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Creating an image recognition solution with Azure IoT Edge and Azure Cognitive Services

Dave Glover on April 05, 2019

Author Dave Glover, Microsoft Cloud Developer Advocate Solution Creating an image recognition solution with Azure IoT Edge and Azure Cognit...
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daveam profile image
Andrea Marson •

This is really a useful and interesting article. Thank you very much.

That being said, I'm trying to run the project on a PC running Ubuntu 18.04.
I'm using a local registry for testing.
It seems that docker images were pushed to the local registry correctly:

$ docker image list         
REPOSITORY                                                    TAG                 IMAGE ID            CREATED             SIZE
mcr.microsoft.com/azureiotedge-simulated-temperature-sensor   1.0                 c86e0d919bd6        4 weeks ago         96.1MB
localhost:5000/image-classifier-service                       1.1.91-amd64        3147a0658034        4 weeks ago         1.71GB
localhost:5000/camera-capture-opencv                          1.1.91-amd64        cdcc320bd8a6        4 weeks ago         1.26GB
python                                                        3.5                 61bbcc36b492        5 weeks ago         909MB
...
mcr.microsoft.com/azureiotedge-agent                          1.0                 46ad173076af        2 months ago        137MB
mcr.microsoft.com/azureiotedge-diagnostics                    1.0.8               d16965225a70        2 months ago        8.71MB
...
mcr.microsoft.com/azureiotedge-hub                            1.0.7               ed05376f97bd        4 months ago        155MB
mcr.microsoft.com/azureiotedge-agent                          1.0.7               219c2aff4adc        4 months ago        140MB
...
registry                                                      2                   f32a97de94e1        6 months ago        25.8MB
hello-world                                                   latest              fce289e99eb9        8 months ago        1.84kB

However, deployment can't be completed. I found this in the edgeAgent logs:

2019-09-25 08:47:51.916 +00:00 [WRN] - Reconcile failed because of invalid configuration format
Microsoft.Azure.Devices.Edge.Agent.Core.ConfigSources.ConfigFormatException: Agent configuration format is invalid. ---> System.ArgumentException: Image localhost:5000/camera-capture-opencv:1.1.91-amd64 is not in the right format
   at Microsoft.Azure.Devices.Edge.Agent.Docker.DockerConfig.ValidateAndGetImage(String image) in /home/vsts/work/1/s/edge-agent/src/Microsoft.Azure.Devices.Edge.Agent.Docker/DockerConfig.cs:line 93

So it seems that there is a syntax error or something like that in the deployment file, but I can't find it. This file looks like this:

{
  "modulesContent": {
    "$edgeAgent": {
      "properties.desired": {
        "schemaVersion": "1.0",
        "runtime": {
          "type": "docker",
          "settings": {
            "minDockerVersion": "v1.25",
            "loggingOptions": "",
            "registryCredentials": {}
          }
        },
        "systemModules": {
          "edgeAgent": {
            "type": "docker",
            "settings": {
              "image": "mcr.microsoft.com/azureiotedge-agent:1.0.7",
              "createOptions": "{}"
            }
          },
          "edgeHub": {
            "type": "docker",
            "status": "running",
            "restartPolicy": "always",
            "settings": {
              "image": "mcr.microsoft.com/azureiotedge-hub:1.0.7",
              "createOptions": "{\"HostConfig\":{\"PortBindings\":{\"5671/tcp\":[{\"HostPort\":\"5671\"}],\"8883/tcp\":[{\"HostPort\":\"8883\"}],\"443/tcp\":[{\"HostPort\":\"443\"}]}}}"
            }
          }
        },
        "modules": {
          "camera-capture": {
            "version": "1.0",
            "type": "docker",
            "status": "running",
            "restartPolicy": "always",
            "settings": {
              "image": "localhost:5000/camera-capture-opencv:1.1.91-amd64",
              "createOptions": "{\"Env\":[\"Video=0\",\"azureSpeechServicesKey=2f57f2d9f1074faaa0e9484e1f1c08c1\",\"AiEndpoint=http://image-classifier-service:80/image\"],\"HostConfig\":{\"PortBindings\":{\"5678/tcp\":[{\"HostPort\":\"5678\"}]},\"Devices\":[{\"PathOnHost\":\"/dev/video0\",\"PathInContainer\":\"/dev/video0\",\"CgroupPermissions\":\"mrw\"},{\"PathOnHost\":\"/dev/snd\",\"PathInContainer\":\"/dev/snd\",\"CgroupPermissions\":\"mrw\"}]}}"
            }
          },
          "image-classifier-service": {
            "version": "1.0",
            "type": "docker",
            "status": "running",
            "restartPolicy": "always",
            "settings": {
              "image": "localhost:5000/image-classifier-service:1.1.91-amd64",
              "createOptions": "{\"HostConfig\":{\"Binds\":[\"/home/pi/images:/images\"],\"PortBindings\":{\"8000/tcp\":[{\"HostPort\":\"80\"}],\"5679/tcp\":[{\"HostPort\":\"5679\"}]}}}"
            }
          }
        }
      }
    },
    "$edgeHub": {
      "properties.desired": {
        "schemaVersion": "1.0",
        "routes": {
          "camera-capture": "FROM /messages/modules/camera-capture/outputs/output1 INTO $upstream"
        },
        "storeAndForwardConfiguration": {
          "timeToLiveSecs": 7200
        }
      }
    }
  }
}

