Alpine pwd x5

Remote and USB cable includedpreamp and speaker-level inputspower, ground, output, and speaker-level input harness included (length: 14-3/4 feet)dimensions: 9-5/8″W x 3-1/4″H x 13-5/8″Dwarranty: 1 yearFeatures & specsGeneralEnclosure TypeSealedFinish MaterialAluminum, plasticGrilleMetal-meshConnector Type26-Pin Molex, RCABi-amp InputsNoWoofer Size (inches)8Woofer MaterialNot givenPassive Radiator—Parts Warranty1 YearLabor Warranty1 YearSpecificationsFrequency Response20-20kAmplifier Power (RMS)265 wattsMaximum Wattage440 wattsSensitivity—Impedance—Second Voice Coil Impedance—Crossover Point— Weight 12 lbs Brand Alpine Number of subwoofers 1 Subwoofer size 8" Built in amp rms power 201-300 Watts Based on 0 reviews 0.00 Overall 0% 0% 0% 0% 0% Be the first to review “Alpine PWD-X5 Compact powered 8″ subwoofer with digital signal processor and 5-channel amp (25 watts RMS x 4 + 165 watts RMS x 1)” You must be logged in to post a review. Reviews There are no reviews yet. CategoriesTesla AudioAlarmsAlarm With PagerAll AlarmsBasic SecurityRemote StartSmart Phone Control SecurityAudioBig Truck SystemsCamera KitsCamerasCar AudioAmps 1 Channel Mono2 Channel Amps4 Channel Amps5 Channel Amps6 Channel AmpsAll AmpsBuilt In DSPFactory Upgrade Plug In Play Brands Alpine Alpine AmplifiersAlpine Head UnitsAlpine Loaded BoxesAlpine SpeakersAlpine Subwoofers Kicker Kicker AmplifiersKicker Loaded BoxesKicker SpeakersKicker Subwoofers Equalizers and Processors Bass ProcessorsDigital Sound Processors DSPEqualizersLine Out Converters (LOC) Factory Stereo Integration Add Apple CarPlay and android auto Installation Parts Install Parts Amps Channels 1/245 Wire Gauge 048 Install Parts Speakers Speaker Wiring HarnessSpeakers Mounting Brackets Install Parts Stereos Antenna AdapterStereo KitWiring Harness Loaded Sub Boxes Non-Powered BoxesPowered Sub Boxes Powered Sub BoxesSpeakers All SpeakersComponents SpeakersCo-Ax SpeakersMidrangeTweeters2.75" Speakers3" Speakers3.5" Speakers4" Speakers4x6" Speakers4x10" Speakers5.25" Speakers5x7 / 6x8 speakers6.5" & 6.75" Speakers6x9 speakers8''10'' Stereos All-StereosAndroid Auto StereosApple Car Play StereosDouble Din Radios No ScreenFactory Plug and PlayFloating Panel RadioGPS Navigation Built-InGPS Navigation w Smart PhoneSingle Din ReceiversTouch Screen Stereo Subwoofers 10" Subwoofers12" Subwoofers15" Subwoofers6.5" Subwoofers8" SubwoofersAll Subwoofers CloseoutsCoil SpringsDrop Down MonitorsExterior PartsTonneau Covers FoldingHardHingedPaintedRetractableRoll UpSoftTool Box Headrest MonitorsLane DepartureLightingheadlightsMarine AudioMarine RadiosMarine SpeakersMotor Sports AudioRyker PackagesAccessoriesMaverick X3 PackagesMotorcycle AudioMotorcycle headunitsPackagesAccord Accord 2002-2006Accord 2007-2011Accord 2012-2016Accord 2017-up Altima Altima 2007-2012Altima 2013-2017Altima 2018-up BMWChallenger Challanger 2008-up Chevrolet Silverado Silverado 1998-2007 Crew Cab (4 Full Doors)Extended Cab Silverado 2007-2013 Crew Cab (4 Full Doors)Crew Cab (4 Full Doors)Extended CabRegular Cab Silverado 2014-2019 Crew Cab (4 Full Doors)Extended Cab (Small

