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Automatic Annotation #2529

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QuarTerll opened this issue Dec 4, 2020 · 65 comments · Fixed by #2725
Closed

Automatic Annotation #2529

QuarTerll opened this issue Dec 4, 2020 · 65 comments · Fixed by #2725
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@QuarTerll
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I deployed my custom model for automatic annotation and it seems perfect.
It shows the inference progress bar and the docker logs show normal.
However, the Annotation did not show on my image dataset in CVAT.

What can I do?
image

More info
The following is what I send to CVAT. In other words, it is the context.Response part.
<class 'list'>---[{'confidence': '0.4071217', 'label': '0.0', 'points': [360.0, 50.0, 1263.0, 720.0], 'type': 'rectangle'}]

@QuarTerll
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@glenn-jocher Can you help me with that? What can I do?

@glenn-jocher
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@QuarTerll I don't maintain this repository, I maintain Ultralytics YOLOv3 and YOLOv5.

@QuarTerll
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@QuarTerll I don't maintain this repository, I maintain Ultralytics YOLOv3 and YOLOv5.

@glenn-jocher ops, srrrrry... I was working on yolov5 recently. So I just remind your name when I get in trouble... Sorry to bother :<

@QuarTerll
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QuarTerll commented Dec 7, 2020

I found that the logs show "Run SiamMask Model" every time when my inference runs. So I guess the SiamMask model is to draw something on the image such as the bounding box.

Then I just found that there is a model called SiamMask in the directory named serverless . So I am trying to deploy this model first then try to do the Automatic Annotation again. Hope it can work :)

@QuarTerll
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QuarTerll commented Dec 7, 2020

@nmanovic Could you please help me with that? What can I do?

For now, my local "SiamMask Model" is still building. It already passed three hours and I cannot see any logs :<

Am I in the right direction?

@QuarTerll
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I have finished deployed the SiamMask model and It is still not working. On the local website of cvat, it shows the following.
image

@nmanovic nmanovic self-assigned this Dec 7, 2020
@nmanovic nmanovic added this to the 1.2.0-release milestone Dec 7, 2020
@QuarTerll
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I have finished deployed the "saic_vul" model in the directory named serverless/pytorch and It is still not working. On the local website of cvat, it shows that "Automatic annotation finished for task 17". However, there is still no annotations showed on my image dataset.
Uploading image.png…

So, @nmanovic, I see that you add a milestone hmmmmm... Does it mean that this function is not finished for the moment?

@jahaniam
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jahaniam commented Dec 8, 2020

@QuarTerll There are a couple of issues in the semi-automatic nuctl pipeline I will address them in my soon-to-be PR for the Automatic annotation probably by tomorrow. The major one is that make sure to install nuctl 1.4.8 for now, until I update the documentation and boost the version to new versions. If you are still having problems I suggest wait a couple of days, I will ping you here when I am done.

@beep-love
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I had a problem with nuctl 1.4.8 while deploying functions, but was able to do with 1.5.7

However, models didn't appear @ http://localhost:8080/models

nucleo error

I have also submitted the full issue at Issue#2541

Can you please help me on this as well?

Is it something related with version issues?

@jahaniam
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jahaniam commented Dec 8, 2020

@beep-love yes, most likely.
Change line
Change the version to 1.5.7
Then rebuild the container.
docker-compose -f docker-compose.yml -f components/serverless/docker-compose.serverless.yml up —build

To debug the functions, you can use nuclio dashboard at localhost:8070 make sure the function is up and running there.

Wait by tonight I will do a PR

@beep-love
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beep-love commented Dec 8, 2020

Changed the line to ### image: quay.io/nuclio/dashboard:1.5.7-amd64 and rebuild the container.

Also, I have a running instance of the function in my nuclio dashboard

Still the same error!

I have renamed my nuclio release file from nuctl-1.5.7-linux-amd64 to nuctl-1.5.7

Does that make any difference to this relating problem?

@jahaniam
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jahaniam commented Dec 8, 2020

How do you deploy your function?

nuctl-1.5.7 deploy ....