Any help would be greatly appreciated.

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daveam profile image
Andrea Marson •

The problem is related to edgeAgent 1.0.7, as explained here.

After updating to 1.0.8, I can deploy the modules:

$ sudo iotedge list
NAME                      STATUS           DESCRIPTION      CONFIG
image-classifier-service  running          Up 11 minutes    localhost:5000/image-classifier-service:1.1.91-amd64
edgeHub                   running          Up 11 minutes    mcr.microsoft.com/azureiotedge-hub:1.0.8
edgeAgent                 running          Up 14 minutes    mcr.microsoft.com/azureiotedge-agent:1.0.8
camera-capture            running          Up 11 minutes    localhost:5000/camera-capture-opencv:1.1.91-amd64
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gloveboxes profile image
Dave Glover •

Ah, fantastic. Thanks and great you got working. I'll update the deployment template so it starts with 1.0.8. Let me know how you get on. Cheers Dave

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daveam profile image
Andrea Marson •

Dave, I'm diving into your project to understand how it works in more detail.

First of all, I'm exploring the camera capture process. I'm running the project on a Intel(R) Core(TM) i3-2100 CPU @ 3.10GHz.
I noticed that if the scene shot by the camera is still, no frames are processed. If the scene changes or if I move the camera, on average about 4 frames per second are processed:

$ iotedge logs camera-capture -f  | ts %F-%H:%M:%.S
2019-09-27-10:42:31.697454 pygame 1.9.6
2019-09-27-10:42:31.697609 Hello from the pygame community. https://www.pygame.org/contribute.html
2019-09-27-10:42:31.697688 sasToken
2019-09-27-10:42:31.697758 
2019-09-27-10:42:31.697833 Python 3.5.2 (default, Nov 12 2018, 13:43:14) 
2019-09-27-10:42:31.697877 [GCC 5.4.0 20160609]
2019-09-27-10:42:31.697904 
2019-09-27-10:42:31.697931 Camera Capture Azure IoT Edge Module. Press Ctrl-C to exit.
2019-09-27-10:42:31.697957 opening camera
...
2019-09-27-10:40:45.445710 sending frame to model: 476
2019-09-27-10:40:45.668826 label: Hand, probability 0.8052769303321838
2019-09-27-10:40:45.925346 sending frame to model: 477
2019-09-27-10:40:46.148182 label: Hand, probability 0.8468263745307922
2019-09-27-10:40:46.404615 sending frame to model: 478
2019-09-27-10:40:46.630166 label: Hand, probability 0.8512248992919922
2019-09-27-10:40:46.886933 sending frame to model: 479
2019-09-27-10:40:47.120413 label: Hand, probability 0.877470850944519
2019-09-27-10:40:47.377079 sending frame to model: 480
2019-09-27-10:40:47.601168 label: Hand, probability 0.8282925486564636
2019-09-27-10:40:47.857675 sending frame to model: 481

Did you achieve similar performances on your development host?
What about the RPi?

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daveam profile image
Andrea Marson •

I've just found this in CameraCapture.py ...

# slow things down a bit - 4 frame a second is fine for demo purposes and less battery drain and lower Raspberry Pi CPU Temperature
            time.sleep(0.25)

It answers my question ;)

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gloveboxes profile image
Dave Glover •

Hey yes, I did some optimisations, 1) if pixel change was greater that 70000 pixels RGB then send frame to ml model. 2) slowed down frame rate down, logic was the model would be more available to process a frame if something changed...

