Question

这是我发现here的修改后的代码。

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.
train = x[:,:50].reshape(-1,400).astype(np.float32) # Size = (2500,400)


img = cv2.imread('1.png')
img1 = cv2.imread('2.png')
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
img = cv2.resize(img, (20,20)).astype(np.float32)
img1 = cv2.resize(img1, (20,20)).astype(np.float32)
img = img.flatten()
img1 = img1.flatten()
arr = [img,img1]
arr = np.asarray(arr)
# Create labels for train and test data
k = np.arange(10)
train_labels = np.repeat(k,250)[:,np.newaxis]

# Initiate kNN, train the data, then test it with test data for k=1

knn = cv2.ml.KNearest_create()
knn.train(train, 0,train_labels)
ret, result, neighbours, dist = knn.findNearest(arr, k=5)
for i in result:
    print i
# save the data
np.savez('knn_data.npz',train=train, train_labels=train_labels)

# Now load the data
with np.load('knn_data.npz') as data:
    print data.files
    train = data['train']
    train_labels = data['train_labels']

完美无缺。但我无法弄清楚如何使用这个knn_data.npz文件。这是我的尝试：

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

with np.load('knn_data.npz') as data:
    print data.files
    train = data['train']
    train_labels = data['train_labels']

    img = cv2.imread('1.png')
    img1 = cv2.imread('2.png')

    img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    img1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)

    img = cv2.resize(img, (20,20)).astype(np.float32)
    img1 = cv2.resize(img1, (20,20)).astype(np.float32)

    img = img.flatten()
    img1 = img1.flatten()

    arr = [img,img1]
    arr = np.asarray(arr)

    knn = cv2.ml.KNearest_create()
    ret, result, neighbours, dist = knn.findNearest(arr, k=5)
    for i in result:
        print i

我收到的错误消息，我无法修复：

OpenCV错误：在findNearest，文件/io/opencv/modules/ml/src/knearest.cpp中，断言失败（test_samples.type（）== 5＆amp;＆amp; test_samples.cols == samples.cols） 325

追踪（最近一次通话）：     文件＆＃34; knn1.py＆＃34;，第20行，in       ret，result，neighbors，dist = knn.findNearest（img，k = 5）   cv2.error：/io/opencv/modules/ml/src/knearest.cpp:325：错误：（-215）test_samples.type（）== 5＆amp;＆amp;函数findNearest
中的test_samples.cols == samples.cols

我在opencv 3.2.0的{{1}}上使用python 2.7.15。文件 1.png 和 2.png 是RGB图像文件。

Answer 1

在您的示例中，您创建变量train和train_labels但从不使用它们。

在致电knn.findNearest(arr, k=5)之前在任何地方添加以下内容：

train = data['train']
train_labels = data['train_labels']
knn = cv2.ml.KNearest_create()
knn.train(train, 0,train_labels)

实现knn的以下代码有什么问题？

1 个答案: