"""
A class that is responsible for detecting a barcode and extracting information
from said barcode.
"""
import numpy as np
import cv2
import zbar.misc
__author__ = "Stephan Nell"
__copyright__ = "Copyright 2017, Java the Hutts"
__license__ = "BSD"
__maintainer__ = "Stephan Nell"
__email__ = "nellstephanj@gmail.com"
__status__ = "Development"
# The minimum area a rectangle must be to be considered a barcode.
BOUNDING_RECTANGLE_THRESHOLD = 200
[docs]class BarCodeManager:
"""
The BarCode Manager is responsible for:
1. Detecting the barcode.
2. Extracting any information on the detected barcode.
3. Applying blurring to the detected barcode to reduce noise.
"""
def __init__(self):
self.scanner = zbar.Scanner()
[docs] def detect(self, image):
"""
This function detects a region containing a barcode if a barcode is present in the image passed.
Barcodes supported:
EAN
UPC
Code 39
Code 93
Code 128
ITF
For more information on Barcode types: https://www.scandit.com/types-barcodes-choosing-right-barcode/
Args:
image (:obj:'OpenCV image'): Image containing the potential barcode.
Returns:
boolean: A value of True is returned if a barcode was detected.
If however a barcode was not detected a value of false is returned.
obj:'OpenCV image': If a Barcode was successfully detected the detected barcode is returned.
If a barcode was not detected return the original image.
integer list: This list contains the box coordinates for the region in which the barcode resides.
Raises:
TypeError: If a none numpy array value has been passed
Todo:
Find a way to support PDF417 format.
"""
if type(image) is not np.ndarray:
raise TypeError(
'Bad type for arg image - expected image in numpy array. Received type "%s".' %
type(image).__name__
)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
grad_x = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)
grad_y = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1)
gradient = cv2.subtract(grad_x, grad_y)
gradient = cv2.convertScaleAbs(gradient)
blurred = cv2.blur(gradient, (7, 7))
(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (27, 7))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
eroded = cv2.erode(closed, None, iterations=4)
dilated = cv2.dilate(eroded, None, iterations=4)
(_, contours, _) = cv2.findContours(dilated.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
c = sorted(contours, key=cv2.contourArea, reverse=True)[0]
rectangle = cv2.minAreaRect(c)
box = cv2.boxPoints(rectangle)
box = np.int0(box)
(x, y, w, h) = cv2.boundingRect(box)
# The Difference between the upper and lower Y-value is calculated to ensure a Barcode is detected.
# This reduces the chance of a false positive detection.
diff = y - (y+h)
if abs(diff) < BOUNDING_RECTANGLE_THRESHOLD:
return True, image[y:y + h, x:x + w], box
else:
return False, image, box
[docs] def get_barcode_info(self, image):
"""
This function returns scanned barcode information.
Args:
image (:obj:'OpenCV image'): Image containing a Barcode.
Returns:
boolean: A value of True if the function was able to extract information from the barcode.
If no information was extracted from the barcode a value of False is returned.
string: A UTF-8 String containing the information extracted from the Barcode.
If no information was extracted from the barcode a empty string is returned.
obj:'OpenCV image': A copy of the original image.
Todo:
Find a way to support PDF417 format.
"""
(detection, detected_image, box) = self.detect(image.copy())
if detection:
gray = cv2.cvtColor(detected_image, cv2.COLOR_BGR2GRAY)
results = self.scanner.scan(gray)
if not results:
return False, "", image
else:
image = self.apply_barcode_blur(image, box)
return True, results[0].data, image
else:
return False, "", image
[docs] def apply_barcode_blur(self, image, box, dilation_intensity=2):
"""
This function applies blurring to a detected barcode region to reduce noise in the image.
The barcode region is first extracted, then blurring is applied, after blurring is applied
the blurred out barcode is reapplied to the original image.
Args:
image (:obj:'OpenCV image'): Image containing a Barcode.
box: The box is an integer list containing the box region coordinates of the barcode location.
dilation_intensity (int): Indicates the intensity by which the barcode should be dilated.
The greater the intensity the greater the extent of dilation. Greater intensity
leads to reduce of speed with every iterations.
Returns:
obj:'OpenCV image': The Original image with blurring applied to the barcode region in the image.
"""
(x, y, w, h) = cv2.boundingRect(box)
sub_bar_code = image[y:y + h, x:x + w]
sub_bar_code = cv2.dilate(sub_bar_code, None, iterations=dilation_intensity)
sub_bar_code = cv2.GaussianBlur(sub_bar_code, (31, 31), 0)
image[y:y + sub_bar_code.shape[0], x:x + sub_bar_code.shape[1]] = sub_bar_code
return image