Closed jiiins closed 4 years ago
Mind sharing your node-red flow? I currently get spammed with images while motion is detected and like your approach.
Could you track the file_saved events to the object_detected events and use that information?
In node red I think you could combine the two events into one message. Wait for 10 combined messages and only pass the highest confidence?
Here is the flow with 4 snaps. It's a bit contorted and still work in progress, but so far it's been pretty accurate at filtering false positives:
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msg.data.imgproc_states.tf || false;\nvar tf = 0;\nif (tf_data !== false){\n tf = tf_data.attributes.summary.person || 0;\n}\n\nvar ds_data = msg.data.imgproc_states.ds || false;\nvar ds = 0;\nif (ds_data !== false){\n ds = msg.data.imgproc_states.ds.attributes.all_predictions.person || 0; \n}\n\nmsg.fullMatch = false;\n\nvar msid = flow.get('motionArray');\n\n// retrieve saved highest score\nscores = msid[msg._msgid][1] || {};\n//scores = msid[msg._msgid][1];\n\n\n// get image processing scores\nif (tf > 0){\n var tf_person1 = (Math.round(msg.data.imgproc_states.tf.attributes.matches.person[0].score * 100) / 100) || 0;\n //node.warn('t' + tf_person1);\n}\n\nif (ds > 0){\n var ds_person1 = (Math.round(msg.data.imgproc_states.ds.attributes.target_confidences[0] * 100) / 100) || 0;\n //node.warn('d' + ds_person1);\n}\n\n// sum up scores\nvar new_sum = tf_person1 || 0 + ds_person1 || 0;\nvar old_sum = scores['tf'] || 0 + scores['ds'] || 0;\n\n//node.warn(msg._msgid + \" - \" + new_sum);\n\n// keep the highest composite score so far\nif (new_sum > old_sum){\n scores = {\"tf\" : tf_person1, \"ds\" : ds_person1, \"snapshot\" : msg.snapshot};\n //node.warn('new highest score');\n \n}\n\nmsid[msg._msgid][1] = scores;\n\n// if it's a clear match, just send it out - everything ends\nif (tf_person1 > 75 && ds_person1 > 60){ \n msg.payload = msg.base_id + \" - TF: \" + tf_person1 + \" - DS: \" + ds_person1;\n msg.fullMatch = true;\n\n msid[msg._msgid][0] = true;\n node.warn(msg._msgid + ' clear match');\n\n\n// else, if it's the last img, send out the best we got (if any)\n} else if (msg.img_last || false){\n \n node.warn(msg._msgid + ' final score: TF: ' + scores['tf'] + ' DS: ' + scores['ds']);\n \n var temp_snapshot = msg.snapshot;\n \n if (scores['tf'] > 70 || scores['ds'] > 40) {\n msg.payload = msg.base_id + \" - TF: \" + (scores['tf'] || 'x') + \" - DS: \" + (scores['ds'] || 'x');\n\n msg.snapshot = scores['snapshot'];\n msid[msg._msgid][0] = true;\n node.warn(msg._msgid + ' partial match');\n \n \n } else if (scores['tf'] > 50 || scores['ds'] > 35) {\n msg.payload = \"Trash? \" + msg.base_id + \" - TF: \" + (scores['tf'] || 'x') + \" - DS: \" + (scores['ds'] || 'x');\n\n msg.snapshot = scores['snapshot'];\n msid[msg._msgid][0] = true;\n node.warn(msg._msgid + ' probably trash');\n \n } else {\n node.warn(msg._msgid + ' score too low, discard');\n var msg2 = { payload : temp_snapshot };\n msg = undefined;\n }\n\n} else {\n \n //node.warn('delete snapshot');\n //var msg2 = { payload : msg.snapshot };\n msg = undefined;\n}\n\n// store back the flow array\nflow.set('motionArray', msid);\n\n//node.warn(msg || '');\nreturn [msg, 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state for positive check upstream","func":"// retrive existing positive state\nmsid = flow.get('motionArray');\n\nmsid[msg._msgid][0] = true;\n \n// store the new state\nflow.set('motionArray', msid);\n\nnode.warn(msg._msgid + ' sending');\n\nreturn msg;","outputs":1,"noerr":0,"x":690,"y":830,"wires":[["1cdc4ae.81b6eb5"]]},{"id":"cfe3e11c.b9cec","type":"function","z":"1556434c.2d9d7d","name":"No positives yet?","func":"// retrieve positive state\nmsid = flow.get('motionArray');\n\n//node.warn(msid[msg._msgid]);\n\n// continue only if a positive hasn't been found\nif (msid[msg._msgid][0] === false ){\n return msg;\n}","outputs":1,"noerr":0,"x":490,"y":390,"wires":[["7a2ab355.bd35fc"]]},{"id":"b210b7ea.8a6658","type":"delay","z":"1556434c.2d9d7d","name":"","pauseType":"delay","timeout":"5","timeoutUnits":"seconds","rate":"1","nbRateUnits":"1","rateUnits":"second","randomFirst":"1","randomLast":"5","randomUnits":"seconds","drop":false,"x":540,"y":310,"wires":[["74b1a13.216f96"]]},{"id":"5ecd7f.dd05028","type":"switch","z":"1556434c.2d9d7d","name":"","property":"fullMatch","propertyType":"msg","rules":[{"t":"true"},{"t":"false"}],"checkall":"true","repair":false,"outputs":2,"x":1010,"y":990,"wires":[["250da80d.a558a8"],["250da80d.a558a8"]]},{"id":"3bf631df.4c34be","type":"function","z":"1556434c.2d9d7d","name":"Create positive state","func":"// retrieve existing flow array and add our msgid entry as false to initialize it\n\nmsid = flow.get('motionArray') || [];\n\n//msid[msg._msgid] = [];\n\nvar msid_done = false;\nvar msid_scores = [];\n\n\nmsid[msg._msgid] = [msid_done, msid_scores];\n\n// get entries for delayed clean up\ncleanUp = flow.get('motionCleanUp') || [];\n\n\n// clean up old entries\nfor (const [key, value] of Object.entries(cleanUp)) {\n \n //node.warn(value);\n \n // delete entries older than 5 minutes in both arrays\n var age = Date.now() - Date.parse(value);\n \n if (age > 300000) {\n delete cleanUp[key];\n delete msid[key];\n }\n}\n\n// add current one\ncleanUp[msg._msgid] = msg.data.new_state.last_updated;\n\n// put back arrays in as flow vars\nflow.set('motionArray', msid);\nflow.set('motionCleanUp', cleanUp);\n\nreturn msg;","outputs":1,"noerr":0,"x":640,"y":50,"wires":[["bc870bfc.53bb38"]]},{"id":"74b1a13.216f96","type":"change","z":"1556434c.2d9d7d","name":"","rules":[{"t":"set","p":"img_last","pt":"msg","to":"true","tot":"bool"}],"action":"","property":"","from":"","to":"","reg":false,"x":750,"y":310,"wires":[["21f4a465.81c92c"]]},{"id":"31d2968c.bfafda","type":"server","z":"","name":"Home 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I can clarify if you want...
PS: this might not work as expected with NR 1.0 as the sync logic was changed.
The correct approach is to use folder_watcher
When running several automations in parallel, it becomes very hard to grab the right saved image via the event, as the
image_scan
processing time can vary quite a bit. It would be very useful to be able to set the filename when calling theimage_processing.scan
service.For example, in NodeRED I capture 10 frames upon motion detection, keep the one with the highest score and send the image out. With the current system I can't guarantee that the image is the correct one. Or maybe I'm missing something...