如何在图像中找到多个条形码

Question

]2下面的python代码在一张图片中只找到一个条形码。我需要找到图像中存在的多个条形码，不胜感激。提前致谢。

import numpy as np
import argparse
import imutils
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
help = "path to the image file")
args = vars(ap.parse_args())

image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
gradX = cv2.Sobel(gray, ddepth=ddepth, dx=1, dy=0, ksize=-1)
gradY = cv2.Sobel(gray, ddepth=ddepth, 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)
cnts = imutils.grab_contours(cnts)
print(len(cnts))
#c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
c = max(cnts, key = cv2.contourArea)

rect = cv2.minAreaRect(c)
box = cv2.cv.BoxPoints(rect) if imutils.is_cv2() else cv2.boxPoints(rect)
box = np.int0(box)

cv2.drawContours(image, [box], -1, (0, 255, 0), 3)
cv2.imshow("Image", image)
cv2.waitKey(0)

Answer 1

我做的最重要的假设是条码是水平对齐的。

find_rectangles 由 OpenCv Squares example 修改而来。我们用它来获得我们的候选轮廓。然后我们对轮廓进行分组，按 x 顺序处理。如果当前轮廓足够接近，我们只能添加到组中，在其中心具有相似的高度并且与组的最后添加的计数具有相似的高度。

最后，我们检查每组中最小的等高线区域，并估计条形的数量作为该区域占总组面积的数量。我们会丢弃任何少于 10 个柱的组。

我们留下了应该是条形码的组，剩下的就是在原始图像上绘制矩形。

import cv2
import math
import numpy as np
from google.colab.patches import cv2_imshow

def get_center(contour):
    M = cv2.moments(contour)
    cX = int(M["m10"] / max(M["m00"], 1e-6))
    cY = int(M["m01"] / max(M["m00"], 1e-6))

    return cX, cY

def find_rectangles(img):
    filtered = np.zeros((img.shape[0], img.shape[1], 1), dtype=np.uint8) 
    img = cv2.GaussianBlur(img, (5, 5), 0)
    for gray in cv2.split(img):
      for thrs in range(50, 200, 1):
          _retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
          contours, h = cv2.findContours(~bin, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
          contours = [contours[i] for i in range(len(contours)) if h[0][i][3] == -1]
          for cnt in contours:
              cnt_len = cv2.arcLength(cnt, True)
              poly = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
              w, h = cv2.minAreaRect(cnt)[1]
              if len(poly) <= 8 and cv2.contourArea(cnt) > 10 and cv2.contourArea(poly) < 1000 and (h / w) > 5:
                cv2.drawContours(filtered, [cnt], -1, 255, -1)

    return filtered

def dist(p1, p2):
  return math.sqrt((p1[0] - p2[0]) **2 + (p1[1] - p2[1]) ** 2)  

def findBarCodes(image):

  thresh = find_rectangles(image)
  contours, h = cv2.findContours(thresh, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
  contours = sorted([contours[i] for i in range(len(contours)) if h[0][i][3] == -1], key = lambda x: cv2.boundingRect(x)[0])

  groups = []

  for cnt in contours:
    x, y, w, h = cv2.boundingRect(cnt)
    center_1 = get_center(cnt)
    found = False
    if w * h > 50 and w * h < thresh.shape[0] * thresh.shape[1] / 2:
      for group in groups:
        x2, y2, w2, h2 = cv2.boundingRect(group[-1])
        center_2 = get_center(group[-1])
        if abs(center_1[1] - center_2[1]) < 20 and (abs(h - h2) / max(h, h2)) < 0.3 and any(map(lambda p: dist((x, y), p) < 20, [(x2, y2), (x2 + w2, y2), (x2, y2 + h2), (x2 + w2, y2 + h2)])):
          group.append(cnt)
          found = True
          break
      if not found:
        groups.append([cnt])

  for group in groups[:]:
    mn = 1000000
    total = 0
    for c in group:
      x, y, w, h = cv2.boundingRect(c)
      total += w * h
      mn = min(mn, w * h)
    estimatedBars = total / mn
    if estimatedBars < 10:
      groups.remove(group)

  for idx, group in enumerate(groups):
    boxes = []
    for c in group:
      x, y, w, h = cv2.boundingRect(c)
      boxes.append([x,y, x+w,y+h])
      cv2.rectangle(thresh, (x,y), (x+w,y+h), 255, 2)
      cv2.putText(thresh, str(idx), (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, 255, 2)
      cv2.drawContours(thresh, [c], -1, 128, -1)

    boxes = np.asarray(boxes)

    left = np.min(boxes[:,0])
    top = np.min(boxes[:,1])
    right = np.max(boxes[:,2])
    bottom = np.max(boxes[:,3])

    cv2.rectangle(image, (left,top), (right,bottom), 255, 2)


  cv2_imshow(image)
  cv2_imshow(thresh)

findBarCodes(cv2.imread('tQp93.jpg'))

结果：

Answer 2

你需要有更好的形态学运算，并且对连通分量的大小有一个阈值。这是我对您的代码所做的更改以捕获所有条形码：

import numpy as np
#import argparse
import imutils
import cv2

image = cv2.imread('D:/1.jpg')

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
gradX = cv2.Sobel(gray, ddepth=ddepth, dx=1, dy=0, ksize=-1)
gradY = cv2.Sobel(gray, ddepth=ddepth, dx=0, dy=1, ksize=-1)

gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)

blurred = cv2.blur(gradient, (9, 9))

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 5))
blurred = cv2.erode(blurred, kernel, iterations = 4)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 1))
blurred = cv2.dilate(blurred, kernel, iterations = 4)

(_, thresh) = cv2.threshold(blurred, 230, 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)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 5))
closed = cv2.dilate(closed, kernel, iterations = 4)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1))
closed = cv2.erode(closed, kernel, iterations = 2)

cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts)

for i in range(len(cnts)):
    if cv2.contourArea(cnts[i]) > 2000:
        cv2.drawContours(image, cnts, i, (0, 255, 0), 3)

cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()