OpenCV

ERROR: Could not build wheels for opencv-python which use PEP 517 and cannot be installed directly

由 谁说胖子不能爱 提交于 2021-01-26 07:42:50
问题 I was trying to install OpenCV4 in a docker on jetson nano. It has jetpack 4.4 s os. The docker was successfully created and Tensorflow is running but while installing OpenCV using pip it is showing CMake error. root@5abf405fb92d:~# pip3 install opencv-python Collecting opencv-python Downloading opencv-python-4.4.0.42.tar.gz (88.9 MB) |████████████████████████████████| 88.9 MB 2.5 kB/s Installing build dependencies ... done Getting requirements to build wheel ... done Preparing wheel metadata

ERROR: Could not build wheels for opencv-python which use PEP 517 and cannot be installed directly

由 只谈情不闲聊 提交于 2021-01-26 07:42:17
问题 I was trying to install OpenCV4 in a docker on jetson nano. It has jetpack 4.4 s os. The docker was successfully created and Tensorflow is running but while installing OpenCV using pip it is showing CMake error. root@5abf405fb92d:~# pip3 install opencv-python Collecting opencv-python Downloading opencv-python-4.4.0.42.tar.gz (88.9 MB) |████████████████████████████████| 88.9 MB 2.5 kB/s Installing build dependencies ... done Getting requirements to build wheel ... done Preparing wheel metadata

用于手语识别的自注意力机制

由 ↘锁芯ラ 提交于 2021-01-26 07:16:33
点击上方 “ 小白学视觉 ”，选择加" 星标 "或“ 置顶 ” 重磅干货，第一时间送达 小白导读 论文是学术研究的精华和未来发展的明灯。小白决心每天为大家带来经典或者最新论文的解读和分享，旨在帮助各位读者快速了解论文内容。个人能力有限，理解难免出现偏差，建议对文章内容感兴趣的读者，一定要下载原文，了解具体内容。 摘要 提出了一种用于连续手语识别的注 意网络。 该方法利用相互独立的数据流对手 语模态进行建模。 这些不同的信息渠道可以在彼此之间共享一个复杂的时间结构。 出于这个原因，我们将注意力应用于同步，并帮助捕获不同符号语言组件之间的相互依赖关系。 尽管手语是多通道的，但手形是手语解释的中心实体。 在正确的语境中看到手形可以定义符号的含义。 考虑到这一点，我们利用注意机制来有效地聚合具有适当时空背景的手部特征，从而更好地进行符号识别。 我们发现，通过这样做，该模型能够识别 围绕支配手和面部区域的基本手语成分。 我们在rth - phoenix - weather 2014基准数据集上测试了我们的模型，得出了竞争结果。 本文创新点 本文提出了一种基于注意的序列符号语言比对识别方法。 与以前的作品不同，我们的方法的独创性在于明确地从非手工手语组件中提取和聚合上下文信息。 在没有任何领域注释的情况下，我们的方法能够在预测手势时独家识别与手势形状相关的最相关的特征。

Can Canny in OpenCV deal with both grayscale and color images?

由 假装没事ソ 提交于 2021-01-26 04:21:20
问题 I have some questions about the Canny edge detector in OpenCV . Here is the code I tried. def auto_canny(image, sigma=0.33): v = np.median(image) lower = int(max(0, (1.0 - sigma) * v)) upper = int(min(255, (1.0 + sigma) * v)) edged = cv2.Canny(image, lower, upper) then, ##### first situation ##### img = cv2.imread('mango.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) auto = auto_canny(gray) cv2.imwrite('mango_gray_edge.jpg', auto) In this situation, I got an image like this: ##### second

Can Canny in OpenCV deal with both grayscale and color images?

由 断了今生、忘了曾经 提交于 2021-01-26 04:17:17
问题 I have some questions about the Canny edge detector in OpenCV . Here is the code I tried. def auto_canny(image, sigma=0.33): v = np.median(image) lower = int(max(0, (1.0 - sigma) * v)) upper = int(min(255, (1.0 + sigma) * v)) edged = cv2.Canny(image, lower, upper) then, ##### first situation ##### img = cv2.imread('mango.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) auto = auto_canny(gray) cv2.imwrite('mango_gray_edge.jpg', auto) In this situation, I got an image like this: ##### second

Can Canny in OpenCV deal with both grayscale and color images?