ESA SNAP 9 docker imagesDocker images of ESA Sentinel Application Platform (SNAP) from related docker images are created and available for download from here: branchBase imageSizedocker pull commands1tbxOnly s1tbx toolboxs1tbxAlpine 3.18 based1.4 GBdocker pull mundialis/esa-snap:s1tbxlatestOnly s1tbx toolboxs1tbxAlpine 3.18 based1.4 GBdocker pull mundialis/esa-snap:latestubuntuAll SNAP toolboxesubuntuUbuntu 18.04 based2 GBdocker pull mundialis/esa-snap:ubuntuInstallationPull the Alpine Linux based image (only SNAP Sentinel-1 toolbox):docker pull mundialis/esa-snap:latestTutorialWe recommend the following tutorial: examplesSNAP Graphical User Interface - GUIUsing the GUI, among other functionality the GraphBuilder is available.Start of GUI, with volume mapping of current directory (pwd; may be set to adifferent directory) to /data/ within docker:docker run -it --rm --volume="$(pwd)/:/data" \ --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" \ --env DISPLAY=$DISPLAY --device="/dev/dri/card0:/dev/dri/card0" \ mundialis/esa-snap:latest \ /usr/local/snap/bin/snapSNAP Graph Processing Tool - command lineUsing the SNAP Graph Processing Tool (GPT):# show help of gpt tooldocker run -it --rm mundialis/esa-snap:latest /usr/local/snap/bin/gpt -hOne can pass the required processing settings in an XML-encoded graph file which is passing thisgraph as parameter to the gpt tool: [options] [ ...]">docker run -it --rm mundialis/esa-snap:latest /usr/local/snap/bin/gpt [options] [ ...]For further gpt usage please refer to the official documentation.Background infoThis docker image is based on Alpine Linux and only contains the s1tbx toolbox. Furthermore,the original installer provided by ESA ships its own oracle javaAlpine is based on musl libc and busybox. As Oracle JAVA depends on glibc, itdoesn't work smoothly. With thiscan be workarounded to a certain way but in our case not sufficient (conflicting dependencies).From SNAP Version 8, oracle java > 8 and openjdk in general will be supportedofficially ( is the Ubuntu based docker image?Find the Ubuntu based docker image related files in branch ubuntu (see here).See there for related instructions.Alpine dev stuffAlternatively, a build approach was tried out. Kept here if needed further.As the stable SNAP version 7.0.2 needs maven 3.6.0 (while alpine offers 3.6.3), so SNAP 8 was built for testing.ENV JAVA_HOME "/usr/lib/jvm/java-1.8-openjdk"RUN apk add git openjdk8 mavenRUN git clone /src/snap/snap-engineWORKDIR /src/snap/snap-engineRUN sed -i 's+snap-classification+snap-classification-->+g' pom.xmlRUN mvn clean install -DskipTestsWORKDIR /src/snap/s1tbxgit clone /src/snap/s1tbxcd s1tbxmvn clean installWORKDIR /src/snap/snap-engingejava -cp snap-runtime/target/snap-runtime.jar org.esa.snap.runtime.BundleCreator ../snap.zip "/src/snap/snap-engine" "/src/snap/s1tbx"">FROM alpine:edgeENV JAVA_HOME "/usr/lib/jvm/java-1.8-openjdk"RUN apk add git openjdk8 mavenRUN git clone /src/snap/snap-engineWORKDIR /src/snap/snap-engineRUN sed -i 's+snap-classification+snap-classification-->+g' pom.xmlRUN mvn clean install -DskipTestsWORKDIR /src/snap/s1tbxgit clone /src/snap/s1tbxcd s1tbxmvn clean installWORKDIR /src/snap/snap-engingejava -cp snap-runtime/target/snap-runtime.jar org.esa.snap.runtime.BundleCreator ../snap.zip "/src/snap/snap-engine" "/src/snap/s1tbx"

Options are available to suit your unique taste.That said, G-Technology's recent ArmorLock drive gives Samsung a run for its secure storage money, by using an app and key that's stored on your Android or iOS phone to unlock your drive. It may not be as convenient as swiping your finger across a sensor on your external SSD, but it might just be more secure.Read: Samsung T7 Touch Portable SSD Review(Image credit: Tom's Hardware)Best Thunderbolt 5 Portable SSDSpecificationsCapacities: 2TB, 4TBDrive Type: SSDTransfer Protocol: Thunderbolt 5Sequential Reads: 6,700 MBpsWarranty: 5 YearsReasons to buy+6GBps / 5GBps reads / writes over Thunderbolt 5+Rugged, premium design+Warranty includes 5 years of data recovery serviceReasons to avoid-Slower