Put a screenshot from your docker ps -a and also the nuclio dashboard -> in cvat that shows the function is up and running

@jahaniam
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jahaniam commented Dec 8, 2020

@QuarTerll it might be due to the label. It might be an int instead of 0.0 .double check that

@beep-love
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Command to deploy function

sudo ./nuctl-1.5.7 create project cvat
sudo ./nuctl-1.5.7 deploy --project-name cvat \
--path serverless/openvino/dextr/nuclio \
--volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
--platform local

And the sc for docker ps -a with nuclio dashboard's sc :

docker ps -a

nuclio dashboard

@yuanliu939
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I have finished deployed the "saic_vul" model in the directory named serverless/pytorch and It is still not working. On the local website of cvat, it shows that "Automatic annotation finished for task 17". However, there is still no annotations showed on my image dataset.

So, @nmanovic, I see that you add a milestone hmmmmm... Does it mean that this function is not finished for the moment?

Same here. Able to finish the automatic annotation, but no actual annotation is displayed after complete and click into the job. While the result of using AI tools is fine. It seems there is a problem in saving the results of automatic annotation.

@QuarTerll
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@QuarTerll it might be due to the label. It might be an int instead of 0.0 .double check that

@jahaniam Thanks for your help.
For the moment, I checked that in my YAML file, there is something like int such as id: 0, but my response is 0.0.
However, it is still not working after I just tried to use labels of an int instead of float, such as 0 instead of 0.0.

And another question:
Is my response type correct?
I didn't find the interface docs. I just followed one of the sample code in dirs named serverless.
I would like to know how to check the interface type? Lol...

@jahaniam
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jahaniam commented Dec 9, 2020

Command to deploy function

sudo ./nuctl-1.5.7 create project cvat
sudo ./nuctl-1.5.7 deploy --project-name cvat \
--path serverless/openvino/dextr/nuclio \
--volume `pwd`/serverless/openvino/common:/opt/nuclio/common \
--platform local

And the sc for docker ps -a with nuclio dashboard's sc :

docker ps -a

nuclio dashboard

looks ok to me. I don't know why it's not working for you. Try without sudo and give it chmod +x nuctl-1.5.7
I've seen in the logs some errors of permissions for nuctl. Before deploying remove the current function as well using dashboard.

@leemengxing
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the same to me, look the docker contrainer logs.
image
image

@leemengxing
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I tested the base64 images in nuclio dashboard and it returned the result normally.
image

@QuarTerll
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QuarTerll commented Dec 14, 2020

@leemengxing
I guess it may be some bugs there about the function of drawing a rectangle.

I am trying to do this semi-anno myself. I will do the inference and save the results by some type of dataset and load them to the CVAT platform and then do the annotations.

@QuarTerll
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@leemengxing @jahaniam @gen-ko @beep-love

I have finished the semi-auto annotation myself. Here is the way that may have a little help.

  1. Create your task
  2. Download dataset. (bug here, not just annotation, see issues 2473)
  3. Unzip the dataset. And change the dir_path and dir_txt to the path of the dataset where save images and annotations.
  4. Do the inference and save results of needed format to txt.
  5. Upload the dataset to your task.
  6. Finished.

Here is my python code for example.

dir_path = './path/to/obj_train_data/your_task'
dir_txt = './path/to/obj_train_data/your_task'

for file_name in os.listdir(dir_path):
    if '.txt' in file_name:
        continue
    name, ext = os.path.splitext(file_name)

    file_path = os.path.join(dir_path, file_name)
    txt_path = os.path.join(dir_txt, f"{name}.txt")

    im = cv2.imread(file_path)
    
    if im is None:
        print('Image is None')
        continue

    inference(model, im, txt_path)

@Inquisitive-ME
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Inquisitive-ME commented Dec 18, 2020

I am also seeing that automatic annotation does not get displayed in the images if you try to do it for a whole task, but it seems to work if you run the annotation on the images individually.

I added in the python code for the annotation function to print out the result.

