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AxisCommunications / acap-computer-vision-sdk-examples

Example applications that provide developers with the tools and knowledge to use Axis Camera Application Platform (ACAP) Computer Vision solution
Apache License 2.0
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Changing default detection model to run on Axis camera (Urgent) #107

Closed BenJam29 closed 2 years ago

BenJam29 commented 2 years ago

Hi everyone,

I was developing a computer vision pipeline on Axis camera model Q1656-LE Box. I installed Axis ACAP and Axis computer vision SDK using Docker and everything is functional when I use the default detection model which is SSD Mobilenet V2. (Screenshot below). Screenshot from 2022-10-17 18-40-03

This detection model is configured in the following files as far as I learned.

FROM arm64v8/alpine as model-image-aarch64

FROM model-image-${ARCH}

Get SSD Mobilenet V2

ADD https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite models/ ADD https://github.com/google-coral/edgetpu/raw/master/test_data/ssd_mobilenet_v2_coco_quant_postprocess.tflite models/ ADD https://github.com/google-coral/edgetpu/raw/master/test_data/coco_labels.txt models/ ADD https://github.com/google-coral/edgetpu/raw/master/test_data/COPYRIGHT models/

CMD /bin/ls /models`


- config/env.aarch64.artpec8

MODEL_PATH=/models/ssd_mobilenet_v2_coco_quant_postprocess.tflite INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:0.6-aarch64 INFERENCE_SERVER_COMMAND=/usr/bin/acap_runtime -p 8501 -j 12


What I need to do is to replace this default detection model with other open-source model like EfficientDet Lite0 (from coral AI model zoo [here](https://coral.ai/models/object-detection/#trained-models)) or any other newly created model. I tried to change the two files above to accomodate these changes but the Axis device keeps loading the default model SSD Mobilenet V2 and returns an error. 

- app/object-detector-python/Dockerfile.model (Modified)

`ARG ARCH=aarch64

FROM arm64v8/alpine as model-image-aarch64

FROM model-image-${ARCH}

Get EfficientDet 0 Model

ADD https://raw.githubusercontent.com/google-coral/test_data/master/efficientdet_lite0_320_ptq_edgetpu.tflite models/ ADD https://raw.githubusercontent.com/google-coral/test_data/master/efficientdet_lite0_320_ptq.tflite models/ ADD https://github.com/google-coral/edgetpu/raw/master/test_data/coco_labels.txt models/ ADD https://github.com/google-coral/edgetpu/raw/master/test_data/COPYRIGHT models/

CMD /bin/ls /models`


- config/env.aarch64.artpec8

MODEL_PATH=/models/efficientdet_lite0_320_ptq_edgetpu.tflite INFERENCE_SERVER_IMAGE=axisecp/acap-runtime:0.6-aarch64 INFERENCE_SERVER_COMMAND=/usr/bin/acap_runtime -p 8501 -j 12



Then when I run rebuild the object detector according to the instructions and run the detector, I received the following error:
![Screenshot from 2022-10-17 18-43-26](https://user-images.githubusercontent.com/64437555/196297471-0b7b7ab9-913e-43be-878b-04934117ed3b.png)

Thank you for your help in advance.
garaujo23 commented 2 years ago

Try running docker system prune -f --volumes on the camera to remove the model volume, should then have your new model. From your host PC you can also run docker -H tcp://$AXIS_TARGET_IP:$DOCKER_PORT system prune -af

BenJam29 commented 2 years ago

Thanks a lot @garaujo23 for your comment. When I want to rebuild the object detector I use the following bash script. The command you mentioned is executed and old volumes are deteted. The code is attached below. 

