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P2L.py
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P2L.py
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import cv2
import os
import glob
import numpy as np
from matplotlib import pyplot as plt
from skimage import filters
#import thinning
def segment (image):
#convert BGR image to graycale
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#Binary image
ret, thresh = cv2.threshold(image_gray, 125, 255, cv2.THRESH_BINARY)
#Resize image
scale_percent = 40 #percent of original size
width = int(thresh.shape[1] * scale_percent / 100)
height = int(thresh.shape[0] * scale_percent / 100)
dim = (width, height)
#resize
result = cv2.resize(thresh, dim, interpolation=cv2.INTER_AREA)
#Thinning
kernel = np.ones((5,5), np.uint8)
thinned = cv2.dilate(result, kernel, iterations = 3 )
#opening = cv2.morphologyEx(erosion, cv2.MORPH_OPEN, kernel)
#thinned = thinning.guo_hall_thinning(result)
return thinned
#return erosion
if __name__ == '__main__' :
img_dir = "/media/sena/Data1/SKRIPSICODE/python_mark16/data/dataset/ya"
data_path = os.path.join(img_dir,'*JPG')
files = glob.glob(data_path)
data = []
result = []
for f1 in files:
img = cv2.imread(f1)
data.append(img)
result.append(segment(img))
#save each image
iteratorName = 1
prefixPathDir = "/media/sena/Data1/SKRIPSICODE/python_mark16/data/hasil/ya/"
for img in result :
cv2.imwrite(prefixPathDir+str(iteratorName)+".png", img)
iteratorName+=1