Separate Positive and Negative Samples for SVM Custom Object Detector - python-2.7

Question

I am trying to train a Custom Object Detector by using the HOG+SVM method on OpenCV.
I have managed to extract HOG features from my positive and negative samples using the below line of code:
import cv2
hog = cv2.HOGDescriptor()
def poshoggify():
for i in range(1,20):
image = cv2.imread("/Users/munirmalik/cvprojek/cod/pos/" + str(i)+ ".jpg")
(winW, winH) = (500, 500)
for resized in pyramid(image, scale=1.5):
# loop over the sliding window for each layer of the pyramid
for (x, y, window) in sliding_window(resized, stepSize=32, windowSize=(winW, winH)):
# if the window does not meet our desired window size, ignore it
if window.shape[0] != winH or window.shape[1] != winW:
continue
img_pos = hog.compute(image)
np.savetxt('posdata.txt',img_pos)
return img_pos
And the equivalent function for the negative samples.
How do I format the data in such a way that the SVM knows which is positive and which is negative?
Furthermore, how do I translate this training to the "test" of detecting the desired objects through my webcam?

Answer 1

How do I format the data in such a way that the SVM knows which is positive and which is negative?
You would now create another list called labels which would store the class value associated with a corresponding image. For example, if you have a training set of features that looks like this:
features = [pos_features1, pos_features2, neg_features1, neg_features2, neg_features3, neg_features4]
you would have a corresponding labels class like
labels = [1, 1, 0, 0, 0, 0]
You would then feed this to a classifier like so:
clf=LinearSVC(C=1.0, class_weight='balanced')
clf.fit(features,labels)
Furthermore, how do I translate this training to the "test" of detecting the desired objects through my webcam?
Before training, you should have split your labelled dataset (groundtruth) into training and testing datasets. You can do this using skilearns KFold module.