如何在构建cv.imread()时包含opencv.js？
如何在构建cv.imread()时包含opencv.js？
提问于 2021-04-21 07:03:09
我克隆了OpenCV Git存储库：git clone https://github.com/opencv/opencv.git，没有改变任何东西。
然后，我一步一步地遵循OpenCV上的build OpenCV文档，并使用以下命令成功地构建了opencv.js和tests
python ./platforms/js/build_js.py build_js --build_test
然后我用一个实时服务器打开了创建的test.html，并看到所有的测试都成功了："581 assertions of 581 passed, 0 failed."。
但是，当我在opencv.js标记之间插入构建的<script> </script>并使用它时，除了cv.imread()之外，它仍然工作。我收到错误消息：‘
"Uncaught : cv.imread不是一个函数“.`
下面是代码块：
let canvas = document.getElementById("canvas");
let ctx = canvas.getContext("2d");
let matrix = cv.imread("canvas");然后，我使用了正式的opencv.js和imread()工作。但是问题是，opencv.js的大小是7.7MB，因为它包含了OpenCV的大部分功能，我只需要一些核心和图像处理功能。
如何将imread()包含在构建中？到目前为止，我还没有找到解决办法。我只找到了构建白名单:它位于OpenCV存储库中，名为opencv_js.config.py，包括以下内容。知道如何将imread()包含在构建中吗？由于OpenCV JavaScript文档在几乎每个示例中都使用imread()，所以应该有一种简单的方法来包含它。
# Classes and methods whitelist
core = {
'': [
'absdiff', 'add', 'addWeighted', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'cartToPolar',
'compare', 'convertScaleAbs', 'copyMakeBorder', 'countNonZero', 'determinant', 'dft', 'divide', 'eigen',
'exp', 'flip', 'getOptimalDFTSize','gemm', 'hconcat', 'inRange', 'invert', 'kmeans', 'log', 'magnitude',
'max', 'mean', 'meanStdDev', 'merge', 'min', 'minMaxLoc', 'mixChannels', 'multiply', 'norm', 'normalize',
'perspectiveTransform', 'polarToCart', 'pow', 'randn', 'randu', 'reduce', 'repeat', 'rotate', 'setIdentity', 'setRNGSeed',
'solve', 'solvePoly', 'split', 'sqrt', 'subtract', 'trace', 'transform', 'transpose', 'vconcat',
'setLogLevel', 'getLogLevel',
],
'Algorithm': [],
}
imgproc = {'': ['Canny', 'GaussianBlur', 'Laplacian', 'HoughLines', 'HoughLinesP', 'HoughCircles', 'Scharr','Sobel', \
'adaptiveThreshold','approxPolyDP','arcLength','bilateralFilter','blur','boundingRect','boxFilter',\
'calcBackProject','calcHist','circle','compareHist','connectedComponents','connectedComponentsWithStats', \
'contourArea', 'convexHull', 'convexityDefects', 'cornerHarris','cornerMinEigenVal','createCLAHE', \
'createLineSegmentDetector','cvtColor','demosaicing','dilate', 'distanceTransform','distanceTransformWithLabels', \
'drawContours','ellipse','ellipse2Poly','equalizeHist','erode', 'filter2D', 'findContours','fitEllipse', \
'fitLine', 'floodFill','getAffineTransform', 'getPerspectiveTransform', 'getRotationMatrix2D', 'getStructuringElement', \
'goodFeaturesToTrack','grabCut','initUndistortRectifyMap', 'integral','integral2', 'isContourConvex', 'line', \
'matchShapes', 'matchTemplate','medianBlur', 'minAreaRect', 'minEnclosingCircle', 'moments', 'morphologyEx', \
'pointPolygonTest', 'putText','pyrDown','pyrUp','rectangle','remap', 'resize','sepFilter2D','threshold', \
'undistort','warpAffine','warpPerspective','warpPolar','watershed', \
'fillPoly', 'fillConvexPoly', 'polylines',
],
'CLAHE': ['apply', 'collectGarbage', 'getClipLimit', 'getTilesGridSize', 'setClipLimit', 'setTilesGridSize'],
'segmentation_IntelligentScissorsMB': [
'IntelligentScissorsMB',
'setWeights',
'setGradientMagnitudeMaxLimit',
'setEdgeFeatureZeroCrossingParameters',
'setEdgeFeatureCannyParameters',
'applyImage',
'applyImageFeatures',
'buildMap',
