hog函数的用法 python_基于HOG描述子和支持向量机的python-cod行人检测

我试图理解python中使用HOG和SVM进行行人检测的代码，并用FPGA来加速。在

下面的代码工作精细复制自一个网站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