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基于聚类的图像分割(image segmentation)从下面这张图可以看出两个问题:过分割和欠分割。首先说一下分割得目标:就是将具有相似性质的像素分在一起。Group together similar-looking pixels for efficiency of further processing.1、超像素分割(superpixels)X. Ren and J. Malik. Learning a classification model for segmentation. ICCV 2003."Bottom-up" process这种自底向上的处理方式,通过分析像素底层的相似性去连接。“top-down”自顶向下是凭借语义的相似性把图像进行分割。如何将两种方式融合?这是一个思考题!超像素分割是一种无监督的分割方法。基于超像素算法的图像分割与LIME结合对于神经网络的可解释性以后有时间单独写一篇详细介绍。2、Inspiration from psychology格式塔(Gestalt)理论是心理学中的理性主义理论之一,强调经验和行为的整体性。格式塔心理学研究认为,人类具有不需要学习的组织倾向,使我们能够在视觉环境中组织排列事物的位置,感受和知觉出环境的整体与连续。上图中我们总是先看到总体,再关注局部。总体不等于局部之和,意识不等于感觉元素的集合。而且有时候看到不一定是真是的,就拿下面这张图来说,上下两个图像直线的长度是否一致?这是因为人类在自然界中的视觉是基于群组感受的。所以,从这个角度来考虑,人是从整体来考虑这个事物的。此外,元素的累加可能会带来不同的效应。Elements in a collection can have properties that result from relationship。在现实生活中的群组现象:假如下面是电梯按钮,如果按照上面一排,人类视觉对于按键的组合需要思考,甚至有时会出现误判,但是如果通过一个黑色的形状将其连接,对于这种群组出错的概率会大大降低!3、基于聚类的图像分割算法k-means clustering based on intensity or color is essentially vector quantization of the image attributes
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normalize的目的是什么?
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kubeadm在安装时需要从k8s.io官网下载一些必要的docker images,因为k8s.io网络不通,root@ecs-b769:~# kubeadm init --apiserver-advertise-address=192.168.0.142 --pod-network-cidr=10.244.0.0/16[init] Using Kubernetes version: v1.20.5[preflight] Running pre-flight checks [WARNING SystemVerification]: this Docker version is not on the list of validated versions: 20.10.5. Latest validated version: 19.03[preflight] Pulling images required for setting up a Kubernetes cluster[preflight] This might take a minute or two, depending on the speed of your internet connection[preflight] You can also perform this action in beforehand using 'kubeadm config images pull'^C先从国内的源拉去docker images,然后打标签,进行官网的镜像替换,就可以顺利调起k8s了。root@ecs-b769:~# docker info | grep -i cgroup //查看cgroup是否是systemd Cgroup Driver: systemd Cgroup Version: 1 WARNING: No swap limit support root@ecs-b769:~# kubeadm config print init-defaults > kubeadm.conf //将默认配置文件导出修改,主要有三个地方 root@ecs-b769:~# vi kubeadm.conf kubernetesVersion: v1.20.0 dnsDomain: cluster.local networking: serviceSubnet: 172.18.0.0/16 apiVersion: kubeadm.k8s.io/v1beta2 bootstrapTokens: - groups: - system:bootstrappers:kubeadm:default-node-token token: abcdef.0123456789abcdef ttl: 24h0m0s usages: - signing - authentication kind: InitConfiguration localAPIEndpoint: advertiseAddress: 192.168.0.142 //本机地址 bindPort: 6443 nodeRegistration: criSocket: /var/run/dockershim.sock name: ecs-b769 taints: - effect: PreferNoSchedule //调度策略 key: node-role.kubernetes.io/master --- apiServer: timeoutForControlPlane: 4m0s apiVersion: kubeadm.k8s.io/v1beta2 certificatesDir: /etc/kubernetes/pki clusterName: kubernetes controllerManager: {} dns: type: CoreDNS etcd: local: dataDir: /var/lib/etcd imageRepository: registry.cn-hangzhou.aliyuncs.com/google_containers //*重点在这里!!!!修改为国内源 kind: ClusterConfiguration kubernetesVersion: v1.20.0 networking: dnsDomain: cluster.local podSubnet: 192.168.0.0/16 serviceSubnet: 172.18.0.0/16 scheduler: {} ~ ~ ~ ~ ~ "kubeadm.conf"\ 39L, 910C written root@ecs-b769:~# kubeadm config images pull --config kubeadm.conf //拉取image [config/images] Pulled registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver:v1.20.0 [config/images] Pulled registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager:v1.20.0 [config/images] Pulled registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:v1.20.0 [config/images] Pulled registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy:v1.20.0 [config/images] Pulled registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.2 [config/images] Pulled registry.cn-hangzhou.aliyuncs.com/google_containers/etcd:3.4.13-0 [config/images] Pulled registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:1.7.0 //计下相关的版本号,打tag root@ecs-b769:~# docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:v1.20.0 k8s.gcr.io/kube-scheduler:v1.20.0 docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:v1.20.0 docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy:v1.20.0 k8s.gcr.io/kube-proxy:v1.20.0 docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy:v1.20.0 docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager:v1.20.0 k8s.gcr.io/kube-controller-manager:v1.20.0 docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager:v1.20.0 root@ecs-b769:~# docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:v1.20.0 docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver:v1.20.0 k8s.gcr.io/kube-apiserver:v1.20.0 docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver:v1.20.0 docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:1.7.0 k8sUntagged: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:v1.20.0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler@sha256:beaa710325047fa9c867eff4ab9af38d9c2acec05ac5b416c708c304f76bdbef root@ecs-b769:~# docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy:v1.20.0 k8s.gcr.io/kube-proxy:v1.20.0 .gcr.io/coredns:1.7.0 docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:1.7.0 docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/etcd:3.4.13-0 k8s.gcr.io/etcd:3.4.13-0 docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/etcd:3.4.13-0 root@ecs-b769:~# docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy:v1.20.0 docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.2 k8s.gcr.io/pause:3.2 docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.2 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy:v1.20.0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-proxy@sha256:40423415eebbd598d1c2660a0a38606ad1d949ea9404c405eaf25929163b479d root@ecs-b769:~# docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager:v1.20.0 k8s.gcr.io/kube-controller-manager:v1.20.0 root@ecs-b769:~# docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager:v1.20.0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager:v1.20.0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-controller-manager@sha256:00ccc3a5735e82d53bc26054d594a942fae64620a6f84018c057a519ba7ed1dc root@ecs-b769:~# docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver:v1.20.0 k8s.gcr.io/kube-apiserver:v1.20.0 root@ecs-b769:~# docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver:v1.20.0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver:v1.20.0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/kube-apiserver@sha256:8b8125d7a6e4225b08f04f65ca947b27d0cc86380bf09fab890cc80408230114 root@ecs-b769:~# docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:1.7.0 k8s.gcr.io/coredns:1.7.0 root@ecs-b769:~# docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:1.7.0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/coredns:1.7.0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/coredns@sha256:73ca82b4ce829766d4f1f10947c3a338888f876fbed0540dc849c89ff256e90c root@ecs-b769:~# docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/etcd:3.4.13-0 k8s.gcr.io/etcd:3.4.13-0 root@ecs-b769:~# docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/etcd:3.4.13-0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/etcd:3.4.13-0 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/etcd@sha256:4ad90a11b55313b182afc186b9876c8e891531b8db4c9bf1541953021618d0e2 root@ecs-b769:~# docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.2 