images.findMultiColors(
* capt, "#123456",
* [[10, 20, "#FFFFFF"], [30, 40, "#000000"]],
* {region: [0, 960, 1080, 960]}
* );
* @example Source code summary (zh-CN: 源代码摘要)
* const defaultColorThreshold = 4;
* let javaImages = runtime.getImages();
* let colorFinder = javaImages.colorFinder;
* images.findMultiColors = function (img, firstColor, paths, options) {
* initIfNeeded();
* options = options || {};
* firstColor = parseColor(firstColor);
* let list = java.lang.reflect.Array.newInstance(java.lang.Integer.TYPE, paths.length * 3);
* for (let i = 0; i < paths.length; i++) {
* let p = paths[i];
* list[i * 3] = p[0];
* list[i * 3 + 1] = p[1];
* list[i * 3 + 2] = parseColor(p[2]);
* }
* let region = options.region ? buildRegion(options.region, img) : null;
* let threshold = options.threshold === undefined ? defaultColorThreshold : options.threshold;
* return colorFinder.findMultiColors(img, firstColor, threshold, region, list);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function parseColor(color) {
* if (typeof (color) === 'string') {
* color = colors.parseColor(color);
* }
* return color;
* }
* function buildRegion(region, img) {
* if (region === undefined) {
* region = [];
* }
* let x = region[0] === undefined ? 0 : region[0];
* let y = region[1] === undefined ? 0 : region[1];
* let width = region[2] === undefined ? img.getWidth() - x : region[2];
* let height = region[3] === undefined ? (img.getHeight() - y) : region[3];
* let r = new org.opencv.core.Rect(x, y, width, height);
* if (x < 0 || y < 0 || x + width > img.width || y + height > img.height) {
* throw new Error('out of region: region = [' + [x, y, width, height] + "], image.size = [' + [img.width, img.height] + ']");
* }
* return r;
* }
* @see org.autojs.autojs.core.image.ColorFinder.findMultiColors
*/
findMultiColors(
img: ImageWrapper, firstColor: Color$, paths: [X, Y, Color$][],
options?: {
region?: OmniRegion;
threshold?: number;
},
): OpenCV.Point | null;
// noinspection SpellCheckingInspection
/**
* @example
* // "data:\\w+/\\w+;base64," could be omitted
* let base64 = "data:image/jpg;base64," + "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQYGBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYaKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wAARCAAhACEDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwB/i3xJfeI9VmubqaQwFz5MO75Y17AD1x1PetHwf4C1bxPAbm28q3swdommJAYjrtAGT/KuSr0F/HTz+CrTw1pNlcQXZWOHzYpOXOeQABn5j/Ov03ExrUKUaWDilra/RLufm2GlSrVZVMW29L26t9iPxJ8LtZ0awkvIpIL2CJS0giyHUDqcEcj6GuR0XWL7Rb2O6025khlQ5wp+VvZh3HtXtfw6F74X8G39z4rd7e2EheOK4bLBccgA+p6L/jXgzkFiQMAngelYZdiKmJdWjXtNRdrpaP8ATQ3zChTwypVaN4OSvZvVfrqezf8AC44v+gf/AOPUV4xRS/sDA/yfix/27jf5vwQV1E3hbXNE0mz8RgRLbgxTxSI4YqTgqSPrjrWNr2lXOi6tc2F5GySwuRyPvDsw9iK7rwT8RodM0YaPr9k17YKCqMoViFP8LK3DD8a6cZVrezjUw0VNdV3Xkc2EpUfaSp4luD6Ps/M634XeILzxlp2qWHiKOK8ijCDe0YAYNn5SAMZ4yCK8W1u1Sx1m/tIW3RwXEkSse4ViB/KvUNQ+KOlafpclr4S0k2sj5wzxpGiE/wAW1Sdx+uK8pt4LjUL1IYEknuZ3wqryzsa48roVKdWrWlD2cJWtH03fkdmZ14VKdKjGftJq95euy8yCivT/APhUOqf8/MH50V0/2zgv+fiOb+x8Z/z7ZsfHv/j30/8AGvGKKKxyH/cYfP8AM2z3/fZfL8gr0/4E/wDIeuf+uRoorbOf9yqehjlH++U/U9zooor80P0c/9k=";
* let img = images.fromBase64(base64);
* console.log(img.width + " × " + img.height);
* @example Source code summary (zh-CN: 源代码摘要)
* images.fromBase64 = function (base64) {
* return javaImages.fromBase64(base64);
* };
* @see org.autojs.autojs.runtime.api.Images.fromBase64
*/
fromBase64(base64: string): ImageWrapper;
/**
* images.fromBytes = function (bytes) {
* return javaImages.fromBytes(bytes);
* };
* @see org.autojs.autojs.runtime.api.Images.fromBytes
*/
fromBytes(bytes: number[]): ImageWrapper;
/**
* Blurs an image using a Gaussian filter.