than competing USB4 drives if you don’t have a TB5 port-Not supported at all over Thunderbolt 3 in Windows, or USB ports with The LaCie Rugged SSD Pro 5 combines a tried-and-true rugged design with by far the fastest single-drive speeds we’ve ever seen on an external SSD, when tested on a Thunderbolt 5-equipped Mac.It also ships with five years of Seagate's data recovery service, making it easy to recommend for Mac users – particularly those who only or primarily use current-gen Mac hardware and need the fastest possible performance for media creation or other write-heavy purposes.Just note that its support on older hardware is so complex that it requires its own compatibility page, and in our testing in Windows 11 over a USB4 / Thunderbolt 4 port, it was slower than recent native USB4 drives. So at least until Thunerbolt 5 becomes more widespread on Macs and PCs, this isn't the best drive for workflows that include Windows PCs.Read: LaCieRugged SSD Pro5 reviewSamsung 1TB Portable SSD X5 (Image credit: Tom's Hardware)8. Samsung X5Best Thunderbolt 3 Portable SSDSpecificationsCapacities: 500GB, 1TB, 2TBDrive Type: SSDTransfer Protocol: Thunderbolt 3Sequential Reads: 2,800 MBpsWarranty: 5 YearsReasons to buy+Fast Thunderbolt 3+Sequential read and write performance+Full hardware-based encryption+Attractive design Reasons to avoid-Slow write speed after write cache fills-Lacks AES hardware encryption or IP ratingDriven by an OEM variant of a Samsung 970 EVO and an Alpine Ridge Thunderbolt 3-to-PCIe bridge, Samsung’s X5 is the fastest Thunderbolt 3

Bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \$(pwd)/inference/random_thick_512 \$(pwd)/inference/random_thick_512_metrics.csv"># Download data from Places365-Standard: Train(105GB)/Test(19GB)/Val(2.1GB) from High-resolution images sectionwget Unpack train/test/val data and create .yaml config for itbash fetch_data/places_standard_train_prepare.shbash fetch_data/places_standard_test_val_prepare.sh# Sample images for test and viz at the end of epochbash fetch_data/places_standard_test_val_sample.shbash fetch_data/places_standard_test_val_gen_masks.sh# Run trainingpython3 bin/train.py -cn lama-fourier location=places_standard# To evaluate trained model and report metrics as in our paper# we need to sample previously unseen 30k images and generate masks for thembash fetch_data/places_standard_evaluation_prepare_data.sh# Infer model on thick/thin/medium masks in 256 and 512 and run evaluation # like this:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier_/ \indir=$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \outdir=$(pwd)/inference/random_thick_512 model.checkpoint=last.ckptpython3 bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/places_standard_dataset/evaluation/random_thick_512/ \$(pwd)/inference/random_thick_512 \$(pwd)/inference/random_thick_512_metrics.csvDocker: TODOCelebAOn the host machine:__lama-fourier-celeba_/ \indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckpt"># Make shure you are in lama foldercd lamaexport TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)# Download CelebA-HQ dataset# Download data256x256.zip from unzip & split into train/test/visualization & create config for itbash fetch_data/celebahq_dataset_prepare.sh# generate masks for test and visual_test at the end of epochbash fetch_data/celebahq_gen_masks.sh# Run trainingpython3 bin/train.py -cn lama-fourier-celeba data.batch_size=10# Infer model on thick/thin/medium masks in 256 and run evaluation # like this:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier-celeba_/ \indir=$(pwd)/celeba-hq-dataset/visual_test_256/random_thick_256/ \outdir=$(pwd)/inference/celeba_random_thick_256 model.checkpoint=last.ckptDocker: TODOPlaces ChallengeOn the host machine:# This script downloads multiple .tar files in parallel and unpacks them# Places365-Challenge: Train(476GB) from High-resolution images (to train Big-Lama) bash places_challenge_train_download.shTODO: prepareTODO: train TODO: evalDocker: TODOCreate your dataPlease check bash scripts for data preparation and mask generation from CelebaHQ section,if you stuck at one of the following steps.On the host machine:_512.yaml \ # thick, thin, mediummy_dataset/val_source/ \my_dataset/val/random__512.yaml \# thick, thin, medium--ext jpg# So the mask generator will: # 1. resize and crop val images and save them as .png# 2. generate masksls my_dataset/val/random_medium_512/image1_crop000_mask000.pngimage1_crop000.pngimage2_crop000_mask000.pngimage2_crop000.png...# Generate thick, thin, medium masks for visual_test folder:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, mediummy_dataset/visual_test_source/ \my_dataset/visual_test/random__512/ \ #thick, thin, medium--ext jpgls my_dataset/visual_test/random_thick_512/image1_crop000_mask000.pngimage1_crop000.pngimage2_crop000_mask000.pngimage2_crop000.png...# Same process for eval_source image folder:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ #thick, thin, mediummy_dataset/eval_source/ \my_dataset/eval/random__512/ \ #thick, thin, medium--ext jpg# Generate location config file which locate these folders:touch my_dataset.yamlecho "data_root_dir: $(pwd)/my_dataset/" >> my_dataset.yamlecho "out_root_dir: $(pwd)/experiments/" >> my_dataset.yamlecho "tb_dir: $(pwd)/tb_logs/" >> my_dataset.yamlmv my_dataset.yaml ${PWD}/configs/training/location/# Check data config for consistency with my_dataset folder structure:$ cat ${PWD}/configs/training/data/abl-04-256-mh-dist...train: indir: ${location.data_root_dir}/train ...val: indir: ${location.data_root_dir}/val img_suffix: .pngvisual_test: indir: ${location.data_root_dir}/visual_test img_suffix: .png# Run trainingpython3 bin/train.py -cn lama-fourier location=my_dataset data.batch_size=10# Evaluation: LaMa training procedure picks best few models according to # scores on my_dataset/val/ # To evaluate one of your best models (i.e. at epoch=32) # on previously unseen my_dataset/eval do the following # for thin, thick and medium:# infer:python3 bin/predict.py \model.path=$(pwd)/experiments/__lama-fourier_/ \indir=$(pwd)/my_dataset/eval/random__512/ \outdir=$(pwd)/inference/my_dataset/random__512 \model.checkpoint=epoch32.ckpt# metrics calculation:python3 bin/evaluate_predicts.py \$(pwd)/configs/eval2_gpu.yaml \$(pwd)/my_dataset/eval/random__512/ \$(pwd)/inference/my_dataset/random__512 \$(pwd)/inference/my_dataset/random__512_metrics.csv"># Make shure you are in lama foldercd lamaexport TORCH_HOME=$(pwd) && export PYTHONPATH=$(pwd)# You need to prepare following image folders:$ ls my_datasettrainval_source # 2000 or more imagesvisual_test_source # 100 or more imageseval_source # 2000 or more images# LaMa generates random masks for the train data on the flight,# but needs fixed masks for test and visual_test for consistency of evaluation.# Suppose, we want to evaluate and pick best models # on 512x512 val dataset with thick/thin/medium masks # And your images have .jpg extention:python3 bin/gen_mask_dataset.py \$(pwd)/configs/data_gen/random__512.yaml \ # thick, thin, mediummy_dataset/val_source/ \my_dataset/val/random__512.yaml \# thick, thin, medium--ext jpg# So the mask generator will: # 1.

