def main():
'''
'''
parser = argparse.ArgumentParser(
description=
'Contact Front estimation from video file or glob spec of image files')
parser.add_argument('-f',
dest='video_file',
type=str,
help='Input file path (MP4, AVI, etc)')
parser.add_argument(
'-g',
dest='glob_spec',
type=str,
help=
'Glob spec of image files to process (/path/to/folder/2015-08-04_DFI28-*.png)'
)
parser.add_argument(
'-s',
dest='strategy',
type=str,
help='Strategy to use (supported: HoughCircle | CumSumDiff)')
parser.add_argument('-o',
dest='video_out_file',
type=str,
help='Video output file')
parser.add_argument('-d',
dest='delay_s',
type=float,
default=0.020,
help='Delay between frames in seconds (default: 0.02)')
parser.add_argument('-v',
dest='verbose',
action='store_true',
help='Run in verbose mode (default: True)')
parser.add_argument(
'--capture_video',
dest='do_capture_video',
action='store_true',
help='Capture video of processing output (default: False)')
parser.add_argument('--plot',
dest='do_plot',
action='store_true',
help='Do plot data (default: True)')
parser.add_argument('--no-plot',
dest='do_plot',
action='store_false',
help='Do NOT plot data')
parser.set_defaults(video_file='')
parser.set_defaults(strategy='HoughCircle')
parser.set_defaults(glob_spec='')
parser.set_defaults(do_capture_video=False)
parser.set_defaults(do_plot=True)
parser.set_defaults(verbose=True)
args = parser.parse_args()
if len(args.glob_spec) > 0:
eng = util.Image_Engine(args.glob_spec, is_glob=True)
p, n = os.path.split(args.glob_spec)
result_name = n.replace('-*', '').split('.')[0] + '_' + args.strategy
elif len(args.video_file) > 0:
eng = util.Image_Engine(args.video_file, is_glob=False)
p, n = os.path.split(args.video_file)
result_name = n.split('.')[0] + '_' + args.strategy
else:
print("\n\tError: invalid input: %s\n\n" % (args))
parser.print_help()
sys.exit(1)
if args.do_capture_video:
capture_video_filename = '../results/%s_Capture.mp4' % (result_name)
else:
capture_video_filename = ''
ret, stats_HoughCircles = ip.processImages_HoughCircles(
eng=eng,
delay_s=args.delay_s,
do_plot=args.do_plot,
verbose=args.verbose,
capture_video_filename=capture_video_filename)
print "Completed HoughCircle. ret= ", ret
ret, stats_CumSumDiff = ip.processImages_CumSumDiff(
eng=eng,
delay_s=args.delay_s,
do_plot=args.do_plot,
verbose=args.verbose,
capture_video_filename=capture_video_filename)
print "Completed CumSumDiff. ret= ", ret
#if not ret:
# print "!! Error processing images! ", result_name
if 'areas' in stats_HoughCircles and len(stats_HoughCircles['areas']) > 0:
plt.figure(figsize=(10 * 2, 5))
plt.plot(stats_HoughCircles['areas'], '-b', label='HoughCircles')
plt.plot(stats_CumSumDiff['areas'], '-r', label='CumSumDiff')
plt.title('Contact Areas')
plt.savefig('../results/%s_ContactAreas.png' % (result_name))
if args.do_plot:
plt.show()
eng.cleanup()
if args.do_plot:
while cv2.waitKey(1) & 0xFF != ord('q'):
time.sleep(0.1)
cv2.destroyAllWindows()
def main():
'''
'''
parser = argparse.ArgumentParser(description='Contact Front estimation from video file or glob spec of image files')
parser.add_argument('-f', dest='video_file', type=str, help='Input file path (MP4, AVI, etc)')
parser.add_argument('-g', dest='glob_spec', type=str, help='Glob spec of image files to process (/path/to/folder/2015-08-04_DFI28-*.png)')
parser.add_argument('-s', dest='strategy', type=str, help='Strategy to use (supported: HoughCircle, EdgeCircle, CumSumDiff)')
parser.add_argument('-o', dest='video_out_file', type=str, help='Video output file')
parser.add_argument('-d', dest='delay_s', type=float, default=0.020, help='Delay between frames in seconds (default: 0.02)')
parser.add_argument('-v', dest='verbose', action='store_true', help='Run in verbose mode (default: True)')
parser.add_argument('--capture_video', dest='do_capture_video', action='store_true', help='Capture video of processing output (default: False)')
parser.add_argument('--plot', dest='do_plot', action='store_true', help='Do plot data (default: True)')
