使用 OpenCV 增强灯的角检测

Question

我正在使用以下代码来检测明亮的灯。照明可能会有所不同。我正在使用以下代码来检测相同的内容。

img = cv2.imread("input_img.jpg")
rgb = img.copy()
img_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
while True:

    th3 = cv2.adaptiveThreshold(img_grey, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \
                                cv2.THRESH_BINARY, 11, 2)

    cv2.imshow("th3",th3)

    edged = cv2.Canny(th3, 50, 100)
    edged = cv2.dilate(edged, None, iterations=1)
    edged = cv2.erode(edged, None, iterations=1)

    cv2.imshow("edge", edged)

    cnts = cv2.findContours(edged.copy(), cv2.RETR_TREE,
                            cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    areaArray = []

    for i, c in enumerate(cnts):
        area = cv2.contourArea(c)
        areaArray.append(area)
    sorteddata = sorted(zip(areaArray, cnts), key=lambda x: x[0], reverse=True)

    thirdlargestcontour = sorteddata[2][1]
    x, y, w, h = cv2.boundingRect(thirdlargestcontour)
    cv2.drawContours(rgb, thirdlargestcontour, -1, (255, 0, 0), 2)

    cv2.rectangle(rgb, (x, y), (x + w, y + h), (0, 255, 0), 2)

    cv2.imshow("rgb", rgb)
    if cv2.waitKey(1) == 27:
        break

上面的代码有效，但是，

1. 它只给出包围灯的矩形。如何精确获取灯的四个角点？
2. 如何改进检测？目前我正在选择第三大轮廓，但不能保证它始终是灯，因为环境构成挑战？

ApproxPolydp 在轮廓完整时工作，但如果轮廓不完整，ApproxPolydp 不会返回正确的坐标。例如在下图中 approxpolydp 返回一个错误的坐标。

Answer 1

这是在 Python/OpenCV 中执行此操作的一种方法。

• 读取输入图像并转换为灰度
• 使用自适应阈值获取灯区域的粗轮廓
• 寻找轮廓
• 过滤区域上的轮廓以去除无关区域并仅保留两者中较大的一个（阈值区域的内部和外部轮廓）
• 获取边界
• 将周长拟合为多边形，该多边形应该是具有正确选择参数的四边形。
• 在输入图像的副本上绘制轮廓（红色）和多边形（蓝色）作为结果

输入：

import cv2
import numpy as np

# load image
img = cv2.imread("lamp.jpg")

# convert to gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# threshold image
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 10)
thresh = 255 - thresh

# find contours
cntrs = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cntrs = cntrs[0] if len(cntrs) == 2 else cntrs[1]

# Contour filtering -- remove small objects and those that are too large
# Keep the larger of the two contours (inner and outer contours from thresh)
area_thresh = 0
for c in cntrs:
    area = cv2.contourArea(c)
    if area > 200 and area > area_thresh:
        big_contour = c
        area_thresh = area

# draw big_contour on image in red and polygon in blue and print corners
results = img.copy()
cv2.drawContours(results,[big_contour],0,(0,0,255),1)
peri = cv2.arcLength(big_contour, True)
corners = cv2.approxPolyDP(big_contour, 0.04 * peri, True)
cv2.drawContours(results,[corners],0,(255,0,0),1)
print(len(corners))
print(corners)

# write result to disk
cv2.imwrite("lamp_thresh.jpg", thresh)
cv2.imwrite("lamp_corners.jpg", results)

cv2.imshow("THRESH", thresh)
cv2.imshow("RESULTS", results)
cv2.waitKey(0)
cv2.destroyAllWindows()

阈值图像：

结果图片：

角坐标：

[[[233 145]]

 [[219 346]]

 [[542 348]]

 [[508 153]]]