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gloveboxes profile image
Dave Glover •

A raspberry pi 4 take approx 0.8 seconds per inference. Raspberry pi 3b plus approx 1.2 seconds per inference

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daveam profile image
Andrea Marson •

Thank you, Dave. These numbers are very useful.
How did you get them?

My goal is

  • to run your project on a couple of ARM-based embedded platforms we manufacture
  • to perform some basic profiling
  • to figure out if and how the project could be optimized.

That's why I would like to measure the inference time the same way you did.

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gloveboxes profile image
Dave Glover •

hey from Bash I just did 'time curl ....' and just used the curl example in the readme from the downloaded custom vision docker container

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daveam profile image
Andrea Marson • • Edited

Hi Dave

I tested the custom docker container on my PC first and it worked fine:

$ time curl -X POST http://127.0.0.1:32769/image -F imageData=@red-apple.jpg
{"created":"2019-10-01T12:40:10.052750","id":"","iteration":"","predictions":[{"boundingBox":null,"probability":1.5830000847927295e-05,"tagId":"","tagName":"Avocado"},{"boundingBox":null,"probability":2.420000100755715e-06,"tagId":"","tagName":"Banana"},{"boundingBox":null,"probability":0.026290949434041977,"tagId":"","tagName":"Green Apple"},{"boundingBox":null,"probability":2.8750000637955964e-05,"tagId":"","tagName":"Hand"},{"boundingBox":null,"probability":0.00048392999451607466,"tagId":"","tagName":"Orange"},{"boundingBox":null,"probability":0.9731781482696533,"tagId":"","tagName":"Red Apple"}],"project":""}

real    0m0,285s
user    0m0,005s
sys     0m0,008s

Then, I built your project for arm32v7 architecture and pulled the resulting image from my embedded device (I had to use a registry on the docker Hub because I couldn't pull from the local registry running on my PC).
I tried to run the same test on my embedded device running armbian distribution, but it didn't work although the container seems up and running:

root@sbcx:~# docker images
REPOSITORY                                                    TAG                 IMAGE ID            CREATED             SIZE
dave1am/image-classifier-service                              1.1.91-arm32v7      804d48001df8        6 days ago          1.05GB
mcr.microsoft.com/azureiotedge-simulated-temperature-sensor   1.0                 a626b1a36236        2 months ago        200MB
mcr.microsoft.com/azureiotedge-hub                            1.0                 3a84bfb86c7d        2 months ago        252MB
mcr.microsoft.com/azureiotedge-agent                          1.0                 58276103181c        2 months ago        238MB
mcr.microsoft.com/azureiotedge-diagnostics                    1.0.8               a480fa622e2a        2 months ago        7.34MB
root@sbcx:~# docker run -P -d 804d48001df8
9f197d878088d97b33f5ef6338bbd5a1eeaa87fd8890a94e05f78614af1ebdc6
root@sbcx:~# docker ps
CONTAINER ID        IMAGE               COMMAND                  CREATED             STATUS              PORTS                                            NAMES
9f197d878088        804d48001df8        "/usr/bin/entry.sh p…"   34 seconds ago      Up 27 seconds       0.0.0.0:32769->80/tcp, 0.0.0.0:32768->5679/tcp   sweet_kepler
root@sbcx:/home/armbian/devel/azure-iot-edge/image-classifier# time curl -X POST http://127.0.0.1:32769/image -F imageData=@red-apple.jpg
curl: (52) Empty reply from server                                                                                                   

real    0m1.669s                                                                                                                                 
user    0m0.030s
sys     0m0.040s

Any advice on how I could analyze this issue?

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daveam profile image
Andrea Marson •

I just noticed that, after running this test on the embedded device, the container stops and the following warning message appears in its log:

# docker logs --details -f 9f197d878088
 Loading model... * Serving Flask app "app" (lazy loading)
...
WARNING:tensorflow:From /app/predict.py:123: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
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gloveboxes profile image
Dave Glover •

Hey there, I've not tried armbian. Those Tensorflow messages are just warnings. You can try the arm image I built from glovebox/image-classifier-service:1.1.111-arm32v7 ie docker run -it --rm -p 80:80 glovebox/image-classifier-service:1.1.111-arm32v7. And test with 'curl -X POST xxx.xxx.xxx.xxx/image -F imageData=@image.jpg' My Pi is running Docker version 19.03.3, build a872fc2. Cheers Dave

