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contour_edges.py
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contour_edges.py
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#####################################################################
# Example : contour edges for a video file
# specified on the command line (e.g. python FILE.py video_file) or from an
# attached web camera
# Author : Toby Breckon, [email protected]
# Copyright (c) 2016 School of Engineering & Computing Science,
# Durham University, UK
# License : LGPL - http://www.gnu.org/licenses/lgpl.html
#####################################################################
import cv2
import argparse
import sys
#####################################################################
keep_processing = True;
# parse command line arguments for camera ID or video file
parser = argparse.ArgumentParser(description='Perform ' + sys.argv[0] + ' example operation on incoming camera/video image')
parser.add_argument("-c", "--camera_to_use", type=int, help="specify camera to use", default=0)
parser.add_argument('video_file', metavar='video_file', type=str, nargs='?', help='specify optional video file')
args = parser.parse_args()
#####################################################################
# this function is called as a call-back everytime the trackbar is moved
# (here we just do nothing)
def nothing(x):
pass
#####################################################################
# define video capture object
cap = cv2.VideoCapture();
# define display window name
windowName = "Largest Area Contour"; # window name
windowName2 = "All Contours"; # window name
# if command line arguments are provided try to read video_name
# otherwise default to capture from attached H/W camera
if (((args.video_file) and (cap.open(str(args.video_file))))
or (cap.open(args.camera_to_use))):
# create window by name (as resizable)
cv2.namedWindow(windowName, cv2.WINDOW_NORMAL);
cv2.namedWindow(windowName2, cv2.WINDOW_NORMAL);
# add some track bar controllers for settings
lower_threshold = 112;
cv2.createTrackbar("lower", windowName2, lower_threshold, 255, nothing);
upper_threshold = 170;
cv2.createTrackbar("upper", windowName2, upper_threshold, 255, nothing);
smoothing_neighbourhood = 3;
cv2.createTrackbar("smoothing", windowName2, smoothing_neighbourhood, 15, nothing);
sobel_size = 3; # greater than 7 seems to crash
cv2.createTrackbar("sobel size", windowName2, sobel_size, 7, nothing);
while (keep_processing):
# if video file successfully open then read frame from video
if (cap.isOpened):
ret, frame = cap.read();
# get parameters from track bars
lower_threshold = cv2.getTrackbarPos("lower", windowName2);
upper_threshold = cv2.getTrackbarPos("upper", windowName2);
smoothing_neighbourhood = cv2.getTrackbarPos("smoothing", windowName2);
sobel_size = cv2.getTrackbarPos("sobel size", windowName2);
# check neighbourhood is greater than 3 and odd
smoothing_neighbourhood = max(3, smoothing_neighbourhood);
if not(smoothing_neighbourhood % 2):
smoothing_neighbourhood = smoothing_neighbourhood + 1;
sobel_size = max(3, sobel_size);
if not(sobel_size % 2):
sobel_size = sobel_size + 1;
# convert to grayscale
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY);
# performing smoothing on the image using a 5x5 smoothing mark (see manual entry for GaussianBlur())
smoothed = cv2.GaussianBlur(gray_frame,(smoothing_neighbourhood,smoothing_neighbourhood),0);
# perform canny edge detection
canny = cv2.Canny(smoothed, lower_threshold, upper_threshold, apertureSize=sobel_size);
# convert the canny edges into contours (check OpenCV version >= 4.x)
if (int(cv2.__version__.split(".")[0]) >= 4):
contours, hierarchy = cv2.findContours(canny,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
else:
_, contours, hierarchy = cv2.findContours(canny,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# find largest contour by area
max_contour_area = -1;
for cnt in contours:
area = cv2.contourArea(cnt);
if (area > max_contour_area):
max_contour_area = area;
largest_contour = cnt;
# draw contours (one display for all of them, one for the largest only)
gray_frame = cv2.cvtColor(gray_frame, cv2.COLOR_GRAY2BGR); # make 3 channel to draw on it in colour
cv2.drawContours(gray_frame, contours, -1, (0,255,0), 3); # in green
cv2.drawContours(frame, [largest_contour], 0, (0,0,255), 3);# in red
# display image
cv2.imshow(windowName,frame);
cv2.imshow(windowName2,gray_frame)
# start the event loop - essential
# cv2.waitKey() is a keyboard binding function (argument is the time in milliseconds).
# It waits for specified milliseconds for any keyboard event.
# If you press any key in that time, the program continues.
# If 0 is passed, it waits indefinitely for a key stroke.
# (bitwise and with 0xFF to extract least significant byte of multi-byte response)
key = cv2.waitKey(40) & 0xFF; # wait 40ms (i.e. 1000ms / 25 fps = 40 ms)
# It can also be set to detect specific key strokes by recording which key is pressed
# e.g. if user presses "x" then exit / press "f" for fullscreen display
if (key == ord('x')):
keep_processing = False;
elif (key == ord('f')):
cv2.setWindowProperty(windowName, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN);
# close all windows
cv2.destroyAllWindows()
else:
print("No video file specified or camera connected.");
#####################################################################