图片显示特效效果 jQuery Image Viewer 产品图片 多角度 展示特效 代码 jQuery Image Viewer 产品图片多角度展示特效代码,jQuery就有功这臣,不过本特效中CSS也起到举足轻重的作用,一个好的效果离不开JS,同样也需要CSS的大力支持,本特效完成的效果主要是仿Flash,每次在切换的时候都不是单一的切换,而是具有动感的变化效果
2023-01-08 16:44:11 235KB 图片显示特效效果 jQuery Image Viewer
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imgViewer imgViewer是一个jQuery插件,允许缩放和平移图像。 缩放和平移在桌面浏览器上进行,使用鼠标滚轮来放大和缩小鼠标左键单击并拖动以进行平移。 在启用触摸的设备上,捏合手势可用于放大和缩小以及点击和拖动以平移。 它是基于jQuery小部件工厂的易于扩展的响应式UI组件,并使用来支持触摸事件。 它应该可以在支持的所有浏览器。 如果找不到此插件,请查看我的插件,该插件提供相同的功能,但使用映射库进行基础图像显示。 例子 文献资料 执照 该插件是根据提供的。 版权所有(c)2017 Wayne Mogg。
2023-01-08 14:42:41 785KB jquery jquery-plugin image responsive
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cifar_image_recognition 使用带有pytorch的cifar10的图像识别 快速入门指南 在克隆的存储库中,在终端中运行以下命令: $ conda env创建-f environment.yml $ conda激活cifar_env 如果使用pycharm,请在创建的conda env中将解释器设置为python版本,例如: ... / anaconda3 / envs / sheep_env / bin / python 从environment.yml列表添加或删除依赖项时,请运行: $ conda env更新--file environment.yml 二手货源/依赖 待定 系统依赖关系: 待定 去做: 待定
2023-01-08 13:48:06 7KB Python
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单个图像除雾 介绍 该程序使用暗通道先验实现单图像去雾。 编译依赖 OpenCV 脾气暴躁的 例子 演算法 使用暗通道先验去除单个图像混浊,何凯明,孙建和唐小鸥”,在CVPR 2009中 指导图像过滤,何凯明,孙健和汤小鸥”,在ECCV 2010中。
2023-01-08 10:23:00 771KB Python
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Preface xi Acknowledgements xvii 1 Image Processing 1 1.1 Basic Definitions 2 1.2 Image Formation 3 1.3 Image Processing Operations 7 1.4 Example Application 9 1.5 Real-Time Image Processing 11 1.6 Embedded Image Processing 12 1.7 Serial Processing 12 1.8 Parallelism 14 1.9 Hardware Image Processing Systems 18 2 Field Programmable Gate Arrays 21 2.1 Programmable Logic 21 2.1.1 FPGAs vs. ASICs 24 2.2 FPGAs and Image Processing 25 2.3 Inside an FPGA 26 2.3.1 Logic 27 2.3.2 Interconnect 28 2.3.3 Input and Output 29 2.3.4 Clocking 30 2.3.5 Configuration 31 2.3.6 Power Consumption 32 2.4 FPGA Families and Features 33 2.4.1 Xilinx 33 2.4.2 Altera 38 2.4.3 Lattice Semiconductor 44 2.4.4 Achronix 46 2.4.5 SiliconBlue 47 2.4.6 Tabula 47 2.4.7 Actel 48 2.4.8 Atmel 49 2.4.9 QuickLogic 50 2.4.10 MathStar 50 2.4.11 Cypress 51 2.5 Choosing an FPGA or Development Board 51 3 Languages 53 3.1 Hardware Description Languages 56 3.2 Software-Based Languages 61 3.2.1 Structural Approaches 63 3.2.2 Augmented Languages 64 3.2.3 Native Compilation Techniques 69 3.3 Visual Languages 72 3.3.1 Behavioural 73 3.3.2 Dataflow 73 3.3.3 Hybrid 74 3.4 Summary 77 4 Design Process 79 4.1 Problem Specification 79 4.2 Algorithm Development 81 4.2.1 Algorithm Development Process 82 4.2.2 Algorithm Structure 83 4.2.3 FPGA Development Issues 86 4.3 Architecture Selection 86 4.3.1 System Level Architecture 87 4.3.2 Computational Architecture 89 4.3.3 Partitioning between Hardware and Software 93 4.4 System Implementation 96 4.4.1 Mapping to FPGA Resources 97 4.4.2 Algorithm Mapping Issues 100 4.4.3 Design Flow 101 4.5 Designing for Tuning and Debugging 102 4.5.1 Algorithm Tuning 102 4.5.2 System Debugging 104 5 Mapping Techniques 107 5.1 Timing Constraints 107 5.1.1 Low Level Pipelining 107 5.1.2 Process Synchronisation 110 5.1.3 Multiple Clock