20.12.18 02:24:10.052 processor (D) Starting triggers {"triggers": [{"ID":"myHttpTrigger","Logger":{},"WorkerAllocator":{},"Class":"sync","Kind":"http","Name":"myHttpTrigger","Statistics":{"EventsHandledSuccessTotal":0,"EventsHandledFailureTotal":0,"WorkerAllocatorStatistics":{"WorkerAllocationCount":0,"WorkerAllocationSuccessImmediateTotal":0,"WorkerAllocationSuccessAfterWaitTotal":0,"WorkerAllocationTimeoutTotal":0,"WorkerAllocationWaitDurationMilliSecondsSum":0,"WorkerAllocationWorkersAvailablePercentage":0}},"Namespace":"nuclio","FunctionName":"tf-efficientdet-D4-1024-coco"}]} 20.12.18 02:24:10.052 processor.http (I) Starting {"listenAddress": ":8080", "readBufferSize": 16384, "maxRequestBodySize": 33554432, "cors": null} 20.12.18 02:24:10.052 processor.webadmin.server (I) Listening {"listenAddress": ":8081"} 20.12.18 02:24:10.052 processor (D) Processor started 20.12.18 02:34:21.173 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 2020-12-18 02:34:28.392762: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7 2020-12-18 02:34:29.669322: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10 20.12.18 02:34:31.042 sor.http.w0.python.logger (I) [{'confidence': '0.7150219', 'label': 'car', 'points': [305.58237075805664, 254.18848514556885, 381.05960845947266, 286.3814306259155], 'type': 'rectangle'}, {'confidence': '0.4878593', 'label': 'car', 'points': [277.7707290649414, 254.48116779327393, 327.9302978515625, 288.5830307006836], 'type': 'rectangle'}] {"worker_id": "0"} 20.12.18 02:37:42.945 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 20.12.18 02:37:44.393 sor.http.w0.python.logger (I) [{'confidence': '0.7150219', 'label': 'car', 'points': [305.58237075805664, 254.18848514556885, 381.05960845947266, 286.3814306259155], 'type': 'rectangle'}, {'confidence': '0.4878593', 'label': 'car', 'points': [277.7707290649414, 254.48116779327393, 327.9302978515625, 288.5830307006836], 'type': 'rectangle'}] {"worker_id": "0"} 20.12.18 02:37:45.002 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 20.12.18 02:37:46.397 sor.http.w0.python.logger (I) [{'confidence': '0.8557838', 'label': 'car', 'points': [235.73516845703125, 262.96679735183716, 440.93788146972656, 388.50183963775635], 'type': 'rectangle'}, {'confidence': '0.68439513', 'label': 'car', 'points': [629.4608306884766, 273.2975935935974, 687.7771759033203, 314.53559160232544], 'type': 'rectangle'}, {'confidence': '0.6654121', 'label': 'car', 'points': [413.3760070800781, 274.4899535179138, 451.6234588623047, 313.1935429573059], 'type': 'rectangle'}, {'confidence': '0.60768425', 'label': 'car', 'points': [444.1271209716797, 271.0985469818115, 486.52244567871094, 303.53201150894165], 'type': 'rectangle'}, {'confidence': '0.54911983', 'label': 'truck', 'points': [700.1108551025391, 227.99441814422607, 946.0396575927734, 405.6583642959595], 'type': 'rectangle'}, {'confidence': '0.5388368', 'label': 'car', 'points': [938.4970092773438, 249.15764808654785, 1063.8406372070312, 324.970178604126], 'type': 'rectangle'}, {'confidence': '0.41315943', 'label': 'car', 'points': [566.1328506469727, 268.943874835968, 587.0766830444336, 283.44505548477173], 'type': 'rectangle'}] {"worker_id": "0"} 20.12.18 02:37:46.604 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 20.12.18 02:37:48.005 sor.http.w0.python.logger (I) [{'confidence': '0.8391146', 'label': 'car', 'points': [172.90611267089844, 429.1703939437866, 403.3717346191406, 580.8685398101807], 'type': 'rectangle'}, {'confidence': '0.8203543', 'label': 'car', 'points': [541.8906784057617, 