This answer for this Question in StackOverflow.
Theory: The barcode consists of vertical black zone to represent each bar, so I did the summation of the rows, and thresholded the whole image depending on an empirical value near the mean of the summation.
Python Code
#========================
# Import Libraies
#========================
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
from pyzbar import pyzbar
#------------------------
# Read Image
#========================
img = cv.imread('barcode_example.jpg', cv.IMREAD_GRAYSCALE)
# #------------------------
# # Morphology
# #========================
# # Closing
# #------------------------
closed = cv.morphologyEx(img, cv.MORPH_CLOSE, cv.getStructuringElement(cv.MORPH_RECT, (1, 21)))
# #------------------------
# # Statistics
# #========================
print(img.shape)
dens = np.sum(img, axis=0)
mean = np.mean(dens)
print(mean)
#------------------------
# Thresholding
#========================
thresh = closed.copy()
for idx, val in enumerate(dens):
if val< 10800:
thresh[:,idx] = 0
(_, thresh2) = cv.threshold(thresh, 128, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
#------------------------
# plotting the results
#========================
plt.figure(num='barcode')
plt.subplot(221)
plt.imshow(img, cmap='gray')
plt.title('Original')
plt.axis('off')
plt.subplot(224)
plt.imshow(thresh, cmap='gray')
plt.title('Thresholded')
plt.axis('off')
plt.subplot(223)
plt.imshow(thresh2, cmap='gray')
plt.title('Result')
plt.axis('off')
plt.subplot(222)
plt.hist(dens)
plt.axvline(dens.mean(), color='k', linestyle='dashed', linewidth=1)
plt.title('dens hist')
plt.show()
#------------------------
# Printing the Output
#========================
barcodes = pyzbar.decode(thresh2)
print(barcodes)
Output:
[Decoded(data=b'00004980072868003004',
type='CODE128',
rect=Rect(left=34, top=0, width=526, height=99),
polygon=[Point(x=34, y=1), Point(x=34, y=99),
Point(x=560, y=98), Point(x=560, y=0)])]