通过python opencv找到齿轮齿

Question

我正在学习OpenCv。我有一个斜齿轮图像来寻找牙齿。

直到现在我已经试图找到轮廓，然后计算牙齿。我能够找到轮廓也是轮廓的坐标。但我坚持要数牙。因为我是OpenCV的新手可能是我试图找到牙齿不正确的方式。

我的守则

import cv2
import numpy as np
import scipy as sp
import imutils
from skimage.morphology import reconstruction

import csv

raw_image = cv2.imread('./Gear Image/new1.jpg')
#cv2.imshow('Original Image', raw_image)
#cv2.waitKey(0)

bilateral_filtered_image = cv2.bilateralFilter(raw_image, 5, 175, 175)
#cv2.imshow('Bilateral', bilateral_filtered_image)
#cv2.waitKey(0)

edge_detected_image = cv2.Canny(bilateral_filtered_image, 75, 200)
#cv2.imshow('Edge', edge_detected_image)
#cv2.waitKey(0)



contours, hierarchy = cv2.findContours(edge_detected_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)





contour_list = []
for contour in contours:
    approx = cv2.approxPolyDP(contour,0.01*cv2.arcLength(contour,True),True)
    area = cv2.contourArea(contour)
    if ((len(approx) > 5) & (len(approx) < 25) & (area > 50) ):
        contour_list.append(contour)



cv2.drawContours(raw_image, contour_list,  -1, (255,0,0), 2)


c = max(contours, key = cv2.contourArea)
M = cv2.moments(c)

cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])

cv2.circle(raw_image, (cX, cY), 5, (142, 152, 100), -1)
cv2.putText(raw_image, "centroid", (cX - 25, cY - 25),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (142, 152, 100), 2)

contour_length = "Number of contours detected: {}".format(len(contours))
cv2.putText(raw_image,contour_length , (20,40),  cv2.FONT_HERSHEY_SIMPLEX, 0.5, (142, 152, 100), 2)

for c in range(len(contours)):
        n_contour = contours[c]
        for d in range(len(n_contour)):
            XY_Coordinates = n_contour[d]


print(len(coordinates))
print(XY_Coordinates)
print(type(XY_Coordinates))
print(XY_Coordinates[0,[0]])
print(XY_Coordinates[0,[1]])



cv2.imshow('Objects Detected',raw_image)
cv2.waitKey(0)

输入图片：

我得到的输出图像：

在这个阶段之后，我该如何计算牙齿？我可以使用坐标来计算间隔并计算牙齿。

或者在这个阶段之后还有其他计算牙齿的方法吗？谢谢你的进步。

Answer 1

我的解决方案的第一部分类似于@HansHirse发布的答案，但我使用了一种不同的方法来计算牙齿。我的完整代码可以在这里找到：link to full code for python3 opencv4。在继续之前检查齿轮的外轮廓是否被正确检测。如果未正确检测到齿轮，则其余部分将无效。

在计算牙齿之前，我'打开'齿轮。我通过扫描齿轮并计算从齿轮中心到齿轮外侧的距离来做到这一点。

这是我用来扫过齿轮并找到从齿轮中心到齿轮外部的距离的代码：

# Start at angle 0, and increment the angle 1/200 rad
angle = 0
increment = 1/200
# Create a list for the distances from the centroid to the edge of the gear tooth
distances = []
# Create an image for display purposes
display_image = raw_image.copy()
# Sweep around the circle (until one full revolution)
while angle < 2*math.pi:
    # Compute a ray from the center of the circle with the current angle
    img_size = max(raw_image.shape)
    ray_end = int(math.sin(angle) * img_size + cX), int(math.cos(angle) * img_size + cY)
    center = cX, cY
    # Create mask
    mask = np.zeros((raw_image.shape[0], raw_image.shape[1]), np.uint8)
    # Draw a line on the mask
    cv2.line(mask, center, ray_end, 255, 2)
    # Mask out the gear slice (this is the portion of the gear the us below the line)
    gear_slice = cv2.bitwise_and(raw_image, raw_image, mask = mask)
    # Threshold the image
    _, thresh = cv2.threshold(cv2.cvtColor(gear_slice, cv2.COLOR_BGR2GRAY), 0 , 255, 0)
    # Find the contours in the edge_slice
    _, edge_slice_contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # Get the center of the edge slice contours
    M = cv2.moments(max(edge_slice_contours, key = cv2.contourArea))
    edge_location = int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])
    cv2.circle(display_image, edge_location, 0, (0,255,0), 4)
    # Find the distance from the center of the gear to the edge of the gear...at this specific angle
    edge_center_distance = distance(center, edge_location)
    # Find the xy coordinates for this point on the graph - draw blue circle
    graph_point = int(angle*0.5*raw_image.shape[1]/math.pi), int(edge_center_distance+ 1.5*gear_radius)
    cv2.circle(display_image, graph_point, 0, (0,255,0), 2)    
    # Add this distance to the list of distances
    distances.append(-edge_center_distance)
    # Create a temporary image and draw the ray on it
    temp = display_image.copy()
    cv2.line(temp, ray_end, (cX,cY), (0,0,255), 2)
    # Show the image and wait
    cv2.imshow('raw_image', temp)
    vid_writer.write(temp)
    k = cv2.waitKey(1)
    if k == 27: break
    # Increment the angle
    angle += increment
# Clean up
cv2.destroyAllWindows()

