try:
fn1, fn2 = args
except:
- fn1 = '../data/aero1.jpg'
- fn2 = '../data/aero3.jpg'
+ fn1 = 'aero1.jpg'
+ fn2 = 'aero3.jpg'
- img1 = cv.imread(fn1, 0)
- img2 = cv.imread(fn2, 0)
+ img1 = cv.imread(cv.samples.findFile(fn1), cv.IMREAD_GRAYSCALE)
+ img2 = cv.imread(cv.samples.findFile(fn2), cv.IMREAD_GRAYSCALE)
detector, matcher = init_feature(feature_name)
if img1 is None:
print()
if len(sys.argv) > 1:
- fn = sys.argv[1]
+ fn = cv.samples.findFile(sys.argv[1])
print('loading %s ...' % fn)
img = cv.imread(fn)
if img is None:
obj_points = []
img_points = []
- h, w = cv.imread(img_names[0], 0).shape[:2] # TODO: use imquery call to retrieve results
+ h, w = cv.imread(img_names[0], cv.IMREAD_GRAYSCALE).shape[:2] # TODO: use imquery call to retrieve results
def processImage(fn):
print('processing %s... ' % fn)
def main():
parser = argparse.ArgumentParser(description='Plot camera calibration extrinsics.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- parser.add_argument('--calibration', type=str, default="../data/left_intrinsics.yml",
+ parser.add_argument('--calibration', type=str, default='left_intrinsics.yml',
help='YAML camera calibration file.')
parser.add_argument('--cam_width', type=float, default=0.064/2,
help='Width/2 of the displayed camera.')
help='The calibration board is static and the camera is moving.')
args = parser.parse_args()
- fs = cv.FileStorage(args.calibration, cv.FILE_STORAGE_READ)
+ fs = cv.FileStorage(cv.samples.findFile(args.calibration), cv.FILE_STORAGE_READ)
board_width = int(fs.getNode('board_width').real())
board_height = int(fs.getNode('board_height').real())
square_size = fs.getNode('square_size').real()
try:
fn = sys.argv[1]
except:
- fn = '../data/baboon.jpg'
+ fn = 'baboon.jpg'
- src = cv.imread(fn)
+ src = cv.imread(cv.samples.findFile(fn))
def nothing(*argv):
pass
fn = sys.argv[1]
except:
fn = 0
- cam = video.create_capture(fn, fallback='synth:bg=../data/baboon.jpg:class=chess:noise=0.05')
+ cam = video.create_capture(fn, fallback='synth:bg=baboon.jpg:class=chess:noise=0.05')
while True:
flag, frame = cam.read()
ESC - exit
Examples:
- deconvolution.py --angle 135 --d 22 ../data/licenseplate_motion.jpg
+ deconvolution.py --angle 135 --d 22 licenseplate_motion.jpg
(image source: http://www.topazlabs.com/infocus/_images/licenseplate_compare.jpg)
- deconvolution.py --angle 86 --d 31 ../data/text_motion.jpg
- deconvolution.py --circle --d 19 ../data/text_defocus.jpg
+ deconvolution.py --angle 86 --d 31 text_motion.jpg
+ deconvolution.py --circle --d 19 text_defocus.jpg
(image source: compact digital photo camera, no artificial distortion)
try:
fn = args[0]
except:
- fn = '../data/licenseplate_motion.jpg'
+ fn = 'licenseplate_motion.jpg'
win = 'deconvolution'
- img = cv.imread(fn, 0)
+ img = cv.imread(cv.samples.findFile(fn), cv.IMREAD_GRAYSCALE)
if img is None:
print('Failed to load file:', fn)
sys.exit(1)
h, w = src.shape[:2]
- cx1 = cx2 = w/2
- cy1 = cy2 = h/2
+ cx1 = cx2 = w // 2
+ cy1 = cy2 = h // 2
# if the size is odd, then adjust the bottom/right quadrants
if w % 2 != 0:
if __name__ == "__main__":
if len(sys.argv) > 1:
- im = cv.imread(sys.argv[1])
+ fname = sys.argv[1]
else:
- im = cv.imread('../data/baboon.jpg')
+ fname = 'baboon.jpg'
print("usage : python dft.py <image_file>")
+ im = cv.imread(cv.samples.findFile(fname))
+
# convert to grayscale
im = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
h, w = im.shape[:2]
'''
SVM and KNearest digit recognition.
-Sample loads a dataset of handwritten digits from '../data/digits.png'.
+Sample loads a dataset of handwritten digits from 'digits.png'.
Then it trains a SVM and KNearest classifiers on it and evaluates
their accuracy.