* zh-CN: 高斯滤波: 使用高斯滤波器平滑图像 (高斯模糊)
* @param src - input image; the image can have any number of channels, which are processed independently,
* but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
* zh-CN: 源图像: 通道数不限, 位深需为 CV_8U / CV_16U / CV_16S / CV_32F / CV_64F
* @param ksize - Gaussian kernel size.
* zh-CN: 高斯内核大小 (ksize[0] 和 ksize[1] 可以不相同但这值必须均为正奇数, 若两个值为 0, 他们的值将由 sigma 参数计算)
* @param [sigmaX=0] - Gaussian kernel standard deviation in X direction.
* zh-CN: 高斯核函数在X方向上的标准偏差
* @param [sigmaY=0] - Gaussian kernel standard deviation in Y direction;
* if sigmaY is zero, it is set to be equal to sigmaX,
* if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively
* zh-CN: 高斯核函数在Y方向上的标准偏差 (若 sigmaY 是 0, 值将被设置为 sigmaX 值; 若 sigmaX 和 sigmaY 均为 0, 这两个值将由 ksize 计算而来)
* @param [type="DEFAULT"] - pixel extrapolation method (without "BORDER_" prefix).
* zh-CN: 推断图像外部像素的方法
* @example
* images.requestScreenCapture();
* let dst = images.gaussianBlur(images.captureScreen(), [3, 3]);
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.gaussianBlur = function (src, ksize, sigmaX, sigmaY, type) {
* initIfNeeded();
* let mat = new Mat();
* ksize = newSize(ksize);
* sigmaX = sigmaX === undefined ? 0 : sigmaX;
* sigmaY = sigmaY === undefined ? 0 : sigmaY;
* type = Core["BORDER_" + (type || "DEFAULT")];
* Imgproc.GaussianBlur(src.mat, mat, ksize, sigmaX, sigmaY, type);
* return images.matToImage(mat);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function newSize(size) {
* if (!Array.isArray(size)) {
* size = [size, size];
* }
* if (size.length === 1) {
* size = [size[0], size[0]];
* }
* return new Size(size[0], size[1]);
* }
* @see org.autojs.autojs.core.opencv.Mat
* @see org.opencv.imgproc.Imgproc
* @see org.opencv.core.Size
* @see org.opencv.core.Core
* @see https://docs.opencv.org/3.4/javadoc/org/opencv/imgproc/Imgproc.html#GaussianBlur(org.opencv.core.Mat,org.opencv.core.Mat,org.opencv.core.Size,double,double,int)
* @see https://blog.csdn.net/keith_bb/article/details/54412493
*/
gaussianBlur(
src: ImageWrapper, ksize: Side | [Width, Height?],
sigmaX?: number, sigmaY?: number, type?: Images.BorderTypes,
): ImageWrapper;
/**
* Applies the bilateral filter to an image
* @param img - Source 8-bit or floating-point, 1-channel or 3-channel image
* @param [d=0] - Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace
* @param [sigma_color=40] - Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color
* @param [sigma_space=20] - Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace
* @param [border_type='DEFAULT'] - border mode used to extrapolate pixels outside the image
* @see https://docs.opencv.org/3.4.3/d4/d86/group__imgproc__filter.html#ga9d7064d478c95d60003cf839430737ed
*/
bilateralFilter(
img: ImageWrapper, d?: number, sigma_color?: number,
sigma_space?: number, border_type?: Images.BorderTypes,
): ImageWrapper;
/**
* Grayscale an image.
* zh-CN: 灰度化
* @param src - input image: 8-bit unsigned, 16-bit unsigned (CV_16UC...), or single-precision floating-point.
* zh-CN: 输入图像, 支持 8 位无符号整形, 16 位无符号整形或单精度浮点数
* @param dstCn - number of channels in the destination image;
* if the parameter is 0, the number of the channels is derived automatically from src and code.
