Removing Text from Images

This app uses OCR to remove text from an image. You might use this as a stand-alone microservice, or as a pre-processing step in a computer vision pipeline. This tutorial is an adaptation of this post.

Define the environment

You’ll start by creating a Beam app definition. In this file, you’re defining a few things:

The libraries you want installed in the environment
The compute settings (some of the CV operations are heavy, so 16Gi of memory is a safe choice)

app.py
from beam import App, Runtime, Image, Output

app = App(
    name="rmtext",
    runtime=Runtime(
        cpu=1,
        memory="16Gi",
        image=Image(
            python_packages=[
                "numpy",
                "matplotlib",
                "opencv-python",
                "keras_ocr",
                "tensorflow",
            ],
            commands=["apt-get update && apt-get install -y libgl1"],
        ),
    ),
)

Removing the text from an image

You’ll use the code below to accomplish the following:

Identify text in the base-64 encoded image and create bounding boxes around each block of text
Add a mask around each box of text
Paint over each text-mask to remove the text

We’ve added an app.run() decorator to remove_text. This decorator will allow us to run this code on Beam, instead of your laptop.

app.py
from beam import App, Runtime, Image, Output

import base64
import matplotlib.pyplot as plt
import keras_ocr
import cv2
import math
import numpy as np


app = App(
    name="rmtext",
    runtime=Runtime(
        cpu=1,
        memory="16Gi",
        image=Image(
            python_packages=[
                "numpy",
                "matplotlib",
                "opencv-python",
                "keras_ocr",
                "tensorflow",
            ],
        ),
    ),
)


def midpoint(x1, y1, x2, y2):
    x_mid = int((x1 + x2) / 2)
    y_mid = int((y1 + y2) / 2)
    return (x_mid, y_mid)


@app.run()
def remove_text(**inputs):
    # Grab the base64 from the kwargs
    encoded_image = inputs["image"]
    # Convert the base64-encoded input image to a buffer
    image_buffer = base64.b64decode(encoded_image)

    pipeline = keras_ocr.pipeline.Pipeline()

    # Read the image
    img = keras_ocr.tools.read(image_buffer)
    # Generate (word, box) tuples
    prediction_groups = pipeline.recognize([img])
    mask = np.zeros(img.shape[:2], dtype="uint8")
    for box in prediction_groups[0]:
        x0, y0 = box[1][0]
        x1, y1 = box[1][1]
        x2, y2 = box[1][2]
        x3, y3 = box[1][3]

        x_mid0, y_mid0 = midpoint(x1, y1, x2, y2)
        x_mid1, y_mi1 = midpoint(x0, y0, x3, y3)

        thickness = int(math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2))

        cv2.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255, thickness)
        img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)

    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    # Save the generated image to the Beam Output path
    cv2.imwrite("output.png", img_rgb)


if __name__ == "__main__":
    input_image = "./coffee.jpeg"
    with open(input_image, "rb") as image_file:
        encoded_image = base64.b64encode(image_file.read())
        remove_text(image=encoded_image)

You can run this code on Beam by running beam run:

beam run app.py:remove_text

Make sure to include a sample image in your working directory, and update the script with the path. In this example, I’m using this image as a sample:

Deployment

If you’re satisfied with this function and want to deploy it as an API, you can do so by updating the decorator:

Just replace @app.run() with @app.task_queue()

# This function will be exposed as a web API when deployed!
@app.task_queue()
  def remove_text(**inputs):
  ...

You can deploy this app by running:

beam deploy app.py

You’ll call the API by copying the task queue URL from the dashboard.

Since this task runs asynchronously, you’ll use the /v1/task/{task_id}/status/ API to retrieve the task status and a link to download the image output.

This will return a response, which contains:

Task ID
The start and end time
A dictionary with pre-signed URLs to download the outputs

{
  "task_id": "edbcf7ff-e8ce-4199-8661-8e15ed880481",
  "started_at": "2023-04-24T22:44:06.911920Z",
  "ended_at": "2023-04-24T22:44:07.184763Z",
  "outputs": {
    "my-output-1": {
      "path": "output_path",
      "name": "my-output-1",
      "url": "http://data.beam.cloud/outputs/6446df99cf455a04e0335d9b/hw6hx/hw6hx-0001/edbcf7ff-e8ce-4199-8661-8e15ed880481/my-output-1.zip?..."
    }
  },
  "status": "COMPLETE",
}

Enter the outputs url in your browser to download the image. You’ll see that the text has been removed:

Start Here

Customizing the Environment

Managing Data

Autoscaling and Concurrency

Endpoints and Web Servers

Task Queues

Serverless Functions

Agents (New)

Other Topics

Self-Hosting

Resources

Security

Removing Text from Images

Define the environment

Removing the text from an image

Deployment

Start Here

Customizing the Environment

Managing Data

Autoscaling and Concurrency

Endpoints and Web Servers

Task Queues

Serverless Functions

Agents (New)

Other Topics

Self-Hosting

Resources

Security

​Define the environment

​Removing the text from an image

​Deployment

Define the environment

Removing the text from an image

Deployment