483.39380264282227, 642.4507141113281, 558.4080648422241], 'type': 'rectangle'}, {'confidence': '0.6969765', 'label': 'bus', 'points': [3.745136260986328, 416.86150074005127, 185.68147659301758, 517.9606103897095], 'type': 'rectangle'}, {'confidence': '0.68772507', 'label': 'car', 'points': [652.8412628173828, 497.9164409637451, 687.9523468017578, 532.858157157898], 'type': 'rectangle'}, {'confidence': '0.678098', 'label': 'traffic_light', 'points': [337.7826690673828, 182.7047824859619, 373.4300231933594, 257.8242087364197], 'type': 'rectangle'}, {'confidence': '0.6073227', 'label': 'person', 'points': [963.6312866210938, 490.5925941467285, 1004.95361328125, 605.0199222564697], 'type': 'rectangle'}, {'confidence': '0.58004767', 'label': 'bus', 'points': [682.5164794921875, 388.3858823776245, 876.5226745605469, 590.8449411392212], 'type': 'rectangle'}] {"worker_id": "0"} 20.12.18 02:37:48.261 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 20.12.18 02:37:49.682 sor.http.w0.python.logger (I) [{'confidence': '0.85061854', 'label': 'truck', 'points': [581.2115478515625, 209.6308135986328, 1032.7020263671875, 391.99214458465576], 'type': 'rectangle'}, {'confidence': '0.81700516', 'label': 'car', 'points': [0.18790245056152344, 292.0550537109375, 245.8984375, 380.5064535140991], 'type': 'rectangle'}, {'confidence': '0.5602961', 'label': 'traffic_light', 'points': [345.5408477783203, 33.68543654680252, 381.2101745605469, 89.90786612033844], 'type': 'rectangle'}, {'confidence': '0.5263241', 'label': 'car', 'points': [1222.5276947021484, 328.851056098938, 1279.0593719482422, 400.8018922805786], 'type': 'rectangle'}] {"worker_id": "0"} 20.12.18 02:37:49.953 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 20.12.18 02:37:51.338 sor.http.w0.python.logger (I) [{'confidence': '0.80725086', 'label': 'car', 'points': [480.3098678588867, 305.0113034248352, 571.6457748413086, 375.3591012954712], 'type': 'rectangle'}, {'confidence': '0.7996338', 'label': 'car', 'points': [752.7581024169922, 310.4746198654175, 879.3225860595703, 381.2298774719238], 'type': 'rectangle'}, {'confidence': '0.76808906', 'label': 'car', 'points': [274.3927764892578, 309.7818160057068, 375.8354949951172, 364.992470741272], 'type': 'rectangle'}, {'confidence': '0.6982929', 'label': 'car', 'points': [381.07444763183594, 295.3595781326294, 451.260986328125, 343.0009961128235], 'type': 'rectangle'}, {'confidence': '0.67496926', 'label': 'car', 'points': [0.0, 311.5396499633789, 181.21234893798828, 467.24231243133545], 'type': 'rectangle'}, {'confidence': '0.5257714', 'label': 'car', 'points': [310.96471786499023, 294.1240668296814, 381.4679718017578, 330.24672746658325], 'type': 'rectangle'}, {'confidence': '0.43731278', 'label': 'car', 'points': [102.87278175354004, 280.21331548690796, 257.70179748535156, 368.63396644592285], 'type': 'rectangle'}] {"worker_id": "0"} 20.12.18 02:37:51.587 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 20.12.18 02:37:52.990 sor.http.w0.python.logger (I) [{'confidence': '0.85429806', 'label': 'car', 'points': [808.3771514892578, 267.3688817024231, 1043.8465118408203, 426.82888984680176], 'type': 'rectangle'}, {'confidence': '0.8060608', 'label': 'car', 'points': [688.3963012695312, 279.3934178352356, 759.7428894042969, 354.9244022369385], 'type': 'rectangle'}, {'confidence': '0.777873', 'label': 'car', 'points': [736.7932891845703, 266.82782649993896, 864.4783782958984, 377.96135902404785], 'type': 'rectangle'}, {'confidence': '0.7537456', 'label': 'car', 'points': [402.83214569091797, 