There are two connection options in Camlytics smart camera software for Windows PC to login and view your Night Owl IP camera. Those are automatic discovery (you'll see your camera in our software) and manual discovery (without camera website). If you cannot find your Night Owl CCTV camera in the left section or it isn't working with Camlytics software app, click "Manual" in Discovery section to setup your Night Owl cameras with direct RTSP or HTTP stream URL. If you could not connect your camera, please refer to the documentation You can connect Night Owl to Camlytics to add the following video analytics capabilities to your camera: Model Protocol Path Port 3 m http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 cam-wnr2p-0u http:// snapshot.jpg?account=[USERNAME]&password=[PASSWORD] 80 CAM-WNR2P-OU http:// snapshot 80 cam-wnvr29-in http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 cam-wnvr2p-ou http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 cam-wnvr2p-ou_20170811 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 81 Other http:// cgi-bin/snapshot.cgi?chn=[CHANNEL]&u=[USERNAME]&p=[PASSWORD] 80 unknown54285708 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 WINR2P-OU http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 wnr http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 wnr http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 0v600-365-kd http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 54285708 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 CAM-WNR2P-0U http:// snapshot 80 cam-wnr2p-ou http:// snapshot.jpg 80 CAM-WNR2P-OU http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 CAM-WNR2P-OU http:// snapshot.jpg?account=[USERNAME]&password=[PASSWORD] 80 CAM-WNR2P-OU http:// cgi-bin/snapshot.cgi?chn=[CHANNEL]&u=[USERNAME]&p=[PASSWORD] 80 cam-wnvr2p-in http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 CAM-WNVR2P-IN http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 CAM-WNVR2P-OU http:// snapshot 80 CAM-WNVR2P-OU http:// snapshot.jpg?account=[USERNAME]&password=[PASSWORD] 80 cl-a10-841 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 81 DVR-THD30B rtsp:// /Streaming/channels/301 10554 Other http:// videostream.asf?user=[USERNAME]&pwd=[PASSWORD]&resolution=[WIDTH]x[HEIGHT] 80 Other http:// current[CHANNEL].jpg 80 Other http:// video.cgi 80 Other http:// cgi-bin/video.jpg?cam=[CHANNEL]&quality=3&size=2 80 Other http:// /control/faststream.jpg?stream=MxPEG&needlength&fps=6 80 Other rtsp:// 554 Other http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 Other http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 Other http:// snapshot.jpg 80 ov600 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 WG4 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 WINR2P-OU http:// snapshot 80 WINR2P-OU http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 wnr2p-ou http:// snapshot 80 2.W http:// cgi-bin/snapshot.cgi?chn=[CHANNEL]&u=[USERNAME]&p=[PASSWORD] 80 54285708 http:// snapshot.jpg?account=[USERNAME]&password=[PASSWORD] 80 CAM1 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 CAM-1 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 CAM2 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 CAM-2 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 CAM-WNR2P-0U http:// snapshot.jpg 80 CAM-WNR2P-0U http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 CAM-WNVR2P-OU http:// cgi-bin/snapshot.cgi?chn=[CHANNEL]&u=[USERNAME]&p=[PASSWORD] 80 CAM-WNVR2P-OU_20170811 http:// snapshot 81 Door Bell http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 Doorbell http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 5544 DoorBell http:// snapshot.jpg 80 NIGHTOWL http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 NIGHTOWL2 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 Other http:// snapshot 80 Other http:// snapshot 9000 Other http:// snapshot.jpg?account=[USERNAME]&password=[PASSWORD] 80 scotty http:// snapshot.jpg 80 WCM-PWNVR20W-BU-JUN http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 81 WCM-PWNVR20W-BU-JUN http:// /control/faststream.jpg?stream=MxPEG&needlength&fps=6 80 wcm-sd2pou-bu-v2 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 WD2CLM