由 回眸只為那壹抹淺笑 提交于 2021-01-26 04:17:12
问题 I have some questions about the Canny edge detector in OpenCV . Here is the code I tried. def auto_canny(image, sigma=0.33): v = np.median(image) lower = int(max(0, (1.0 - sigma) * v)) upper = int(min(255, (1.0 + sigma) * v)) edged = cv2.Canny(image, lower, upper) then, ##### first situation ##### img = cv2.imread('mango.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) auto = auto_canny(gray) cv2.imwrite('mango_gray_edge.jpg', auto) In this situation, I got an image like this: ##### second

linux工程构建：make，cmake

由 我与影子孤独终老i 提交于 2021-01-25 07:47:26
make cmake C Make是一个比make更高级的编译配置工具，它可以根据不同平台、不同的编译器，生成相应的Makefile或者vcproj项目。通过编写CMakeLists.txt，可以控制生成的Makefile，从而控制编译过程。 CMake自动生成的Makefile不仅可以通过make命令构建项目生成目标文件，还支持安装（make install）、测试安装的程序是否能正确执行（make test，或者ctest）、生成当前平台的安装包（make package）、生成源码包（make package_source）、产生Dashboard显示数据并上传等高级功能，只要在CMakeLists.txt中简单配置，就可以完成很多复杂的功能，包括写测试用例。如果有嵌套目录，子目录下可以有自己的CMakeLists.txt。 外部编译 外部编译，一个最大的好处是，对于原有的工程没 有任何影响，所有动作全部发生在编译目录。通过这一点，也足以说服我们全部采用外部编 译方式构建工程。 #确定cmake最低版本需求 cmake_minimum_required(VERSION 3.0 . 0 ) #打印 MESSAGE(STATUS " This is install dir " ${CMAKE_INSTALL_PREFIX}) #确定工程名 #(这一行会引入两个变量XXX

Java 身份证号码识别系统

由 橙三吉。 提交于 2021-01-24 14:21:16
大家好，我是阿逛！ 最近发现一个有趣的项目。 这个 项目是通过学习https://gitee.com/nbsl/idCardCv 后整合 tess4j,不需要经过训练直接使用的，当然，你也可以进行训练后进行使用。 该项目修改原有的需要安装 opencv 的过程，全部使用 javaccp 技术重构,通过 javaccp 引入需要的 c++ 库进行开发。不需要安装 opencv 新增的了前端控制识别区域的功能，新增了后端识别后验证 ，页面样式主要适应 paid，重新修改了后面的识别过程，用户 opencv 进行图片优化和区域 选择，使用 tess4j 进行数字和 x 的识别 配合样式中的区域在后台裁剪相关区域图片 /idCardCv/src/main/resources/static/js/plugins/cropper/cropper.css 身份证号码识别 请求地址 http://localhost:8080/idCard/index 它基于 openCV 开源库。这意味着你可以获取全部源代码，并且移植到 opencv 支持的所有平台。它是基于 java 开发。它的识别率较高。图片清晰情况下，号码检测与识别准确率在90%以上。 Required Software 本版本在以下平台测试通过： windows7 64bit jdk1.8.0_45 junit 4 opencv4.3

Extract boxes from sudoku in opencv [duplicate]

由 旧城冷巷雨未停 提交于 2021-01-24 12:03:10
问题 This question already has answers here : How to get the cells of a sudoku grid with OpenCV? (3 answers) Closed 8 days ago . i have converted sudoku image into sudoku grid using opencv now i want to extract each box from image what is best way to do this? as per my knowledge i am trying to find intersection points of lines to find corner of each box class SudokuSolverPlay: def __init__(self, image): def __preProcess(self, img): """return grayscale image""" def __maskSudoku(self, img): ""

Extract boxes from sudoku in opencv [duplicate]

由 江枫思渺然 提交于 2021-01-24 12:01:32
问题 This question already has answers here : How to get the cells of a sudoku grid with OpenCV? (3 answers) Closed 8 days ago . i have converted sudoku image into sudoku grid using opencv now i want to extract each box from image what is best way to do this? as per my knowledge i am trying to find intersection points of lines to find corner of each box class SudokuSolverPlay: def __init__(self, image): def __preProcess(self, img): """return grayscale image""" def __maskSudoku(self, img): ""
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