Accelerating HOG algorithm with Pynq


I am using Xfopencv library in Vivado HLS to accelerate the opencv detection algorithm of HOG + SVM.

Looking into the software code (python):

hog = cv2.HOGDescriptor()

def detector(image):
rects, weights = hog.detectMultiScale(image, winStride=(4, 4), padding=(8, 8),scale=1.05)
for (x, y, w, h) in rects:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
result = non_max_suppression(rects, probs=None, overlapThresh=0.7)
return result

frame = cv2.imread(“/…/pedestrian2.jpg”)
result = detector(frame.copy())
for (xA, yA, xB, yB) in result: # draw the final bounding boxes after non-maxima supression
cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img_out = PIL.Image.fromarray(img)

I don´t understand exactly what is the output of xf::HOGDescriptor, and how could I link with the software code. Is this function equivalent to “hog = cv2.HOGDescriptor()”? I don´t guess so. As far as I know, xf::HOGDescriptor operate with the image pixels, and cv2.HOGDescriptor() doesn´t.

Anyone has worked with this algorithm that can please guide me? Thanks