http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=[CHANNEL] 80 WDB-20 http:// snapshot 80 WDB-20 http:// snapshot.jpg?account=[USERNAME]&password=[PASSWORD] 80 WEBCAM http:// snapshot 80 WG4 http:// snapshot.jpg 80 WG4 http:// snapshot 80 WG4 http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 WNR http:// cgi-bin/snapshot.cgi?chn=[CHANNEL]&u=[USERNAME]&p=[PASSWORD] 80 WNR2P-OU http:// snapshot.jpg?account=[USERNAME]&password=[PASSWORD] 80 54285708 http:// snapshot 80 54285708 http:// snapshot.jpg 80 CAM1 http:// snapshot.jpg?account=[USERNAME]&password=[PASSWORD] 80 CAM-WNR2P-0U http:// cgi-bin/snapshot.cgi?chn=[CHANNEL]&u=[USERNAME]&p=[PASSWORD] 80 cam-wnvr2p-ou_20170811 http:// /snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD]&strm=0 80 DVR-THD30B http:// snapshot.jpg 80 DVR-THD30B http:// snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 NIGHTOWL http:// /snapshot.jpg?user=[USERNAME]&pwd=[PASSWORD] 80 QM-CAM-WNP2LBU rtsp:// /ch0_0.264 554 wcm-sd2pouv2 http:// snapshot.jpg 80 WM-CAM-WAWNP2L rtsp:// / 554 WM-CAM-WAWNP2L rtsp:// /Streaming/channels/301 554 wmvr-wnip2 rtsp:// /Streaming/channels/301 554 WNIP2 (CAM1) rtsp:// /ch0_1.264 554 WNIP2 CAM2 rtsp:// /ch1_1.264 554 WNIP2 CAM3 rtsp:// /ch3_1.264 554 WNIP2 CAM3 rtsp:// /ch2_1.264 554 WNIP2-CM rtsp:// /Streaming/channels/301 554 WNIP2-CM rtsp:// /ch0_0.264 554 WNIP-2lta-bs rtsp:// /Streaming/channels/301 554 WNIP-2LTA-BS rtsp:// /ch0_1.264 554 WNIP-2LTA-BS rtsp:// /ch3_1.264 554 WNIP-2LTA-BS rtsp:// /ch2_1.264 554 WNIP-2LTA-BS rtsp:// /ch1_1.264 554 WNIP-2LTAW-BS-U rtsp:// /ch0_0.264 554 WNIP-TLTA-BS-U rtsp:// / 554 Other rtsp:// /Streaming/channels/301 10554

There are two connection options in Camlytics smart camera software for Windows PC to login and view your Kaikong IP camera. Those are automatic discovery (you'll see your camera in our software) and manual discovery (without camera website). If you cannot find your Kaikong CCTV camera in the left section or it isn't working with Camlytics software app, click "Manual" in Discovery section to setup your Kaikong cameras with direct RTSP or HTTP stream URL. If you could not connect your camera, please refer to the documentation You can connect Kaikong to Camlytics to add the following video analytics capabilities to your camera: Model Protocol Path Port 1016 http:// videostream.asf?user=[USERNAME]&pwd=[PASSWORD]&resolution=[WIDTH]x[HEIGHT] 80 1016 http:// videostream.asf?user=[USERNAME]&pwd=[PASSWORD]&resolution=32&rate=0 80 1201 http:// videostream.cgi?usr=[USERNAME]&pwd=[PASSWORD] 80 1201 rtsp:// /11 554 1214 rtsp:// /11 554 1303 rtsp:// /11 554 1303 http:// tmpfs/auto.jpg 80 1406 http:// videostream.cgi?rate=11 80 1406 rtsp:// H264 554 1601 http:// videostream.cgi?rate=11 80 1601 http:// videostream.cgi?rate=0 80 1601 http:// videostream.cgi 80 1601 http:// videostream.cgi?user=[USERNAME]&pwd=[PASSWORD] 80 1602 http:// snapshot.cgi?user=[USERNAME]&pwd=[PASSWORD] 80 1602 http:// img/snapshot.cgi?size=2 80 1602 http:// videostream.cgi?rate=11 80 1602 http:// videostream.cgi 80 1602 http:// user/videostream.cgi 80 1602 http:// videostream.cgi? 80 1602 http:// videostream.cgi?user=[USERNAME]&pwd=[PASSWORD]&resolution=32 80 1602 http:// videostream.cgi?rate=0&user=[USERNAME]&pwd=[PASSWORD] 81 1602 http:// videostream.cgi?user=[USERNAME]&pwd=[PASSWORD]&resolution=32&rate=0 80 1602w http:// snapshot.cgi?user=[USERNAME]&pwd=[PASSWORD] 80 1602w http:// videostream.cgi 80 1602w http:// videostream.cgi?rate=0&user=[USERNAME]&pwd=[PASSWORD] 80 1602w http:// videostream.cgi?user=[USERNAME]&pwd=[PASSWORD]&resolution=32&rate=0 80 1602W http:// videostream.asf?user=[USERNAME]&pwd=[PASSWORD]&resolution=[WIDTH]x[HEIGHT] 80 1602W http:// videostream.cgi?user=[USERNAME]&pwd=[PASSWORD]&resolution=32 80 720P rtsp:// live/ch00_0 554 CAM01 rtsp:// 11 554 ip1018 http:// videostream.asf?user=[USERNAME]&pwd=[PASSWORD]&resolution=[WIDTH]x[HEIGHT] 80 ip1018 http:// videostream.asf?user=[USERNAME]&pwd=[PASSWORD]&resolution=64&rate=0 80 ip1018 http:// [CHANNEL].jpg 80 ip1018 http:// videostream.cgi?rate=11 80 ip-1801 http:// videostream.asf?user=[USERNAME]&pwd=[PASSWORD]&resolution=[WIDTH]x[HEIGHT] 80 Other http:// videostream.asf?user=[USERNAME]&pwd=[PASSWORD]&resolution=[WIDTH]x[HEIGHT]