'getContour'
],
}
objdetect = {'': ['groupRectangles'],
'HOGDescriptor': ['load', 'HOGDescriptor', 'getDefaultPeopleDetector', 'getDaimlerPeopleDetector', 'setSVMDetector', 'detectMultiScale'],
'CascadeClassifier': ['load', 'detectMultiScale2', 'CascadeClassifier', 'detectMultiScale3', 'empty', 'detectMultiScale'],
'QRCodeDetector': ['QRCodeDetector', 'decode', 'decodeCurved', 'detect', 'detectAndDecode', 'detectMulti', 'setEpsX', 'setEpsY']}
video = {'': ['CamShift', 'calcOpticalFlowFarneback', 'calcOpticalFlowPyrLK', 'createBackgroundSubtractorMOG2', \
'findTransformECC', 'meanShift'],
'BackgroundSubtractorMOG2': ['BackgroundSubtractorMOG2', 'apply'],
'BackgroundSubtractor': ['apply', 'getBackgroundImage']}
dnn = {'dnn_Net': ['setInput', 'forward'],
'': ['readNetFromCaffe', 'readNetFromTensorflow', 'readNetFromTorch', 'readNetFromDarknet',
'readNetFromONNX', 'readNet', 'blobFromImage']}
features2d = {'Feature2D': ['detect', 'compute', 'detectAndCompute', 'descriptorSize', 'descriptorType', 'defaultNorm', 'empty', 'getDefaultName'],
'BRISK': ['create', 'getDefaultName'],
'ORB': ['create', 'setMaxFeatures', 'setScaleFactor', 'setNLevels', 'setEdgeThreshold', 'setFirstLevel', 'setWTA_K', 'setScoreType', 'setPatchSize', 'getFastThreshold', 'getDefaultName'],
'MSER': ['create', 'detectRegions', 'setDelta', 'getDelta', 'setMinArea', 'getMinArea', 'setMaxArea', 'getMaxArea', 'setPass2Only', 'getPass2Only', 'getDefaultName'],
'FastFeatureDetector': ['create', 'setThreshold', 'getThreshold', 'setNonmaxSuppression', 'getNonmaxSuppression', 'setType', 'getType', 'getDefaultName'],
'AgastFeatureDetector': ['create', 'setThreshold', 'getThreshold', 'setNonmaxSuppression', 'getNonmaxSuppression', 'setType', 'getType', 'getDefaultName'],
'GFTTDetector': ['create', 'setMaxFeatures', 'getMaxFeatures', 'setQualityLevel', 'getQualityLevel', 'setMinDistance', 'getMinDistance', 'setBlockSize', 'getBlockSize', 'setHarrisDetector', 'getHarrisDetector', 'setK', 'getK', 'getDefaultName'],
# 'SimpleBlobDetector': ['create'],
'KAZE': ['create', 'setExtended', 'getExtended', 'setUpright', 'getUpright', 'setThreshold', 'getThreshold', 'setNOctaves', 'getNOctaves', 'setNOctaveLayers', 'getNOctaveLayers', 'setDiffusivity', 'getDiffusivity', 'getDefaultName'],
'AKAZE': ['create', 'setDescriptorType', 'getDescriptorType', 'setDescriptorSize', 'getDescriptorSize', 'setDescriptorChannels', 'getDescriptorChannels', 'setThreshold', 'getThreshold', 'setNOctaves', 'getNOctaves', 'setNOctaveLayers', 'getNOctaveLayers', 'setDiffusivity', 'getDiffusivity', 'getDefaultName'],
'DescriptorMatcher': ['add', 'clear', 'empty', 'isMaskSupported', 'train', 'match', 'knnMatch', 'radiusMatch', 'clone', 'create'],
'BFMatcher': ['isMaskSupported', 'create'],
'': ['drawKeypoints', 'drawMatches', 'drawMatchesKnn']}
photo = {'': ['createAlignMTB', 'createCalibrateDebevec', 'createCalibrateRobertson', \
'createMergeDebevec', 'createMergeMertens', 'createMergeRobertson', \
'createTonemapDrago', 'createTonemapMantiuk', 'createTonemapReinhard', 'inpaint'],
'CalibrateCRF': ['process'],
'AlignMTB' : ['calculateShift', 'shiftMat', 'computeBitmaps', 'getMaxBits', 'setMaxBits', \
