- 综合排序
- 最热优先
- 最新优先
- 来自专栏测试开发技术
Magnitude：强！一款基于 AI 视觉的 Web 自动化框架
什么是 Magnitude？ Magnitude 是一款基于AI视觉 的 Web 自动化框架，它彻底摆脱了传统自动化工具对 DOM 结构的依赖，通过模拟人类视觉和交互方式来操控浏览器。 ，有两种方式可以快速开始： 方式一：创建自动化项目 npx create-magnitude-app 这条命令会创建一个新的项目，并引导你完成 Magnitude 的设置，还会生成一个可以立即运行的示例脚本 方式二：在现有项目中使用测试运行器 npm i --save-dev magnitude-test && npx magnitude init 初始化后会生成： magnitude.config.ts ', { data: { title: '使用 Magnitude', description: '运行 "npx create-magnitude-app" 并跟随指示 如果你受够了维护脆弱的自动化脚本，不妨试试 Magnitude，让 AI 来处理那些繁琐的界面交互细节。
50110编辑于 2025-12-31 - 来自专栏云深之无迹
Hog图像特征提取算法,HOG
, gradient_angle = self.global_gradient() gradient_magnitude = abs(gradient_magnitude) = gradient_magnitude[i * self.cell_size:(i + 1) * self.cell_size, j = mag(block_vector) if magnitude ! = 0: normalize = lambda block_vector, magnitude: [element / magnitude for element = [0] * self.bin_size for i in range(cell_magnitude.shape[0]): for j in range(cell_magnitude.shape
5.2K20发布于 2020-08-12 - 来自专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏
Google Earth Engine ——MCD43A4 V6天底双向反射分布函数调整反射率（NBAR）这个产品结合了Terra和Aqua航天器的数据，从16天的时间里选择最好的代表像素。
QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude
44810编辑于 2024-02-02 - 来自专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏
Google Earth Engine ——MCD43A2 V6双向反射分布函数和反照率（BRDF/Albedo）质量500m数据集是一个500米的每日16天产品。包含质量信息！
those with a Solar Zenith Angle that is > 70 degrees even though WoDs and RMSE majority are good)2: Magnitude those with a Solar Zenith Angle that is > 70 degrees even though WoDs and RMSE majority are good)2: Magnitude those with a Solar Zenith Angle that is > 70 degrees even though WoDs and RMSE majority are good)2: Magnitude those with a Solar Zenith Angle that is > 70 degrees even though WoDs and RMSE majority are good)2: Magnitude inversion (numobs ≥ 7)3: Magnitude inversion (numobs ≥ 2 and < 7)4: Fill value BRDF_Albedo_Uncertainty
27710编辑于 2024-02-02 - 来自专栏AutoML(自动机器学习)
Python数据增强(data augmentation)库--Augmentor 使用介绍
Augmentor 使用介绍 原图 1.random_distortion(probability, grid_height, grid_width, magnitude) 最终选择参数为 p.random_distortion (probability=0.8, grid_height=3, grid_width=3, magnitude=6) 其他参数效果： magnitude和grid_width,grid_height越大 ，扭曲程度越大 p.random_distortion(probability=0.6, grid_height=6, grid_width=6, magnitude=5) p.random_distortion (probability=0.6, grid_height=6, grid_width=6, magnitude=9) p.random_distortion(probability=0.6, grid_height =10, grid_width=10, magnitude=5) 2.random_erasing(probability, rectangle_area) rectangle_area表示覆盖区域的比例
2.1K80发布于 2018-04-11 - 来自专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏
Google Earth Engine ——MCD43A3 V6反照率模型数据集是一个每天16天的产品。它提供了MODIS表面反射波段（波段1到波段7）以及3个宽光谱波段（可见光、近红外和短波）
QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude
51610编辑于 2024-02-02 - 来自专栏tkokof 的技术,小趣及杂念
Sweet Snippet 之 Gram-Schmidt 正交化
, #a do ret = ret + a[i] * b[i] end return ret end end -- magnitude return math.sqrt(val) end end -- normalize, do not change param function norm(a) local magnitude = mag(a) if magnitude and magnitude ~= 0 then local normalize = {} for i = 1, #a do table.insert(normalize, a[i] / magnitude) end return
69340发布于 2019-08-29 - 来自专栏全栈程序员必看
扩频调制matlab仿真
=4096;%设定进行fft变换的点数为4096个 subplot(2,1,2) m_sequ_fft=fft(bipolar_m_sequ, sample_num);%双极性M序列进行fft变换 magnitude =abs(m_sequ_fft);%采样后的振幅magnitude frequency_sequence=(1:sample_num)*200/sample_num;%频率序列，按采样频率为200进行采样 %进行fft变换得到的幅值不是真实的，需要将变换后的结果乘2再除以个数N plot(frequency_sequence,magnitude(1:sample_num)*2/sample_num); =abs(y_bpsk); frequency_bpsk=(1:N/2)*100000/N; subplot(2,1,1) plot(frequency_unbpsk,magnitude_unbpsk *magnitude_recovery; %观察经过低通滤波器后无扩频与扩频系统的时域波形 %对扩频系统做iift变换 y_bitlist_recovery_ifft=real(ifft(bs.
