def main():
ldr_images = [
cv2.imread('media/PlasmaWink_0.jpg'),
cv2.imread('media/PlasmaWink_1.jpg')]
hdr_processor = cv2.createMergeMertens()
hdr_image = hdr_processor.process(ldr_images) * 255
cv2.imwrite('media/PlasmaWink_HDR.jpg', hdr_image)
def main():
ldr_images = [
cv2.imread('media/PlasmaWink_0.jpg'),
cv2.imread('media/PlasmaWink_1.jpg')
]
hdr_processor = cv2.createMergeMertens()
hdr_image = hdr_processor.process(ldr_images) * 255
cv2.imwrite('media/PlasmaWink_HDR.jpg', hdr_image)
def main():
num_input_files = 56
input_filename_pattern = 'media/JosephineChair_%02d.jpg'
output_filename = 'media/JosephineChair_TimeLapse.avi'
fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
fps = 60.0
writer = None
for i in range(num_input_files):
input_filename = input_filename_pattern % i
image = cv2.imread(input_filename)
if writer is None:
is_color = (len(image.shape) > 2)
h, w = image.shape[:2]
writer = cv2.VideoWriter(output_filename, fourcc, fps, (w, h),
is_color)
writer.write(image)
def main():
parser = argparse.ArgumentParser(
description='This script detects colors using an '
'attached webcam. When it detects colors '
'that match the histogram of a reference '
'image, it captures photos on an '
'attached gPhoto2-compatible photo '
'camera.')
parser.add_argument(
'--debug', type=bool, default=False,
help='print debugging information')
parser.add_argument(
'--cam-index', type=int, default=-1,
help='device index for detection camera '
'(default=0)')
parser.add_argument(
'--width', type=int, default=320,
help='capture width for detection camera '
'(default=320)')
parser.add_argument(
'--height', type=int, default=240,
help='capture height for detection camera '
'(default=240)')
parser.add_argument(
'--detection-interval', type=float, default=0.25,
help='interval between detection frames, in seconds '
'(default=0.25)')
parser.add_argument(
'--reference-image', type=str, required=True,
help='path to reference image, whose colors will be '
'detected in scene')
parser.add_argument(
'--min-similarity', type=float, default=0.02,
help='similarity score that histogram comparator '
'must find in order to trigger similarity event '
'(default=0.02, valid_range=[0.0, 1.0])')
parser.add_argument(
'--photo-count', type=int, default=1,
help='number of photo frames per motion event '
'(default=1)')
parser.add_argument(
'--photo-interval', type=float, default=3.0,
help='interval between photo frames, in seconds '
'(default=3.0)')
parser.add_argument(
'--photo-ev-step', type=float, default=None,
help='exposure step between photo frames, in EV. If '
'this is specified, --photo-interval is ignored '
'and --photo-count refers to the length of an '
'exposure bracketing sequence, not a time-lapse '
'sequence.')
args = parser.parse_args()
debug = args.debug
cam_index = args.cam_index
w, h = args.width, args.height
detection_interval = args.detection_interval
reference_image = cv2.imread(args.reference_image,
cv2.IMREAD_COLOR)
if reference_image is None:
print('Failed to read reference image: %s' %
args.reference_image, file=sys.stderr)
return
min_similarity = args.min_similarity
photo_count = args.photo_count
photo_interval = args.photo_interval
photo_ev_step = args.photo_ev_step
cap = cv2.VideoCapture(cam_index)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
bgr = None
channels = range(3)
hist_size = [256] * 3
ranges = [0, 255] * 3
def create_normalized_hist(image, hist=None):
hist = cv2.calcHist(
[image], channels, None, hist_size, ranges, hist)
return cv2.normalize(
hist, hist, norm_type=cv2.NORM_L1)
reference_hist = create_normalized_hist(reference_image)
query_hist = None
cc = CameraCommander.CameraCommander()
while True:
time.sleep(detection_interval)
success, bgr = cap.read(bgr)
if success:
query_hist = create_normalized_hist(
bgr, query_hist)
similarity = cv2.compareHist(
reference_hist, query_hist,
cv2.HISTCMP_INTERSECT)
if debug:
print('similarity=%f' % similarity)
if similarity >= min_similarity and not cc.capturing:
if photo_ev_step is not None:
cc.capture_exposure_bracket(
photo_ev_step, photo_count)
else:
cc.capture_time_lapse(
photo_interval, photo_count)
