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I am facing problem in the hog.setSVMDetector(). I get the following error:

OpenCV Error: Assertion failed (checkDetectorSize()) in setSVMDetector

I have searched for this problem, and found out the winsize in the input arguments must be the same. I have used the same parameters for both training and testing. However I still get the same error.

Here is the code below:

####TESITNG

hog = cv2.HOGDescriptor((64,64), (16,16), (8,8), (8,8), 9) svm = cv2.SVM() svm.load('trained.xml')

img = cv2.imread('t_.png', cv2.IMREAD_GRAYSCALE) h = hog.compute(img) p = svm.predict(h) print p

model = pickle.load(open("svm.pickle")) hog.setSVMDetector(np.array(model))

######## HOG FEATURES COMPUTING METHOD

train_list = [] response_list = []

hog = cv2.HOGDescriptor((64,64), (16,16), (8,8), (8,8), 9) hog = cv2.HOGDescriptor() for i in range(2,23): img = cv2.imread('/home/taseer/Python/Image_Processing/Project/resized/' + str(i) + "_.png", cv2.IMREAD_COLOR) h = hog.compute(img) h = h.ravel() train_list.append(h)

if i > 7:
    response_list.append(1)
else:
    response_list.append(-1)

model = cv2.SVM() model.train(np.array(train_list), np.array(response_list)) model.save('trained.xml')

I also found that the winsize must be equal to the size of images. I have resized all my images to (150,150).

for i in range(1,17): img = Image.open('/home/taseer/Python/Image_Processing/Project/positive/pos/' + str(i) + ".jpg") r = img.resize((150,150)) r.save('/home/taseer/Python/Image_Processing/Project/resized/' + str(i+7) + '_.png')

I am facing problem in the hog.setSVMDetector(). I get the following error:

OpenCV Error: Assertion failed (checkDetectorSize()) in setSVMDetector
setSVMDetector

hog = cv2.HOGDescriptor((64,64), (16,16), (8,8), (8,8), 9)
svm = cv2.SVM()
svm.load('trained.xml')
 
svm.load('trained.xml')
img = cv2.imread('t_.png', cv2.IMREAD_GRAYSCALE)
h = hog.compute(img)
p = svm.predict(h)
print p
 
p
model = pickle.load(open("svm.pickle"))
hog.setSVMDetector(np.array(model))
 
######## hog.setSVMDetector(np.array(model))

train_list = []
response_list = []
 
[]
hog = cv2.HOGDescriptor((64,64), (16,16), (8,8), (8,8), 9)
hog = cv2.HOGDescriptor()
for i in range(2,23):
img = cv2.imread('/home/taseer/Python/Image_Processing/Project/resized/' + str(i) + "_.png", cv2.IMREAD_COLOR)
h = hog.compute(img)
h = h.ravel()
train_list.append(h)
 
train_list.append(h)
if i > 7:
 response_list.append(1)
 else:
 response_list.append(-1)
 
 model = cv2.SVM()
model.train(np.array(train_list), np.array(response_list))
model.save('trained.xml')
 
model.save('trained.xml')

for i in range(1,17):
img = Image.open('/home/taseer/Python/Image_Processing/Project/positive/pos/' + str(i) + ".jpg")
r = img.resize((150,150))
r.save('/home/taseer/Python/Image_Processing/Project/resized/' + str(i+7) + '_.png')
'_.png')