如何可靠地检测条形码的四个角? [英] How to reliably detect a barcode's 4 corners?

问题描述

我正在尝试使用Python + zbar模块检测此 Code128 条码:

I'm trying to detect this Code128 barcode with Python + zbar module:

(图像下载链接此处).

这有效:

import cv2, numpy
import zbar
from PIL import Image 
import matplotlib.pyplot as plt

scanner = zbar.ImageScanner()
pil = Image.open("000.jpg").convert('L')
width, height = pil.size    
plt.imshow(pil); plt.show()
image = zbar.Image(width, height, 'Y800', pil.tobytes())
result = scanner.scan(image)

for symbol in image:
    print symbol.data, symbol.type, symbol.quality, symbol.location, symbol.count, symbol.orientation

但仅检测到一个点:(596, 210).

如果我应用黑白阈值:

pil = Image.open("000.jpg").convert('L')
pil = pil .point(lambda x: 0 if x<100 else 255, '1').convert('L')    

更好，我们有3分:(596，210)，(482，211)，(596，212).但这又增加了一个难度(为每个新图像自动找到最佳阈值-在这里为100).

it's better, and we have 3 points: (596, 210), (482, 211), (596, 212). But it adds one more difficulty (finding the optimal threshold - here 100 - automatically for every new image).

仍然，我们没有条形码的四个角.

Still, we don't have the 4 corners of the barcode.

问题:如何使用Python可靠地找到图像上条形码的4个角?(也许还有OpenCV或其他库?)

Question: how to reliably find the 4 corners of a barcode on an image, with Python? (and maybe OpenCV, or another library?)

注意:

• 有可能是 ，这是一个很好的例子(但不幸的是，注释中没有提到开源):

• It is possible, this is a great example (but sadly not open-source as mentioned in the comments):

对象检测，用于实时应用的非常快速和强大的模糊一维条形码检测

角落检测似乎非常好而且非常快，即使条形码只是整个图像的一小部分(这对我来说很重要).

The corners detection seems to be excellent and very fast, even if the barcode is only a small part of the whole image (this is important for me).

有趣的解决方案:使用Python和OpenCV在视频中进行实时条形码检测，但是该方法存在一些局限性(请参见文章:条形码应封闭等)，从而限制了潜在的使用范围.另外，我正在为此寻找一个现成的库.

Interesting solution: Real-time barcode detection in video with Python and OpenCV but there are limitations of the method (see in the article: the barcode should be close up, etc.) that limit the potential use. Also I'm more looking for a ready-to-use library for this.

有趣的解决方案2:检测条形码在使用Python和OpenCV的图像中，但它似乎还不是生产就绪的解决方案，而是更多的正在进行中的研究.的确，我在此图像上尝试了他们的代码，但检测未获得成功的结果.必须注意的是，检测并不会考虑条形码的任何规格(事实是开始/停止符号等)

Interesting solution 2: Detecting Barcodes in Images with Python and OpenCV but again, it does not seem like a production-ready solution, but more a research in progress. Indeed, I tried their code on this image but the detection does not yield successful result. It has to be noted that it doesn't take any spec of the barcode in consideration for the detection (the fact there's a start/stop symbol, etc.)

import numpy as np
import cv2
image = cv2.imread("000.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gradX = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 1, dy = 0, ksize = -1)
gradY = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 0, dy = 1, ksize = -1)
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
blurred = cv2.blur(gradient, (9, 9))
(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
closed = cv2.erode(closed, None, iterations = 4)
closed = cv2.dilate(closed, None, iterations = 4)
(_, cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
rect = cv2.minAreaRect(c)
box = np.int0(cv2.boxPoints(rect))
cv2.drawContours(image, [box], -1, (0, 255, 0), 3)
cv2.imshow("Image", image)
cv2.waitKey(0)

推荐答案

解决方案2很好.导致图像失效的关键因素是阈值.如果将参数225降到55，则会得到更好的结果.

Solution 2 is pretty good. The critical factor that made it fail on your image was the thresholding. If you drop the parameter 225 way down to 55, you'll get much better results.

我已经重新编写了代码，在这里和那里进行了一些调整.如果您愿意，可以使用原始代码. OpenCV的文档非常好，并且有非常好的

I've reworked the code, making some tweaks here and there. The original code is fine if you prefer. The documentation for OpenCV is quite good, and there are very good Python tutorials.

import numpy as np
import cv2

image = cv2.imread("barcode.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# equalize lighting
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
gray = clahe.apply(gray)

# edge enhancement
edge_enh = cv2.Laplacian(gray, ddepth = cv2.CV_8U, 
                         ksize = 3, scale = 1, delta = 0)
cv2.imshow("Edges", edge_enh)
cv2.waitKey(0)
retval = cv2.imwrite("edge_enh.jpg", edge_enh)

# bilateral blur, which keeps edges
blurred = cv2.bilateralFilter(edge_enh, 13, 50, 50)

# use simple thresholding. adaptive thresholding might be more robust
(_, thresh) = cv2.threshold(blurred, 55, 255, cv2.THRESH_BINARY)
cv2.imshow("Thresholded", thresh)
cv2.waitKey(0)
retval = cv2.imwrite("thresh.jpg", thresh)

# do some morphology to isolate just the barcode blob
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 9))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
closed = cv2.erode(closed, None, iterations = 4)
closed = cv2.dilate(closed, None, iterations = 4)
cv2.imshow("After morphology", closed)
cv2.waitKey(0)
retval = cv2.imwrite("closed.jpg", closed)

# find contours left in the image
(_, cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
rect = cv2.minAreaRect(c)
box = np.int0(cv2.boxPoints(rect))
cv2.drawContours(image, [box], -1, (0, 255, 0), 3)
print(box)
cv2.imshow("found barcode", image)
cv2.waitKey(0)
retval = cv2.imwrite("found.jpg", image)

edge.jpg

edge.jpg

thresh.jpg

thresh.jpg

closed.jpg

closed.jpg

found.jpg

found.jpg

控制台的输出:

[[596 249]
 [470 213]
 [482 172]
 [608 209]]

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