SVM classifier based on HOG features for object detection in OpenCV
In order to detect arbitrary objects with using opencv HOG descriptors and SVM classifier, you need to first train the classifier. Playing with the parameters will not help here, sorry :( .
In broad terms, you will need to complete the following steps:
Step 1) Prepare some training images of the objects you want to detect (positive samples). Also you will need to prepare some images with no objects of interest (negative samples).
Step 2) Detect HOG features of the training sample and use this features to train an SVM classifier (also provided in OpenCV).
Step 3) Use the coefficients of the trained SVM classifier in HOGDescriptor::setSVMDetector() method.
Only then, you can use the peopledetector.cpp sample code, to detect the objects you want to detect.
Apply HOG+SVM Training to Webcam for Object Detection
You already have three of the most important pieces available at your disposal. hoggify
creates a list of HOG descriptors - one for each image. Note that the expected input for computing the descriptor is a grayscale image and the descriptor is returned as a 2D array with 1 column which means that each element in the HOG descriptor has its own row. However, you are using np.squeeze
to remove the singleton column and replacing it with a 1D numpy array instead, so we're fine here. You would then use list_to_matrix
to convert the list into a numpy
array. Once you do this, you can use svmClassify
to finally train your data. This assumes that you already have your labels
in a 1D numpy
array. After you train your SVM, you would use the SVC.predict
method where given input HOG features, it would classify whether the image belonged to a chair or not.
Therefore, the steps you need to do are:
Use
hoggify
to create your list of HOG descriptors, one per image. It looks like the inputx
is a prefix to whatever you called your chair images as, whilez
denotes the total number of images you want to load in. Remember thatrange
is exclusive of the ending value, so you may want to add a+ 1
afterint(z)
(i.e.int(z) + 1
) to ensure that you include the end. I'm not sure if this is the case, but I wanted to throw it out there.x = '...' # Whatever prefix you called your chairs
z = 100 # Load in 100 images for example
lst = hoggify(x, z)Convert the list of HOG descriptors into an actual matrix:
data = list_to_matrix(lst)
Train your SVM classifier. Assuming you already have your labels stored in
labels
where a value0
denotes not a chair and1
denotes a chair and it is a 1Dnumpy
array:labels = ... # Define labels here as a numpy array
clf = svmClassify(data, labels)Use your SVM classifer to perform predictions. Assuming you have a test image you want to test with your classifier, you will need to do the same processing steps like you did with your training data. I'm assuming that's what
hoggify
does where you can specify a differentx
to denote different sets to use. Specify a new variablextest
to specify this different directory or prefix, as well as the number of images you need, then usehoggify
combined withlist_to_matrix
to get your features:xtest = '...' # Define new test prefix here
ztest = 50 # 50 test images
lst_test = hoggify(xtest, ztest)
test_data = list_to_matrix(lst_test)
pred = clf.predict(test_data)pred
will contain an array of predicted labels, one for each test image that you have. If you want, you can see how well your SVM did with the training data, so since you have this already at your disposal, just usedata
again from step #2:pred_training = clf.predict(data)
pred_training
will contain an array of predicted labels, one for each training image.
If you ultimately want to use this with a webcam, the process would be to use a VideoCapture
object and specify the ID of the device that is connected to your computer. Usually there's only one webcam connected to your computer, so use the ID of 0. Once you do this, the process would be to use a loop, grab a frame, convert it to grayscale as HOG descriptors require a grayscale image, compute the descriptor, then classify the image.
Something like this would work, assuming that you've already trained your model and you've created a HOG descriptor object from before:
cap = cv2.VideoCapture(0)
dim = 128 # For HOG
while True:
# Capture the frame
ret, frame = cap.read()
# Show the image on the screen
cv2.imshow('Webcam', frame)
# Convert the image to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Convert the image into a HOG descriptor
gray = cv2.resize(gray, (dim, dim), interpolation = cv2.INTER_AREA)
features = hog.compute(gray)
features = features.T # Transpose so that the feature is in a single row
# Predict the label
pred = clf.predict(features)
# Show the label on the screen
print("The label of the image is: " + str(pred))
# Pause for 25 ms and keep going until you push q on the keyboard
if cv2.waitKey(25) == ord('q'):
break
cap.release() # Release the camera resource
cv2.destroyAllWindows() # Close the image window
The above process reads in an image, displays it on the screen, converts the image into grayscale so we can compute its HOG descriptor, ensures that the data is in a single row compatible for the SVM you trained and we then predict its label. We print this to the screen, and we wait for 25 ms before we read in the next frame so we don't overload your CPU. Also, you can quit the program at any time by pushing the q key on your keyboard. Otherwise, this program will loop forever. Once we finish, we release the camera resource back to the computer so that it can be made available for other processes.
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