You should create a Modules folder which will only contain the modules that you wish to test in Bisque. You should name your {ModuleName} folder how you would like your module to appear in bisque, for ex. EdgeDetection. Create a folder named src and place all your source code inside it. Finally, create a python file named BQ_run_module.py inside the src folder.
BQ_run_module.py
Include all necessary data reading and pre-processing code in BQ_run_module.py as well as a function named run_module that will take input_path_dict and output_folder_path. Hyper parameters for running the module will have to be hardcoded for now but future releases will extend functionality for these as well. This function should load input resources from input_path_dict, do any preprocessing steps, run the algorithm, save all outputs to output_folder_path, AND return the outputs_path_dict.
input_path_dict
The input_path_dict parameter is a dictionary with input names as keys and their corresponding paths as values.
NOTE
It is important to note that these input names will be the labels that Bisque will display in your module web page.
****
Input Example
These input names must also match the input names specified with the cli in a later step. This will become clear later, for now, just choose some descriptive and unique input names that you would like Bisque to display in the module web page. You will use these input names to index the input_path_dict dictionary and load each resource from its respective path. Ex:
The output_folder_path parameter is a path to the directory where output results should be saved.
output_paths_dict
You should save all output result paths into a dictionary with descriptive and unique output names as keys. These output names will be used by Bisque as labels in your module results web page.
These output paths must also be the same names used to specify outputs with the cli at a later step. The run_module function must return this dictionary of output paths in order for Bisque to read and post results back to the module web page.
A sample BQ_run_module.py is shown below:
import cv2
import os
from detection import canny_detector
# input_path_dict will have input file paths with keys corresponding to the input names set in the cli.
def run_module(input_path_dict, output_folder_path, min_hysteresis=100, max_hysteresis=200):
"""
This function should load input resources from input_path_dict, do any pre-processing steps, run the algorithm,
save all outputs to output_folder_path, AND return the outputs_path_dict.
:param input_path_dict: Dictionary of input resource paths indexed by input names.
:param output_folder_path: Directory where to save output results.
:param min_hysteresis: Tunable parameter must have default values.
:param max_hysteresis: Tunable parameter must have default values.
:return: Dictionary of output result paths.
"""
##### Preprocessing #####
# Get input file paths from dictionary
input_img_path = input_path_dict['Input Image'] # KEY MUST BE DESCRIPTIVE, UNIQUE, AND MATCH INPUT NAME SET IN CLI
# Load data
img = cv2.imread(input_img_path, 0)
##### Run algorithm #####
edges_detected = canny_detector(img, min_hysteresis, max_hysteresis)
##### Save output #####
# Get filename
input_img_name = os.path.split(input_img_path)[-1][:-4]
# Generate desired output file names and paths
output_img_path = "%s/%s_out.jpg" % (output_folder_path, input_img_name) # CHECK FILE EXTENSION!
# Save output files
cv2.imwrite(output_img_path, edges_detected)
# Create dictionary of output paths
output_paths_dict = {}
output_paths_dict['Output Image'] = output_img_path # KEY MUST BE DESCRIPTIVE, UNIQUE, AND MATCH OUTPUT NAME SET IN CLI
##### Return output paths dictionary ##### -> IMPORTANT STEP
return output_paths_dict
if __name__ == '__main__':
# Place some code to test implementation
# Define input_path_dict and output_folder_path
input_path_dict = {}
current_directory = os.getcwd()
# Place test image in current directory
input_path_dict['Input Image'] = os.path.join(current_directory,'test_image.jpg') # KEY MUST MATCH INPUT NAME SET IN CLI
output_folder_path = current_directory
# Run algorithm and return output_paths_dict
output_paths_dict = run_module(input_path_dict, output_folder_path, min_hysteresis=100, max_hysteresis=200)
# Get outPUT file path from dictionary
output_img_path = output_paths_dict['Output Image'] # KEY MUST MATCH OUTPUT NAME SET IN CLI
# Load data
out_img = cv2.imread(output_img_path, 0)
# Display output image and ensure correct output
cv2.imshow("Results",out_img)
🚨 ATTENTION
IT IS IMPORTANT TO TRIPLE CHECK OUTPUT FILE EXTENSIONS TO AVOID BUGS WHEN UPLOADING RESULTS BACK TO BISQUE!
Containerizing application
Test your BQ_run_module.py file by writing some test code in the if __name__ == '__main__': code block. A simple test implementation is shown above. Once BQ_run_module.py is working as expected, you can containerize your application with docker. Follow the instructions on downloading docker, creating a Dockerfile, and running a container. Here is an example of a Dockerfile for a simple edge detection module.
You must include the section**** Copy Source Code ****in your own Dockerfile.
# ==================================================================
# module list
# ------------------------------------------------------------------
# python 3.6 (apt)
# ==================================================================
FROM python:3.6.15-buster
ENV DEBIAN_FRONTEND noninteractive
RUN apt-get update
RUN apt-get install ffmpeg libsm6 libxext6 -y
# ===================Module Dependencies============================
RUN pip3 install cycler imageio kiwisolver matplotlib numpy opencv-python Pillow pyparsing python-dateutil scipy
# ===================Copy Source Code===============================
RUN mkdir /module
WORKDIR /module
COPY src /module/src
In your {ModuleName} folder, you can create your image by running
Terminal
docker build -t {modulemame}:v0.0.0 .
Note that docker images are only allowed to have lowercase letters.
Run your docker container with docker run -it {modulemame}:v0.0.0 bash and test your application inside the container by calling python BQ_run_module.py.
