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Three questions about HOG detector

have following code and I have some questions about it: 1. What is detection window size for HOG detector and how does it change within program run? 2. What classificators are being used in OpenCV HOG detector? 3. Confusion matrix for HOG in OpenCV?

# detect people in the image and start counting time
start = datetime.datetime.now()
(rects, weights) = hog.detectMultiScale(image, winStride=winStride,
    padding=padding, scale=scale, useMeanshiftGrouping=meanShift)
#end time counting
print("[INFO] detection took: {}s".format(
    (datetime.datetime.now() - start).total_seconds()))

# draw the original bounding boxes
for (x, y, w, h) in rects:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

# show the output image
cv2.imshow("Detections", image)
cv2.waitKey(0)
print ("dws: ", dws)

Three questions about HOG detector

have following code and I have some questions about it: 1. What is detection window size for HOG detector and how does it change within program run? 2. What classificators are being used in OpenCV HOG detector? 3. Confusion matrix for HOG in OpenCV?

# detect people in the image and start counting time
start = datetime.datetime.now()
(rects, weights) = hog.detectMultiScale(image, winStride=winStride,
    padding=padding, scale=scale, useMeanshiftGrouping=meanShift)
#end time counting
print("[INFO] detection took: {}s".format(
    (datetime.datetime.now() - start).total_seconds()))

# draw the original bounding boxes
for (x, y, w, h) in rects:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

# show the output image
cv2.imshow("Detections", image)
cv2.waitKey(0)
print ("dws: ", dws)