Image Distance Calculation Example with OpenCV

         Distance calculation is a fundamental concept in image processing that plays a crucial role in various applications, from object tracking to edge detection. It involves measuring the spatial separation between pixels or points in an image using different distance metrics.

        In this blog post, we'll explore the importance of distance calculation and its applications in image processing. The tutorial covers:

1. What is Distance Calculation in Image Processing?
2. Why is Distance Calculation Important?
3. Distance Calculation with OpenCV
   
4. Conclusion
   

     Let's get started.

 

What is Distance Calculation in Image Processing?

      Distance calculation, in the context of image processing, measures the spatial separation between a pixel and specific features or objects in an image. It's commonly used for tasks like edge detection and object tracking. The distance can be calculated using various metrics like Euclidean distance or Manhattan distance, depending on the application's requirements. The result is a new image where each pixel represents its distance from a feature. Shorter distances typically indicate proximity to the feature, while longer distances signify greater separation. This information is valuable for identifying objects, boundaries, or regions of interest in images and plays a critical role in many computer vision tasks.

 

 Why is Distance Calculation Important?

    Distance transform is a valuable tool for various image processing tasks, and here's why it's important:

    Object Detection: Distance calculation helps identify objects in an image by measuring the proximity of pixels. It's crucial for tasks like locating the center of mass of an object or determining if two objects are touching or separate.

    Edge Detection: In edge detection, distances between neighboring pixels are examined to find abrupt changes in intensity, indicating edges and boundaries between objects.

    Object Tracking: For tracking moving objects, distance calculation helps associate object positions in consecutive frames, enabling us to follow their trajectories accurately.

    Segmentation: Distance transforms are used in image segmentation to delineate regions or objects based on their proximity to a particular feature or seed point.

    Pattern Recognition: In pattern recognition, calculating distances between feature vectors (e.g., in machine learning applications) is essential for classifying and recognizing objects within images.

 

Distance Calculation with OpenCV

   The cv2.distanceTransform() function in OpenCV is used to compute the distance transform of a binary image. The distance transform calculates the distance of each pixel in the binary image to the nearest zero pixel (background pixel) and stores this distance information in the output image. It is a valuable tool in image processing and computer vision for various applications, including shape analysis, object recognition, and image segmentation.

Here's an explanation of the function's parameters and how it works:

 

dst = cv2.distanceTransform(src, distanceType, maskSize)

 

 

    src: This is the input binary image, where pixel values are either 0 (background) or 1 (foreground). The function calculates the distance transform based on this image.
    distanceType: This parameter determines the type of distance metric used for computing distances. It can take one of the following values:
    cv2.DIST_L1: Calculates the distance using the L1 (Manhattan) distance metric.
    cv2.DIST_L2: Calculates the distance using the L2 (Euclidean) distance metric (default).
    cv2.DIST_C: Calculates the distance using the Chebyshev distance metric.
   maskSize: This parameter specifies the size of the neighborhood used for the distance calculation.  

 

     Now, let's put theory into practice with an example using Python and OpenCV. In this example, we'll create binary image and draw a white rectangle on it. Then, we calculate the distance transform. We'll check the values of image before and after the distance calculation and finally we'll display the both images.

 

 

import cv2

import numpy as np

import matplotlib.pyplot as plt

# Create a binary image with a single object (white) on 

# a black background (black)

image = np.zeros((15, 15), dtype=np.uint8)

# Draw a white rectangle 

cv2.rectangle(image, (9, 9), (5, 5), 255, -1)

for row in image:

# Loop through columns in each row

for pixel in row:

print(f"{pixel:3}", end=' ')

print()

# Calculate the distance transform

dist_transform = cv2.distanceTransform(image, 

cv2.DIST_L2, cv2.DIST_MASK_PRECISE)

print("After computing the distance transform")

for row in dist_transform:

# Loop through columns in each row

for pixel in row:

print(f"{pixel:3}", end=' ')

print()

# Display the original binary image and the distance transform

plt.figure(figsize=(10, 5))

plt.subplot(1, 2, 1)

plt.imshow(image, cmap='gray')

plt.title('Binary Image')

plt.subplot(1, 2, 2)

plt.imshow(dist_transform, cmap="gray")

plt.title('Distance Transform')

plt.colorbar()

plt.show()

 

 

      

 

 

 

 

 

 

 

 

Next, we apply the distance calculation to image file and check the output results. 

 

import cv2

import matplotlib.pyplot as plt

# Load image and convert to binary

image = cv2.imread('items.jpg', cv2.IMREAD_GRAYSCALE)

# Calculate the distance transform

dist_transform = cv2.distanceTransform(image, 

cv2.DIST_L1, cv2.DIST_MASK_PRECISE)

# Display the original binary image and the distance transform

plt.figure(figsize=(10, 5))

plt.subplot(1, 2, 1)

plt.imshow(image, cmap='gray')

plt.title('Binary Image')

plt.subplot(1, 2, 2)

# Use a colormap for visualization 

plt.imshow(dist_transform, cmap="jet")

plt.title('Distance Transform')

plt.colorbar()

plt.show()

 

 Conclusion

  

    In this tutorial, we've briefly explored the concepts of distance calculation in image processing and learned how to implement it with OpenCV. 

     Distance calculation is a fundamental concept in image processing, enabling us to quantify spatial relationships between pixels or points. Its applications span various domains, making it a valuable tool for tasks like object detection, edge detection, segmentation, and more.

 

 

References:

1. OpenCV image processing