countNeuriteIntensityMeijering

# from skimage.filters import frangi, hessian, sato, meijering
# import matplotlib.pyplot as plt
# import cv2
# import numpy as np


# # Default values for lowerbound = 2/255, upperbound = 0.05"
# #Output the neuritness of the cell image and images of the cells, the Meijering transformed image, 
# # and image quantifying neuriteness
# def countNeuriteIntensityMeijering(pathToFile, lowerbound, upperbound):

#     image =  cv2.imread(pathToFile)
#     image_m =  cv2.imread(pathToFile)

#     greyscale_m = cv2. cvtColor(image, cv2.COLOR_BGR2GRAY)

#     dns_greyscale_m = cv2.fastNlMeansDenoising(greyscale_m)

#     meijering_filtered_image  = meijering(dns_greyscale_m, [1], black_ridges = True)

#     image_m[((meijering_filtered_image > lowerbound) & (meijering_filtered_image < upperbound))] = (255, 0, 0)

#     lower_red = np.array([255, 0, 0])
#     upper_red = np.array([255, 0, 0])

#     red_mask = cv2.inRange(image_m, lower_red, upper_red)

#     red_pixel_count = cv2.countNonZero(red_mask)

#     print('The relative neurite intensity is '+ str(red_pixel_count))

#     plt.figure(figsize=(10,5))
#     plt.subplot(1, 3, 1)
#     plt.imshow(image, cmap='gray')
#     plt.title('Original Image')
#     plt.axis('off')

# # Display the filtered image
#     plt.subplot(1, 3, 2)
#     plt.imshow(meijering_filtered_image, cmap='gray')
#     plt.title('Meijering-Filtered Image')
#     plt.axis('off')

# # Display the filtered image
#     plt.subplot(1, 3, 3)
#     plt.imshow(image_m, cmap='gray')
#     plt.title('Neuriteness')
#     plt.axis('off')

#     plt.show()

# countNeuriteIntensityMeijering('/Users/stevensu/Downloads/images/livecell_train_val_images/MCF7_Phase_G4_2_03d16h00m_4.tif', 2/255, 0.05)