您的函数仅返回沿轮廓的实际点，然后您尝试在其上调用plt.imshow。这就是您收到此错误的原因。你需要做的是使用cv2.drawContour 这个轮廓来得到你想要的。在这种情况下，我们应该重构您的 getContours 函数，以便它返回坐标（以便您以后可以使用它）和在图像本身上绘制的实际轮廓。与其改变imgContour 并将其视为全局变量，不如仅在此图像上绘制一次，这将是循环中找到的最大轮廓：
def getContours(img):
biggest = np.array([])
maxArea = 0
imgContour = img.copy() # Change - make a copy of the image to return
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
index = None
for i, cnt in enumerate(contours): # Change - also provide index
area = cv2.contourArea(cnt)
if area > 500:
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt,0.02*peri, True)
if area > maxArea and len(approx) == 4:
biggest = approx
maxArea = area
index = i # Also save index to contour
if index is not None: # Draw the biggest contour on the image
cv2.drawContours(imgContour, contours, index, (255, 0, 0), 3)
return biggest, imgContour # Change - also return drawn image
最后我们可以通过以下方式在你的整体代码中使用它：
import cv2
import numpy as np
from matplotlib import pyplot as plt
kernel = np.ones((3,3))
image = cv2.imread('NoPlate0.jpg')
imgGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(5,5),1)
imgCanny = cv2.Canny(imgBlur,150,200)
imgDial = cv2.dilate(imgCanny,kernel,iterations=2)
imgThres = cv2.erode(imgDial,kernel,iterations=2)
biggest, imgContour = getContours(imgThres) # Change
titles = ['original', 'Blur', 'Canny', 'Dilate', 'Threshold', 'Contours']
images = [image, imgBlur, imgCanny, imgDial, imgThres, imgContour] # Change
for i in range(6):
plt.subplot(3, 3, i+1), plt.imshow(images[i], 'gray')
plt.title(titles[i])
plt.show()
最后一点，如果您想扭曲车牌图像以使其与图像平面平行，您可以使用cv2.getPerspectiveTransform 定义从原始源图像（源点）到扭曲的单应性图像（目标点），然后使用cv2.warpPerspective 最终扭曲图像。请注意，源点和目标点的方式需要对它们进行排序，以便它们的对应位置在透视上匹配。也就是说，如果定义区域四边形的点集的第一个点是左上角，则源点和目标点都应该定义左上角。您可以通过找到源和目标的四边形的质心来做到这一点，然后找到从质心到每个角的角度，并通过对角度进行排序来对它们进行排序。
这是我编写的以下函数，称为order_points：
def order_points(pts):
# Step 1: Find centre of object
center = np.mean(pts)
# Step 2: Move coordinate system to centre of object
shifted = pts - center
# Step #3: Find angles subtended from centroid to each corner point
theta = np.arctan2(shifted[:, 0], shifted[:, 1])
# Step #4: Return vertices ordered by theta
ind = np.argsort(theta)
return pts[ind]
最后，用你返回的角点，尝试做：
src = np.squeeze(biggest).astype(np.float32) # Source points
height = image.shape[0]
width = image.shape[1]
# Destination points
dst = np.float32([[0, 0], [0, height - 1], [width - 1, 0], [width - 1, height - 1]])
# Order the points correctly
src = order_points(src)
dst = order_points(dst)
# Get the perspective transform
M = cv2.getPerspectiveTransform(src, dst)
# Warp the image
img_shape = (width, height)
warped = cv2.warpPerspective(img, M, img_shape, flags=cv2.INTER_LINEAR)
src 是包围车牌的源多边形的四个角。请注意，因为它们是从 cv2.approxPolyDP 返回的，它们将是一个 4 x 1 x 2 NumPy 整数数组。您将需要删除单例第二维并将它们转换为 32 位浮点，以便它们可以与 cv2.getPerspectiveTransform 一起使用。 dst 是目标点，源多边形中的每个角都映射到实际输出图像尺寸的角点，其大小与输入图像相同。最后要记住的一件事是，使用cv2.warpPerspective，您将图像的大小指定为(width, height)。
如果您最终想将所有这些整合在一起并让getContours 函数返回扭曲的图像，我们可以很容易地做到这一点。我们必须修改一些东西才能使其按预期工作：
-
getContours 还将获取原始 RGB 图像，以便我们可以正确地可视化轮廓并更好地了解车牌是如何定位的。
- 添加逻辑以扭曲
getContours 中的图像，如上所示。
- 更改绘图代码以包含此变形图像，并从
getContours 返回变形图像。
- 稍微修改绘图代码以在 Matplotlib 中显示原始图像，因为
cv2.imread 读取 BGR 格式的图像，但 Matplotlib 期望图像为 RGB 格式。
因此：
import cv2
import numpy as np
from matplotlib import pyplot as plt
def order_points(pts):
# Step 1: Find centre of object
center = np.mean(pts)
# Step 2: Move coordinate system to centre of object
shifted = pts - center
# Step #3: Find angles subtended from centroid to each corner point
theta = np.arctan2(shifted[:, 0], shifted[:, 1])
# Step #4: Return vertices ordered by theta
ind = np.argsort(theta)
return pts[ind]
def getContours(img, orig): # Change - pass the original image too
biggest = np.array([])
maxArea = 0
imgContour = orig.copy() # Make a copy of the original image to return
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
index = None
for i, cnt in enumerate(contours): # Change - also provide index
area = cv2.contourArea(cnt)
if area > 500:
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt,0.02*peri, True)
if area > maxArea and len(approx) == 4:
biggest = approx
maxArea = area
index = i # Also save index to contour
warped = None # Stores the warped license plate image
if index is not None: # Draw the biggest contour on the image
cv2.drawContours(imgContour, contours, index, (255, 0, 0), 3)
src = np.squeeze(biggest).astype(np.float32) # Source points
height = image.shape[0]
width = image.shape[1]
# Destination points
dst = np.float32([[0, 0], [0, height - 1], [width - 1, 0], [width - 1, height - 1]])
# Order the points correctly
biggest = order_points(src)
dst = order_points(dst)
# Get the perspective transform
M = cv2.getPerspectiveTransform(src, dst)
# Warp the image
img_shape = (width, height)
warped = cv2.warpPerspective(orig, M, img_shape, flags=cv2.INTER_LINEAR)
return biggest, imgContour, warped # Change - also return drawn image
kernel = np.ones((3,3))
image = cv2.imread('NoPlate0.jpg')
imgGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(5,5),1)
imgCanny = cv2.Canny(imgBlur,150,200)
imgDial = cv2.dilate(imgCanny,kernel,iterations=2)
imgThres = cv2.erode(imgDial,kernel,iterations=2)
biggest, imgContour, warped = getContours(imgThres, image) # Change
titles = ['Original', 'Blur', 'Canny', 'Dilate', 'Threshold', 'Contours', 'Warped'] # Change - also show warped image
images = [image[...,::-1], imgBlur, imgCanny, imgDial, imgThres, imgContour, warped] # Change
# Change - Also show contour drawn image + warped image
for i in range(5):
plt.subplot(3, 3, i+1)
plt.imshow(images[i], cmap='gray')
plt.title(titles[i])
plt.subplot(3, 3, 6)
plt.imshow(images[-2])
plt.title(titles[-2])
plt.subplot(3, 3, 8)
plt.imshow(images[-1])
plt.title(titles[-1])
plt.show()
我现在得到的图是：