#!/bin/bash

: ' Description:

object-detector-python
├── app
├── |- detector.py
├── |- dog416.png
├── docker-compose.yml
├── static-image.yml
├── Dockerfile
├── Dockerfile.model

detector.py - The inference client main program
dog416.png - Static image used with static-image.yml
docker-compose.yml - Docker compose file for streaming camera video example using larod inference service
static-image.yml - Docker compose file for static image debug example using larod inference service
Dockerfile - Build Docker image with inference client for camera
Dockerfile.model - Build Docker image with inference model
'

# cd to hello world app directory
SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]:-$0}"; )" &> /dev/null && pwd 2> /dev/null; )"
cd $SCRIPT_DIR/../apps/object-detector-python

# For arm64 (Q1656-LE the architecture is arm64)
export ARCH=aarch64
# Valid options for chip on aarch64 are 'artpec8' (hardware accelerator) or 'cpu'
export CHIP=artpec8

# Camera Setup
export AXIS_TARGET_IP=206.174.180.54
export DOCKER_PORT=7581

# Define APP name
export APP_NAME=acap4-object-detector-python
export MODEL_NAME=acap-dl-models

# Clean docker memory
printf "\n#################### Cleaning docker memory ... ####################\n"
sudo docker -H tcp://$AXIS_TARGET_IP:$DOCKER_PORT system prune -af

# Install qemu to allow build flask for a different architecture
printf "\n#################### Installing qemu to allow build flask ... ####################\n"
sudo docker run -it --rm --privileged multiarch/qemu-user-static --credential yes --persistent yes

# Build and upload inference client for camera
printf "\n#################### Building and uploading inference client for camera ... ####################\n"
sudo docker build . -t $APP_NAME --build-arg ARCH
sudo docker save $APP_NAME | docker -H tcp://$AXIS_TARGET_IP:$DOCKER_PORT load

# Build and upload inference models
printf "\n#################### Building and uploading inference models for camera ... ####################\n"
sudo docker build . -f Dockerfile.model -t $MODEL_NAME --build-arg ARCH
sudo docker save $MODEL_NAME | docker -H tcp://$AXIS_TARGET_IP:$DOCKER_PORT load

The output for rebuilding is: 


#################### Cleaning docker memory ... ####################
[sudo] password for behnam:             
Deleted Containers:
e95cb52a1d0275bcce18b7a16e0553433cacd1ec1e64ecc7f0fe63846a59e7fd
3cc1d47d05013ca4ffa0f5aee2d417ead829180016f1814248cee49c68be2f65
bc2f39af66ba4706d67d13717ac2de4186813e9c65b11265875ad810826a3230

