[pedestrianDetection] HOG to SVM with autoscaler in OpenCV python

detect.py

from __future__ import print_function
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import imutils
import argparse
import cv2

ap = argparse.ArgumentParser()
ap.add_argument('-i', "--images", required=True, help="path to images directory")
args = vars(ap.parse_args())

# initialize the HOG descriptor (person detector)
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

#loop over the image paths
for imagePath in paths.list_images(args["images"]):
    # load the image and resize it to reduce detection time and improve accrcy
    image = cv2.imread(imagePath)
    image = imutils.resize(image, width=min(400, image.shape[1]))
    orig = image.copy()
    

    #detect people
    (rects, weights) = hog.detectMultiScale(image, winStride=(4,4),
                                           padding=(8,8),scale=1.05)
    #draw the original bounding boxes
    for (x, y, w, h) in rects:
        cv2.rectangle(orig,(x,y),(x + w, y + h), (0,0,255), 2)

    #apply non-maxima suppresssion to the bounding boxes using
    #pretty large overlap threshold to try to maintain overlapping people boxs
    rects = np.array([[x,y, x + w, y + h] for (x, y, w, h) in rects])
    pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)

    #draw the final bounding boxes
    for (xA, yA, xB, yB) in pick:
        cv2.rectangle(image, (xA, yA), (xB, yB), (0,255,0),2)


    #show some information on the number of bounding box
        filename = imagePath[imagePath.rfind("/") + 1:]
        print("[INFO]{}: {} original boxes, {} after suppression".format(
            filename, len(rects), len(pick)))

    #show the different frames
    cv2.imshow("Before NMS",orig)
    cv2.imshow("After NMS",image)
    cv2.waitKey(0)