opencv knn TypeError：只有长度为1的数组可以转换为Python标量

Question

我正在尝试按照教程http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_knn/py_knn_opencv/py_knn_opencv.html 并用cv2.m1.KNearest_create() 替换KNearest，但我得到TypeError: only length-1 arrays can be converted to Python scalars

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('digits.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# Now we split the image to 5000 cells, each 20x20 size
cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]

# Make it into a Numpy array. It size will be (50,100,20,20)
x = np.array(cells)

# Now we prepare train_data and test_data.
train = x[:,:50].reshape(-1,400).astype(np.float32) # Size = (2500,400)
test = x[:,50:100].reshape(-1,400).astype(np.float32) # Size = (2500,400)

# Create labels for train and test data
k = np.arange(10)
train_labels = np.repeat(k,250)[:,np.newaxis]
test_labels = train_labels.copy()

# Initiate kNN, train the data, then test it with test data for k=1
cv2.m1.KNearest_create()
knn.train(train,train_labels)
ret,result,neighbours,dist = knn.find_nearest(test,k=5)

# Now we check the accuracy of classification
# For that, compare the result with test_labels and check which are wrong
matches = result==test_labels
correct = np.count_nonzero(matches)
accuracy = correct*100.0/result.size
print accuracy

（我正在使用树莓派并按照本教程安装 open cv http://www.pyimagesearch.com/2015/10/26/how-to-install-opencv-3-on-raspbian-jessie/ 随后我 pip 安装了 matplotlib）

Answer 1

参数 cv2.ml.ROW_SAMPLE 丢失，将 knn.find_nearest(test,k=5) 改成下面的代码。这是 openCv3 中新增的，请参考 openCv 官网http://docs.opencv.org/3.0.0/dd/de1/classcv_1_1ml_1_1KNearest.html

    ` knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
      ret, result, neighbours, dist = knn.findNearest(test, k=5)`

Answer 2

您只是缺少一个参数，但我注意到很多人对本教程的这一部分有疑问，所以这里是调整后的整个最后一部分以使用 python3 和现代 openCV 库。

knn = cv2.ml.KNearest_create()
knn.train(trainData, cv2.ml.ROW_SAMPLE, responses)
ret, results, neighbours, dist = knn.findNearest(newcomer, k=5)

print("result: ", results,"\n")
print("neighbours: ", neighbours,"\n")
print("distance: ", dist)

plt.show()

Answer 3

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('digits.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# Now we split the image to 5000 cells, each 20x20 size
cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]

# Make it into a Numpy array. It size will be (50,100,20,20)
x = np.array(cells)

# Now we prepare train_data and test_data.
train = x[:,:50].reshape(-1,400).astype(np.float32) # Size = (2500,400)
test = x[:,50:100].reshape(-1,400).astype(np.float32) # Size = (2500,400)

# Create labels for train and test data
k = np.arange(10)
train_labels = np.repeat(k,250)[:,np.newaxis]
test_labels = train_labels.copy()

# Initiate kNN, train the data, then test it with test data for k=1
knn = cv2.ml.KNearest_create()
knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
ret, results, neighbours, dist = knn.findNearest(test, k=5)

#print("result: ", results,"\n")
#print("neighbours: ", neighbours,"\n")
#print("distance: ", dist)
matches = result=test_labels
correct = np.count_nonzero(matches)
accuracy = correct*100.0/result.size
print(accuracy)

Answer 4

opencv 的文档说：

findNearest(...) |找到最近的（样本，k[，结果[， 邻居响应 [, dist]]]) -> retval，结果，邻居响应， ...

---

不是knn.find_nearest(test,k=5)

你可以运行

help(cv2.ml.KNearest_create())

然后你会看到。 顺便说一下，opencv 网站上有 loss 错误