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Julia机器学习实战——使用Random Forest随机森林进行字符图像识别

, path) x = zeros(size(labelsInfo, 1), imageSize) for (index, idImage) in enumerate(labelsInfo.ID...Gray.(): 将RGB图像转化为灰度图像 reshape(): 在这里做的是平铺工作设置图像大小以及项目路径： imageSize = 400 path = "..."...读取训练图像数据： xTrain = read_data("train", labelsInfoTrain, imageSize, path) ?...读取测试图像数据： xTest = read_data("test", labelsInfoTest, imageSize, path) ?...(temp) x[index, :] = reshape(temp, 1, imageSize) end return x end imageSize = 400 path

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C#创建缩略图的操作类代码

= GetImageSize(image, width, height); return GetThumbnailImage(image, imageSize.Width, imageSize.Height...; int desiredWidth = imageSize.Width; imageSize.Height = desiredHeight; if...(widthRatio > 0) imageSize.Width = Convert.ToInt32(imageSize.Height * widthRatio);...if (imageSize.Width > desiredWidth) { imageSize.Width = desiredWidth;...imageSize.Height = Convert.ToInt32(imageSize.Width * heightRatio); } return imageSize

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Halcon MapImage 复刻 remap

, CV_32S); sub_map2 = sub_map2.zeros(imageSize, CV_16U); sub_map3 = sub_map3.zeros(imageSize, CV_16U)...; sub_map4 = sub_map4.zeros(imageSize, CV_16U); sub_map5 = sub_map5.zeros(imageSize, CV_16U); float x_value..., "uint2", imageSize.width, imageSize.height, (Hlong)(temp)); memcpy(temp, sub_map3.data, size * 2);...HalconCpp::GenImage1(&sub_map_data3, "uint2", imageSize.width, imageSize.height, (Hlong)(temp)); memcpy...(temp, sub_map4.data, size * 2); HalconCpp::GenImage1(&sub_map_data4, "uint2", imageSize.width, imageSize.height

1.2K3 0

小程序canvas生成海报图片压缩和失真问题解决

= {}; if(imageWidth){ imageSize.imageWidth = imageWidth; imageSize.imageHeight =...} }); } return imageSize; } /*** * 按照显示图片的高等比例缩放得到显示图片的宽 * @params...= {}; if(imageHeight){ imageSize.imageWidth = (imageHeight *originalWidth) / originalHeight...} }); } return imageSize; } } export default Util; 复制代码工具库使用 imageSize.imageHeight}) } }) 复制代码绘制背景图用上面的方法动态设置图片宽高，解决失真问题 import Util from '../..

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短视频直播源码，iOS图片去背景

targetData[i + 3] = 255; } } UIImage *result = [self imageFromBRGABytes: targetData imageSize... free(targetData); return result; } - (UIImage *)imageFromBRGABytes:(unsigned char *)imageBytes imageSize...:(CGSize)imageSize { CGImageRef imageRef = [self imageRefFromBGRABytes:imageBytes imageSize:imageSize...:(CGSize)imageSize { CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB(); CGContextRef...context = CGBitmapContextCreate(imageBytes, imageSize.width

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95.HarmonyOS NEXT 图片约束处理教程：深入理解Constrain

偏移计算实现2.1 最大偏移量计算export function getMaxAllowedOffset( winSize: number, imageSize: number, scale: number...) { const maxNum = Math.max(imageSize * scale, winSize); const minNum = Math.min(imageSize * scale...: number, scale: number): number { let maxAllowedOffset = getMaxAllowedOffset(winSize, imageSize,...图片切换判断3.1 切换条件判断function isToggle( offset: number, winSize: number, imageSize: number, scale: number..., TogglePercent: number): boolean { let maxAllowedOffset = getMaxAllowedOffset(winSize, imageSize

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Imageloader-获取图片需要显示的大小

/** * 压缩的宽和高 */ private class ImageSize{ int width ; int height; }...计算压缩的宽和高的方法 /** * 根据imageView获取适当的压缩的宽和高 * @param imageView */ private ImageSize...getImageViewSize(ImageView imageView) { ImageSize imageSize = new ImageSize(); final...// parameter if (height <= 0) height = displayMetrics.heightPixels; imageSize.width...= width; imageSize.height = height; return imageSize; } /** * 为了兼容低版本，没有采用@

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OpenHarmony3.1组件：用Slider组件控制风车旋转和尺寸