SZ = 20 # size of each digit is SZ x SZ
CLASS_N = 10
-DIGITS_FN = '../data/digits.png'
+DIGITS_FN = 'digits.png'
def split2d(img, cell_size, flatten=True):
h, w = img.shape[:2]
return cells
def load_digits(fn):
+ fn = cv.samples.findFile(fn)
print('loading "%s" ...' % fn)
- digits_img = cv.imread(fn, 0)
+ digits_img = cv.imread(fn, cv.IMREAD_GRAYSCALE)
digits = split2d(digits_img, (SZ, SZ))
labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
return digits, labels
try:
fn = sys.argv[1]
except:
- fn = '../data/fruits.jpg'
+ fn = 'fruits.jpg'
print(__doc__)
- img = cv.imread(fn, 0)
+ fn = cv.samples.findFile(fn)
+ img = cv.imread(fn, cv.IMREAD_GRAYSCALE)
if img is None:
print('Failed to load fn:', fn)
sys.exit(1)
except:
video_src = 0
args = dict(args)
- cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
- nested_fn = args.get('--nested-cascade', "../../data/haarcascades/haarcascade_eye.xml")
+ cascade_fn = args.get('--cascade', "data/haarcascades/haarcascade_frontalface_alt.xml")
+ nested_fn = args.get('--nested-cascade', "data/haarcascades/haarcascade_eye.xml")
cascade = cv.CascadeClassifier(cv.samples.findFile(cascade_fn))
nested = cv.CascadeClassifier(cv.samples.findFile(nested_fn))
try:
fn1, fn2 = args
except:
- fn1 = '../data/box.png'
- fn2 = '../data/box_in_scene.png'
+ fn1 = 'box.png'
+ fn2 = 'box_in_scene.png'
- img1 = cv.imread(fn1, 0)
- img2 = cv.imread(fn2, 0)
+ img1 = cv.imread(cv.samples.findFile(fn1), cv.IMREAD_GRAYSCALE)
+ img2 = cv.imread(cv.samples.findFile(fn2), cv.IMREAD_GRAYSCALE)
detector, matcher = init_feature(feature_name)
if img1 is None:
try:
fn = sys.argv[1]
except:
- fn = '../data/fruits.jpg'
+ fn = 'fruits.jpg'
print(__doc__)
- img = cv.imread(fn, True)
+ img = cv.imread(cv.samples.findFile(fn))
if img is None:
print('Failed to load image file:', fn)
sys.exit(1)
try:
img_fn = sys.argv[1]
except:
- img_fn = '../data/baboon.jpg'
+ img_fn = 'baboon.jpg'
- img = cv.imread(img_fn)
+ img = cv.imread(cv.samples.findFile(img_fn))
if img is None:
print('Failed to load image file:', img_fn)
sys.exit(1)
if len(sys.argv) == 2:
filename = sys.argv[1] # for drawing purposes
else:
- print("No input image given, so loading default image, ../data/lena.jpg \n")
+ print("No input image given, so loading default image, lena.jpg \n")
print("Correct Usage: python grabcut.py <filename> \n")
- filename = '../data/lena.jpg'
+ filename = 'lena.jpg'
- img = cv.imread(filename)
+ img = cv.imread(cv.samples.findFile(filename))
img2 = img.copy() # a copy of original image
mask = np.zeros(img.shape[:2],dtype = np.uint8) # mask initialized to PR_BG
output = np.zeros(img.shape,np.uint8) # output image to be shown
if len(sys.argv)>1:
fname = sys.argv[1]
else :
- fname = '../data/lena.jpg'
+ fname = 'lena.jpg'
print("usage : python hist.py <image_file>")
- im = cv.imread(fname)
+ im = cv.imread(cv.samples.findFile(fname))
if im is None:
print('Failed to load image file:', fname)
Usage:
houghcircles.py [<image_name>]
- image argument defaults to ../data/board.jpg
+ image argument defaults to board.jpg
'''
# Python 2/3 compatibility
try:
fn = sys.argv[1]
except IndexError:
- fn = "../data/board.jpg"
+ fn = 'board.jpg'
- src = cv.imread(fn, 1)
+ src = cv.imread(cv.samples.findFile(fn))
img = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
img = cv.medianBlur(img, 5)
cimg = src.copy() # numpy function
Usage:
houghlines.py [<image_name>]
- image argument defaults to ../data/pic1.png
+ image argument defaults to pic1.png
'''
# Python 2/3 compatibility
try:
fn = sys.argv[1]
except IndexError:
- fn = "../data/pic1.png"
+ fn = 'pic1.png'
- src = cv.imread(fn)
+ src = cv.imread(cv.samples.findFile(fn))
dst = cv.Canny(src, 50, 200)
cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
try:
fn = sys.argv[1]
except:
- fn = '../data/fruits.jpg'
+ fn = 'fruits.jpg'
print(__doc__)
- img = cv.imread(fn)
+ img = cv.imread(cv.samples.findFile(fn))
if img is None:
print('Failed to load image file:', fn)
sys.exit(1)
args, dummy = getopt.getopt(sys.argv[1:], '', ['model=', 'data=', 'load=', 'save='])
args = dict(args)
args.setdefault('--model', 'svm')
- args.setdefault('--data', '../data/letter-recognition.data')
+ args.setdefault('--data', 'letter-recognition.data')
- print('loading data %s ...' % args['--data'])
- samples, responses = load_base(args['--data'])