1.3K10编辑于 2022-08-24 - 来自专栏AutoML(自动机器学习)
Augmentor 使用介绍
solid black;border-radius:15px;"> random_distortion(probability, grid_height, grid_width, magnitude ) 最终选择参数为 p.random_distortion(probability=0.8, grid_height=3, grid_width=3, magnitude=6)
- 来自专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏
Google Earth Engine ——MCD43A1 V6双向反射分布函数和反照率（BRDF/Albedo）模型参数数据集是一个500米每日16天的产品2000年至今
QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude QA bit index 0: Processed, good quality (full BRDF inversions) 1: Processed, see other QA (magnitude
49110编辑于 2024-02-02 - 来自专栏大宇笔记
Swift 读标准库源码笔记 -- Integers(基本数据类型篇)
/ 自定义类型想去遵守该协议，需要实现初始化方法和操作符， 和提供一个`magnitude`属性。 associatedtype Magnitude : Comparable, Numeric /// The magnitude of this value. /// /// For any numeric value `x`, `x.magnitude` is the absolute value of `x`. /// You can use the `magnitude` property in var magnitude: Magnitude { get } /// Multiplies two values and produces their product. /// /// The { return unsafeBitCast(x.magnitude, to: T.self) } return x < (0 as T) ?
1.2K20发布于 2019-10-25 - 来自专栏贾志刚-OpenCV学堂
干货 | OpenCV实现边缘模板匹配算法
算法原理 该算法主要是基于图像梯度，实现基于梯度级别的NCC模板匹配，基于Sobel梯度算子得到dx, dy, magnitude ? 通过Canny算法得到边缘图像、基于轮廓发现得到所有的轮廓点集，基于每个点计算该点的dx、dy、magnitude(dxy)三个值。生成模板信息。 算法实现代码详解 梯度图像计算 Mat gx, gy; Sobel(gray, gx, CV_32F, 1, 0); Sobel(gray, gy, CV_32F, 0, 1); Mat magnitude , direction; cartToPolar(gx, gy, magnitude, direction); long contoursLength = 0; double magnitudeTemp <float>(y, x); pointInfo.Magnitude = magnitudeTemp; if (magnitudeTemp !
