ImportError: Unable to find zbar shared library on Flask

Question

Im trying to use pyzbar 0.1.4 on a Flask Server in Docker

The image was created by us, based in python 2.7 taken from alpine.

Install ZBar by

apk update
apk add zbar

Im getting the following error when running dockerfile File "/usr/lib/python2.7/site-packages/pyzbar/pyzbar.py", line 8, in <module> from .wrapper import ( File "/usr/lib/python2.7/site-packages/pyzbar/wrapper.py", line 166, in <module> c_uint_p, # minor File "/usr/lib/python2.7/site-packages/pyzbar/wrapper.py", line 159, in zbar_function return prototype((fname, load_libzbar())) File "/usr/lib/python2.7/site-packages/pyzbar/wrapper.py", line 135, in load_libzbar raise ImportError('Unable to find zbar shared library') ImportError: Unable to find zbar shared library

Im trying to decode a QR image using that library

Dockerfile

FROM buffetcontainerimages.azurecr.io/base/buffetcloud-python:0.1
RUN pip install --upgrade pip setuptools wheel
COPY wheeldir /opt/app/wheeldir
COPY *requirements.txt /opt/app/src/
RUN pip install --use-wheel --no-index --find-links=/opt/app/wheeldir \
-r /opt/app/src/requirements.txt
RUN pip install --use-wheel --no-index --find-links=/opt/app/wheeldir \
-r /opt/app/src/test-requirements.txt
COPY . /opt/app/src/
WORKDIR /opt/app/src
RUN python setup.py install
EXPOSE 5000
CMD dronedemo

And requirements.txt

requests>=2.18.4
flask>=0.12.2
mechanize>=0.3.6
regex>=2.4.136
PyPDF2>=1.26.0
bs4>=4.5.3
pyzbar>=0.1.4
openpyxl>=2.5.0
selenium>=3.9.0
matplotlib>=2.1.2

When pip install zbar ` pip install zbar Collecting zbar Downloading zbar-0.10.tar.bz2 ... zbarmodule.h:26:18: fatal error: zbar.h: No such file or directory

include

compilation terminated. error: command 'gcc' failed with exit status 1 `

Answer 1

In Ubuntu install zbar-tools

sudo apt-get install zbar-tools

Answer 2

In Ubuntu terminal simply run this command and this will install zbar in your global package

sudo apt-get install zbar-tools

Answer 3

I encountered the same issue (happy to have found this thread). Not sure if this has already been solved but this might help you or future devs.

As usual, it worked on my machine locally but couldn't get it to work in a container

What I tried initially:

• Building an image based on a Python3 image

What solved the issue:

• Build with FROM ubuntu:18.04
• Within Ubuntu I was able to install the zbar shared library. According to https://pypi.org/project/pyzbar/ we need sudo apt-get install libzbar0
• Set LC_ALL & LANG ENV variables (not sure why, it was provided in an additional error)
• Within requirements.txt downgrade Pillow==8.4.0 to Pillow==6.2.2

My Dockerfile:

FROM ubuntu:18.04

RUN apt-get update -y
# Get's shared library for zbar
RUN apt-get install -y libzbar0
# Installs Python
RUN apt-get install -y python3-pip python3-dev build-essential

COPY . /app
WORKDIR /app
COPY requirements.txt .
RUN pip3 install -r requirements.txt

# Initially encountered an issue that indicated I had to set these ENVs
ENV LC_ALL C.UTF-8
ENV LANG C.UTF-8

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8080"]

And requirements.txt

fastapi==0.67.0
Pillow==6.2.2
pyzbar==0.1.8
urllib3==1.26.7
uvicorn==0.12.2

Answer 4

I grea with the second commend on your post, but you might want to try to install the pyzbar dependency through pip.

FROM buffetcontainerimages.azurecr.io/base/buffetcloud-python:0.1
RUN pip install --upgrade pip setuptools wheel pyzbar
COPY wheeldir /opt/app/wheeldir
COPY *requirements.txt /opt/app/src/
RUN pip install --use-wheel --no-index --find-links=/opt/app/wheeldir \
-r /opt/app/src/requirements.txt
RUN pip install --use-wheel --no-index --find-links=/opt/app/wheeldir \
-r /opt/app/src/test-requirements.txt
RUN pip install -y pyzbar
COPY . /opt/app/src/
WORKDIR /opt/app/src
RUN python setup.py install
EXPOSE 5000
CMD dronedemo

Answer 5

You can use tf.keras.layers.Concatenatefrom Tensorflow Keras API to combine two models

# Concatenate last layer of model1 and model2
concat = tf.keras.layers.Concatenate()([dense_1, dense_2])

n_classes = 10
# output layer
output = tf.keras.layers.Dense(units=n_classes,
                               activation=tf.keras.activations.softmax)(concat)

Complete _model = tf.keras.Model(inputs=[input_1, input_2], outputs=[output])

complete _model.summary()