Python script to apply Canny edge detection and filter contours based on a dynamic threshold value, invert the image, and apply binary thresholding to create a mask on all images in a directory.

refer to code

 

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import os

import cv2

from tqdm import tqdm

import numpy as np

# Path to input and output directories

input_dir = './background'

output_dir = './background_canny'

# Create the output directory if it doesn't exist

if not os.path.exists(output_dir):

os.makedirs(output_dir)

# Loop over all files in the input directory

for filename in tqdm(os.listdir(input_dir)):

if filename.endswith('.jpg') or filename.endswith('.png'):

# Load the input image

img = cv2.imread(os.path.join(input_dir, filename), cv2.IMREAD_GRAYSCALE)

# Apply Canny edge detection algorithm

edges = cv2.Canny(img, 100, 200)

# Find and draw the contours of the edge map

contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

contours_filtered = []

threshold = int( min(0.1 * (img.shape[0]), 0.1 * (img.shape[1]) ) )

for cnt in contours:

if cv2.arcLength(cnt, True) >= threshold:

contours_filtered.append(cnt)

edges_contours = cv2.drawContours(np.zeros_like(img), contours_filtered, -1, 255, 1)

# Invert the image (black to white, white to black)

img_inv = cv2.bitwise_not(edges_contours)

# Apply binary thresholding to the image

thresh, img_binary = cv2.threshold(img_inv, 127, 255, cv2.THRESH_BINARY)

# Save the result in the output directory with the same file name

output_path = os.path.join(output_dir, filename)

cv2.imwrite(output_path, img_binary)

..

Thank you.

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