300, right: 16 }) .rotate({ x: 0, y: 0, z: 1, angle: this.angle }) .scale({ x: this.imageSize..., y: this.imageSize }) 在Image组件中设置了很多属性，如height、width等，这些都是使用静态值设置的，而旋转角度（this.angle）和图像缩放比例（this.imageSize...() } 本案例完整的代码如下： @Entry @Component struct Index { @State private speed: number = 5 @State private imageSize..., y: this.imageSize }) // 创建Text组件(用于描述Slider组件) this.DescribeText('速度：', this.speed) Slider({...) // 用于控制缩放比例 Slider({ value: this.imageSize, min: 0.5, max: 4.5,

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iOS 图片处理生成文字图片

小图片不放大 static func imageZoomByWidth(_ sourceImage:UIImage,maxWidth:CGFloat) -> UIImage{ let imageSize...= sourceImage.size; let width = imageSize.width; let height = imageSize.height;...static func imageZoomByHeight(_ sourceImage:UIImage,maxHeight:CGFloat) -> UIImage{ let imageSize...= sourceImage.size; let width = imageSize.width; let height = imageSize.height;...= sourceImage.size; let width = imageSize.width; let height = imageSize.height;

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62.Harmonyos NEXT 图片预览组件之工具类实现

最大偏移量计算// 获取最大偏移量export function getMaxAllowedOffset(winSize: number, imageSize: number, scale: number...) { const maxNum = Math.max(imageSize * scale, winSize); const minNum = Math.min(imageSize * scale...计算逻辑如下：当图片尺寸大于窗口尺寸时（imageSize * scale > winSize）：maxNum = imageSize * scaleminNum = winSize最大偏移量 = (imageSize...* scale - winSize) / 2当图片尺寸小于窗口尺寸时（imageSize * scale imageSize *...scale最大偏移量 = (winSize - imageSize * scale) / 24.

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62.Harmonyos NEXT 图片预览组件之工具类实现

最大偏移量计算 // 获取最大偏移量 export function getMaxAllowedOffset(winSize: number, imageSize: number, scale: number...) { const maxNum = Math.max(imageSize * scale, winSize); const minNum = Math.min(imageSize *...计算逻辑如下：当图片尺寸大于窗口尺寸时（imageSize * scale > winSize）： maxNum = imageSize * scale minNum = winSize 最大偏移量...= (imageSize * scale - winSize) / 2 当图片尺寸小于窗口尺寸时（imageSize * scale < winSize）： maxNum = winSize minNum...= imageSize * scale 最大偏移量 = (winSize - imageSize * scale) / 2 4.

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【传感器标定】图片校正（c++、python代码）

(ImgWidth, ImgHeight); cv::Mat NewCameraMatrix = getOptimalNewCameraMatrix(K, D, imageSize, alpha, imageSize..., 0); // 非鱼眼相机 initUndistortRectifyMap(K, D, cv::Mat(), NewCameraMatrix, imageSize, CV_16SC2, map1,...const int ImgWidth = 1920; const int ImgHeight = 1080; cv::Mat map1, map2; cv::Size imageSize...= 0; cv::Mat NewCameraMatrix; cv::fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, imageSize...cv::Mat UndistortImage; cv::fisheye::undistortImage(RawImage, UndistortImage, K, D, K, imageSize

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C++对于大型图片的加载缩放尝试

// 设置图片名 reader.setFileName("D:\\123.jpg"); // 读取图片大小 //sleep(30000); QSize imageSize...= reader.size(); // 缩放图片尺寸以适应屏幕大小 //imageSize.scale(100,100,); //设定宽度高度比例 //imageSize.scale...(100,Qt::KeepAspectRatio); // 设置图片大小 imageSize.setHeight(500); imageSize.setWidth(500); reader.setScaledSize...(imageSize); img= reader.read(); // 读取图片 QLabel *m_label= new QLabel; //m_label->resize(image_width

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使用PaddlePaddle实现人脸对比和人脸识别

, type_size, center_crop_size = 128): self.imageSize = imageSize self.type_size = type_size...********************** myReader = MyReader(imageSize=imageSize, type_size=type_size, center_crop_size..., imageSize)).flatten() return img # 使用训练好的参数进行预测 def to_prediction(inferer, image_paths, imageSize..., imageSize)).flatten() return img # 使用训练好的参数进行预测 def to_prediction(inferer, image_paths, imageSize...=imageSize) print('预测结果为:%d,可信度为:%f' % (result, probability))

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【CV实战】年轻人的第一个深度学习图像分割项目应该是什么样的（Pytorch框架）？

class SegDataset(Dataset): def __init__(self,filetxt,imagesize,cropsize,transform=None):...lines = open(filetxt,'r').readlines() self.samples = [] self.imagesize = imagesize...,self.imagesize),interpolation=cv2.INTER_NEAREST) label = cv2.resize(label,(self.imagesize,self.imagesize...),interpolation=cv2.INTER_NEAREST) offsetx = np.random.randint(self.imagesize-self.cropsize)...(filetxt,imagesize,cropsize) print(mydataset.

