如何可靠地检测条形码的四个角？
如何可靠地检测条形码的四个角？
提问于 2018-07-25 23:51:04
带有Python+的条形码zbar模块：
这样做是可行的：
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但只检测到一点：(596, 210)。
如果我用黑白阈值：
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)。但这又增加了一个困难(为每一幅新图像自动找到最佳阈值(此处为100)。
不过，我们还没有条形码的四个角落。
问：如何使用Python可靠地在图像上找到条形码的4个角？
Stack Overflow用户
发布于 2018-07-26 09:41:01
解决方案2非常好。使它在你的形象上失败的关键因素是阈值。如果您删除参数225远至55，你会得到更好的结果。
我重新编写了代码，在这里和那里做了一些调整。
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
阈值.jpg
Closed.jpg
Found.jpg
控制台输出：
[[596 249]
[470 213]
[482 172]
[608 209]]页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/-100005761
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