Deleted Networks:
object-detector-python_default

Deleted Images:
untagged: acap-dl-models:latest
deleted: sha256:3e638375e811aca74d39efef1b2266b0d58b40d2c478822d70c39fd6694f88f7
deleted: sha256:917deb131fe665a035e67b7af025b0d53cd29d84e8acd08bcd8f7c80caa8079f
deleted: sha256:79fb712db44287bf7b7657449a13bf0f1162dcffef0cd254c88ff1240fd05878
deleted: sha256:ecc28deef19c7fc5036e69cc6d7a0dec47fd7a4e0bfdb94b0f1b858884ebd2dc
deleted: sha256:c695fdbbe117cff430b6567dbb4519464ec9b29cd2861a20ca489be7a2775ef0
deleted: sha256:dd565ff850e7003356e2b252758f9bdc1ff2803f61e995e24c7844f6297f8fc3
untagged: axisecp/acap-runtime:0.6-aarch64
untagged: axisecp/acap-runtime@sha256:f9d4ad68d33ac2e5bca477bae1af9b9d50dbd354dafa53d9424c296423c5c35f
deleted: sha256:7af88c670dc7284e17e6b9fa99c84ec4be13d31f78cc6ad3f33343e8f8eb07c2
deleted: sha256:b77f4975d4d018b7f07443312493178a22cfff851da75e80e53764a9f7f81f34
deleted: sha256:e8e3a4ee8de1ec9e6a48eaf4d5a1a5e833a8073dddba573cce5ed539ecf9d05f
deleted: sha256:4e3a47641eef414a8e4c324c0908d036bde9e4703d7ab12c8c7a1300efb70c78
deleted: sha256:a12b9bea33ecfc12c88e5bf55eb20d6e8522ea2749a163b31fc9f69cd787b828
deleted: sha256:432b3de25d92b22b3c362467bfbbb8bb6611163ad858e8ac662955608e56ee5e
deleted: sha256:f94736c0f7a0f1defeb9f6398c2322ba861fab6e4cd7ab036f8751e70decd66a
deleted: sha256:b753a71ec0efa96f79239330227ba465a297f7f74798d97ac2aa3ffce91e4700
deleted: sha256:b88c694e50d0f2276d37418dd9f81f574cbd3de73d68a84e3d509cc0f72ee697
deleted: sha256:c55ac170e0177d374f6be4136823a070495e8fe80046a5867e1a1477ab9848d4
deleted: sha256:d3636593414b57373747f7df90de0459054dd2b3a1ac35a3b0b647dc702702d5
deleted: sha256:0819900094fcbf63ee4a843c7185f124e8748c2933bcb8fa3f43c6f09a0d8205
deleted: sha256:7dbfce9d1310108bd44fc0b0a84335f26f133ff91ea672b11e5a70dfd1228a51
deleted: sha256:055538203431597fd1b320db1682dd1478395ce42ffa883c30ba7bd19db58497
deleted: sha256:eab5e90eef28773e05560aac1900b203c6f8356bcd4d8dc78f00219a09b4264f
deleted: sha256:81b46cc6b3d699b968a933f403c1cae3fc6daaeddc1abf5ee45617c72ffcaeba
deleted: sha256:038d0573c9f62181b3ef070fccd5b75512b246c8762230617f3a52375df4acfd
deleted: sha256:8941214bcd17e54ff3f98ab30d538a7f61cc0b8165b4ca39aa02e98096d2abbd
deleted: sha256:f615dace8722bb91d54f30ce7bdbbd70ce39ff760a73eadbf2bc88897a1bc83b
deleted: sha256:dc8ce6c30712bfd583e0e8ef27c36886240ac6921c6ead81d28c14f7f3816cd3
deleted: sha256:a8fbf9f278fc7b31173a095a31c4a43a5fca39864c7b65588a81546b29075183
deleted: sha256:992bf51e74e0992dbf9d4a5def169463d13b551f97453fab5c7ba3717e6de42b
deleted: sha256:4fc005e132e201536501a631d6ed22d79db5093e82ec2887f4fefca64e05050d
deleted: sha256:34fed83d22fa86c27902bae18b734ba4bc17158c2ff3ffa831a325d8757ad8b4
deleted: sha256:eb41b83fbfe7d00e5872257db46a4e9741e048b9729273c985177b48471b029f
deleted: sha256:350f36b271dee3d47478fbcd72b98fed5bbcc369632f2d115c3cb62d784edaec
untagged: acap4-object-detector-python:latest
deleted: sha256:d5e0a31dd793a9a195d23da1de7990a101cc17f36e1e2a6d4032e2458c7a65bf
deleted: sha256:7fa6e907c2ad7f67b0086f21566197fd4daad50c80636fe66be254a48f311ea2
deleted: sha256:41abc841d6d99446debbe06f6ef0c5d14ba2108d132cd196cd6a75f6d3f5a6da
deleted: sha256:c2b78bfb4ea1ded88e137e74e4d3313dac708b634e617b1b16847ca61c3204e9
deleted: sha256:929bac34d18325734a02bdc5c1ef5ae8b49a0a4e25bd0af3a6081196378fca5d
deleted: sha256:f0885049cdc187d4c3ee4764c1e1ecd6db15da447e0310eaf754dd5e0f6c39c4
deleted: sha256:040325e4a34ef5868ded9cbc27999a42961fedf01278ffb28487bc50a1c3e7ae
deleted: sha256:0557987b9395d985733b80569a0465aceeaaf2ece0fcbaf9a9ca42aaef647bd6
deleted: sha256:e265835b28ac16782ef429b44427c7a72cdefc642794515d78a390a72a2eab42

