Detecting Irregular Rectangles in an Image Without Using OpenCV

Detecting irregular rectangles in an image without relying on a specialized library like OpenCV can be accomplished using a few basic image processing techniques with Python and its powerful libraries, such as NumPy and PIL Pillow. This guide will walk you through a step-by-step approach to identifying irregular rectangles using only these tools.

1. Load the Image and Convert to Grayscale

The first step is to load the image and convert it to grayscale. Grayscale simplifies the image data to a single channel, making it easier to manipulate and analyze.

#64;import Image
#64;import numpy as np

image  (path_to_your_image)
gray_image  (#39;L#39;)

2. Binarize the Image

Next, you should convert the grayscale image to a binary image, which consists of black and white pixels. This is typically done using thresholding methods.

gray_array  (gray_image)

threshold  128 # This value can be adjusted based on the image
binary_array  (gray_array  threshold).astype(np.uint8) * 255

3. Find Contours

To identify irregular shapes, you can use edge detection methods like Sobel or Canny. After detecting edges, you can find connected components in the image.

from scipy.ndimage import label

structure  ((3, 3), dtype)
labeled_array, num_features  label(binary_array, structurestructure)

# labeled_array contains labels for each connected component

4. Analyze the Contours

For each connected component, you can find its bounding box and check if it resembles a rectangle. This step involves calculating the aspect ratio and determining if it falls within a specific range.

from scipy.ndimage import find_objects

slices  find_objects(labeled_array)

for i, s in enumerate(slices):
    # Get the bounding box coordinates
    y_start, y_end  s[0].start, s[0].stop
    x_start, x_end  s[1].start, s[1].stop

    # Check aspect ratio to determine if it's a rectangle
    width  x_end - x_start
    height  y_end - y_start
    aspect_ratio  width / height if height ! 0 else 0

    # Define a threshold for aspect ratio
    if 0.8  aspect_ratio  1.2: # Adjust this threshold as needed
        print(f#34;Detected rectangle: ({x_start}, {y_start}) to ({x_end}, {y_end}) with aspect ratio: {aspect_ratio}#34;)

5. Draw or Highlight the Detected Rectangles

To visualize the detected rectangles, you can draw them on the original image. This step involves converting the image back to RGB and using the PIL ImageDraw module to draw rectangles.

import  as plt

rgb_image  (#39;RGB#39;)
draw  ImageDraw.Draw(rgb_image)

for i, s in enumerate(slices):
    y_start, y_end  s[0].start, s[0].stop
    x_start, x_end  s[1].start, s[1].stop
    if 0.8  aspect_ratio  1.2: # Adjust this threshold as needed
        ([x_start, y_start, x_end, y_end], outline#34;red#34;)
      # Show the image with rectangles
(rgb_image)
()

Summary

This method involves loading an image, converting it to binary, finding connected components, and checking their aspect ratios to identify irregular rectangles. The thresholds and aspect ratio conditions can be adjusted based on specific requirements to improve detection accuracy.