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@stanchiang
Last active July 31, 2016 16:41
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blurring_nonhumans in video
# import the necessary packages
from __future__ import print_function
from imutils.video import VideoStream
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
# INPUT
# take in the video
vc = cv2.VideoCapture('clip_personwalking.mp4')
# default start with true
success = True
# OUTPUT
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-o", "--output", required=True,
help="path to output video file")
ap.add_argument("-f", "--fps", type=int, default=20,
help="FPS of output video")
ap.add_argument("-c", "--codec", type=str, default="avc1",
help="codec of output video")
args = vars(ap.parse_args())
# initialize the FourCC, video writer, dimensions of the frame, and
fourcc = cv2.VideoWriter_fourcc('a', 'v', 'c', '1')
writer = None
(h, w) = (None, None)
# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
while success:
success, image = vc.read()
if success:
image = imutils.resize(image, width=1920)
# copy a blurred version of the image to a new image
blurred = cv2.medianBlur(image, 7).copy()
if writer is None:
(h, w) = image.shape[:2]
writer = cv2.VideoWriter(args["output"], fourcc, args["fps"],
(w, h), True)
# detect people in the image
(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
padding=(8, 8), scale=1.05)
# 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)
# our hacky way of masking blurry image with hi res objects
# identify important the coordinates of objects we will avoid blurring
# grab the the hi-fi verion and place it in the same spot as the blurred version
for (xA, yA, xB, yB) in pick:
cv2.rectangle(blurred, (xA, yA), (xB, yB), (0, 255, 0), 2)
blurred[yA:yB, xA:xB] = image[yA:yB, xA:xB]
# construct the final output image
h=1080
w=1920
output = np.zeros((h, w, 3), dtype="uint8")
output[0:h, 0:w] = blurred
# write the output image to file
writer.write(output)
# if the `q` key was pressed, break from the loop
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
writer.release()
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