k8s.gcr.io/pause:3.2 root@ecs-b769:~# docker rmi registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.2 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.2 Untagged: registry.cn-hangzhou.aliyuncs.com/google_containers/pause@sha256:927d98197ec1141a368550822d18fa1c60bdae27b78b0c004f705f548c07814f root@ecs-b769:~# docker images //查询image REPOSITORY TAG IMAGE ID CREATED SIZE hello-world latest d1165f221234 3 weeks ago 13.3kB k8s.gcr.io/kube-proxy v1.20.0 10cc881966cf 3 months ago 118MB k8s.gcr.io/kube-scheduler v1.20.0 3138b6e3d471 3 months ago 46.4MB k8s.gcr.io/kube-apiserver v1.20.0 ca9843d3b545 3 months ago 122MB k8s.gcr.io/kube-controller-manager v1.20.0 b9fa1895dcaa 3 months ago 116MB k8s.gcr.io/etcd 3.4.13-0 0369cf4303ff 7 months ago 253MB k8s.gcr.io/coredns 1.7.0 bfe3a36ebd25 9 months ago 45.2MB k8s.gcr.io/pause 3.2 80d28bedfe5d 13 months ago 683kB root@ecs-b769:~# kubeadm init --config /root/kubeadm.conf //拉起k8s [init] Using Kubernetes version: v1.20.0 [preflight] Running pre-flight checks [WARNING SystemVerification]: this Docker version is not on the list of validated versions: 20.10.5. Latest validated version: 19.03 [preflight] Pulling images required for setting up a Kubernetes cluster [preflight] This might take a minute or two, depending on the speed of your internet connection [preflight] You can also perform this action in beforehand using 'kubeadm config images pull' [certs] Using certificateDir folder "/etc/kubernetes/pki" [certs] Generating "ca" certificate and key [certs] Generating "apiserver" certificate and key ... ....记录这里的token,node上需要输入。
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设二进制图像宽image_width, 高image_height, 二进制数据内存地址image_data。要转为opencv bgr,可以使用如下步骤:Mat src(img_height * 3 / 2, img_width, CV_8UC1);int image_size = src.cols * src.rows * src.elemSize();memcpy(src.data, image_data, image_size);mat destcvtColor(src, dest, CV_YUV2BGR_NV12);
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【功能模块】【操作步骤&问题现象】1、参考了https://gitee.com/ascend/samples/blob/master/python/level2_simple_inference/2__detection/face_detection_rtsp2、发现是在图一preprocess.py中调用vedio.py后调用acl_dvpp.py函数进行resize,其中image和resize后的resized_image都是实例化的AclImage类,其data为图片在内存的地址,size大小正好是height*width*3/2.3.我在图二acl_Image.py中增加了一个函数,将nparray重组的格式从一维改成了三维,其中高度是原来的一半,将内存得到的数组存为jpg图片后却发现得不到正确格式的resize图片。其中AdlImage类定义在图三,其_data为int类型的内存地址,其_np_array为None请问有办法从内存地址中还原解码后的图片吗?【截图信息】 【日志信息】(可选,上传日志内容或者附件)
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image caption是指图像描述,在2015年左右,这个方向的主流模型是基于 CNN-RNN 框架的,即输入一张图像,先用一个 pre-trained 的 CNN 去提取图像特征,然后将这些 CNN 特征输入到 RNN中去生成单词序列。这种模型表面上看起来非常吸引人,依赖于强大的深度神经网络,能够用 end-to-end 的方式学习到一个从图像到语言(vision2language)的直接对应关系,但忽略了一个重要的事实——图像和语言之间,其实是存在鸿沟的。虽然用神经网络将图像空间和语言空间 embed 在同一个空间当中,但这两个空间应该需要一个共同的 sub-space 作为桥梁来连接。于是还有方法提出了attributes(attributes 定义是广义的,包括物体名称,属性,动作,形容词,副词,情绪等等),一种图像和语言都拥有的特征。大家可以看下图左侧,基于上面提到的 CNN-RNN 结构,多加了一个 attributes prediction layer。当给定一张图像,先去预测图像当中的各种 attributes,然后再将这些 attributes 代替之前的 CNN 图像特征,输入到 RNN 当中,生成语句。
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我先将图片和标签下载到本地,使用 Pytorch 框架训练好模型再上传到 obs 桶里。将模型部署为在线服务的时候报错 share image failed, retry later。 This is the file structure in obs2. This is the event in modelarts 3. This is the configuration4. This is the content in config.jsonI think the possible cause may be the "url" in config.json should not be "/", and we must get images from "url", so the error "share image failed, retry later".But i don't know which url is correct. I would appreciate it if anyone can help me.
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我发现building image for model 这个步骤是部署上线过程中最慢的步骤了这个步骤花了3分钟,急性子~希望快点,这个步骤到底在做啥呢?
