I generated a texture image like this
I have to compare two textures. I have used histogram comparison method.
image_file = 'output_ori.png'
img_bgr = cv2.imread(image_file)
height, width, channel = img_bgr.shape
hist_lbp = cv2.calcHist([img_bgr], [0], None, [256], [0, 256])
print("second started")
image_fileNew = 'output_scan.png'
img_bgr_new = cv2.imread(image_fileNew)
height_new, width_new, channel_new = img_bgr_new.shape
print("second lbp")
hist_lbp_new = cv2.calcHist([img_bgr_new], [0], None, [256], [0, 256])
print("compar started")
compare = cv2.compareHist(hist_lbp, hist_lbp_new, cv2.HISTCMP_CORREL)
print(compare)
But this method is not effective. It shows similar results for two different image textures. Also it is not showing too much of variation to identify Print & Scan effect. How do I compare the textures? I thought of analysing the GLCM characteristics.
import cv2
import numpy as np
from skimage.feature import greycomatrix
img = cv2.imread('images/noised_img1.jpg', 0)
image = np.array(img, dtype=np.uint8)
g = greycomatrix(image, [1, 2], [0, np.pi/2], levels=4, normed=True, symmetric=True)
contrast = greycoprops(g, 'contrast')
print(contrast)
In this method, I am getting the output as 2*2 matrix. How do I compare two matrices of several features like contrast, similarity, homogeneity, ASM, energy and correlation?
COMMENT CLARIFICATION
import numpy as np
from PIL import Image
class LBP:
def __init__(self, input, num_processes, output):
# Convert the image to grayscale
self.image = Image.open(input).convert("L")
self.width = self.image.size[0]
self.height = self.image.size[1]
self.patterns = []
self.num_processes = num_processes
self.output = output
def execute(self):
self._process()
if self.output:
self._output()
def _process(self):
pixels = list(self.image.getdata())
pixels = [pixels[i * self.width:(i + 1) * self.width] for i in range(self.height)]
# Calculate LBP for each non-edge pixel
for i in range(1, self.height - 1):
# Cache only the rows we need (within the neighborhood)
previous_row = pixels[i - 1]
current_row = pixels[i]
next_row = pixels[i + 1]
for j in range(1, self.width - 1):
# Compare this pixel to its neighbors, starting at the top-left pixel and moving
# clockwise, and use bit operations to efficiently update the feature vector
pixel = current_row[j]
pattern = 0
pattern = pattern | (1 << 0) if pixel < previous_row[j-1] else pattern
pattern = pattern | (1 << 1) if pixel < previous_row[j] else pattern
pattern = pattern | (1 << 2) if pixel < previous_row[j+1] else pattern
pattern = pattern | (1 << 3) if pixel < current_row[j+1] else pattern
pattern = pattern | (1 << 4) if pixel < next_row[j+1] else pattern
pattern = pattern | (1 << 5) if pixel < next_row[j] else pattern
pattern = pattern | (1 << 6) if pixel < next_row[j-1] else pattern
pattern = pattern | (1 << 7) if pixel < current_row[j-1] else pattern
self.patterns.append(pattern)
def _output(self):
# Write the result to an image file
result_image = Image.new(self.image.mode, (self.width - 2, self.height - 2))
result_image.putdata(self.patterns)
result_image.save("output.png")
I generated texture with this code. I have texture and I have methods to calculate the texture properties, but the question is how to identify the similarity between two textures.
