Well, seems this is an increasing interest in todays society being able to monitor your home and other belongings using all kind of video devices. Especially since they have become rather affordable and easy to install and configure So let's start simple, this beginners example flow just demonstrates how you can view live video in the dashboard. Obviously you have to change the url's to fit your specific cameras Next steps could involve adding features to integrate with your home automation & alarm system, take snapshots, view latest as well as to introduce AI object detection and sending notifications to your smart phone. Many features will depend on the capabilities of your cameras and if you have an underlying video system. In my case, I use a video system software named Motion that adds a lot of such capabilities/intelligence and it allows you to use all kind of non-intelligent usb to more intelligent ip cameras. Really recommended. In this project I run everything on a single RPI 3+. To install Motion, just follow the guide here describing "Installing with a release deb package". I used the latest Stretch version: https://github.com/Motion-Project/motion/releases/download/release-4.2.2/pi_stretch_motion_4.2.2-1_armhf.deb My current system structure is rather straight forward & simple: Cameras Motion and other services Node-RED/Dashboard In this example, I have just one camera. A really nice and cheap ESP-EYE configured as a web cam but you can use any camera that is supported Let's see if there is an interest and how this can be developed & extended further! The flow: [{"id":"286dfcc6.d4e334","type":"comment","z":"55f532a.497bccc","name":"Just a live view of cameras","info":"","x":190,"y":60,"wires":[]},{"id":"bb5fdb34.e3dd88","type":"inject","z":"55f532a.497bccc","name":"","topic":"","payload":"checkWeb","payloadType":"str","repeat":"30","crontab":"","once":true,"onceDelay":"10","x":150,"y":160,"wires":[["769295e6.93ea2c"]]},{"id":"769295e6.93ea2c","type":"ui_template","z":"55f532a.497bccc","group":"1c70d4cd.325c0b","name":"Just a view of cameras","order":1,"width":"35","height":"12","format":" \n \n \nimg {\n border: 1px solid #ddd;\n border-radius: 8px;\n padding: 2px;\n width: 405px;\n}\n \n\n \nscope.$watch('msg', function(msg) {\n inMessage(msg.payload);\n});\n\nfunction updatePic() {\n document.getElementById('c51').src = \"http://192.168.0.234:8081/1/stream\";\n document.getElementById('c52').src = \"http://192.168.0.234:8081/1/stream\";\n document.getElementById('c53').src = \"http://192.168.0.234:8081/1/stream\";\n document.getElementById('c54').src = \"http://192.168.0.234:8081/1/stream\";\n document.getElementById('c55').src = \"http://192.168.0.234:8081/1/stream\";\n document.getElementById('c56').src = \"http://192.168.0.234:8081/1/stream\";\n document.getElementById('c57').src = \"http://192.168.0.234:8081/1/stream\";\n document.getElementById('c58').src = \"http://192.168.0.234:8081/1/stream\";\n //alert('updated pics');\n}\n\nfunction inMessage(event) {\n if (event.match('checkWeb')) {\n updatePic();\n }\n}\n\n \n \n \n \n \t\t \n \t\t \n \t\t \n \t\t \n \n \n \t\t \n \t\t \n \t\t \n \t\t \n \n \n \n ","storeOutMessages":false,"fwdInMessages":false,"templateScope":"local","x":380,"y":160,"wires":[[]]},{"id":"debe02e2.7163a","type":"ui_template","z":"55f532a.497bccc","group":"1c70d4cd.325c0b","name":"Clock Toolbar","order":5,"width":"0","height":"0","format":"\n \n $('#clock').remove();\n var toolbar = $('.md-toolbar-tools');\n var div = $(' ');\n var p = $(' ');\n $('#titleScript').parent().hide();\n div.append(p);\n div[0].style.margin = '5px 5px 5px auto';\n toolbar.append(div);\n\n function displayTitle(lh) {\n p.text(lh); \n }\n \n function upTime() {\n var d = new Date();\n p.text(d.toLocaleTimeString('de-AT'));\n }\n\n // Watch the payload and update the title\n (function(scope) {\n scope.$watch('msg.payload', function(data) {\n if (data.match(\"systemsummary\")) { //Systemstatus\n m = data;\n n = m.split(':');\n //alert(n[1]);\n var sSummary = parseInt(n[1], 10);\n if(sSummary === 14){\n document.getElementById(\"clock\").style.backgroundColor =\"transparent\";\n }\n if(sSummary ","storeOutMessages":false,"fwdInMessages":false,"templateScope":"local","x":160,"y":100,"wires":[[]]},{"id":"1c70d4cd.325c0b","type":"ui_group","z":"","name":"LiveVideo","tab":"df07ccac.714e9","disp":false,"width":"35","collapse":false},{"id":"df07ccac.714e9","type":"ui_tab","z":"","name":"LiveVideo","icon":"dashboard","disabled":false,"hidden":false}] Typical Dashboard ui view for 8 cameras: [image]