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快速高斯模糊算法

unsigned int read, ri, p, n; unsigned int imgWidth = w; unsigned int imgHeight = h; unsigned int imageSize...= imgWidth*imgHeight; unsigned char * rgb = (unsigned char *)malloc(sizeof(unsigned char) * imageSize...* 3); unsigned char * r = rgb; unsigned char * g = rgb + imageSize; unsigned char * b = rgb + imageSize...* 2; unsigned char * rgb2 = (unsigned char *)malloc(sizeof(unsigned char) * imageSize * 3); unsigned...char * r2 = rgb2; unsigned char * g2 = rgb2 + imageSize; unsigned char * b2 = rgb2 + imageSize * 2

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《我的PaddlePaddle学习之路》笔记四——自定义图像数据集的识别

): self.imageSize = imageSize def train_mapper(self,sample): ''' map image.../model/model.tar" # 数据的大小 datadim = 3 * imageSize * imageSize paddleUtil = PaddleUtil().../model/model.tar" # 数据的大小 datadim = 3 * imageSize * imageSize # 添加数据 image_path = [].../model/model.tar" # 数据的大小 datadim = 3 * imageSize * imageSize paddleUtil = PaddleUtil().../model/model.tar" # 数据的大小 datadim = 3 * imageSize * imageSize # ************************

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【Matlab】基于特征的全景图像拼接（计算机视觉工具箱）

初始化变量以保存图像大小 imageSize = zeros(numImages,2); % Iterate over remaining image pairs 迭代剩余的图像对 for n = 2...imageSize(n,:) = size(grayImage); % Detect and extract SURF features for I(n)....(i,2)], [1 imageSize(i,1)]); end 接下来，计算每次变换的平均 X 限制，并找到位于中心的图像。...outputLimits for i = 1:numel(tforms) [xlim(i,:), ylim(i,:)] = outputLimits(tforms(i), [1 imageSize...(i,2)], [1 imageSize(i,1)]); end maxImageSize = max(imageSize); % Find the minimum and maximum output

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为volantis主题添加瀑布流相册

const fs = require('fs-extra'); const path = require('path'); const imageSize = require('image-size')...plot.dirName = dirName; plot.fileName = subfileName; const imageInfo = imageSize...= item.split(" ")[0]; imageX = imageSize.split(".")[0]; imageY = imageSize.split...let self = this; var html, imgNameWithPattern, imgName, imageSize...= item.split(" ")[0]; imageX = imageSize.split(".")[0]; imageY = imageSize.split

1.3K1 0

Wolfram语言将有趣且平凡的数学转变为艺术

{\[Theta], 2 \[Pi] i/m, 2 \[Pi] (m + i - 2)/m, 2 \[Pi]/m}], PlotRange -> 3, ImageSize...540, Background -> None], {i,1, m}]; ImageCompose[Graphics[Background -> cols[[5]], ImageSize...Background -> RGBColor["#172940"], PlotStyle -> Directive[Thickness[.004], RGBColor["#acf0f2"]], ImageSize...Background -> RGBColor["#172940"], PlotStyle -> Directive[Thickness[.004], RGBColor["#acf0f2"]], ImageSize...verts, k + 1][[i]]}]}, {i, 1, n - 1, 2}, {t, 1/12, 11/12, 1/12}]}, PlotRange -> 3, ImageSize

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点击加载更多

Julia机器学习实战——使用Random Forest随机森林进行字符图像识别

C#创建缩略图的操作类代码

Halcon MapImage 复刻 remap

小程序canvas生成海报图片压缩和失真问题解决

短视频直播源码，iOS图片去背景

95.HarmonyOS NEXT 图片约束处理教程：深入理解Constrain

Imageloader-获取图片需要显示的大小

OpenHarmony3.1组件：用Slider组件控制风车旋转和尺寸

iOS 图片处理生成文字图片

62.Harmonyos NEXT 图片预览组件之工具类实现

62.Harmonyos NEXT 图片预览组件之工具类实现

【传感器标定】图片校正（c++、python代码）

C++对于大型图片的加载缩放尝试

使用PaddlePaddle实现人脸对比和人脸识别

【CV实战】年轻人的第一个深度学习图像分割项目应该是什么样的（Pytorch框架）？

快速高斯模糊算法

《我的PaddlePaddle学习之路》笔记四——自定义图像数据集的识别

【Matlab】基于特征的全景图像拼接（计算机视觉工具箱）

为volantis主题添加瀑布流相册

Wolfram语言将有趣且平凡的数学转变为艺术

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