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@w-garcia
Created February 16, 2016 04:00
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# import the necessary packages
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())
video_capture = cv2.VideoCapture(0)
count = 0
# loop over the image paths
#for imagePath in paths.list_images(args["images"]):
rectsPersist = None
pickPersist = None
while(True):
# load the image and resize it to (1) reduce detection time
# and (2) improve detection accuracy
retval, im = video_capture.read()
image = im.copy()
count += 1
orig = image.copy()
# draw the previous original bounding boxes
if rectsPersist != None:
for (x, y, w, h) in rectsPersist:
cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
# draw the previous final bounding boxes
if pickPersist != None:
for (xA, yA, xB, yB) in pickPersist:
cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
if not count % 1:
count = 0
#image = cv2.imread(imagePath)
image = imutils.resize(image, width=min(400, image.shape[1]))
# detect people in the image
(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
padding=(8, 8), scale=1.05)
rectsPersist = rects
# 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 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)
pickPersist = pick
# 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 = "webcam"
print("[INFO] {}: {} original boxes, {} after suppression".format(
filename, len(rects), len(pick)))
# show the output images
cv2.imshow("Before NMS", orig)
cv2.imshow("After NMS", image)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
#cv2.waitKey()
video_capture.release()
cv2.destroyAllWindows()
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