Adaptive Threshold, Sobel, Morph Close comparison with Matplotlib sliders

edge_detection_compare_adjust.py

import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider

def sobel_edge_detector(img, threshold=50):
    grad_x = cv.Sobel(img, cv.CV_64F, 1, 0)
    grad_y = cv.Sobel(img, cv.CV_64F, 0, 1)
    grad = np.sqrt(grad_x**2 + grad_y**2)
    grad_norm = (grad * 255 / grad.max()).astype(np.uint8)
    
    _, binary_edge = cv.threshold(grad_norm, threshold, 255, cv.THRESH_BINARY)
    
    return binary_edge

def update(val):
    # Get current slider values
    block_size = int(block_size_slider.val)
    C = int(C_slider.val)
    blur_size = int(blur_size_slider.val)
    closing_size = int(closing_size_slider.val)
    sobel_threshold = int(sobel_threshold_slider.val)

    # Apply Sobel edge detection
    sobel_edges = sobel_edge_detector(gray, sobel_threshold)

    # Apply blur
    blurred_image = cv.GaussianBlur(gray, (blur_size, blur_size), 0)

    # Apply adaptive thresholding
    adaptive_threshold = cv.adaptiveThreshold(
        blurred_image, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, block_size, C
    )

    # Apply morphological closing to close contours
    kernel = np.ones((closing_size, closing_size), np.uint8)
    closed_contours = cv.morphologyEx(adaptive_threshold, cv.MORPH_CLOSE, kernel)

    # Update the displayed images
    ax2.imshow(closed_contours, cmap='gray')
    ax2.set_title('Adaptive Thresholded with Blur and Closed Contours')

    ax3.imshow(sobel_edges, cmap='gray')
    ax3.set_title('Sobel Edge Detection')

    fig.canvas.draw_idle()

# Load the image
img = cv.imread('capture.png')
assert img is not None, "file could not be read, check with os.path.exists()"
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

# Create figure and axes
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 5))
fig.subplots_adjust(left=0.1, right=0.9, bottom=0.45)

# Display the original image
ax1.imshow(gray, cmap='gray')
ax1.set_title('Original Image')

# Initial values for adaptive threshold parameters
initial_block_size = 3
initial_C = 3
initial_blur_size = 3
initial_closing_size = 3
initial_sobel_threshold = 50

# Create sliders
axcolor = 'lightgoldenrodyellow'
ax_block_size = plt.axes([0.1, 0.35, 0.65, 0.03], facecolor=axcolor)
ax_C = plt.axes([0.1, 0.3, 0.65, 0.03], facecolor=axcolor)
ax_blur_size = plt.axes([0.1, 0.25, 0.65, 0.03], facecolor=axcolor)
ax_closing_size = plt.axes([0.1, 0.2, 0.65, 0.03], facecolor=axcolor)
ax_sobel_threshold = plt.axes([0.1, 0.15, 0.65, 0.03], facecolor=axcolor)

block_size_slider = Slider(ax_block_size, 'Block Size', 3, 31, valinit=initial_block_size, valstep=2)
C_slider = Slider(ax_C, 'C', -10, 20, valinit=initial_C, valstep=1)
blur_size_slider = Slider(ax_blur_size, 'Blur Size', 1, 21, valinit=initial_blur_size, valstep=2)
closing_size_slider = Slider(ax_closing_size, 'Closing Size', 1, 21, valinit=initial_closing_size, valstep=2)
sobel_threshold_slider = Slider(ax_sobel_threshold, 'Sobel Threshold', 0, 255, valinit=initial_sobel_threshold, valstep=1)

# Register the update function with the sliders
block_size_slider.on_changed(update)
C_slider.on_changed(update)
blur_size_slider.on_changed(update)
closing_size_slider.on_changed(update)
sobel_threshold_slider.on_changed(update)

# Initial blur and adaptive thresholding
blurred_image = cv.GaussianBlur(gray, (initial_blur_size, initial_blur_size), 0)
adaptive_threshold = cv.adaptiveThreshold(
    blurred_image, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, initial_block_size, initial_C
)

# Initial morphological closing to close contours
kernel = np.ones((initial_closing_size, initial_closing_size), np.uint8)
closed_contours = cv.morphologyEx(adaptive_threshold, cv.MORPH_CLOSE, kernel)

# Initial Sobel edge detection
sobel_edges = sobel_edge_detector(gray, initial_sobel_threshold)

# Display the adaptive thresholded image with closed contours
ax2.imshow(closed_contours, cmap='gray')
ax2.set_title('Adaptive Thresholded with Blur and Closed Contours')

# Display the Sobel edge detection
ax3.imshow(sobel_edges, cmap='gray')
ax3.set_title('Sobel Edge Detection')

plt.show()