* zh-CN: 目标图像的通道数, 该参数为 0 时, 目标图像根据源图像的通道数和具体操作自动决定
* @example
* images.requestScreenCapture(false);
* let dst = images.grayscale(images.captureScreen());
* @example Source code summary (zh-CN: 源代码摘要)
* images.grayscale = function (src, dstCn) {
* return images.cvtColor(src, "BGR2GRAY", dstCn);
* };
* @see cvtColor
*/
grayscale(src: ImageWrapper, dstCn?: number): ImageWrapper;
/**
* Checks if array elements lie between the elements of two other arrays
* @example
* images.inRange(img, "#000000", "#222222");
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.inRange = function (img, lowerBound, upperBound) {
* initIfNeeded();
* let lb = new Scalar(
* colors.red(lowerBound), colors.green(lowerBound),
* colors.blue(lowerBound), colors.alpha(lowerBound)
* );
* let ub = new Scalar(
* colors.red(upperBound), colors.green(upperBound),
* colors.blue(upperBound), colors.alpha(lowerBound)
* );
* let bi = new Mat();
* Core.inRange(img.mat, lb, ub, bi);
* return images.matToImage(bi);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* @see org.opencv.core.Core.inRange
* @see org.autojs.autojs.core.opencv.Mat
* @see org.opencv.core.Scalar
*/
inRange(img: ImageWrapper, lowerBound: Color$, upperBound: Color$): ImageWrapper;
/**
* @example
* // set colors within the range of channel color value ±tolerance into #ffffff,
* // and set colors out of range above to #000000
* // zh-CN: 每个通道的颜色值均为 0x88 ，加减偏差 16 之后的范围是 [0x78, 0x98]
* // 因此此方法将 #787878 - #989898 范围颜色值变成 #ffffff 范围外变成 #000000
* images.interval(img, "#888888", 16);
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.interval = function (img, color, tolerance) {
* initIfNeeded();
* let lb = new Scalar(colors.red(color) - tolerance, colors.green(color) - tolerance,
* colors.blue(color) - tolerance, colors.alpha(color));
* let ub = new Scalar(colors.red(color) + tolerance, colors.green(color) + tolerance,
* colors.blue(color) + tolerance, colors.alpha(color));
* let bi = new Mat();
* Core.inRange(img.mat, lb, ub, bi);
* return images.matToImage(bi);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* @see org.opencv.core.Core.inRange
* @see org.autojs.autojs.core.opencv.Mat
* @see org.opencv.core.Scalar
*/
interval(img: ImageWrapper, color: Color$, tolerance: number): ImageWrapper;
/**
* @param img
* @param template
* @param [options={}]
* @param [options.threshold=0.9]
* @param [options.weakThreshold=0.6]
* @param [options.level=-1]
* @param [options.region]
* @param [options.max=5]
* @example
* images.requestScreenCapture(false);
* let capt = images.captureScreen();
* let img = images.read("/sdcard/capture.png");
* let templ = images.read("/sdcard/template.png");
* let matches = images.matchTemplate(img, templ, {threshold: 20, max: 11});
* matches.points.forEach(point => console.log(point.x + ", " + point.y));
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.matchTemplate = function (img, template, options) {
* initIfNeeded();
* options = options || {};
* let threshold = options.threshold || 0.9;
* let maxLevel = -1;
* if (typeof (options.level) === 'number') {
* maxLevel = options.level;
* }
* let max = options.max || 5;
* let weakThreshold = options.weakThreshold || 0.6;
* let result;
* if (options.region) {
* result = javaImages.matchTemplate(img, template, weakThreshold, threshold, buildRegion(options.region, img), maxLevel, max);
* } else {
* result = javaImages.matchTemplate(img, template, weakThreshold, threshold, null, maxLevel, max);
* }
* return new MatchingResult(result);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* function buildRegion(region, img) {
* if (region === undefined) {
* region = [];
* }
* let x = region[0] === undefined ? 0 : region[0];
* let y = region[1] === undefined ? 0 : region[1];
* let width = region[2] === undefined ? img.getWidth() - x : region[2];
* let height = region[3] === undefined ? (img.getHeight() - y) : region[3];
* let r = new org.opencv.core.Rect(x, y, width, height);
* if (x < 0 || y < 0 || x + width > img.width || y + height > img.height) {
* throw new Error('out of region: region = [' + [x, y, width, height] + "], image.size = [' + [img.width, img.height] + ']");
* }
* return r;
* }
* @see org.autojs.autojs.runtime.api.Images.matchTemplate
*/
matchTemplate(img: ImageWrapper, template: ImageWrapper, options?: {
threshold?: number;
weakThreshold?: number;
level?: number;
region?: OmniRegion;
max?: number;
}): Images.MatchingResult;
/**
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.matToImage = function (mat) {
* initIfNeeded();
* return Image.ofMat(mat);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
*/
matToImage(mat: org.autojs.autojs.core.opencv.Mat): ImageWrapper;
/**
* Blurs an image using the median filter.