Total reclaimed space: 730.8MB

#################### Installing qemu to allow build flask ... ####################
Setting /usr/bin/qemu-alpha-static as binfmt interpreter for alpha
sh: write error: File exists
Setting /usr/bin/qemu-arm-static as binfmt interpreter for arm
sh: write error: File exists
Setting /usr/bin/qemu-armeb-static as binfmt interpreter for armeb
sh: write error: File exists
Setting /usr/bin/qemu-sparc-static as binfmt interpreter for sparc
sh: write error: File exists
Setting /usr/bin/qemu-sparc32plus-static as binfmt interpreter for sparc32plus
sh: write error: File exists
Setting /usr/bin/qemu-sparc64-static as binfmt interpreter for sparc64
sh: write error: File exists
Setting /usr/bin/qemu-ppc-static as binfmt interpreter for ppc
sh: write error: File exists
Setting /usr/bin/qemu-ppc64-static as binfmt interpreter for ppc64
sh: write error: File exists
Setting /usr/bin/qemu-ppc64le-static as binfmt interpreter for ppc64le
sh: write error: File exists
Setting /usr/bin/qemu-m68k-static as binfmt interpreter for m68k
sh: write error: File exists
Setting /usr/bin/qemu-mips-static as binfmt interpreter for mips
sh: write error: File exists
Setting /usr/bin/qemu-mipsel-static as binfmt interpreter for mipsel
sh: write error: File exists
Setting /usr/bin/qemu-mipsn32-static as binfmt interpreter for mipsn32
sh: write error: File exists
Setting /usr/bin/qemu-mipsn32el-static as binfmt interpreter for mipsn32el
sh: write error: File exists
Setting /usr/bin/qemu-mips64-static as binfmt interpreter for mips64
sh: write error: File exists
Setting /usr/bin/qemu-mips64el-static as binfmt interpreter for mips64el
sh: write error: File exists
Setting /usr/bin/qemu-sh4-static as binfmt interpreter for sh4
sh: write error: File exists
Setting /usr/bin/qemu-sh4eb-static as binfmt interpreter for sh4eb
sh: write error: File exists
Setting /usr/bin/qemu-s390x-static as binfmt interpreter for s390x
sh: write error: File exists
Setting /usr/bin/qemu-aarch64-static as binfmt interpreter for aarch64
sh: write error: File exists
Setting /usr/bin/qemu-aarch64_be-static as binfmt interpreter for aarch64_be
sh: write error: File exists
Setting /usr/bin/qemu-hppa-static as binfmt interpreter for hppa
sh: write error: File exists
Setting /usr/bin/qemu-riscv32-static as binfmt interpreter for riscv32
sh: write error: File exists
Setting /usr/bin/qemu-riscv64-static as binfmt interpreter for riscv64
sh: write error: File exists
Setting /usr/bin/qemu-xtensa-static as binfmt interpreter for xtensa
sh: write error: File exists
Setting /usr/bin/qemu-xtensaeb-static as binfmt interpreter for xtensaeb
sh: write error: File exists
Setting /usr/bin/qemu-microblaze-static as binfmt interpreter for microblaze
sh: write error: File exists
Setting /usr/bin/qemu-microblazeel-static as binfmt interpreter for microblazeel
sh: write error: File exists
Setting /usr/bin/qemu-or1k-static as binfmt interpreter for or1k
sh: write error: File exists
Setting /usr/bin/qemu-hexagon-static as binfmt interpreter for hexagon
sh: write error: File exists