277.8368353843689, 456.1515426635742, 352.4603319168091], 'type': 'rectangle'}, {'confidence': '0.74806195', 'label': 'car', 'points': [982.2433471679688, 210.104877948761, 1277.1514892578125, 561.2226247787476], 'type': 'rectangle'}, {'confidence': '0.65498257', 'label': 'car', 'points': [263.3064270019531, 241.56405687332153, 416.4366149902344, 379.8746967315674], 'type': 'rectangle'}, {'confidence': '0.6334168', 'label': 'car', 'points': [0.002460479736328125, 221.89629793167114, 309.68921661376953, 446.5914058685303], 'type': 'rectangle'}, {'confidence': '0.62531567', 'label': 'car', 'points': [669.5706176757812, 287.40833044052124, 705.9028625488281, 322.6821255683899], 'type': 'rectangle'}, {'confidence': '0.6175538', 'label': 'car', 'points': [399.0179443359375, 256.85344219207764, 492.1952819824219, 338.9302396774292], 'type': 'rectangle'}, {'confidence': '0.48883328', 'label': 'car', 'points': [485.5108642578125, 271.1214208602905, 515.6954956054688, 326.12908601760864], 'type': 'rectangle'}, {'confidence': '0.4759105', 'label': 'car', 'points': [504.89349365234375, 279.6710801124573, 534.8953247070312, 315.52470445632935], 'type': 'rectangle'}, {'confidence': '0.43032724', 'label': 'car', 'points': [637.9503631591797, 285.22608518600464, 669.1315460205078, 313.8369941711426], 'type': 'rectangle'}] {"worker_id": "0"} 20.12.18 02:37:53.172 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 20.12.18 02:37:54.584 sor.http.w0.python.logger (I) [{'confidence': '0.85027987', 'label': 'car', 'points': [282.9447937011719, 310.1306104660034, 560.2922821044922, 555.8311700820923], 'type': 'rectangle'}, {'confidence': '0.8252959', 'label': 'car', 'points': [167.78976440429688, 320.5255436897278, 290.92662811279297, 379.9345636367798], 'type': 'rectangle'}, {'confidence': '0.6271201', 'label': 'car', 'points': [80.9302806854248, 310.6858706474304, 177.0273208618164, 365.5087423324585], 'type': 'rectangle'}, {'confidence': '0.6123071', 'label': 'car', 'points': [1068.5894775390625, 401.55754566192627, 1277.4929809570312, 625.2445077896118], 'type': 'rectangle'}, {'confidence': '0.53847194', 'label': 'car', 'points': [3.228464126586914, 292.42730140686035, 89.75964546203613, 320.40149688720703], 'type': 'rectangle'}, {'confidence': '0.4461867', 'label': 'car', 'points': [529.5944595336914, 341.00616216659546, 572.5418472290039, 403.1205224990845], 'type': 'rectangle'}, {'confidence': '0.42748508', 'label': 'traffic_light', 'points': [638.5101318359375, 162.6327931880951, 672.9914855957031, 208.5464072227478], 'type': 'rectangle'}] {"worker_id": "0"} 20.12.18 02:37:54.831 sor.http.w0.python.logger (I) Run efficientdet_d4_1024_coco model {"worker_id": "0"} 20.12.18 02:37:56.226 sor.http.w0.python.logger (I) [{'confidence': '0.92636585', 'label': 'person', 'points': [148.53775024414062, 278.6093330383301, 358.0533981323242, 656.5142154693604], 'type': 'rectangle'}, {'confidence': '0.8458457', 'label': 'person', 'points': [0.0, 253.6021327972412, 128.81412506103516, 665.1172828674316], 'type': 'rectangle'}, {'confidence': '0.6159458', 'label': 'car', 'points': [94.89753723144531, 380.959210395813, 145.67127227783203, 424.5071268081665], 'type': 'rectangle'}, {'confidence': '0.5576205', 'label': 'handbag', 'points': [178.5042953491211, 411.9667053222656, 283.97972106933594, 565.3603506088257], 'type': 'rectangle'}, {'confidence': '0.5391152', 'label': 'car', 'points': [1160.5314636230469, 481.07062339782715, 1278.8795471191406, 714.7406816482544], 'type': 'rectangle'}] {"worker_id": "0"}

The result looks fine so it seems like it must be an issue receiving or saving the multiple requests. Even exporting annotations does not show anything so somehow the result from the detector is lost

@QuarTerll
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QuarTerll commented Dec 18, 2020

I have finished the semi-auto annotation myself. Here is the way that may have a little help.

  1. Create your task
  2. Download dataset. (bug here, not just annotation, see issues 2473)
  3. Unzip the dataset. And change the dir_path and dir_txt to the path of the dataset where save images and annotations.
  4. Do the inference and save results of needed format to txt.
  5. Upload the dataset to your task.
  6. Finished.

Here is my python code for example.

dir_path = './path/to/obj_train_data/your_task'
dir_txt = './path/to/obj_train_data/your_task'

for file_name in os.listdir(dir_path):
    if '.txt' in file_name:
        continue
    name, ext = os.path.splitext(file_name)

    file_path = os.path.join(dir_path, file_name)
    txt_path = os.path.join(dir_txt, f"{name}.txt")

    im = cv2.imread(file_path)
    
    if im is None:
        print('Image is None')
        continue

    inference(model, im, txt_path)

@Inquisitive-ME
YES. What I did is to do the automatic annotations for the whole task. For now, you can do the above to do this.

How do you do to run the automatic annotation for a single image? Maybe my version of CVAT is not the latest?

@Inquisitive-ME
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Yea doing the annotations outside of CVAT is not desirable for me.

But if you click on the AI Tools icon (picture below) in the toolbar you can go to detectors and should be able to select your model to annotate that image.

image

@QuarTerll
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QuarTerll commented Dec 24, 2020

Yea doing the annotations outside of CVAT is not desirable for me.

Hmmmm, yep, it's real semi-auto Lols.

But if you click on the AI Tools icon (picture below) in the toolbar you can go to detectors and should be able to select your model to annotate that image.

image

Thank you.

@jahaniam
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jahaniam commented Jan 1, 2021

I can confirm automatic annotation is broken for tasks. It only works for single images I have tried two models. Faster rcnn and mask rcnn. It works fine for a single image but when I use it on a task containing multiple images although it shows it has completed but it doesn't show any annotation results on the images.
@nmanovic can we have a look on this problem please? I am working on the gpu version of the maskrcnn rn and a fix for cpu version. I will do a PR soon for that.

@turowicz
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turowicz commented Jan 5, 2021

Same problem documented here, possible duplication #2644

F-RCNN on CPU

@turowicz
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turowicz commented Jan 6, 2021

The Faster RCNN on cvat.org works fine detecting persons for me. But now I realize what difference we may have. On my k8s cluster, I am trying to use TensorFlow F-RCNN, which doesn't use the volume handler, and perhaps the TensorFlow models are the ones that are "broken".

@turowicz
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turowicz commented Jan 6, 2021

The Mask RCNN also works on cvat.org for me. Through vino.

@turowicz
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turowicz commented Jan 6, 2021

I have deployed the Open VINO versions of F-RCNN and Mask RCNN to my cluster.

NO LUCK.

@turowicz
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turowicz commented Jan 6, 2021

I've also noticed that Open VINO F-RCNN requires TF F-RCNN to exist. What is the point of this? It makes no sense to me.

I get this error when running Open VINO F-RCNN without a TF F-RCNN running aside to it.

image

@turowicz
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turowicz commented Jan 6, 2021

Regardless, both TF F-RCNN and OpenVINO F-RCNN do not work as a bulk annotation on a Task.

Somehow OpenVINO F-RCNN works on cvat.org for person detection. Perhaps they have a different function.yaml?

@jahaniam
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jahaniam commented Jan 6, 2021

@turowicz Your information was really helpful. I investigated why cvat.org automatic annotation doesn't work for me. I realized if a task is assigned to a project this feature fails, otherwise works fine.

Can you also try it on the develop branch and create a task without assigning it to a project and see if bulk annotation works?
I believe it might work that way.

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jahaniam commented Jan 6, 2021

@nmanovic
The project PR was introduced in #2255
If a task is assigned to a project, the automatic annotation for that task is failing (showing success but there is no result ).

@turowicz
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turowicz commented Jan 6, 2021

@jahaniam woop woop! You're right!

After removing all the projects on my k8s, the automatic annotation works on Tasks!

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turowicz commented Jan 6, 2021

@jahaniam also cancelling automated tasks is broken

image

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turowicz commented Jan 6, 2021

@turowicz Your information was really helpful. I investigated why cvat.org automatic annotation doesn't work for me. I realized if a task is assigned to a project this feature fails, otherwise works fine.

Can you also try it on the develop branch and create a task without assigning it to a project and see if bulk annotation works?
I believe it might work that way.

Yes this is how it works, on develop, thanks!

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nmanovic commented Jan 7, 2021

@turowicz , we will look at the problem after public holidays in Russia. Sorry for the experience. "I realized if a task is assigned to a project this feature fails, otherwise works fine" - it looks like a regression. @ActiveChooN , could you please look?

@nmanovic nmanovic added the bug Something isn't working label Jan 7, 2021
@beep-love
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beep-love commented Jan 7, 2021

Hi, I was also using tf-faster rcnn for automatic annotation.

I was able to deploy the function and run auto annotation using nuclio 1.5.8 and clearing all other error functions at nuclio dashboard. Also, i had to make a change in yaml file in the line no. of worker for 2 to 1 .

I ran auto annotation. It ran well in the first three videos of length less than 3 mins for traffic annotation. The later 3 was also able to run the annotation till the end with a success message and few error message.

