Optical_Flow_Clustering.py

import numpy as np
import matplotlib.pyplot as plt
import os
import cv2



# if __name__ == '__main__':
# Get the current directory
current_directory = os.getcwd()
print(current_directory)

# Go back to the parent directory
parent_directory = os.path.dirname(current_directory)
print(parent_directory)

# Set input directory
image_path  = os.path.join(parent_directory, 'Output', 'Flow_Frames', 'flow_158.jpg')

original_image = cv2.imread(image_path)

img=cv2.cvtColor(original_image,cv2.COLOR_BGR2RGB)


# Check if the image was loaded successfully
if img is None:
    print("Failed to load image:", image_path)
    exit(1)

# Reshape the image array
vectorized = img.reshape((-1,3))
vectorized = np.float32(vectorized)

# Define the stopping criteria and number of clusters (K) for k-means
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

K=2
attempts=10
# Apply k-means algorithm
ret, label, center = cv2.kmeans(vectorized, K, None, criteria, attempts, cv2.KMEANS_PP_CENTERS)

# Convert the center values to uint8 and reconstruct the image
center = np.uint8(center)
res = center[label.flatten()]
result_image = res.reshape((img.shape))

figure_size = 15
plt.figure(figsize=(figure_size,figure_size))
plt.subplot(1,2,1),plt.imshow(img)
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(1,2,2),plt.imshow(result_image)
plt.title('Segmented Image when K = %i' % K), plt.xticks([]), plt.yticks([])
plt.show()

# Display the plots
plt.show()