Coverage for image_processing/barcode_manager.py : 71%

import numpy as np 

import cv2 

import zbar.misc 

# The minimum area a rectangle must be to be considered a barcode. 

BOUNDING_RECTANGLE_THRESHOLD = 200 

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() 

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/ 

Author(s): 

Stephan Nell 

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) 

78 ↛ 81line 78 didn't jump to line 81, because the condition on line 78 was never false if abs(diff) < BOUNDING_RECTANGLE_THRESHOLD: 

return True, image[y:y + h, x:x + w], box 

else: 

return False, image, box 

def get_barcode_info(self, image): 

""" 

This function returns scanned barcode information. 

Author(s): 

Stephan Nell 

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()) 

100 ↛ 109line 100 didn't jump to line 109, because the condition on line 100 was never false if detection: 

gray = cv2.cvtColor(detected_image, cv2.COLOR_BGR2GRAY) 

results = self.scanner.scan(gray) 

103 ↛ 106line 103 didn't jump to line 106, because the condition on line 103 was never false if not results: 

return False, "", image 

else: 

image = self.apply_barcode_blur(image, box) 

return True, results[0].data, image 

else: 

return False, "", image 

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. 

Author(s): 

Stephan Nell 

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