* zh-CN: 中值滤波: 使用中值滤波器平滑图像
* @param src - input 1-, 3-, or 4-channel image;
* when ksize is 3 or 5, the image depth should be CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
* zh-CN: 输入图像: 图像为 1, 3, 4 通道图像; 当模板尺寸为 3 或 5 时, 图像深度只能为 CV_8U / CV_16U / CV_32F , 对于较大孔径尺寸的图片则只能是 CV_8U
* @param ksize - aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
* zh-CN: 滤波模板尺寸: 需为大于 1 的奇数, 如 3, 5, 7...
* @example
* images.requestScreenCapture(false);
* let dst = images.medianBlur(images.captureScreen(), 9);
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.medianBlur = function (src, ksize) {
* initIfNeeded();
* let mat = new Mat();
* Imgproc.medianBlur(src.mat, mat, ksize);
* return images.matToImage(mat);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }
* @see org.autojs.autojs.core.opencv.Mat
* @see org.opencv.imgproc.Imgproc
* @see https://docs.opencv.org/3.4/javadoc/org/opencv/imgproc/Imgproc.html#medianBlur(org.opencv.core.Mat,org.opencv.core.Mat,int)
* @see https://blog.csdn.net/keith_bb/article/details/54426920
*/
medianBlur(src: ImageWrapper, ksize: Side | [Side, Side]): ImageWrapper;
/**
* @example
* console.log(images.readPixels("test.png").data.slice(0, 10));
* @example Source code summary (zh-CN: 源代码摘要)
* images.readPixels = function (path) {
* let img = images.read(path);
* let bitmap = img.getBitmap();
* let w = bitmap.getWidth();
* let h = bitmap.getHeight();
* let pixels = util.java.array("int", w * h);
* bitmap.getPixels(pixels, 0, w, 0, 0, w, h);
* img.recycle();
* return {data: pixels, width: w, height: h};
* };
* @see read
* @see ImageWrapper.getBitmap
*/
readPixels(path: string): { data: number[], width: Width, height: Height };
/**
* @example
* images.requestScreenCapture(); // auto orientation
* @example
* images.requestScreenCapture(true); // landscape orientation
* @example
* images.requestScreenCapture(false); // portrait orientation
* @example Source code summary (zh-CN: 源代码摘要)
* const ResultAdapter = require("result-adapter");
* let javaImages = runtime.getImages();
* images.requestScreenCapture = function (landscape) {
* let ScreenCapturer = org.autojs.autojs.core.image.capture.ScreenCapturer;
* let orientation = ScreenCapturer.ORIENTATION_AUTO;
* if (landscape === true) {
* orientation = ScreenCapturer.ORIENTATION_LANDSCAPE;
* }
* if (landscape === false) {
* orientation = ScreenCapturer.ORIENTATION_PORTRAIT;
* }
* return ResultAdapter.wait(javaImages.requestScreenCapture(orientation));
* };
* @see runtime.getImages
* @see org.autojs.autojs.runtime.api.Images.requestScreenCapture
* @see org.autojs.autojs.core.image.capture.ScreenCapturer
*/
requestScreenCapture(landscape?: boolean): boolean;
/**
* Resizes an image.
* zh-CN: 缩放图像 (按尺寸)
* @param src - input image.
* zh-CN: 源图像
* @param dsize - output image size (Side|[Width,Height]).
* zh-CN: 输出图像尺寸
* @param [interpolation="LINEAR"] - interpolation method (without "INTER_" prefix).
* zh-CN: 插值方法 (参数不包含 “INTER_” 前缀)
* @example
* images.requestScreenCapture(false);
* let dst = images.resize(images.captureScreen(), [720, 1280]);
* @example Source code summary (zh-CN: 源代码摘要)
* let javaImages = runtime.getImages();
* images.resize = function (src, size, interpolation) {
* initIfNeeded();
* let mat = new Mat();
* interpolation = Imgproc["INTER_" + (interpolation || "LINEAR")];
* Imgproc.resize(src.mat, mat, newSize(size), 0, 0, interpolation);
* return images.matToImage(mat);
* };
* function initIfNeeded() {
* javaImages.initOpenCvIfNeeded();
* }