pedestrian.py

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

# construct the argument parse and parse the arguments
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())

for imagePath in paths.list_images(args["images"]):
    # load the image and resize it to reduce detection time & improve  accuracy
    image = cv2.imread(imagePath)
    image = imutils.resize(image, width=min(400, image.shape[1]))
    orig = image.copy()
    (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),padding=(8, 8), scale=1.05)
    for (x, y, w, h) in rects: # draw the original bounding boxes
        cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
        # apply non-maxima suppression to the bounding boxes using a
        # fairly large overlap threshold to try to maintain overlapping
        # boxes that are still people
        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 boxes
        filename = imagePath[imagePath.rfind("/") + 1:]
        print(f'[INFO] {filename}:original boxes, len{rects} after suppression{len(pick)}')

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