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daveam profile image
Andrea Marson •

Hi Dave,
I verified the docker version running on my board:

# docker version
Client: Docker Engine - Community
 Version:           19.03.3
 API version:       1.40
 Go version:        go1.12.10
 Git commit:        a872fc2
 Built:             Tue Oct  8 01:12:57 2019
 OS/Arch:           linux/arm
 Experimental:      false

Server: Docker Engine - Community
 Engine:
  Version:          19.03.3
  API version:      1.40 (minimum version 1.12)
  Go version:       go1.12.10
  Git commit:       a872fc2
  Built:            Tue Oct  8 01:06:58 2019
  OS/Arch:          linux/arm
  Experimental:     false
 containerd:
  Version:          1.2.6
  GitCommit:        894b81a4b802e4eb2a91d1ce216b8817763c29fb
 runc:
  Version:          1.0.0-rc8
  GitCommit:        425e105d5a03fabd737a126ad93d62a9eeede87f
 docker-init:
  Version:          0.18.0
  GitCommit:        fec3683

Unfortunately, the outcome is the same even with your image:

# curl -X POST 127.0.0.1/image -F imageData=@red-apple.jpg
curl: (52) Empty reply from server
root@sbcx:/home/armbian/devel/azure-iot-edge/image-classifier# docker ps
CONTAINER ID        IMAGE                                               COMMAND                  CREATED             STATUS              PORTS                          NAMES
71f9b808440d        glovebox/image-classifier-service:1.1.111-arm32v7   "/usr/bin/entry.sh p…"   3 minutes ago       Up 3 minutes        0.0.0.0:80->80/tcp, 5679/tcp   admiring_mccarthy
root@sbcx:/home/armbian/devel/azure-iot-edge/image-classifier# curl -X POST 127.0.0.1/image -F imageData=@red-apple.jpg
curl: (7) Failed to connect to 127.0.0.1 port 80: Connection refused
root@sbcx:/home/armbian/devel/azure-iot-edge/image-classifier# docker ps
CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES

I'm afraid I have to debug at a lower level to understand what's going on (that is quite common for embedded devices ...).
I'm not an expert of Azure-based development approach, so I don't know what is the best thing to do in such a situation.

If there are no better ideas, I'm thinking of:

  • writing a simple Python application to exercise the model by following this tutorial
  • remote debugging it as described in this article you wrote.
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gloveboxes profile image
Dave Glover •

Hey there, I'm pretty sure that the contents of the container are fine, and they are isolated too. Do you have a Raspberry Pi you can test against? There is nothing to stop you running the contents of the docker project that is exported by Custom Vision directly on the device (ie outside of a container). dg

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gloveboxes profile image
Dave Glover • • Edited

also try curl to localhost curl -X POST localhost/image -F imageData=@red-apple.jpg or by hostname curl -X POST mydevice.local/image -F imageData=@red-apple.jpg. I've seen issues where name resolution doesnt always work as you'd expect...

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daveam profile image
Andrea Marson • • Edited

Hi Dave,
unfortunately, neither localhost nor mydevice.local worked :(

So I tried the other approach that doesn't make use of any container.
For convenience, I first tried to make it work on my development PC. I followed this tutorial, but it didn't work either :(

Apart from several warning messages, the simple Python program I wrote crashes because of this error:

2019-10-17 09:53:43.957158: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3092910000 Hz
2019-10-17 09:53:43.957622: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1da3970 executing computations on platform Host. Devices:
2019-10-17 09:53:43.957668: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
Traceback (most recent call last):
  File "/home/sysadmin/.vscode/extensions/ms-python.python-2019.10.41019/pythonFiles/ptvsd_launcher.py", line 43, in <module>
    main(ptvsdArgs)
  File "/home/sysadmin/.vscode/extensions/ms-python.python-2019.10.41019/pythonFiles/lib/python/old_ptvsd/ptvsd/__main__.py", line 432, in main
    run()
  File "/home/sysadmin/.vscode/extensions/ms-python.python-2019.10.41019/pythonFiles/lib/python/old_ptvsd/ptvsd/__main__.py", line 316, in run_file
    runpy.run_path(target, run_name='__main__')
  File "/usr/lib/python3.6/runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "/usr/lib/python3.6/runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/sysadmin/devel/azure/custom-vision/glover-image-classifier/image-classifier.py", line 143, in <module>
    main()
  File "/home/sysadmin/devel/azure/custom-vision/glover-image-classifier/image-classifier.py", line 138, in main
    predict_image()
  File "/home/sysadmin/devel/azure/custom-vision/glover-image-classifier/image-classifier.py", line 115, in predict_image
    predictions, = sess.run(prob_tensor, {input_node: [augmented_image] })
  File "/home/sysadmin/devel/azure/custom-vision/glover-image-classifier/glover-image-classifier-venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 950, in run
    run_metadata_ptr)
  File "/home/sysadmin/devel/azure/custom-vision/glover-image-classifier/glover-image-classifier-venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1149, in _run
    str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (1,) for Tensor 'Placeholder:0', which has shape '(?, 224, 224, 3)'
Terminated