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【功能模块】【操作步骤&问题现象】1、W处需要填写304,但是现在无法输入,重启依然不好用2、【截图信息】【日志信息】(可选,上传日志内容或者附件)
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工程代码中添加image_transport::ImageTransport it(n)语句后报如下错误/usr/lib/gcc-cross/aarch64-linux-gnu/5/../../../../aarch64-linux-gnu/bin/ld: warning: libPocoFoundation.so.9, needed by /usr/ubuntu_crossbuild_devkit/mdc_crossbuild_sysroot/opt/ros/kinetic/lib/libclass_loader.so, not found (try using -rpath or -rpath-link)/usr/ubuntu_crossbuild_devkit/mdc_crossbuild_sysroot/opt/ros/kinetic/lib/libclass_loader.so:对‘Poco::SharedLibrary::SharedLibrary(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)’未定义的引用/usr/ubuntu_crossbuild_devkit/mdc_crossbuild_sysroot/opt/ros/kinetic/lib/libclass_loader.so:对‘typeinfo for Poco::LibraryLoadException’未定义的引用/usr/ubuntu_crossbuild_devkit/mdc_crossbuild_sysroot/opt/ros/kinetic/lib/libclass_loader.so:对‘Poco::SharedLibrary::suffix[abi:cxx11]()’未定义的引用/usr/ubuntu_crossbuild_devkit/mdc_crossbuild_sysroot/opt/ros/kinetic/lib/libclass_loader.so:对‘typeinfo for Poco::NotFoundException’未定义的引用/usr/ubuntu_crossbuild_devkit/mdc_crossbuild_sysroot/opt/ros/kinetic/lib/libclass_loader.so:对‘typeinfo for Poco::LibraryAlreadyLoadedException’未定义的引用/usr/ubuntu_crossbuild_devkit/mdc_crossbuild_sysroot/opt/ros/kinetic/lib/libclass_loader.so:对‘typeinfo for Poco::RuntimeException’未定义的引用/usr/ubuntu_crossbuild_devkit/mdc_crossbuild_sysroot/opt/ros/kinetic/lib/libclass_loader.so:对‘Poco::SharedLibrary::unload()’未定义的引用
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问题现象描述硬件配置:Atlas300问题现象:客户在进行图片流推理业务的开发,在业务功能测试时,发现测试集中有部分图片解码会占用大量时间,感受明显,严重影响业务:1. 占用较长时间的解码图片,客户测试用两张此类图片跑推理业务共耗时132秒,与实际业务场景性能要求相差约百倍。 关键过程、根本原因分析关键过程:<ol style="margin- class=" list-paddingleft-2">通过客户收集的dlog日志,可以观察到如下内容: • 以此为例,我们识别出了客户的输入图片为JPEG,却报了解码超时,且超时后会自动重试10次,每次超时门限是6s,即一张图片最多解码可耗时一分钟。 • 重试10次后仍未解码成功,则返回解码失败。 2. 为什么超时:下图为将客户超时图片用16进制打开• 红框所示为JPEG解码的结束标识,上层软件将结束标识后的数据也一起送到了芯片进行硬解码,导致解码超时。 结论、解决方案及效果解决方案:在将数据送到芯片硬解之前先将结束标识之后的八个字节置零。效果: 测试后原先解码超时的图片现在可正常解码(但根据图片数据仍有可能失败),不会阻塞业务,时延ms级。
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对于YUV420SP_U8图片类型,load_start_pos_w、load_start_pos_h、crop_size_w与crop_size_h四个参数必须配置为偶数。抠图后的图像的宽、高需和网络模型输入定义的w和h相等。配置样例如下:aipp_op { aipp_mode: static input_format : YUV420SP_U8 src_image_size_w :320 src_image_size_h :240 crop :true load_start_pos_w :10 load_start_pos_h :20 crop_size_w :50 crop_size_h :60 padding : true left_padding_size :10 right_padding_size :20 top_padding_size :10 bottom_padding_size :20}
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Model Image Format和Input Image Format的区别是什么?我处理的是RGB图像,那么我的Input Image Format应该是RGB,Model Image Format就是BGR吗?
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【功能模块】在运行facedetection样例时,已启动presenter server,然后run facedetection,但是报错send JPEG image to presenter failed, error 1,请问有可能是什么问题?【截图信息】【日志信息】(可选,上传日志内容或者附件)
wozailushang 发表于2020-08-07 16:43:55 2020-08-07 16:43:55 最后回复 wozailushang 2020-08-08 23:27:21
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