无法训练线性SVM机器

Question

我正在为我的图像处理项目构建SVM线性机器，我正在提取正面和负面样品的功能并将其保存到目录中。然后我用这些功能训练SVM，但是我收到了一个我无法调试的错误。 下面是我train-classifier.py文件训练分类 -

from skimage.feature import local_binary_pattern 
from sklearn.svm import LinearSVC 
from sklearn.linear_model import LogisticRegression 
from sklearn.externals import joblib 
import argparse as ap 
import glob 
import os 
from config import * 

if __name__ == "__main__": 
    # Parse the command line arguments 
    parser = ap.ArgumentParser() 
    parser.add_argument('-p', "--posfeat", help="Path to the positive features directory", required=True) 
    parser.add_argument('-n', "--negfeat", help="Path to the negative features directory", required=True) 
    parser.add_argument('-c', "--classifier", help="Classifier to be used", default="LIN_SVM") 
    args = vars(parser.parse_args()) 

    pos_feat_path = args["posfeat"] 
    neg_feat_path = args["negfeat"] 

    # Classifiers supported 
    clf_type = args['classifier'] 

    fds = [] 
    labels = [] 
    # Load the positive features 
    for feat_path in glob.glob(os.path.join(pos_feat_path,"*.feat")): 
     fd = joblib.load(feat_path) 
     fds.append(fd) 
     labels.append(1) 

    # Load the negative features 
    for feat_path in glob.glob(os.path.join(neg_feat_path,"*.feat")): 
     fd = joblib.load(feat_path) 
     fds.append(fd) 
     labels.append(0) 

    if clf_type is "LIN_SVM": 
     clf = LinearSVC() 
     print "Training a Linear SVM Classifier" 
     clf.fit(fds, labels) 
     # If feature directories don't exist, create them 
     if not os.path.isdir(os.path.split(model_path)[0]): 
      os.makedirs(os.path.split(model_path)[0]) 
     joblib.dump(clf, model_path) 
     print "Classifier saved to {}".format(model_path) 

我得到一个错误在该行clf.fit（FDS，标签）以下是它说 -

Calculating the descriptors for the positive samples and saving them 
Positive features saved in ../data/features/pos 
Calculating the descriptors for the negative samples and saving them 
Negative features saved in ../data/features/neg 
Completed calculating features from training images 
Training a Linear SVM Classifier 
Traceback (most recent call last): 
    File "../object-detector/train-classifier.py", line 42, in <module> 
    clf.fit(fds, labels) 
    File "/usr/local/lib/python2.7/dist-packages/sklearn/svm/classes.py", line 200, in fit 
    dtype=np.float64, order="C") 
    File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 444, in check_X_y 
    ensure_min_features) 
    File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 344, in check_array 
    array = np.array(array, dtype=dtype, order=order, copy=copy) 
ValueError: setting an array element with a sequence. 
Traceback (most recent call last): 
    File "../object-detector/test-classifier.py", line 68, in <module> 
    fd = hog(im_window, orientations, pixels_per_cell, cells_per_block, visualize, normalize) 
    File "/usr/lib/python2.7/dist-packages/skimage/feature/_hog.py", line 63, in hog 
    raise ValueError("Currently only supports grey-level images") 
ValueError: Currently only supports grey-level images 

Answer 1

您可以使用SVM类的OpenCV而不是scikit的。它很容易使用。

import cv2 

# prepare your test and train datasets 

svm = cv2.SVM() 
svm.train(some_train_data, responses, params) 

exp = svm.predict(some_test_data) 

欲了解更多信息，请OpenCV docs。

Answer 2

我假定代码来自https://github.com/bikz05/object-detector。您需要确保训练样本（pos和neg）具有相同的大小（widthxheight）并且是灰色图像。你的测试图像也应该是灰色的。

我使用ImageMagick的convert命令此：

转换sample.png调整大小100x40 -colorspace灰色sample.png

更新（使用Python转换为灰度图像和调整）：

import cv2 

img = cv2.imread('color_image.jpg',0) 
im = cv2.resize(img, (100,40), interpolation=cv2.INTER_CUBIC) 
cv2.imwrite("gray_image.jpg", im)