Attaching the screenshot here:

https://drive.google.com/file/d/1B8yvmU4KR6K_-QMKv_B0wmI1vjYT6K0M/view?usp=sharing

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Also,

The later videos were of length more than 5 mins. So I will again check with the shorter videos if it throws an error or not.

In my opinion, this issue might be related to any runtime parameters in the YAML file.

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jahaniam commented Jan 8, 2021

@turowicz , we will look at the problem after public holidays in Russia. Sorry for the experience. "I realized if a task is assigned to a project this feature fails, otherwise works fine" - it looks like a regression. @ActiveChooN , could you please look?

@ActiveChooN Would also be nice to support changing project ID of a task through the API or UI.
We cannot change Project ID of a task and set it to null or any other project IDs to be able to move a task between projects or assign a task to a project that is not assigned to any initially.

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jahaniam commented Jan 8, 2021

Hi, I was also using tf-faster rcnn for automatic annotation.

I was able to deploy the function and run auto annotation using nuclio 1.5.8 and clearing all other error functions at nuclio dashboard. Also, i had to make a change in yaml file in the line no. of worker for 2 to 1 .

I ran auto annotation. It ran well in the first three videos of length less than 3 mins for traffic annotation. The later 3 was also able to run the annotation till the end with a success message and few error message.

Attaching the screenshot here:

https://drive.google.com/file/d/1B8yvmU4KR6K_-QMKv_B0wmI1vjYT6K0M/view?usp=sharing

I think the best way to optimize for now is to do it outside and create a task and upload annotation using API. see https://github.com/openvinotoolkit/cvat/tree/develop/utils/cli or localhost:8080/api/swagger

@nmanovic nmanovic modified the milestones: 1.2.0-release, 1.3.0-alpha Jan 8, 2021
@turowicz
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turowicz commented Jan 8, 2021

OK so issues identified:

  1. Tasks assigned to Projects cannot be annotated automagically.
  2. Cancellation of automated Task annotation throws errors when setting timeout.
  3. In the current state of automated annotations some people are forced to run it through the API.

Anything else? How about the fact that OpenVINO F-RCNN requires TF-RCNN to co-exist?

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jahaniam commented Jan 8, 2021

OK so issues identified:

  1. Tasks assigned to Projects cannot be annotated automagically.
  2. Cancellation of automated Task annotation throws errors when setting timeout.
  3. In the current state of automated annotations some people are forced to run it through the API.

Anything else? How about the fact that OpenVINO F-RCNN requires TF-RCNN to co-exist?

Nuclio functions are independent of each other hence OpenVINO F-RCNN requires TF-RCNN to co-exist is wrong. these are two independent functions. The only reason I said to go with TF-RCNN is that I debug and tested it myself.

  1. Tasks assigned to Projects cannot be annotated automagically.
  2. Cancellation of automated Task annotation throws errors when setting timeout.
  3. Moving/assigning a task to a project through UI/API fails (considering two projects have the same annotations)

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jahaniam commented Jan 10, 2021

  1. if a task is assigned to a project, dumping the annotation/dataset as PASCAL VOC and many other formats fails (succeeds but annotation is empty although it shouldn't)

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ashokbalaraman commented Jan 11, 2021

I can confirm automatic annotation is broken for tasks. It only works for single images I have tried two models. Faster rcnn and mask rcnn. It works fine for a single image but when I use it on a task containing multiple images although it shows it has completed but it doesn't show any annotation results on the images.
@nmanovic can we have a look on this problem please? I am working on the gpu version of the maskrcnn rn and a fix for cpu version. I will do a PR soon for that.

Thanks @jahaniam. Appreciate all your work. I can confirm that it works for Tasks not tied to a project. Do you have a ball-park ETA on solving this for tasks tied to a project?

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Guess problem with auto-annotation of the task in project should be fixed with @2725.

@ActiveChooN Would also be nice to support changing project ID of a task through the API or UI.

@jahaniam, do you mean moving task between projects? It's quite complicated task and will be implemented in future releases with developing project feature.

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jahaniam commented Jan 27, 2021

@ActiveChooN There is a project id field for tasks. Wouldn’t changing it change the task project?

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@jahaniam, it would, but there is annotation in the task that depend on project labels. So we need merge annotation with new label somehow before moving the task.

@lchunleo
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This is issue of semi annotation by model is fixed ? And where can I find instructions how to configure for semi annotation? Thanks

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