Domains 111 5.2 Memory Bandwidth Constraints 113 5.2.1 Memory Architectures 113 5.2.2 Caching 116 5.2.3 Row Buffering 117 5.2.4 Other Memory Structures 118 vi Contents 5.3 Resource Constraints 122 5.3.1 Resource Multiplexing 122 5.3.2 Resource Controllers 125 5.3.3 Reconfigurability 130 5.4 Computational Techniques 132 5.4.1 Number Systems 132 5.4.2 Lookup Tables 138 5.4.3 CORDIC 142 5.4.4 Approximations 150 5.4.5 Other Techniques 152 5.5 Summary 154 6 Point Operations 155 6.1 Point Operations on a Single Image 155 6.1.1 Contrast and Brightness Adjustment 155 6.1.2 Global Thresholding and Contouring 159 6.1.3 Lookup Table Implementation 162 6.2 Point Operations on Multiple Images 163 6.2.1 Image Averaging 164 6.2.2 Image Subtraction 166 6.2.3 Image Comparison 170 6.2.4 Intensity Scaling 171 6.2.5 Masking 173 6.3 Colour Image Processing 175 6.3.1 False Colouring 175 6.3.2 Colour Space Conversion 176 6.3.3 Colour Thresholding 192 6.3.4 Colour Correction 193 6.3.5 Colour Enhancement 197 6.4 Summary 197 7 Histogram Operations 199 7.1 Greyscale Histogram 199 7.1.1 Data Gathering 201 7.1.2 Histogram Equalisation 206 7.1.3 Automatic Exposure 210 7.1.4 Threshold Selection 211 7.1.5 Histogram Similarity 219 7.2 Multidimensional Histograms 219 7.2.1 Triangular Arrays 220 7.2.2 Multidimensional Statistics 222 7.2.3 Colour Segmentation 226 7.2.4 Colour Indexing 229 7.2.5 Texture Analysis 231 Contents vii 8 Local Filters 233 8.1 Caching 233 8.2 Linear Filters 239 8.2.1 Noise Smoothing 239 8.2.2 Edge Detection 241 8.2.3 Edge Enhancement 243 8.2.4 Linear Filter Techniques 243 8.3 Nonlinear Filters 248 8.3.1 Edge Orientation 250 8.3.2 Non-maximal Suppression 251 8.3.3 Zero-Crossing Detection 252 8.4 Rank Filters 252 8.4.1 Rank Filter Sorting Networks 255 8.4.2 Adaptive Histogram Equalisation 260 8.5 Colour Filters 261 8.6 Morphological Filters 264 8.6.1 Binary Morphology 264 8.6.2 Greyscale Morphology 269 8.6.3 Colour Morphology 270 8.7 Adaptive Thresholding 271 8.7.1 Error Diffusion 271 8.8 Summary 273 9 Geometric Transformations 275 9.1 Forward Mapping 276 9.1.1 Separable Mapping 277 9.2 Reverse Mapping 282 9.3 Interpolation 285 9.3.1 Bilinear Interpolation 286 9.3.2 Bicubic Interpolation 288 9.3.3 Splines 290 9.3.4 Interpolating Compressed Data 292 9.4 Mapping Optimisations 292 9.5 Image Registration 294 9.5.1 Feature-Based Methods 295 9.5.2 Area-Based Methods 299 9.5.3 Applications 305 10 Linear Transforms 309 10.1 Fourier Transform 310 10.1.1 Fast Fourier Transform 311 10.1.2 Filtering 318 10.1.3 Inverse Filtering 320 10.1.4 Interpolation 321 10.1.5 Registration 322 viii Contents 10.1.6 Feature Extraction 323 10.1.7 Goertzel’s Algorithm 324 10.2 Discrete Cosine Transform 325 10.3 Wavelet Transform 328 10.3.1 Filter Implementations 330 10.3.2 Applications of the Wavelet Transform 335 10.4 Image and Video Coding 336 11 Blob Detection and Labelling 343 11.1 Bounding Box 343 11.2 Run-Length Coding 346 11.3 Chain Coding 347 11.3.1 Sequential Implementation 347 11.3.2 Single Pass Algorithms 348 11.3.3 Feature Extraction 350 11.4 Connected Component Labelling 352 11.4.1 Random Access Algorithms 