I'll try to figure out what's going on, but I don't think I'll be able to solve it quickly, as I'm not an Tensorflow expert ...
That being said, as far as I know, I can't exclude that the docker version of the classifier doesn't work on my embedded device for the same problem ...

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daveam profile image
Andrea Marson •

I had a stupid bug in my code.
I fixed it and now everything works fine. I'm gonna run it on my embedded device.

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gloveboxes profile image
Dave Glover •

Yah awesome!

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daveam profile image
Andrea Marson • • Edited

Hi Dave,

installing tensorflow and all its dependencies wasn't easy on armbian at all!

I tried several TF/Python combinations, but none of them worked :(
This table lists the combinations I tried and the reason why they fail.

I think that the Illegal instruction problem might explain why your container doesn't work either on this device.

By the way, does your container make use of Python 2.x o 3.x?

In the meantime, I think I'm gonna try a different distro.

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daveam profile image
Andrea Marson • • Edited

Hi Dave
I also tried Armbian Stretch (Debian 9), but nothing changed. I got an Illegal Instruction error as well.

Then I managed to get an RPi 3. I set it up by following this tutorial. On this platform, my simple test program runs correctly:

pi@raspberrypi:~/devel/glover-image-classifier-0.1.0 $ python3 image-classifier.py             
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.AttrValue is deprecated. Please use tf.compat.v1.AttrValue instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.COMPILER_VERSION is deprecated. Please use tf.version.COMPILER_VERSION instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.CXX11_ABI_FLAG is deprecated. Please use tf.sysconfig.CXX11_ABI_FLAG instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.ConditionalAccumulator is deprecated. Please use tf.compat.v1.ConditionalAccumulator instead.

2019-10-22 15:42:53,478 - DEBUG - Starting ...
2019-10-22 15:42:53,479 - DEBUG - Importing the TF graph ...
Classified as: Red Apple
2019-10-22 15:42:58,061 - DEBUG - Prediction time = 1.8572380542755127 s
Avocado 2.246000076411292e-05
Banana 3.769999921132694e-06
Green Apple 0.029635459184646606
Hand 4.4839998736279085e-05
Orange 0.0009084499906748533
Red Apple 0.9693851470947266
2019-10-22 15:42:58,067 - DEBUG - Exiting ...

I used mounted the same raspbian root file system used with RPi from my embedded platform and I got an Illegal Instruction error again.
So it seems there is a structural incompatibility between one of the software layers (maybe TensorFlow) and my platform, which is based on NXP i.MX6Q.

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gloveboxes profile image
Dave Glover •

Hey, I had a brief look at armbian and I spotted that it was on a fairly old kernel release - 3.x from memory. I think Stretch on RPi was on 4.3 or something similar. I did wonder if that was where the issue is. There is nothing to stop you from retargeting the Custom Vision model Docker image to different base a image... I think you said you got the CV/Tensorflow running directly on Armbian so that might be a good starting point...

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daveam profile image
Andrea Marson • • Edited

Actually, I used only the armbian root file system.
Regarding the Linux kernel, I used the one that belongs to the latest official BSP of our platform. It is based on release 4.9.11.
Anyway, I agree with you, in the sense that I can't exclude that the root cause is somehow related to the kernel.

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daveam profile image
Andrea Marson •

Hi Dave,
finally, I managed to solve the problem.
The root cause is related to how the Tensor Flow packages I used were built. Because of the compiler's flags, these packages make use of instructions that are not supported by the i.MX6Q SoC.

