mlp/src/utils.py

Overview:

This Python script provides utility functions for handling and processing images, particularly in the context of converting images to and from base64-encoded strings. This functionality is often required in web applications, APIs, and data serialization/deserialization tasks involving image data.

base64_to_img(data):

  • Description: this function is designed to take a base64-encoded string, which typically represents an image, and convert it into a format that can be used for image processing tasks in Python, specifically with OpenCV. The conversion involves decoding the base64 string back into binary data, then transforming this data into a numpy array, which is finally decoded into an image format using OpenCV’s imdecode method.
  • Usage: useful in web applications or APIs where images are transmitted as base64-encoded strings due to the need for text-based transmission over HTTP or storage in JSON format.

img_to_base64(frame):

  • Description: this function converts an image from a numpy array format, commonly used in OpenCV for image manipulation, to a base64-encoded string. This is typically required when an image processed or generated by the server needs to be sent over the internet or stored in a text-based format like JSON. The image is first encoded into a JPEG format, then the binary data is converted into a base64 string.

  • Usage: essential for sending images from server to client in web services, embedding images in HTML or CSS, or serializing images in JSON objects.

process_frame(frame):

  • Description: a utility function for converting a color image (in BGR format) into a grayscale image. This is a common preprocessing step in various computer vision and image processing workflows. The function uses OpenCV’s cvtColor with the COLOR_BGR2GRAY flag to perform the conversion.

  • Usage: particularly useful in image analysis tasks where color information is unnecessary, such as in edge detection, face recognition, or in situations where reducing image complexity may improve algorithm performance or reduce computational costs.