parser.add_argument('--no-plot', dest='do_plot', action='store_false', help='Do NOT plot data')
parser.set_defaults(video_file='')
parser.set_defaults(strategy='HoughCircle')
parser.set_defaults(glob_spec='')
parser.set_defaults(do_capture_video=False)
parser.set_defaults(do_plot=True)
parser.set_defaults(verbose=True)
args = parser.parse_args()
if len(args.glob_spec) > 0:
eng = util.Image_Engine(args.glob_spec, is_glob=True)
p, n = os.path.split(args.glob_spec)
result_name = n.replace('-*', '').split('.')[0] + '_' + args.strategy
elif len(args.video_file) > 0:
eng = util.Image_Engine(args.video_file, is_glob=False)
p, n = os.path.split(args.video_file)
result_name = n.split('.')[0] + '_' + args.strategy
else:
print("\n\tError: invalid input: %s\n\n" % (args))
parser.print_help()
sys.exit(1)
if args.do_capture_video:
capture_video_filename = '../results/%s_Capture.mp4' % (result_name)
else:
capture_video_filename = ''
if args.strategy == 'HoughCircle':
ret, stats = ip.processImages_HoughCircles(eng=eng, delay_s=args.delay_s,
do_plot=args.do_plot, verbose=args.verbose,
capture_video_filename=capture_video_filename)
elif args.strategy == 'CumSumDiff':
ret, stats = ip.processImages_CumSumDiff(eng=eng, delay_s=args.delay_s,
do_plot=args.do_plot, verbose=args.verbose,
capture_video_filename=capture_video_filename)
elif args.strategy == 'EdgeCircle':
ret, stats = ip.processImages_EdgeCircles(eng=eng, delay_s=args.delay_s,
do_plot=args.do_plot, verbose=args.verbose,
capture_video_filename=capture_video_filename)
else:
print("\n\tError: Unsupported strategy: %s\n\n" % (args.strategy))
parser.print_help()
sys.exit(1)
if not ret:
print "!! Error processing images! ", result_name
figs = []
if 'areas' in stats and len(stats['areas']) > 0:
fig = plt.figure(figsize=(10*2,5))
plt.plot(stats['areas'], label='Approx Area (pels)')
plt.title('Contact Areas')
plt.legend()
#plt.savefig('../results/%s_ContactAreas.png' % (result_name))
figs.append(fig)
if 'deltas' in stats and len(stats['deltas']) > 0:
fig = plt.figure(figsize=(10*2,5))
plt.plot(stats['deltas'], label='Delta Area (pels)')
plt.title('Area Deltas')
plt.legend()
#plt.savefig('../results/%s_AreaDeltas.png' % (result_name))
figs.append(fig)
if 'center_pts' in stats and len(stats['center_pts']) > 0:
fig = plt.figure(figsize=(10*2,5))
plt.plot([x for x,_ in stats['center_pts']], label='X')
plt.plot([y for _,y in stats['center_pts']], label='Y')
plt.title('Center XY')
plt.legend()
#plt.savefig('../results/%s_CenterXY.png' % (result_name))
figs.append(fig)
fig = plt.figure(figsize=(10*2,5))
plt.plot([x for x,_ in stats['center_pts']], [y for _,y in stats['center_pts']], label='Center Points')
plt.xticks([t for t in range(50, stats['image_dims'][1], 50)] )
plt.yticks([t for t in range(50, stats['image_dims'][0], 50)] )
plt.title('Center Locs')
plt.legend()
#plt.savefig('../results/%s_CenterLocs.png' % (result_name))
figs.append(fig)
if 'chan_means' in stats and len(stats['chan_means']) > 0:
fig = plt.figure(figsize=(10*2,5))
plt.plot([c for c,_,_ in stats['chan_means']], color='b', label='Blue')
plt.plot([c for _,c,_ in stats['chan_means']], color='g', label='Green')
plt.plot([c for _,_,c in stats['chan_means']], color='r', label='Red')
plt.title('HSV mean values from center ROI')
plt.legend()
#plt.savefig('../results/%s_HSV-Means.png' % (result_name))
figs.append(fig)
if args.do_plot:
plt.show()
timestamp = "{:%y%m%d-%H%M%S}".format(datetime.datetime.now())
with PdfPages('../results/%s_%s_Report.pdf' % (timestamp, result_name)) as pdf:
for fig in figs:
pdf.savefig(fig)
d = pdf.infodict()
d['Title'] = u'Report for %s' % (result_name)
d['Author'] = u'Tom H. Rafferty / CNT'
d['Subject'] = u'Various plots created using %s strategy' % (args.strategy)
d['CreationDate'] = datetime.datetime.today()
d['ModDate'] = datetime.datetime.today()
eng.cleanup()
if args.do_plot:
while cv2.waitKey(1) & 0xFF != ord('q'):
time.sleep(0.1)
cv2.destroyAllWindows()
def main():