So I rebuilt TF with the proper flags ... et voilà:

$ python3 image-classifier.py 
2019-10-25 11:17:15,288 - DEBUG - Starting ...
2019-10-25 11:17:15,289 - DEBUG - Importing the TF graph ...
Classified as: Red Apple
2019-10-25 11:17:21,591 - DEBUG - Prediction time = 2.567471504211426 s
Avocado 2.246000076411292e-05
Banana 3.769999921132694e-06
Green Apple 0.029635440558195114
Hand 4.4839998736279085e-05
Orange 0.0009084499906748533
Red Apple 0.9693851470947266
2019-10-25 11:17:21,594 - DEBUG - Exiting ...
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gloveboxes profile image
Dave Glover •

Woohoo, well done!

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tam66662 profile image
Tam66662 • • Edited

Hi, just wanted to let you know that I got your demo working on a Linux desktop x86_64 architecture, running Ubuntu 18.04.3, using a Logitech USB C922 webcam.

There were several challenges in finding all the things that needed tweaking, so I thought I'd share for others who may run into some of these issues.

1) deployment.template.json: Edit the azureSpeechServicesKey to match your Azure Cognitive Service's Speech service key (not BingKey, as stated in the tutorial)
2) module.json: In each module's folder, edit the "repository" line to point to your localhost:5000 instead of glovebox
3) azure_text_speech.py: Edit the TOKEN_URL to point to the one that Azure provides for you when you set up your speech service. Also edit the BASE_URL to point to the text-to-speech base URL for your region. For example, I had to edit mine to point to my region:

  TOKEN_URL = "https://westus2.api.cognitive.microsoft.com/sts/v1.0/issuetoken"
  BASE_URL = "https://westus2.tts.speech.microsoft.com/"

4) text2speech.py: For whatever reason, wf.getframerate() would not return the correct frame rate of my audio, causing an error.

Expression 'paInvalidSampleRate' failed in 'src/hostapi/alsa/pa_linux_alsa.c', line: 2048

So I ran 'pacmd list-sinks' to find my actual audio sample rate (48000) and hardcoded it in place of wf.getframerate.
5) predict.py: Lastly, my camera-capture module kept getting connectivity issues, which was actually because it kept returning a response error stating:

Error: Could not preprocess image for prediction. module 'tensorflow' has no attribute 'Session'

This method was deprecated, so to fix this, edit the predict.py's line from 'tf.Session()' to 'tf.compat.v1.Session()'

After all was said and done, I was able to get it working:

Image

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heldss profile image
heldss •

Hi ,

Thanks for sharing.I am receiving the same error of your 5 point. In my predict.py file "tf.Session" is missing. Any help will be great.

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heldss profile image
heldss •

if possible could u share your this predict.py file.

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tam66662 profile image
Tam66662 •

Sure I could share my predict.py edit. It's simply a one line edit on line 123 from tf.Session() to tf.compat.v1.Session().

from urllib.request import urlopen
from datetime import datetime
import time
import tensorflow as tf
from PIL import Image
import numpy as np
import sys


class Predict():

    def __init__(self):

        self.filename = 'model.pb'
        self.labels_filename = 'labels.txt'
        self.network_input_size = 0
        self.output_layer = 'loss:0'
        self.input_node = 'Placeholder:0'
        self.graph_def = tf.compat.v1.GraphDef()
        self.labels = []
        self.graph = None

        self._initialize()

    def _initialize(self):
        print('Loading model...', end=''),
        with tf.io.gfile.GFile(self.filename, 'rb') as f:
            self.graph_def.ParseFromString(f.read())

        tf.import_graph_def(self.graph_def, name='')
        self.graph = tf.compat.v1.get_default_graph()

        # Retrieving 'network_input_size' from shape of 'input_node'
        input_tensor_shape = self.graph.get_tensor_by_name(
            self.input_node).shape.as_list()

        assert len(input_tensor_shape) == 4
        assert input_tensor_shape[1] == input_tensor_shape[2]

        self.network_input_size = input_tensor_shape[1]

        with open(self.labels_filename, 'rt') as lf:
            self.labels = [l.strip() for l in lf.readlines()]

    def _log_msg(self, msg):
        print("{}: {}".format(time.time(), msg))

    def _resize_to_256_square(self, image):
        w, h = image.size
        new_w = int(256 / h * w)
        image.thumbnail((new_w, 256), Image.ANTIALIAS)
        return image

    def _crop_center(self, image):
        w, h = image.size
        xpos = (w - self.network_input_size) / 2
        ypos = (h - self.network_input_size) / 2
        box = (xpos, ypos, xpos + self.network_input_size,
               ypos + self.network_input_size)
        return image.crop(box)