'getExcludeRange', 'setExcludeRange', 'getCut', 'setCut'],
'CalibrateDebevec' : ['getLambda', 'setLambda', 'getSamples', 'setSamples', 'getRandom', 'setRandom'],
'CalibrateRobertson' : ['getMaxIter', 'setMaxIter', 'getThreshold', 'setThreshold', 'getRadiance'],
'MergeExposures' : ['process'],
'MergeDebevec' : ['process'],
'MergeMertens' : ['process', 'getContrastWeight', 'setContrastWeight', 'getSaturationWeight', \
'setSaturationWeight', 'getExposureWeight', 'setExposureWeight'],
'MergeRobertson' : ['process'],
'Tonemap' : ['process' , 'getGamma', 'setGamma'],
'TonemapDrago' : ['getSaturation', 'setSaturation', 'getBias', 'setBias', \
'getSigmaColor', 'setSigmaColor', 'getSigmaSpace','setSigmaSpace'],
'TonemapMantiuk' : ['getScale', 'setScale', 'getSaturation', 'setSaturation'],
'TonemapReinhard' : ['getIntensity', 'setIntensity', 'getLightAdaptation', 'setLightAdaptation', \
'getColorAdaptation', 'setColorAdaptation']
}
aruco = {'': ['detectMarkers', 'drawDetectedMarkers', 'drawAxis', 'estimatePoseSingleMarkers', 'estimatePoseBoard', 'estimatePoseCharucoBoard', 'interpolateCornersCharuco', 'drawDetectedCornersCharuco'],
'aruco_Dictionary': ['get', 'drawMarker'],
'aruco_Board': ['create'],
'aruco_GridBoard': ['create', 'draw'],
'aruco_CharucoBoard': ['create', 'draw'],
'aruco_DetectorParameters': ['create']
}
calib3d = {'': ['findHomography', 'calibrateCameraExtended', 'drawFrameAxes', 'estimateAffine2D', \
'getDefaultNewCameraMatrix', 'initUndistortRectifyMap', 'Rodrigues', \
'solvePnP', 'solvePnPRansac', 'solvePnPRefineLM']}
white_list = makeWhiteList([core, imgproc, objdetect, video, dnn, features2d, photo, aruco, calib3d])Stack Overflow用户
发布于 2022-03-19 11:58:07
我也面临过同样的问题。看起来，opencv.js文档还没有为新版本的emscripten进行更新(或者说，这只是我正在做的与OpenCV文档不同的事情，而且碰巧起作用了)。
如点缀的文档中所示，您可以使用onRuntimeInitialized函数检测WASM模块何时完成加载，然后通过Module对象访问其导出的函数。这就是我如何能够使用opencv.js，包括cv.imread
<html>
<head>
</head>
<body>
<h1>Hello OpenCV.js</h1>
<div>
<div>
<canvas id="canvas"></canvas>
</div>
<div>
<input type="file" id="fileInput" name="file" />
</div>
</div>
<script>
var Module = {
onRuntimeInitialized: function() {
console.log(Module.imread)
const cv = Module;
let fileInput = document.getElementById('fileInput');
fileInput.onchange = (e) => {
const image = new Image();
image.src = URL.createObjectURL(e.target.files[0]);
image.onload = () => {
const canvas = document.getElementById('canvas');
const context = canvas.getContext('2d');
canvas.height = image.height;
canvas.width = image.width;
context.drawImage(image, 0, 0, image.width, image.height);
const imageData = context.getImageData(0, 0, image.width, image.height);
const cvImage = cv.imread("canvas");
const mean = cv.mean(cvImage)
console.log(`Mean: ${mean[0]}, ${mean[1]}, ${mean[2]}`)
}
};
}
};
</script>
<script src="opencv.js"></script>
</body>
</html>重要：确保您没有编译OpenCV imcodecs模块，因为imread的浏览器版本将被C++版本覆盖。浏览器实现(在opencv/modules/js/src/helpers.js中定义)从浏览器加载图像，而C++版本则从文件加载图像。
请记住，我刚刚开始使用WASM，emscripten和JavaScript，所以请原谅任何可能的术语误用。如果我做错了什么或者做错了什么，我也会非常乐意得到一些反馈。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/67190799
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