'''
'''
parser = argparse.ArgumentParser(description='Contact Front estimation from video file or glob spec of image files')
parser.add_argument('-f', dest='video_file', type=str, help='Input file path (MP4, AVI, etc)')
parser.add_argument('-g', dest='glob_spec', type=str, help='Glob spec of image files to process (/path/to/folder/2015-08-04_DFI28-*.png)')
parser.add_argument('-s', dest='strategy', type=str, help='Strategy to use (supported: HoughCircle | CumSumDiff)')
parser.add_argument('-o', dest='video_out_file', type=str, help='Video output file')
parser.add_argument('-d', dest='delay_s', type=float, default=0.020, help='Delay between frames in seconds (default: 0.02)')
parser.add_argument('-v', dest='verbose', action='store_true', help='Run in verbose mode (default: True)')
parser.add_argument('--capture_video', dest='do_capture_video', action='store_true', help='Capture video of processing output (default: False)')
parser.add_argument('--plot', dest='do_plot', action='store_true', help='Do plot data (default: True)')
parser.add_argument('--no-plot', dest='do_plot', action='store_false', help='Do NOT plot data')
parser.set_defaults(video_file='')
parser.set_defaults(strategy='HoughCircle')
parser.set_defaults(glob_spec='')
parser.set_defaults(do_capture_video=False)
parser.set_defaults(do_plot=True)
parser.set_defaults(verbose=True)
args = parser.parse_args()
if len(args.glob_spec) > 0:
eng = util.Image_Engine(args.glob_spec, is_glob=True)
p, n = os.path.split(args.glob_spec)
result_name = n.replace('-*', '').split('.')[0] + '_' + args.strategy
elif len(args.video_file) > 0:
eng = util.Image_Engine(args.video_file, is_glob=False)
p, n = os.path.split(args.video_file)
result_name = n.split('.')[0] + '_' + args.strategy
else:
print("\n\tError: invalid input: %s\n\n" % (args))
parser.print_help()
sys.exit(1)
if args.do_capture_video:
capture_video_filename = '../results/%s_Capture.mp4' % (result_name)
else:
capture_video_filename = ''
ret, stats_HoughCircles = ip.processImages_HoughCircles(eng=eng, delay_s=args.delay_s,
do_plot=args.do_plot, verbose=args.verbose,
capture_video_filename=capture_video_filename)
print "Completed HoughCircle. ret= ", ret
ret, stats_CumSumDiff = ip.processImages_CumSumDiff(eng=eng, delay_s=args.delay_s,
do_plot=args.do_plot, verbose=args.verbose,
capture_video_filename=capture_video_filename)
print "Completed CumSumDiff. ret= ", ret
#if not ret:
# print "!! Error processing images! ", result_name
if 'areas' in stats_HoughCircles and len(stats_HoughCircles['areas']) > 0:
plt.figure(figsize=(10*2,5))
plt.plot(stats_HoughCircles['areas'], '-b', label='HoughCircles')
plt.plot(stats_CumSumDiff['areas'],'-r', label='CumSumDiff')
plt.title('Contact Areas')
plt.savefig('../results/%s_ContactAreas.png' % (result_name))
if args.do_plot:
plt.show()
eng.cleanup()
if args.do_plot:
while cv2.waitKey(1) & 0xFF != ord('q'):
time.sleep(0.1)
cv2.destroyAllWindows()
def main():
'''
'''
parser = argparse.ArgumentParser(
description=
'Contact Front estimation from video file or glob spec of image files')
parser.add_argument('-f',
dest='video_file',
type=str,
help='Input file path (MP4, AVI, etc)')
parser.add_argument(
'-g',
dest='glob_spec',
type=str,
help=
'Glob spec of image files to process (/path/to/folder/2015-08-04_DFI28-*.png)'
)
parser.add_argument(
'-s',
dest='strategy',
type=str,
help='Strategy to use (supported: HoughCircle, EdgeCircle, CumSumDiff)'
)
parser.add_argument('-o',
dest='video_out_file',
type=str,
help='Video output file')
parser.add_argument('-d',
dest='delay_s',
type=float,
default=0.020,
help='Delay between frames in seconds (default: 0.02)')
parser.add_argument('-v',
dest='verbose',
action='store_true',
help='Run in verbose mode (default: True)')
parser.add_argument(
'--capture_video',
dest='do_capture_video',
action='store_true',
help='Capture video of processing output (default: False)')
parser.add_argument('--plot',
dest='do_plot',
action='store_true',
help='Do plot data (default: True)')
