I am training my dataset with the below code:
for file in glob.glob('C:\*.png'):
image = cv2.imread(file, 1)
image = cv2.resize(img, (60, 120))
hog = cv2.HOGDescriptor((60,120), (8,8), (4,4), (4,4), 9)
hist = hog.compute(image)
samples.append(hist)
labels.append(-1)
I am using `hist = hog.compute(image)`. This code is in the training part, but when I do the prediction part:
hog = cv2.HOGDescriptor((60,120), (8,8), (4,4), (4,4), 9)
svm = cv2.ml.SVM_load('svm_data.xml')
sv = svm.getSupportVectors()
rho, alpha, svidx = svm.getDecisionFunction(0)
svm_new = np.append(sv, -rho)
hog.setSVMDetector(svm_new)
I am not using `hist = hog.compute(image)`, and my results are not as good. Do I need to use hog.compute in prediction part while using `Multiscale`?
found, w = hog.detectMultiScale(img,hitThreshold=0,winStride=(8,8),padding=(16,16), scale=1.05, finalThreshold = 2.0,useMeanshiftGrouping=False)
When I try to use it, it gives an error, and without it, I am not getting good results. Am I doing wrong in the training part or in the prediction part?
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