    def _resize_down_to_1600_max_dim(self, image):
        w, h = image.size
        if h < 1600 and w < 1600:
            return image

        new_size = (1600 * w // h, 1600) if (h > w) else (1600, 1600 * h // w)
        self._log_msg("resize: " + str(w) + "x" + str(h) + " to " +
                      str(new_size[0]) + "x" + str(new_size[1]))
        if max(new_size) / max(image.size) >= 0.5:
            method = Image.BILINEAR
        else:
            method = Image.BICUBIC
        return image.resize(new_size, method)

    def _convert_to_nparray(self, image):
        # RGB -> BGR
        image = np.array(image)
        return image[:, :, (2, 1, 0)]

    def _update_orientation(self, image):
        exif_orientation_tag = 0x0112
        if hasattr(image, '_getexif'):
            exif = image._getexif()
            if exif != None and exif_orientation_tag in exif:
                orientation = exif.get(exif_orientation_tag, 1)
                self._log_msg('Image has EXIF Orientation: ' +
                              str(orientation))
                # orientation is 1 based, shift to zero based and flip/transpose based on 0-based values
                orientation -= 1
                if orientation >= 4:
                    image = image.transpose(Image.TRANSPOSE)
                if orientation == 2 or orientation == 3 or orientation == 6 or orientation == 7:
                    image = image.transpose(Image.FLIP_TOP_BOTTOM)
                if orientation == 1 or orientation == 2 or orientation == 5 or orientation == 6:
                    image = image.transpose(Image.FLIP_LEFT_RIGHT)
        return image

    def predict_url(self, imageUrl):
        self._log_msg("Predicting from url: " + imageUrl)
        with urlopen(imageUrl) as testImage:
            image = Image.open(testImage)
            return self.predict_image(image)

    def predict_image(self, image):
        try:
            if image.mode != "RGB":
                self._log_msg("Converting to RGB")
                image = image.convert("RGB")

            # Update orientation based on EXIF tags
            image = self._update_orientation(image)

            image = self._resize_down_to_1600_max_dim(image)

            image = self._resize_to_256_square(image)

            image = self._crop_center(image)

            cropped_image = self._convert_to_nparray(image)

            with self.graph.as_default():
                with tf.compat.v1.Session() as sess:
                    prob_tensor = sess.graph.get_tensor_by_name(
                        self.output_layer)
                    predictions, = sess.run(
                        prob_tensor, {self.input_node: [cropped_image]})

                    result = []
                    for p, label in zip(predictions, self.labels):
                        truncated_probablity = np.float64(round(p, 8))
                        if truncated_probablity > 1e-8:
                            result.append({
                                'tagName': label,
                                'probability': truncated_probablity,
                                'tagId': '',
                                'boundingBox': None})
                    print('[%s]' % ', '.join(map(str, result)))

                    response = {
                        'id': '',
                        'project': '',
                        'iteration': '',
                        'created': datetime.utcnow().isoformat(),
                        'predictions': result
                    }

                return response

        except Exception as e:
            self._log_msg(str(e))
            return 'Error: Could not preprocess image for prediction. ' + str(e)
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heldss profile image
heldss •

Hi ,
This article is helpful.
How can i make the same image classification module without raspberry pi and on my Ubuntu platform.
Any help would be great.

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gloveboxes profile image
Dave Glover •

Yes absolutely. I mostly built the project on Ubuntu 18.04 on my laptop and then ported to Raspberry Pi. You will see there are Dockerfiles for x86 in the project. Cheers Dave

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heldss profile image
heldss •

Thanks .
Also do u have any document for connecting a physical device like camera to this project.

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gloveboxes profile image
Dave Glover •

On the bottom bar of Visual Studio Code there is the option to switch the project from armv32 to amd64. That is how you build the containers for arm64. The project will work with most USB cameras and the camera module is using OpenCV to capture frames from the USB camera.

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heldss profile image
heldss •

Thanks a lot sir for your efforts.

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heldss profile image
heldss •

Also , do i have to delete the arm 32v7 files and from platform also if i am not using raspberry pi.

Thanks a lot again.

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heldss profile image
heldss •

I tried it but it's showing a 500 error in azure portal. How to know whether my camera is connected or not.

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t04glovern profile image
Nathan Glover •

Thank you very much, you've saved me countless hours over the last couple days while I've been learning Azure IoT.