353 11.4.2 Multiple-Pass Algorithms 353 11.4.3 Two-Pass Algorithms 354 11.4.4 Single-Pass Algorithms 356 11.4.5 Multiple Input Labels 358 11.4.6 Further Optimisations 358 11.5 Distance Transform 359 11.5.1 Morphological Approaches 360 11.5.2 Chamfer Distance 360 11.5.3 Separable Transform 362 11.5.4 Applications 365 11.5.5 Geodesic Distance Transform 365 11.6 Watershed Transform 366 11.6.1 Flow Algorithms 366 11.6.2 Immersion Algorithms 367 11.6.3 Applications 369 11.7 Hough Transform 370 11.7.1 Line Hough Transform 371 11.7.2 Circle Hough Transform 373 11.7.3 Generalised Hough Transform 374 11.8 Summary 375 12 Interfacing 377 12.1 Camera Input 378 12.1.1 Camera Interface Standards 378 12.1.2 Deinterlacing 383 12.1.3 Global and Rolling Shutter Correction 384 12.1.4 Bayer Pattern Processing 384 Contents ix 12.2 Display Output 387 12.2.1 Display Driver 387 12.2.2 Display Content 390 12.3 Serial Communication 393 12.3.1 PS2 Interface 393 12.3.2 I2C 395 12.3.3 SPI 397 12.3.4 RS-232 397 12.3.5 USB 398 12.3.6 Ethernet 398 12.3.7 PCI Express 399 12.4 Memory 400 12.4.1 Static RAM 400 12.4.2 Dynamic RAM 401 12.4.3 Flash Memory 402 12.5 Summary 402 13 Testing, Tuning and Debugging 405 13.1 Design 405 13.1.1 Random Noise Sources 406 13.2 Implementation 409 13.2.1 Common Implementation Bugs 410 13.3 Tuning 412 13.4 Timing Closure 412 14 Example Applications 415 14.1 Coloured Region Tracking 415 14.2 Lens Distortion Correction 418 14.2.1 Characterising the Distortion 419 14.2.2 Correcting the Distortion 421 14.3 Foveal Sensor 424 14.3.1 Foveal Mapping 425 14.3.2 Using the Sensor 429 14.4 Range Imaging 429 14.4.1 Extending the Unambiguous Range 431 14.5 Real-Time Produce Grading 433 14.5.1 Software Algorithm 434 14.5.2 Hardware Implementation 436 14.6 Summary 439 References 441 Index 475 x Content
2023-01-07 18:32:43 27.35MB FPGA 图像处理
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Shenzhen_Spreadview_Century_Technologies_Co,_Ltd__SiI-DS-1107-0.80
2023-01-04 18:16:25 1.8MB silicon image
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肿瘤图片分类代码,可应用于医疗病理学图片的分类(可通用于各种医疗图像图片的分类),具有良好的效果,可供大家学习
2023-01-03 19:52:43 13.58MB 人工智能
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Efficient gamut clipping for color image processing using LHS and YIQ 很经典的一篇图像增强处理的方法介绍,在LHS和YIQ空间都可以,调整饱和度,亮度等,还有超出RGB范围时,快速做gamut clipping,在显示和拍照里都用的到
2023-01-03 13:28:26 545KB gamutclipping LHS YUV
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图片字幕 介绍 建立一个模型以从图像生成字幕。 给定图像后,模型可以用英语描述图像中的内容。 为了实现这一点,我们的模型由一个编码器(一个CNN)和一个解码器(一个RNN)组成。 为CNN编码器提供了用于分类任务的图像,其输出被馈送到RNN解码器,后者输出英语句子。 该模型及其超参数的调整基于论文和。 我们使用微软Çommon在CO NTEXT(MS COCO)O bjects为这个项目。 它是用于场景理解的大规模数据集。 该数据集通常用于训练和基准化对象检测,分段和字幕算法。 有关下载数据的说明,请参见下面的“数据”部分。 代码 该代码可以分为两类: 笔记本-该项目的主要代码由一系列Jupyter笔记本构成: 0_Dataset.ipynb介绍数据集并绘制一些样本图像。 1_Preliminaries.ipynb加载和预处理数据并使用模型进行实验。 2_Training.ip
2023-01-02 13:00:14 2.09MB nlp computer-vision cnn pytorch
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An Image is worth 16x16 Words: Transformer for image recognition手写笔记
2022-12-31 19:25:24 7.98MB 深度学习 论文阅读笔记
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