其结果是距离齿轮中心的齿距作为角度的函数。

import matplotlib.pyplot as plt
plt.plot(distances)
plt.show()

现在，计算牙齿要容易得多，因为它们是函数中的峰值（或者在这种情况下是山谷 - 后面会更多）。为了计算峰值，我采用了齿距函数的Fourier transform。

import scipy.fftpack
# Calculate the Fourier transform
yf = scipy.fftpack.fft(distances)
fig, ax = plt.subplots()
# Plot the relevant part of the Fourier transform (a gear will have between 2 and 200 teeth)
ax.plot(yf[2:200])
plt.show()

傅立叶变换的峰值出现在37处。因此，有37个山谷和38个齿轮齿。

num_teeth = list(yf).index(max(yf[2:200])) - 1
print('Number of teeth in this gear: ' + str(num_teeth))

Answer 2

也许以下解决方案适合您。

• 我在双边滤波后添加了一些轻微的中值模糊，以改善后续边缘检测（边缘较小）。
• 在findContours，我从RETR_TREE切换到RETR_EXTERNAL，只获得最外面的轮廓。
• 为此，我确定了轮廓的凸包，并确保每个齿只有一个凸包壳点。
• 这些“稀疏”凸包船点的最终数量是齿数。

（我删除了一些不必要的代码以保持答案简短。）

import cv2
import numpy as np

raw_image = cv2.imread('images/vChAL.jpg')

bilateral_filtered_image = cv2.bilateralFilter(raw_image, 5, 175, 175)

# Added median blurring to improve edge detection
median_blurred_images = cv2.medianBlur(bilateral_filtered_image, 5)

edge_detected_image = cv2.Canny(median_blurred_images, 75, 200)

# Switched from RETR_TREE to RETR_EXTERNAL to only extract most outer contours
contours, _ = cv2.findContours(edge_detected_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

contour_list = []
for contour in contours:
    approx = cv2.approxPolyDP(contour,0.01*cv2.arcLength(contour,True),True)
    area = cv2.contourArea(contour)
    if ((len(approx) > 5) & (len(approx) < 25) & (area > 50) ):
        contour_list.append(contour)

cv2.drawContours(raw_image, contour_list, -1, (255, 0, 0), 2)

c = max(contours, key = cv2.contourArea)

contour_length = "Number of contours detected: {}".format(len(contours))
cv2.putText(raw_image,contour_length , (20, 40),  cv2.FONT_HERSHEY_SIMPLEX, 0.5, (142, 152, 100), 2)

# Determine convex hull of largest contour
hull = cv2.convexHull(c, clockwise = True, returnPoints = False)

# Debug: Draw "raw" convex hull points (green)
cv2.drawContours(raw_image, c[hull], -1, (0, 255, 0), 3)

# Determine convex hull, such that nearby convex hull points are "grouped"
sparsehull = []
for idx in hull:
    if (len(sparsehull) == 0):
        sparsehull.append(idx)
    else:
        last = sparsehull[-1]
        diff = c[idx] - c[last]
        if (cv2.norm(diff) > 40):
            sparsehull.append(idx)
sparsehull = np.asarray(sparsehull)

# Debug: Draw "sparse2 convex hull points (red)
cv2.drawContours(raw_image, c[sparsehull], -1, (0, 0, 255), 3)

# Additional output on image
teeth_length = "Number of teeth detected: {}".format(len(sparsehull))
cv2.putText(raw_image, teeth_length , (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (142, 152, 100), 2)

cv2.imshow('Objects Detected', raw_image)
cv2.waitKey(0)

免责声明：我是Python的新手，特别是OpenCV的Python API（获胜的C ++）。非常欢迎评论，改进，突出Python no-gods！