使用HOG描述符和SVM的行人检测了解python代码

Question

I am trying to understand the code in python of pedestrian detection with HOG and SVM to accelerate it with an FPGA.

Below the code working fine copied from a website

hog = cv2.HOGDescriptor()                              
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

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)
img_out

Following the tutorial https://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial/ I understand that the main function is hog.compute(im,descriptor) which compute the HOG features of an image, but where is this function on the first code? Is it inside of one of the functions?

Answer 1

While "explain this code" is a bit too broad for this site, the narrow question of "where is this function on the first code": it's not. It is, however, discussed in the tutorial you've linked.

At the beginning of the posted code, you instantiate the cv2.HOGDescriptor() object as hog . Once created, the object has all of the bound attributes and methods of the cv2.HOGDescriptor() class, including the .compute() method, once you invoke it.

There's some discussion in this question about the basic usage and here is a link to some of the basic documentation for the HOGDescriptor` class