python - python、opencvで画像の行数を数える方法

Question

紙を数えたいので、線検出を使おうと思います。Canny、HoughLines、FLD などの方法をいくつか試しました。しかし、私は処理された写真しか得られません。それを数える方法がわかりません。必要な線である小さな線分がいくつかあります。len(lines)またはを使用しlen(contours)ました。しかし、結果は私の期待とはかけ離れています。結果は百か千です。それで、誰かが良い考えを持っていますか？

元の写真

processd by Cannyで処理 LSD で処理 HoughLinesP で処理

#Canny
samplename = "sam04.jpg"
img = cv2.imread('D:\\Users\\Administrator\\PycharmProjects\\EdgeDetect\\venv\\sample\\{}'.format(samplename),0)

edges = cv2.Canny(img,100,200)
cv2.imwrite('.\\detected\\{}'.format("p03_"+samplename),edges)
plt.subplot(121),plt.imshow(img,cmap = 'gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap = 'gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
plt.show()

#LSD
samplename = "sam09.jpg"

img0 = cv2.imread('D:\\Users\\Administrator\\PycharmProjects\\EdgeDetect\\venv\\sample\\{}'.format(samplename))

img = cv2.cvtColor(img0,cv2.COLOR_BGR2GRAY)


fld = cv2.ximgproc.createFastLineDetector()

dlines = fld.detect(img)


# drawn_img = fld.drawSegments(img0,dlines, )
for dline in dlines:
    x0 = int(round(dline[0][0]))
    y0 = int(round(dline[0][1]))
    x1 = int(round(dline[0][2]))
    y1 = int(round(dline[0][3]))
    cv2.line(img0, (x0, y0), (x1,y1), (0,255,0), 1, cv2.LINE_AA)


cv2.imwrite('.\\detected\\{}'.format("p12_"+samplename), img0)
cv2.imshow("LSD", img0)
cv2.waitKey(0)
cv2.destroyAllWindows()

#HoughLine
import cv2
import numpy as np
samplename = "sam09.jpg"

#First, get the gray image and process GaussianBlur.
img = cv2.imread('D:\\Users\\Administrator\\PycharmProjects\\EdgeDetect\\venv\\sample\\{}'.format(samplename))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)

#Second, process edge detection use Canny.
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)
cv2.imshow('photo2',edges)
cv2.waitKey(0)
#Then, use HoughLinesP to get the lines. You can adjust the parameters for better performance.

rho = 1  # distance resolution in pixels of the Hough grid
theta = np.pi / 180  # angular resolution in radians of the Hough grid
threshold = 15  # minimum number of votes (intersections in Hough grid cell)
min_line_length = 50  # minimum number of pixels making up a line
max_line_gap = 20  # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0  # creating a blank to draw lines on

# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
                    min_line_length, max_line_gap)
print(lines)
print(len(lines))
for line in lines:
    for x1,y1,x2,y2 in line:
        cv2.line(line_image,(x1,y1),(x2,y2 ),(255,0,0),5)

#Finally, draw the lines on your srcImage.
# Draw the lines on the  image
lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)
cv2.imshow('photo',lines_edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('.\\detected\\{}'.format("p14_"+samplename),lines_edges)