python - 在轮廓opencv上找到主色
问题描述
我试图找到轮廓内的主色(黑色或白色)。我正在使用 OpenCV 读取图像并在黑色图像上提取白色。这是我到目前为止得到的:
绿色轮廓是轮廓,蓝色线条是边界框。所以在这个例子中,我试图提取数字 87575220,但正如你所看到的,它还可以识别一些随机伪影,例如字母 G。我认为解决方案是找到轮廓内的主要颜色,该颜色应该是接近白色。我不知道如何做到这一点。
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()
解决方案
在开始搜索数字之前,我会尝试获得投资回报率。您没有提供原始图像,因此此示例是使用您发布的图像(已绘制框和轮廓)制作的。不过,也应该与原版一起使用。步骤写在示例代码中。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:
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