在轮廓opencv上找到主色

Question

我试图找到轮廓（黑色或白色）内的主要颜色。 我正在使用OpenCV读取图像并提取黑色图像上的白色。这是我到目前为止所得到的：

绿色轮廓为轮廓，蓝色为边界框。因此，在这个实例中，我尝试提取数字87575220，但是正如您所看到的，它还可以识别一些随机伪像，例如字母G。我认为解决方案是在轮廓内部找到主要颜色，并且该颜色应为接近白色。我不知道该怎么做。

这是我目前拥有的代码：

import argparse
import cv2
import imutils
import numpy as np

parser = argparse.ArgumentParser()
parser.add_argument("--image", "-i", required=True, help="Image to detect blobs from")
args = vars(parser.parse_args())

image = cv2.imread(args["image"])
image = imutils.resize(image, width=1200)
grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(grey)
maxval_10 = maxVal * 0.5
ret, threshold = cv2.threshold(grey, maxval_10, 255, cv2.THRESH_BINARY)
canny = cv2.Canny(grey, 200, 250)
lines = cv2.HoughLines(canny, 1, np.pi / 180, 140)

print(maxVal)

theta_min = 60 * np.pi / 180.
theta_max = 120 * np.pi / 180.0
theta_avr = 0
theta_deg = 0
filteredLines = []
for rho, theta in lines[0]:
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a * rho
    y0 = b * rho
    x1 = int(x0 + 1000 * (-b))
    y1 = int(y0 + 1000 * (a))
    x2 = int(x0 - 1000 * (-b))
    y2 = int(y0 - 1000 * (a))

    cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)

    if theta_min <= theta <= theta_max:
        filteredLines.append(theta)
        theta_avr += theta

if len(filteredLines) > 0:
    theta_avr /= len(filteredLines)
    theta_deg = (theta_avr / np.pi * 180) - 90
else:
    print("Failed to detect skew")

image = imutils.rotate(image, theta_deg)
canny = imutils.rotate(canny, theta_deg)

im2, contours, hierarchy = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# cv2.drawContours(image, contours, -1, (0, 255, 0), 1)
cv2.imshow('Contours', im2)

boundingBoxes = []
filteredContours = []

for cnt in contours:
    (x, y, w, h) = cv2.boundingRect(cnt)
    if (h > 20 and h < 90 and w > 5 and w < h):
        if cv2.contourArea(cnt, True) <= 0:
            boundingBoxes.append((x, y, w, h))
            filteredContours.append(cnt)

for x, y, w, h in boundingBoxes:
    cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)

cv2.drawContours(image, filteredContours, -1, (0, 255, 0), 1)

cv2.imshow('Image', image)
cv2.imshow('Edges', canny)
cv2.imshow('Threshold', threshold)

cv2.waitKey(0)
cv2.destroyAllWindows()

这是原始图片：

Answer 1

在我开始搜索数字之前，我会尝试获得投资回报。您没有给出原始图像，因此该示例使用您发布的图像（已绘制好框和轮廓）完成。应该也与原始作品一起工作。步骤以示例代码编写。希望能帮助到你。干杯！

示例代码：

import cv2
import numpy as np

# Read the image and make a copy then create a blank mask
img = cv2.imread('dominant.jpg')
img2 = img.copy()
h,w = img.shape[:2]
mask = np.zeros((h,w), np.uint8)

# Transform to gray colorspace and perform histogram equalization
gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
equ = cv2.equalizeHist(gray)

# Transform all pixels above thershold to white
black = np.where(equ>10)
img2[black[0], black[1], :] = [255, 255, 255]

# Transform to gray colorspace and make a thershold then dilate the thershold
gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
kernel = np.ones((15,15),np.uint8)
dilation = cv2.dilate(thresh,kernel,iterations = 1)

# Search for contours and select the biggest one and draw it on mask
_, contours, hierarchy = cv2.findContours(dilation,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)
cv2.drawContours(mask, [cnt], 0, 255, -1)

# Perform a bitwise operation
res = cv2.bitwise_and(img, img, mask=mask)

# Display the ROI
cv2.imshow('img', res)

结果：

Answer 2

您可以在每个轮廓之外创建一个遮罩：

mask = np.zeros(image.shape, dtype="uint8")
cv2.drawContours(mask, [cnt], -1, 255, -1)

，然后计算蒙版内部所有像素的平均值：

mean = cv2.mean(image, mask=mask)

然后检查mean是否足够接近白色

Answer 3

由于色彩空间属性，颜色和均值不能很好地匹配。我将创建一个直方图并选择最频繁的直方图（也可以应用一些颜色向下采样）