Your Dockerfile for OpenCV are also a life saver (i found the ones in the Azure-Samples github.com/Azure-Samples/Custom-vi... to fail to build properly)

p.s. nice surname.

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rayssoftware profile image
Chandra Mohan •

Very helpful tutorial and relevant to my current work.

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gloveboxes profile image
Dave Glover •

Hey, that is awesome and great that you find helpful.

Would you mind telling me how you found the posting - did you come to dev.to and what did you search for as I was not sure how to tag this post.

Cheers and thanks Dave

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rayssoftware profile image
Chandra Mohan •

I follow you on twitter and came across this post in twitter updates. Also I came to know about dev.to only through your post.

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gloveboxes profile image
Dave Glover •

ah cool - thanks for the follow. Feel free to ask any questions. Cheers Dave

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heldss profile image
heldss •

HI,need a little help.I am receiving this in my camera capture log. Camera is not opening.I am using Ubuntu and not raspberry pi. Following is my code:

Camera Capture Azure IoT Edge Module. Press Ctrl-C to exit.
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side
ALSA lib confmisc.c:1286:(snd_func_refer) Unable to find definition 'cards.ICH.pcm.surround71.0:CARD=0'
ALSA lib conf.c:4292:(_snd_config_evaluate) function snd_func_refer returned error: No such file or directory
ALSA lib conf.c:4771:(snd_config_expand) Evaluate error: No such file or directory
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM surround71
ALSA lib setup.c:548:(add_elem) Cannot obtain info for CTL elem (MIXER,'IEC958 Playback Default',0,0,0): No such file or directory
ALSA lib setup.c:548:(add_elem) Cannot obtain info for CTL elem (MIXER,'IEC958 Playback Default',0,0,0): No such file or directory
ALSA lib setup.c:548:(add_elem) Cannot obtain info for CTL elem (MIXER,'IEC958 Playback Default',0,0,0): No such file or directory
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.hdmi
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.hdmi
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.modem
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.modem
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.phoneline
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.phoneline

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tam66662 profile image
Tam66662 •

@heldss

All of those "ALSA lib" lines have nothing to do with your camera, those are a result of your audio device, so you can ignore them as they are just warnings.

Is there any LED light on your camera that light up when the camera-capture module is running? For example, I have a C922 Logitech webcam, and on the device you can see the white LED lights turn on when camera-capture module starts running.

If you see no lights, or suspect camera-capture module isn't connecting to your camera, then most likely it means your camera device index doesn't match what is in your deployment.template.json file. For example, my webcam shows up as "/dev/video0" and "/dev/video1" whenever I plug it in. Find yours by opening up a terminal window, unplug your camera, type "ls /dev/video*" and see what shows up, then plug in your camera and type "ls /dev/video*" again to determine the number of your camera index. Then in your deployment.template.json, edit the "PathOnHost" and "PathInContainer" parameters to match your device.

                "modules": {
                    "camera-capture": {
                        "version": "1.0",
                        "type": "docker",
                        "status": "running",
                        "restartPolicy": "always",
                        "settings": {
                            "image": "${MODULES.CameraCaptureOpenCV.amd64}",
                            "createOptions": {
                                "Env": [
                                    "Video=0",
                                    "azureSpeechServicesKey=d4f26304e1cc4507b0185e9f257ff292",
                                    "AiEndpoint=http://image-classifier-service:80/image"
                                ],
                                "HostConfig": {
                                    "PortBindings": {
                                        "5678/tcp": [
                                            {
                                                "HostPort": "5678"
                                            }
                                        ]
                                    },
                                    "Devices": [
                                        {
                                            "PathOnHost": "/dev/video0",
                                            "PathInContainer": "/dev/video0",
                                            "CgroupPermissions": "mrw"
                                        },

Restart your camera-capture from Terminal with "iotedge restart camera-capture", and see if it works.

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heldss profile image
heldss •

Hi,
I am facing this issue.
Camera Capture Azure IoT Edge Module. Press Ctrl-C to exit.
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe
ALSA lib pcm.c:2266:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side
ALSA lib pcm_route.c:867:(find_matching_chmap) Found no matching channel map

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gloveboxes profile image
Dave Glover •

Hey there - as Tom pointed out about the ALSA messages are to do with audio and are just warnings. I think there must be an issue with your USB camera and OpenCV. What camera are you using? Cheers Dave