#################### Building and uploading inference client for camera ... ####################
Sending build context to Docker daemon  439.3kB
Step 1/14 : ARG ARCH=aarch64
Step 2/14 : ARG SDK_VERSION=1.2
Step 3/14 : ARG REPO=axisecp
Step 4/14 : FROM arm64v8/ubuntu:20.04 as runtime-image-aarch64
 ---> db1bc6aa58da
Step 5/14 : FROM $REPO/acap-computer-vision-sdk:$SDK_VERSION-$ARCH AS cv-sdk
 ---> 0eaa6bb57def
Step 6/14 : FROM runtime-image-${ARCH}
 ---> db1bc6aa58da
Step 7/14 : COPY --from=cv-sdk /axis/python /
 ---> Using cache
 ---> 979e8195a241
Step 8/14 : COPY --from=cv-sdk /axis/python-numpy /
 ---> Using cache
 ---> a7ff49c5a010
Step 9/14 : COPY --from=cv-sdk /axis/python-tfserving /
 ---> Using cache
 ---> dbb56f713381
Step 10/14 : COPY --from=cv-sdk /axis/opencv /
 ---> Using cache
 ---> f0447d4cacf4
Step 11/14 : COPY --from=cv-sdk /axis/openblas /
 ---> Using cache
 ---> b57f43d55de9
Step 12/14 : WORKDIR /app
 ---> Using cache
 ---> 9abcf84e5341
Step 13/14 : COPY app/* /app/
 ---> Using cache
 ---> a686434caf70
Step 14/14 : CMD ["python3", "stage-1.py"]
 ---> Using cache
 ---> d5e0a31dd793
Successfully built d5e0a31dd793
Successfully tagged acap4-object-detector-python:latest
Corallo commented 2 years ago

Hi @BenJam29 The answer of @garaujo23 is correct, you need to make sure that the old volumes in your camera are removed. First of all, make sure to run the command docker-compose --tlsverify --host tcp://$DEVICE_IP:$DOCKER_PORT --env-file ./config/env.$ARCH.$CHIP down --volumes when you terminate your execution, to leave the camera in a "clean state" Try to run it now, and see if it already solves your issue, Otherwise run docker -H tcp://$DEVICE_IP:$DOCKER_PORT volumes ls to check if there are volumes left and docker volume prune -f to do a forced cleaning

BenJam29 commented 2 years ago

Hi @Corallo, Thanks a lot for your message. It was very helpful. In termination, the "--volumes" was missed in my script and now it removes all old volumes left on device (image attached below). Screenshot from 2022-10-19 11-22-38

When rerun the program, the model name is correctly identified (image below), it failed to load the model. "ERROR in Inference: Failed to load model efficientdet_lite0_320_ptq_edgetpu.tflite (Could not load model: Could not set VX delegate)." Screenshot from 2022-10-19 11-24-08

I use EfficientDet-Lite0 model from here, by copy pasting model url in Dockerfile.model.

Dockerfile.model content:

ARG ARCH=aarch64

FROM arm64v8/alpine as model-image-aarch64

FROM model-image-${ARCH}

# Get EfficientDet 0 Model
ADD https://raw.githubusercontent.com/google-coral/test_data/master/efficientdet_lite0_320_ptq_edgetpu.tflite models/
ADD https://raw.githubusercontent.com/google-coral/test_data/master/efficientdet_lite0_320_ptq.tflite models/
ADD https://github.com/google-coral/edgetpu/raw/master/test_data/coco_labels.txt models/
ADD https://github.com/google-coral/edgetpu/raw/master/test_data/COPYRIGHT models/

CMD /bin/ls /models

Any idea, why it failed to load new model?

BenJam29 commented 2 years ago

Just tried to look deeper into the issue. I persistently receive the "<_InactiveRpcError of RPC that terminated with:status = StatusCode.DEADLINE_EXCEEDED" by using various models. Image attached below.
Screenshot from 2022-10-19 17-22-12

I took a look at gRPC to have a better understanding of the problem but I couldn't resolve the issue yet.

Any help would be appreciated. Best,

Corallo commented 2 years ago

HI @BenJam29

I am going to move this issue into the discussion tab, as this is not an issue nor an enchantment.