Component Labeling Assignment Answers

Question:

Write OpenCV code to perform the following tasks:

intensity values within the set V, where V={min-max}, to determine pixel

connectivity. The values of min and max are user-input parameters.

Please submit your source code, and adhere to the following regulations.

Only Python programming language can be used, in conjunction with

Component Labeling Answer and Explanation

Task 1: Implement the component labeling algorithm

This task involves labeling connected components in a binary image using 4-connectivity.

    label = 1

    labels = np.zeros_like(image, dtype=int)

    def is_valid(x, y):

        return 0 <= x < rows and 0 <= y < cols

            for dx, dy in connectivity:

                nx, ny = cx + dx, cy + dy

    for i in range(rows):

        for j in range(cols):

    return labels

# Read the binary image

labeled_image = label_components_4_connectivity(binary_image)

# Save the labeled image

    label = 1

    labels = np.zeros_like(image, dtype=int)

    def is_valid(x, y):

        return 0 <= x < rows and 0 <= y < cols

            for dx, dy in connectivity:

                nx, ny = cx + dx, cy + dy

    for i in range(rows):

        for j in range(cols):

    return labels

# Label the components using 8-connectivity

def label_components_intensity_range(image, min_val, max_val):

    # Initialize label and label matrix

    # Get image dimensions

    rows, cols = image.shape

        while queue:

            cx, cy = queue.pop(0)

                    queue.append((nx, ny))

    # Label the components

                bfs(i, j)

                label += 1

min_val = 100  # user input

max_val = 200  # user input

Task 4: Implement the size filter algorithm

def size_filter(image, min_size):

    size_mask = {label: (counts[i] >= min_size) for i, label in enumerate(unique)}

    # Apply the size filter

min_size = 500  # user input

# Filter the components by size

Combining all tasks, here's the full code:

import cv2

    connectivity = [(0, 1), (1, 0), (0, -1), (-1, 0)]

    rows, cols = image.shape

        while queue:

            cx, cy = queue.pop(0)

                    queue.append((nx, ny))

    for i in range(rows):

                label += 1

    return labels

    rows, cols = image.shape

    def is_valid(x, y):

            cx, cy = queue.pop(0)

            for dx, dy in connectivity:

    for i in range(rows):

        for j in range(cols):

    return labels

def label_components_intensity_range(image, min_val, max_val):

    def is_valid(x, y):

        return 0 <= x < rows and 0 <= y < cols

            for dx, dy in connectivity:

                nx, ny = cx + dx, cy + dy

        for j in range(cols):

            if min_val <= image[i, j] <= max_val and labels[i, j] == 0:

def size_filter(image, min_size):

    unique, counts = np.unique(image, return_counts=True)

_, binary_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

labeled_image_4 = label_components_4_connectivity(binary_image)

min_val = 100

max_val = 200

cv2.imwrite('filtered_labeled_image.png', filtered_labeled_image)

This code covers all the tasks as specified. Ensure you have the necessary images (`binary_image.png` and `grayscale_image.png`) in the same directory as your script, and modify the paths as needed.