使用OpenCV检测数字的小点或小数点

Question

我正在关注Adrian Rosebrock关于识别RPi数字的教程，所以没有tesseract或其他：https://www.pyimagesearch.com/2017/02/13/recognizing-digits-with-opencv-and-python/

但是它不能识别小数点，所以我一直在努力创造一个有助于做到这一点的部分。我想我已经接近了，但我不确定我做错了什么。

这是我预处理后的图像

这是在尝试识别部分之后发生的事情

如你所见，我在某处做错了。已经尝试在houghCircles中调整param1和param2

更多例子：

谁能引导我做我应该做的事情？我真的迷失在这里

================================================================

我正在使用的图像

我正在使用的代码

from imutils.perspective import four_point_transform
from imutils import contours
import imutils
import cv2
import numpy

DIGITS_LOOKUP = {
        # Old Library
    #(1, 1, 1, 0, 1, 1, 1): 0, # same as new 8
    (0, 0, 1, 0, 0, 1, 0): 1,
    (1, 0, 1, 1, 1, 1, 0): 2,
    (1, 0, 1, 1, 0, 1, 1): 3,
    (0, 1, 1, 1, 0, 1, 0): 4,
    (1, 1, 0, 1, 0, 1, 1): 5,
    #(1, 1, 0, 1, 1, 1, 1): 6,
    (1, 0, 1, 0, 0, 1, 0): 7,
    (1, 1, 1, 1, 1, 1, 1): 8,
    (1, 1, 1, 1, 0, 1, 1): 9,

    # New Digital Library
        (0, 0, 1, 1, 1, 0, 1): 0,
        (1, 0, 1, 0, 0, 1, 1): 2,

        (0, 0, 1, 1, 0, 1, 1): 4,
        (0, 0, 0, 0, 0, 1, 1): 4,

        (1, 1, 0, 0, 0, 1, 1): 5,
        (1, 1, 0, 1, 1, 0, 1): 5,
        (1, 0, 0, 0, 0, 1, 1): 5,

        (1, 1, 1, 0, 0, 0, 0): 7,

        (1, 1, 0, 1, 1, 1, 1): 8,
        (1, 1, 1, 0, 1, 1, 1): 8
}

image = cv2.imread("10.jpg")

image = imutils.resize(image, height=100)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 120, 255, 1)
cv2.imshow("1", edged)

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
    cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None

for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    if len(approx) == 4:
        displayCnt = approx
        break

warped = four_point_transform(gray, displayCnt.reshape(4, 2))
output = four_point_transform(image, displayCnt.reshape(4, 2))

thresh = cv2.threshold(warped, 0, 255,
    cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imshow("2", thresh)
print(thresh.shape)

circles = cv2.HoughCircles(warped, cv2.HOUGH_GRADIENT, 7, 14, param1=0.1, param2=20, minRadius=3, maxRadius=7)

# ensure at least some circles were found
if circles is not None:
    circles = numpy.round(circles[0, :]).astype("int")

    for (x, y, r) in circles:
        cv2.circle(output, (x, y), r, (0, 255, 0), 4)
        cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)


    # show the output image
    cv2.imshow("test", output)
    cv2.waitKey(0)

Answer 1

由于小数可能是正方形而不是圆形，因此使用cv2.HoughCircles()可能不是最佳选择。此外，由于您可能有背景噪音，尝试查找连接的组件可能会给您带来误报结果。

这是使用cv2.boundingRect()和cv2.contourArea()检测小数的方法。我们可以设置阈值最小和最大区域，这样它只会检测小数，但也可以避免检测噪声。

试图检测图像

from imutils.perspective import four_point_transform
from imutils import contours
import imutils
import cv2
import numpy

DIGITS_LOOKUP = {
    (1, 1, 1, 0, 1, 1, 1): 0,
    (0, 0, 1, 0, 0, 1, 0): 1,
    (1, 0, 1, 1, 1, 1, 0): 2,
    (1, 0, 1, 1, 0, 1, 1): 3,
    (0, 1, 1, 1, 0, 1, 0): 4,
    (1, 1, 0, 1, 0, 1, 1): 5,
    (1, 1, 0, 1, 1, 1, 1): 6,
    (1, 0, 1, 0, 0, 1, 0): 7,
    (1, 1, 1, 1, 1, 1, 1): 8,
    (1, 1, 1, 1, 0, 1, 1): 9
}

image = cv2.imread("10.jpg")

image = imutils.resize(image, height=100)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 120, 255, 1)
cv2.imshow("1", edged)

cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
    cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None

for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    if len(approx) == 4:
        displayCnt = approx
        break

warped = four_point_transform(gray, displayCnt.reshape(4, 2))

thresh = cv2.threshold(warped, 0, 255,
    cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imshow("2", thresh)

digit_cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
digit_cnts = imutils.grab_contours(digit_cnts)

threshold_max_area = 25
threshold_min_area = 5
contour_image = thresh.copy()

for c in digit_cnts:
    (x,y,w,h) = cv2.boundingRect(c)
    area = cv2.contourArea(c) 
    if area < threshold_max_area and area > threshold_min_area:
        cv2.drawContours(contour_image,[c], 0, (100,5,10), 3)

cv2.imshow("detect decimal", contour_image)
cv2.waitKey(0)

Answer 2

参与其中的工作：How to remove small connected objects using OpenCV

得到这些作为结果

但它们使用起来不是很好或不稳定。如果点突然大于之前的点（例如，超过9个像素，或者图像被调整大小），那么它就不能再使用了，所以这不是一个动态的答案，这是不好的。但是，如果有人有兴趣，我会把它留在这里

码

#find all your connected components (white blobs in your image)
nb_components, dotput, stats, centroids = cv2.connectedComponentsWithStats(thresh, connectivity=8)
#connectedComponentswithStats yields every seperated component with information on each of them, such as size
#the following part is just taking out the background which is also considered a component, but most of the time we don't want that.
sizes = stats[1:, -1]; nb_components = nb_components - 1

# minimum size of particles we want to keep (number of pixels)
#here, it's a fixed value, but you can set it as you want, eg the mean of the sizes or whatever
min_size = 50

#your answer image
img2 = numpy.zeros((dotput.shape))
#for every component in the image, you keep it only if it's above min_size
#thresh[output == 5 + 1] = 0
dots = []
for i in range(0, nb_components):
    if sizes[i] < min_size:
        dots.append(centroids[i])

#print(dots)
if dots:
    dots.sort(key = lambda x: abs(x[1]-digitCenY))
    print(dots)
    pDot = -1

    for i in range(len(digitCenX)):
        if (dots[0][0] <= digitCenX[i]) and (i > 0):
            pDot = 0
            break
        elif (digitCenX[i] <= dots[0][0]) and (i != len(digitCenX)-1):
            pDot = 0
            break
        else:
            pDot = 1

    cv2.rectangle(output, (int(dots[pDot][0]), int(dots[pDot][1])), (int(dots[pDot][0]) + 3, int(dots[pDot][1]) + 3), (0, 255, 0), 1)