This repository is a collection of codes using docker and kubernetes to deploy a machine learning model as a Rest API for performing image segmentation. I used docker to build an image segmentation API using PixelLib segmentation library and deploy it in Kubernetes.
1 Docker Image Segmentation Deployment
2 Kubernetes Image Segmentation Deployment
Read this article I published on the basic concepts of docker and how it is used to build MachineLearning Rest APIs.
sudo docker pull ayoolaolafenwa/pixellibapi
Run the API
sudo docker run -p 80:5000 ayoolaolafenwa/pixellibapi
Code For API Testing
import requests
results = requests.post("http://localhost:85/segmentapi", files = {"image": open("sample.jpeg", "rb")}).json()
print(results["outputs"])Results
{'boxes': [[372, 158, 528, 504], [530, 163, 605, 374], [1, 219, 81, 299], [374, 309, 542, 542], [227, 204, 420, 332], [477, 151, 596, 239], [589, 195, 703, 257], [742, 213, 774, 259], [376, 181, 429, 218], [172, 167, 264, 206], [279, 190, 294, 200], [299, 185, 334, 205]], 'class_ids': [0, 0, 2, 1, 2, 5, 2, 2, 2, 5, 2, 2], 'class_names': ['person', 'person', 'car', 'bicycle', 'car', 'bus', 'car', 'car', 'car', 'bus', 'car', 'car'], 'mask_shape': [581, 774, 12], 'object_counts': {'bicycle': 1, 'bus': 2, 'car': 7, 'person': 2}, 'scores': [99, 99, 99, 99, 99, 98, 98, 97, 86, 81, 57, 54]}
The outputs include a lot of details about the objects detected in the image which include the box coordinates values, class ids, class names, object counts, segmentation mask shape and prediction scores.
Obtain the segmentation masks
Obtain the segmentation masks of the objects segmented using this code below;
import requests
import numpy as np
results = requests.post("http://localhost:85/segmentapi", files = {"image": open("sample.jpeg", "rb")}).json()
segmentation_masks = results["mask_values"]
#convert the masks to numpy arrays for proper output format
masks_numpy_array = np.array(segmentation_masks)
print(masks_numpy_array)Note: If you print the results["mask_values"] directly it will be too long for a proper view format because the segmentation results by the API is in list. It is better you convert it to numpy arrays to view the actual segmentation values.
I provided a simple web API to test on images directly and visualize the results. Visit the port you are running the API e.g localhost:80 and perform a simple web image segmentation.
You can choose any image and press the button segment, it will display an output segmented image within seconds.
The code for building this API is available in this repository, clone it, make modifications and build your own custom image.
Clone Repository
git clone ayoolaolafenwa/pixellibapi
Download the PointRend model used for image segmentation and put it in the folder directory PixelLibAPI.
sample folder directory
└── ImageSegmentationAPI
├── app.py
├── Dockerfile
├── pointrend_resnet50.pkl
└── templates
├── segment.html
Build Docker Image
cd ImageSegmentationAPI
docker build -t yourimagename
Note: Ensure that you have docker installed before installing kubectl.
Follow this guide for Minikube Installation.
minikube start
Clone Repository
git clone ayoolaolafenwa/pixellibapi
cd KubernetesDeployment
Create PixelLibAPI deployment
kubectl apply -f pixellib_deployment.yml
This command will create the pixellib pod deployment and service. This will be the log:
deployment.apps/pixellib-deployment created
service/pixellib-service created
Check the kubernetes pod deployment logs
kubectl get pods
NAME READY STATUS RESTARTS AGE
pixellib-deployment-5b9f884bd5-f9k2m 0/1 ContainerCreating 0 8s
pixellib-deployment-5b9f884bd5-k5b62 0/1 ContainerCreating 0 8s
In the logs the status of the pods is ContainerCreating which means that the pods are still creating. If the internet speed is fast the pods will create within seconds but if not, it will take some minutes. When it is done the status of the pods will change to Running.
NAME READY STATUS RESTARTS AGE
pixellib-deployment-5b9f884bd5-f9k2m 1/1 Running 0 2m32s
pixellib-deployment-5b9f884bd5-k5b62 1/1 Running 0 2m32s
Start the PixelLib deployment Service
minikube service pixellib-service
This will be the output log:
|-----------|------------------|-------------|---------------------------|
| NAMESPACE | NAME | TARGET PORT | URL |
|-----------|------------------|-------------|---------------------------|
| default | pixellib-service | 80 | http://192.168.49.2:30184 |
|-----------|------------------|-------------|---------------------------|
* Starting tunnel for service pixellib-service.
|-----------|------------------|-------------|------------------------|
| NAMESPACE | NAME | TARGET PORT | URL |
|-----------|------------------|-------------|------------------------|
| default | pixellib-service | | http://127.0.0.1:50780 |
|-----------|------------------|-------------|------------------------|
* Opening service default/pixellib-service in default browser...
! Because you are using a Docker driver on windows, the terminal needs to be open to run it.
The pixellib service will open in your default browser and if not, copy the second url and paste it in a browser.
This will be the page loaded. You can upload any image to test the service.
Uploaded Image
Segmented Image
import requests
res = requests.post("path-to-service-url", files = {"image":open("sample.jpg", "rb")}).json()
print(res["outputs"])test image
import requests
out = requests.post("http://127.0.0.1:50780/segmentapi", files = {"image":open("sample.jpeg", "rb")}).json()
print(out["outputs"])Ouputs
{'boxes': [[32, 380, 127, 612], [245, 334, 324, 549], [584, 208, 649, 356], [58, 433, 106, 495], [787, 521, 1021, 767], [487, 250, 555, 405], [49, 537, 101, 646], [738, 428, 1012, 674], [109, 197, 155, 255], [789, 462, 873, 518], [890, 8, 962, 60], [252, 458, 299, 565], [839, 334, 891, 369], [941, 193, 1024,
341], [870, 582, 937, 650], [485, 318, 545, 423], [293, 181, 553, 392], [403, 146, 643, 348], [584, 244, 630, 320], [101, 240, 368, 494], [885, 13, 947, 38], [793, 277, 1023, 501], [859, 155, 1002, 278], [101, 233, 359, 496], [128, 183, 167, 249], [255, 214, 456, 430], [588, 297, 639, 374], [969, 227, 1017, 263], [687, 229, 719, 283], [271, 224, 451, 428]], 'class_ids': [0, 0, 0, 24, 2, 0, 1, 2, 24, 0, 9, 1, 0, 2, 0, 3, 7, 7, 24, 2, 9, 2, 2, 7, 0, 7, 1, 0, 2, 2], 'class_names': ['person', 'person', 'person', 'backpack', 'car', 'person', 'bicycle', 'car', 'backpack', 'person', 'traffic light', 'bicycle', 'person', 'car', 'person', 'motorcycle', 'truck', 'truck', 'backpack', 'car', 'traffic light', 'car', 'car', 'truck', 'person', 'truck', 'bicycle', 'person', 'car', 'car'], 'mask_shape': [768, 1024, 30], 'object_counts': {'backpack': 3, 'bicycle': 3, 'car': 8, 'motorcycle': 1, 'person': 9, 'traffic light': 2, 'truck': 4}, 'scores': [99, 99, 99, 99, 97, 96, 95, 92, 91, 90, 89, 88, 87, 86, 84, 84, 84, 84, 76, 72, 72, 71, 68, 68, 59, 58, 57, 57, 55, 50]}