+ datafile = cv.samples.findFile(args['--data'])
+
+ print('loading data %s ...' % datafile)
+ samples, responses = load_base(datafile)
Model = models[args['--model']]
model = Model()
try:
fn = sys.argv[1]
except IndexError:
- fn = '../data/fruits.jpg'
+ fn = 'fruits.jpg'
- img = cv.imread(fn)
+ img = cv.imread(cv.samples.findFile(fn))
if img is None:
print('Failed to load image file:', fn)
sys.exit(1)
try:
fn = sys.argv[1]
except:
- fn = '../data/baboon.jpg'
+ fn = 'baboon.jpg'
- img = cv.imread(fn)
+ img = cv.imread(cv.samples.findFile(fn))
if img is None:
print('Failed to load image file:', fn)
hog = cv.HOGDescriptor()
hog.setSVMDetector( cv.HOGDescriptor_getDefaultPeopleDetector() )
- default = ['../data/basketball2.png '] if len(sys.argv[1:]) == 0 else []
+ default = [cv.samples.findFile('basketball2.png')] if len(sys.argv[1:]) == 0 else []
for fn in it.chain(*map(glob, default + sys.argv[1:])):
print(fn, ' - ',)
if __name__ == '__main__':
print('loading images...')
- imgL = cv.pyrDown( cv.imread('../data/aloeL.jpg') ) # downscale images for faster processing
- imgR = cv.pyrDown( cv.imread('../data/aloeR.jpg') )
+ imgL = cv.pyrDown(cv.imread(cv.samples.findFile('aloeL.jpg'))) # downscale images for faster processing
+ imgR = cv.pyrDown(cv.imread(cv.samples.findFile('aloeR.jpg')))
# disparity range is tuned for 'aloe' image pair
window_size = 3
try:
fn = sys.argv[1]
except:
- fn = '../data/starry_night.jpg'
+ fn = 'starry_night.jpg'
- img = cv.imread(fn)
+ img = cv.imread(cv.samples.findFile(fn))
if img is None:
print('Failed to load image file:', fn)
sys.exit(1)
if __name__ == '__main__':
- backGr = cv.imread('../data/graf1.png')
- fgr = cv.imread('../data/box.png')
+ backGr = cv.imread(cv.samples.findFile('graf1.png'))
+ fgr = cv.imread(cv.samples.findFile('box.png'))
render = TestSceneRender(backGr, fgr)
## [Load image]
parser = argparse.ArgumentParser(description='Code for Histogram Calculation tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/lena.jpg')
+parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
## [Load image]
parser = argparse.ArgumentParser(description='Code for Histogram Equalization tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/lena.jpg')
+parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
window_name = 'filter2D Demo'
## [load]
- imageName = argv[0] if len(argv) > 0 else "../data/lena.jpg"
+ imageName = argv[0] if len(argv) > 0 else 'lena.jpg'
# Loads an image
- src = cv.imread(imageName, cv.IMREAD_COLOR)
+ src = cv.imread(cv.samples.findFile(imageName), cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print ('Error opening image!')
- print ('Usage: filter2D.py [image_name -- default ../data/lena.jpg] \n')
+ print ('Usage: filter2D.py [image_name -- default lena.jpg] \n')
return -1
## [load]
## [init_arguments]
def main(argv):
## [load]
- default_file = "../../../../data/smarties.png"
+ default_file = 'smarties.png'
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
- src = cv.imread(filename, cv.IMREAD_COLOR)
+ src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
def main(argv):
## [load]
- default_file = "../../../../data/sudoku.png"
+ default_file = 'sudoku.png'
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
- src = cv.imread(filename, cv.IMREAD_GRAYSCALE)
+ src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)
# Check if image is loaded fine
if src is None:
# [variables]
# [load]
- imageName = argv[0] if len(argv) > 0 else "../data/lena.jpg"
+ imageName = argv[0] if len(argv) > 0 else 'lena.jpg'
- src = cv.imread(imageName, cv.IMREAD_COLOR) # Load an image
+ src = cv.imread(cv.samples.findFile(imageName), cv.IMREAD_COLOR) # Load an image
# Check if image is loaded fine
if src is None:
print ('Error opening image')
- print ('Program Arguments: [image_name -- default ../data/lena.jpg]')
+ print ('Program Arguments: [image_name -- default lena.jpg]')
return -1
# [load]
window_name = "copyMakeBorder Demo"
## [variables]
## [load]
- imageName = argv[0] if len(argv) > 0 else "../data/lena.jpg"
+ imageName = argv[0] if len(argv) > 0 else 'lena.jpg'
# Loads an image
- src = cv.imread(imageName, cv.IMREAD_COLOR)
+ src = cv.imread(cv.samples.findFile(imageName), cv.IMREAD_COLOR)
# Check if image is loaded fine
if src is None:
print ('Error opening image!')