parser.add_argument('--no-plot',
dest='do_plot',
action='store_false',
help='Do NOT plot data')
parser.set_defaults(video_file='')
parser.set_defaults(strategy='HoughCircle')
parser.set_defaults(glob_spec='')
parser.set_defaults(do_capture_video=False)
parser.set_defaults(do_plot=True)
parser.set_defaults(verbose=True)
args = parser.parse_args()
if len(args.glob_spec) > 0:
eng = util.Image_Engine(args.glob_spec, is_glob=True)
p, n = os.path.split(args.glob_spec)
result_name = n.replace('-*', '').split('.')[0] + '_' + args.strategy
elif len(args.video_file) > 0:
eng = util.Image_Engine(args.video_file, is_glob=False)
p, n = os.path.split(args.video_file)
result_name = n.split('.')[0] + '_' + args.strategy
else:
print("\n\tError: invalid input: %s\n\n" % (args))
parser.print_help()
sys.exit(1)
if args.do_capture_video:
capture_video_filename = '../results/%s_Capture.mp4' % (result_name)
else:
capture_video_filename = ''
if args.strategy == 'HoughCircle':
ret, stats = ip.processImages_HoughCircles(
eng=eng,
delay_s=args.delay_s,
do_plot=args.do_plot,
verbose=args.verbose,
capture_video_filename=capture_video_filename)
elif args.strategy == 'CumSumDiff':
ret, stats = ip.processImages_CumSumDiff(
eng=eng,
delay_s=args.delay_s,
do_plot=args.do_plot,
verbose=args.verbose,
capture_video_filename=capture_video_filename)
elif args.strategy == 'EdgeCircle':
ret, stats = ip.processImages_EdgeCircles(
eng=eng,
delay_s=args.delay_s,
do_plot=args.do_plot,
verbose=args.verbose,
capture_video_filename=capture_video_filename)
else:
print("\n\tError: Unsupported strategy: %s\n\n" % (args.strategy))
parser.print_help()
sys.exit(1)
if not ret:
print "!! Error processing images! ", result_name
figs = []
if 'areas' in stats and len(stats['areas']) > 0:
fig = plt.figure(figsize=(10 * 2, 5))
plt.plot(stats['areas'], label='Approx Area (pels)')
plt.title('Contact Areas')
plt.legend()
#plt.savefig('../results/%s_ContactAreas.png' % (result_name))
figs.append(fig)
if 'deltas' in stats and len(stats['deltas']) > 0:
fig = plt.figure(figsize=(10 * 2, 5))
plt.plot(stats['deltas'], label='Delta Area (pels)')
plt.title('Area Deltas')
plt.legend()
#plt.savefig('../results/%s_AreaDeltas.png' % (result_name))
figs.append(fig)
if 'center_pts' in stats and len(stats['center_pts']) > 0:
fig = plt.figure(figsize=(10 * 2, 5))
plt.plot([x for x, _ in stats['center_pts']], label='X')
plt.plot([y for _, y in stats['center_pts']], label='Y')
plt.title('Center XY')
plt.legend()
#plt.savefig('../results/%s_CenterXY.png' % (result_name))
figs.append(fig)
fig = plt.figure(figsize=(10 * 2, 5))
plt.plot([x for x, _ in stats['center_pts']],
[y for _, y in stats['center_pts']],
label='Center Points')
plt.xticks([t for t in range(50, stats['image_dims'][1], 50)])
plt.yticks([t for t in range(50, stats['image_dims'][0], 50)])
plt.title('Center Locs')
plt.legend()
#plt.savefig('../results/%s_CenterLocs.png' % (result_name))
figs.append(fig)
if 'chan_means' in stats and len(stats['chan_means']) > 0:
fig = plt.figure(figsize=(10 * 2, 5))
plt.plot([c for c, _, _ in stats['chan_means']],
color='b',
label='Blue')
plt.plot([c for _, c, _ in stats['chan_means']],
color='g',
label='Green')
plt.plot([c for _, _, c in stats['chan_means']],
color='r',
label='Red')
plt.title('HSV mean values from center ROI')
plt.legend()
#plt.savefig('../results/%s_HSV-Means.png' % (result_name))
figs.append(fig)
if args.do_plot:
plt.show()
timestamp = "{:%y%m%d-%H%M%S}".format(datetime.datetime.now())
with PdfPages('../results/%s_%s_Report.pdf' %
(timestamp, result_name)) as pdf:
for fig in figs:
pdf.savefig(fig)
d = pdf.infodict()
d['Title'] = u'Report for %s' % (result_name)
d['Author'] = u'Tom H. Rafferty / CNT'
d['Subject'] = u'Various plots created using %s strategy' % (
args.strategy)
d['CreationDate'] = datetime.datetime.today()
d['ModDate'] = datetime.datetime.today()
eng.cleanup()
if args.do_plot:
while cv2.waitKey(1) & 0xFF != ord('q'):
time.sleep(0.1)
cv2.destroyAllWindows()