6.1K52发布于 2019-04-29 - 来自专栏大宇笔记
iOS 悬浮可拖动可点击按钮
{ //计算速度向量的长度，当他小于200时，滑行会很短 CGPoint velocity = [recognizer velocityInView:self.view]; CGFloat magnitude = sqrtf((velocity.x * velocity.x) + (velocity.y * velocity.y)); CGFloat slideMult = magnitude / 200 ; //NSLog(@"magnitude: %f, slideMult: %f", magnitude, slideMult); //e.g. 397.973175, slideMult: 1.989866
2.6K20编辑于 2022-12-22 - 来自专栏机器视觉工坊
OpenCV与图像处理（五）
, gradient_angle = self.global_gradient() gradient_magnitude = abs(gradient_magnitude) = gradient_magnitude[i * self.cell_size:(i + 1) * self.cell_size, j = mag(block_vector) if magnitude ! = 0: normalize = lambda block_vector, magnitude: [element / magnitude for element [0]): for j in range(cell_magnitude.shape[1]): gradient_strength = cell_magnitude
89320发布于 2020-07-28 - 来自专栏大宇笔记
iOS 悬浮可拖动可点击按钮
//计算速度向量的长度，当他小于200时，滑行会很短 CGPoint velocity = [recognizer velocityInView:self.view]; CGFloat magnitude = sqrtf((velocity.x * velocity.x) + (velocity.y * velocity.y)); CGFloat slideMult = magnitude / 200 ; //NSLog(@"magnitude: %f, slideMult: %f", magnitude, slideMult); //e.g. 397.973175, slideMult: 1.989866
3.1K10发布于 2019-01-15 - 来自专栏FPGA技术江湖
FPGA DSP：Vivado 中带有 DDS 的 FIR 滤波器
frequency response [H, f] = freqz(filter_taps, 1, N, Sample_Rate); % Calculate the frequency response % Magnitude and phase response magnitude = abs(H); % Magnitude response % Plot the filter response % Magnitude response plot figure; plot(f, 20*log10(magnitude),'linewidth',1.3); % Plot magnitude in dB grid on; title ('Magnitude Response (dB)',FontSize=22); xlabel_txt = 'Frequency (Hz)'; xlabel(xlabel_txt,FontSize=22 ); ylabel('Magnitude (dB)',FontSize=22); %xlim([0 30e6]) figure freqz(filter_taps,1) figure stem(filter_taps
95510编辑于 2025-02-21 - 来自专栏Swift-开源分析
标准库中的主要关联类型
UnsignedInteger -- IntegerLiteralType, Magnitude SignedInteger -- IntegerLiteralType, Magnitude FixedWidthInteger -- IntegerLiteralType, Magnitude FloatingPoint -- IntegerLiteralType, Magnitude BinaryFloatingPoint -- IntegerLiteralType, FloatLiteralType, Magnitude
87040编辑于 2022-11-29 - 来自专栏深度学习和计算机视觉
干货 | OpenCV实现边缘模板匹配算法
算法原理 该算法主要是基于图像梯度，实现基于梯度级别的NCC模板匹配，基于Sobel梯度算子得到dx, dy, magnitude ? 通过Canny算法得到边缘图像、基于轮廓发现得到所有的轮廓点集，基于每个点计算该点的dx、dy、magnitude(dxy)三个值。生成模板信息。 算法实现代码详解 梯度图像计算 Mat gx, gy; Sobel(gray, gx, CV_32F, 1, 0); Sobel(gray, gy, CV_32F, 0, 1); Mat magnitude , direction; cartToPolar(gx, gy, magnitude, direction); long contoursLength = 0; double magnitudeTemp <float>(y, x); pointInfo.Magnitude = magnitudeTemp; if (magnitudeTemp !
7.5K70发布于 2019-05-31 - 来自专栏猫头虎博客专区
深入解析向量数据库：定义、原理和应用的全面指南
computeSimilarity 计算两个向量之间的余弦相似度 func computeSimilarity(vec1, vec2 []float64) float64 { dotProduct := 0.0 magnitude1 := 0.0 magnitude2 := 0.0 for i := 0; i < len(vec1); i++ { dotProduct += vec1[i] * vec2[i] magnitude1 += vec1[i] * vec1[i] magnitude2 += vec2[i] * vec2[i] } magnitude1 = sqrt(magnitude1) magnitude2 = sqrt(magnitude2) return dotProduct / (magnitude1 * magnitude2) } // sqrt 计算一个数的平方根 func sqrt(num
2.5K10编辑于 2024-04-08 - 来自专栏猫头虎博客专区
什么是向量数据库？
computeSimilarity 计算两个向量之间的余弦相似度 func computeSimilarity(vec1, vec2 []float64) float64 { dotProduct, magnitude1 , magnitude2 := 0.0, 0.0, 0.0 for i := 0; i < len(vec1); i++ { dotProduct += vec1[i] * vec2[i] magnitude1 += vec1[i] * vec1[i] magnitude2 += vec2[i] * vec2[i] } magnitude1 = sqrt(magnitude1) magnitude2 = sqrt(magnitude2) return dotProduct / (magnitude1 * magnitude2) } // sqrt 计算一个数的平方根 func
1.1K10编辑于 2024-04-08
腾讯云开发者
Copyright © 2013 - 2026 Tencent Cloud. All Rights Reserved. 腾讯云 版权所有
深圳市腾讯计算机系统有限公司 ICP备案/许可证号：粤B2-20090059 
粤公网安备44030502008569号
腾讯云计算（北京）有限责任公司 京ICP证150476号 | 京ICP备11018762号