This is the first trial and it already works as expected!! Thanks to many contributors providing great nodes, I have just put them together in a simple flow I will share the flow, required nodes etc very soon, asap we finished easter lunch etc etc, so later today or tomorrow Functionality: when an image is dropped in the watched folder, manually or created by Motion, it will be recognized, sent for coco ssd analyze, if a person is detected, boxes will be drawn and finally it will be sent via email as additional feature, there is a mqtt in node in the flow. This allows image data to be sent via mqtt. This is how I communicate images over the network from my Motion installations on several RPi's to one common analyzing computer (to make this happen, on motion detected, a python script is started by Motion where the image is taken and forwarded over mqtt) [image]

Yes, I am using RPi 3. Its ok, I will continue with PIR sensor.

If you are interested, you can try this flow, it might look complex but is rather straight forward. You could add it to a separate tab and let it watch the same Monitor image directory (/var/lib/motion), keeping your PIR solution unchanged Required nodes in addition to standard nodes (I use the latest current versions): Install via "Manage palette" node-red-contrib-image-tools node-red-contrib-tfjs-coco-ssd node-red-node-email [{"id":"345bf70a.8ff7c8","type":"comment","z":"62e04cc7.69cf04","name":"If motion detection, then send Picture to email","info":"","x":250,"y":40,"wires":[]},{"id":"411fa684.d527d8","type":"tensorflowCoco","z":"62e04cc7.69cf04","name":"","scoreThreshold":"","passthru":true,"x":580,"y":160,"wires":[["d7499d10.9b50b"]]},{"id":"24206a8a.65a716","type":"mqtt in","z":"62e04cc7.69cf04","name":"","topic":"epic","qos":"2","datatype":"auto","broker":"2a019090.5ba4d","x":170,"y":240,"wires":[["a357a64f.869c78"]]},{"id":"a357a64f.869c78","type":"switch","z":"62e04cc7.69cf04","name":"","property":"payload","propertyType":"msg","rules":[{"t":"istype","v":"buffer","vt":"buffer"}],"checkall":"true","repair":false,"outputs":1,"x":380,"y":240,"wires":[["411fa684.d527d8"]]},{"id":"23db01de.10b36e","type":"switch","z":"62e04cc7.69cf04","name":"detect persons","property":"classes","propertyType":"msg","rules":[{"t":"hask","v":"person","vt":"str"}],"checkall":"true","repair":false,"outputs":1,"x":580,"y":310,"wires":[["cf65cd55.3b0ef"]]},{"id":"d7499d10.9b50b","type":"change","z":"62e04cc7.69cf04","name":"pay to detect","rules":[{"t":"move","p":"payload","pt":"msg","to":"detect","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":580,"y":240,"wires":[["23db01de.10b36e"]]},{"id":"cf65cd55.3b0ef","type":"jimp-image","z":"62e04cc7.69cf04","name":"","data":"image","dataType":"msg","ret":"img","parameter1":"","parameter1Type":"msg","parameter2":"","parameter2Type":"msg","parameter3":"","parameter3Type":"msg","parameter4":"","parameter4Type":"msg","parameter5":"","parameter5Type":"msg","parameter6":"","parameter6Type":"msg","parameter7":"","parameter7Type":"msg","parameter8":"","parameter8Type":"msg","parameterCount":0,"jimpFunction":"none","selectedJimpFunction":{"name":"none","fn":"none","description":"Just loads the image.","parameters":[]},"x":580,"y":380,"wires":[["ef5baf2e.aaf7a"]]},{"id":"ef5baf2e.aaf7a","type":"function","z":"62e04cc7.69cf04","name":"draw Rects","func":"const LINE_WIDTH = 2;\nconst LINE_COLOR = 0xED143DFF;\n\nvar jimpImage = msg.payload;\nlet imgW = jimpImage.bitmap.width;\nlet imgH = jimpImage.bitmap.height;\n\n//create clamping functions to avoid printing outside of image\nlet clampX = (val) => clamp(val,0,imgW);\nlet clampY = (val) => clamp(val,0,imgH);\n\n//add imgBatchOps to msg for drawing text in next node \nmsg.imgBatchOps = [];\n\n\nif (msg.detect.length>0) {\n drawBoxes(jimpImage, msg.detect);\n}\n\nfunction drawBox(img, box) {\n //get box ccords\n let x = box.bbox[0];\n let y = box.bbox[1];\n let w = box.bbox[2];\n let h = box.bbox[3];\n \n scanHLine(img, x, y+h-14, w, 14, 0xfa758e22); //draw box for text\n scanRectangle(img, x, y, w, h, LINE_WIDTH, LINE_COLOR);//draw outer rect\n \n //built batch operations for next image node to draw text\n msg.imgBatchOps.push( \n {\n \"name\": \"print\",\n \"parameters\": [\n \"FONT_SANS_10_BLACK\",\n clampX(x+2),\n clampY(y+h-16),\n box.class\n ]\n }\n \n ); \n}\n\nfunction drawBoxes(img, detections) {\n detections.forEach(element => drawBox(img, element))\n}\n\n\nfunction makeColorIterator(color) {\n return function (x, y, offset) {\n this.bitmap.data.writeUInt32BE(color, offset, true);\n }\n}\n\nfunction scanRectangle(image,x,y,w,h,linePX,color){\n let iterator = makeColorIterator(color);\n \n let xw, yh;\n x = clampX(x-(linePX/2));\n y = clampY(y-(linePX/2));\n w = clampX(w);\n h = clampY(h);\n xw = clampX(x+w);\n yh = clampY(y+h);\n\n \n if(y+linePX <= imgH){\n image.scan(x, y, w, linePX, iterator);// scan linePX height line - TOP\n }\n if(yh+linePX <= imgH){\n image.scan(x, yh, w, linePX, iterator);// scan linePX height line - BOTTOM\n }\n if(x+linePX <= imgW){\n image.scan(x, y, linePX, h, iterator);// scan linePX width line - LEFT\n }\n if(xw+linePX <= imgW){\n image.scan(xw, y, linePX, h, iterator);// scan linePX width line - RIGHT\n }\n \n}\n\n\nfunction scanHLine(image,x,y,length,linePX,color){\n let iterator = makeColorIterator(color);\n x = clampX(x);\n y = clampY(y);\n length = clampX(length);\n if(y+linePX <= imgH){\n image.scan(x, y, length, linePX, iterator);// \n }\n}\n\nfunction scanVLine(image,x,y,height,linePX,color){\n let iterator = makeColorIterator(color);\n x = clampX(x);\n y = clampY(y);\n height = clampY(y+height);\n if(x+linePX <= imgW){\n image.scan(x, y, linePX, height, iterator);// \n }\n}\n\n\n//scanCircle(jimpImage, 92, 170, 43, 0x000000FF);\n//scanCircle(jimpImage, 92, 170, 42, 0xFFCC00FF);\n\nfunction clamp(value, min, max) {\n return Math.min(Math.max(value, min), max);\n}\n\nfunction scanCircle(image, x, y, radius, color) {\n let iterator = makeColorIterator(color);\n return image.scan(x - radius, y - radius, radius*2, radius*2, function (pixX, pixY, idx) {\n if (Math.pow(pixX - x, 2) + Math.pow(pixY - y, 2) < radius*radius) {\n iterator.call(this, pixX, pixY, idx);\n }\n });\n}\n\n \n\nreturn msg;","outputs":1,"noerr":0,"x":580,"y":450,"wires":[["c9964923.b3e558"]]},{"id":"c9964923.b3e558","type":"jimp-image","z":"62e04cc7.69cf04","name":"add text","data":"payload","dataType":"msg","ret":"img","parameter1":"imgBatchOps","parameter1Type":"msg","parameter2":"","parameter2Type":"msg","parameter3":"","parameter3Type":"msg","parameter4":"","parameter4Type":"msg","parameter5":"","parameter5Type":"msg","parameter6":"","parameter6Type":"msg","parameter7":"","parameter7Type":"msg","parameter8":"","parameter8Type":"msg","parameterCount":1,"jimpFunction":"batch","selectedJimpFunction":{"name":"batch","fn":"batch","description":"apply one or more functions","parameters":[{"name":"options","type":"json","required":true,"hint":"an object or an array of objects containing {\"name\" : \"function_name\", \"parameters\" : [x,y,z]}. Refer to info on side panel}"}]},"x":580,"y":520,"wires":[["8e6fe5d.4d2f018","171ad863.c30528"]]},{"id":"8e6fe5d.4d2f018","type":"jimp-image","z":"62e04cc7.69cf04","name":"","data":"payload","dataType":"msg","ret":"buf","parameter1":"","parameter1Type":"msg","parameter2":"","parameter2Type":"msg","parameter3":"","parameter3Type":"msg","parameter4":"","parameter4Type":"msg","parameter5":"","parameter5Type":"msg","parameter6":"","parameter6Type":"msg","parameter7":"","parameter7Type":"msg","parameter8":"","parameter8Type":"msg","parameterCount":0,"jimpFunction":"none","selectedJimpFunction":{"name":"none","fn":"none","description":"Just loads the image.","parameters":[]},"x":580,"y":590,"wires":[["883190ad.6dc53"]]},{"id":"fa145cb8.aa157","type":"file in","z":"62e04cc7.69cf04","name":"","filename":"","format":"","chunk":false,"sendError":false,"x":170,"y":160,"wires":[["4360aa96.6a4b34"]]},{"id":"efefc67f.4b58d8","type":"watch","z":"62e04cc7.69cf04","name":"","files":"/home/pi/Pictures","recursive":"","x":170,"y":80,"wires":[["4f3cf324.67896c"]]},{"id":"4f3cf324.67896c","type":"delay","z":"62e04cc7.69cf04","name":"","pauseType":"delay","timeout":"100","timeoutUnits":"milliseconds","rate":"1","nbRateUnits":"1","rateUnits":"second","randomFirst":"1","randomLast":"5","randomUnits":"seconds","drop":false,"x":380,"y":80,"wires":[["fa145cb8.aa157"]]},{"id":"4360aa96.6a4b34","type":"delay","z":"62e04cc7.69cf04","name":"","pauseType":"rate","timeout":"5","timeoutUnits":"seconds","rate":"1","nbRateUnits":"10","rateUnits":"second","randomFirst":"1","randomLast":"5","randomUnits":"seconds","drop":true,"x":380,"y":160,"wires":[["411fa684.d527d8"]]},{"id":"171ad863.c30528","type":"image viewer","z":"62e04cc7.69cf04","name":"","width":160,"data":"payload","dataType":"msg","x":840,"y":380,"wires":[[]]},{"id":"883190ad.6dc53","type":"e-mail","z":"62e04cc7.69cf04","server":"smtp.gmail.com","port":"465","secure":true,"tls":true,"name":"","dname":"","x":840,"y":590,"wires":[]},{"id":"2a019090.5ba4d","type":"mqtt-broker","z":"","name":"","broker":"192.168.0.240","port":"1883","clientid":"","usetls":false,"compatmode":true,"keepalive":"60","cleansession":true,"birthTopic":"","birthQos":"0","birthPayload":"","closeTopic":"","closePayload":"","willTopic":"","willQos":"0","willPayload":""}] [image]

Could you tell me which software you are using for image processing. I am looking for a lightweight one, tks a lot. In your diagram, some how 2 node "pay to detect" and "detect persons" doing the image processing, that is so interesting. However, if I understand you are using PIR for triggering capture, not realtime processing camera stream, right?

@linhtranvu If you are asking me, I do not use PIR, I use only the cameras. The cameras are handled by a software named Motion. Motion is detecting movements in the video and saving images to a folder/directory when this happens. In Motion you can setup a lot of params and filters, you can enable/disable detection etc etc. To make a long story short, please read about Motion and it's features here NR example above is configured to watch the folder for new images and to do object analyze on those and send an email when a known object is recognized. Everything is from this point handled by nodes available for NR

@krambriw tks for your quick reply. I will try motion

Take a look at my project on GitHub using Coral TPU AI accelerator and handle net cameras, up to 4K, directly: [image] wb666greene/AI-Person-Detector Python Ai "person detector" using Coral TPU or Movidius NCS/NCS2 - wb666greene/AI-Person-Detector Its Python with some straight forward node-red to handle notifications and spatial filtering to not alert on persons in the frame but not of interest -- people walking along the sidewalk in front of the house etc.

@wb666greene I'm starting to look at using a second AI that only runs on current detections in an attempt to reject false positives Reading "It's Not Perfect" I think this sounds as an interesting idea. I also think it could be worth trying to change the detection model/type for the second analyze to something else not based on ssd. Even if it is heavy loading your cpu, you could try to run YOLOv3 for the second run, it might be that would help (and then it is just a matter of finding enough powerful cpu:s :wink: ) I will try to run your images with those false alarms through my YOLO analyzer and see what happens

After testing your bogus images, the result is: The image with the bird is the only one where "person" was detected when running it through my RPi3 with Tensorflow JS Coco SSD node in Node-RED. For the other images, no false detection at all None of the bogus images resulted in false detections of "persons" when running them through: NVIDIA Jetson Nano: jetson inference engine with ssd-inception-v2 using the GPU of the device Lenovo laptop w debian: YOLOv3 handled by Python So for a second run, I think using either would filter out those false detections. I think I would go for and try out YOLOv3 EDIT: Forgot to mention, the analyze of your bogus images took around 0.17 sec in the Jetson and around 0.78 sec in my Lenovo laptop

Thanks for doing this and the info! I've got bogged down in other things,since my current false alarm rate is low enough to not be a bother, but this motivates me. In my node-red I filter detections to reject people walking by on the sidewalk and only alert when they get a certain distance towards the house. Sending the image to a Nano for final verification before the alert and letting the Nano do the alert would be a minor change once I get the Nano AI running.

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A Dashboard setup for video cameras

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krambriw 9 December 2020 14:42 90

I would try this instead, a lot to read but works great

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A Dashboard setup for video cameras

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