- print ('Usage: copy_make_border.py [image_name -- default ../data/lena.jpg] \n')
+ print ('Usage: copy_make_border.py [image_name -- default lena.jpg] \n')
return -1
## [load]
# Brief how-to for this program
cv.imshow(window_name, dst)
parser = argparse.ArgumentParser(description='Code for Canny Edge Detector tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/fruits.jpg')
+parser.add_argument('--input', help='Path to input image.', default='fruits.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
parser = argparse.ArgumentParser(description='Code for Image Segmentation with Distance Transform and Watershed Algorithm.\
Sample code showing how to segment overlapping objects using Laplacian filtering, \
in addition to Watershed and Distance Transformation')
-parser.add_argument('--input', help='Path to input image.', default='../data/cards.png')
+parser.add_argument('--input', help='Path to input image.', default='cards.png')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
## [Update]
parser = argparse.ArgumentParser(description='Code for Remapping tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/chicky_512.png')
+parser.add_argument('--input', help='Path to input image.', default='chicky_512.png')
args = parser.parse_args()
## [Load]
-src = cv.imread(args.input, cv.IMREAD_COLOR)
+src = cv.imread(cv.samples.findFile(args.input), cv.IMREAD_COLOR)
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
## [Load the image]
parser = argparse.ArgumentParser(description='Code for Affine Transformations tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/lena.jpg')
+parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
## [setup]
# Load source image
parser = argparse.ArgumentParser(description='Code for Creating Bounding boxes and circles for contours tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/stuff.jpg')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
## [setup]
# Load source image
parser = argparse.ArgumentParser(description='Code for Creating Bounding rotated boxes and ellipses for contours tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/stuff.jpg')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Load source image
parser = argparse.ArgumentParser(description='Code for Finding contours in your image tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/HappyFish.jpg')
+parser.add_argument('--input', help='Path to input image.', default='HappyFish.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Load source image
parser = argparse.ArgumentParser(description='Code for Convex Hull tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/stuff.jpg')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
## [setup]
# Load source image
parser = argparse.ArgumentParser(description='Code for Image Moments tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/stuff.jpg')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Load source image and convert it to gray
parser = argparse.ArgumentParser(description='Code for Shi-Tomasi corner detector tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/pic3.png')
+parser.add_argument('--input', help='Path to input image.', default='pic3.png')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Load source image and convert it to gray
parser = argparse.ArgumentParser(description='Code for Creating your own corner detector tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/building.jpg')
+parser.add_argument('--input', help='Path to input image.', default='building.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Load source image and convert it to gray
parser = argparse.ArgumentParser(description='Code for Shi-Tomasi corner detector tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/pic3.png')
+parser.add_argument('--input', help='Path to input image.', default='pic3.png')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
# Load source image and convert it to gray
parser = argparse.ArgumentParser(description='Code for Harris corner detector tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/building.jpg')
+parser.add_argument('--input', help='Path to input image.', default='building.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
if 0 <= alpha <= 1:
alpha = input_alpha
# [load]
-src1 = cv.imread('../../../../data/LinuxLogo.jpg')
-src2 = cv.imread('../../../../data/WindowsLogo.jpg')
+src1 = cv.imread(cv.samples.findFile('LinuxLogo.jpg'))
+src2 = cv.imread(cv.samples.findFile('WindowsLogo.jpg'))
# [load]
if src1 is None:
print("Error loading src1")
This program demonstrated the use of the discrete Fourier transform (DFT).
The dft of an image is taken and it's power spectrum is displayed.
Usage:
- discrete_fourier_transform.py [image_name -- default ../../../../data/lena.jpg]''')
+ discrete_fourier_transform.py [image_name -- default lena.jpg]''')
def main(argv):
print_help()
- filename = argv[0] if len(argv) > 0 else "../../../../data/lena.jpg"
+ filename = argv[0] if len(argv) > 0 else 'lena.jpg'
- I = cv.imread(filename, cv.IMREAD_GRAYSCALE)
+ I = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)
if I is None:
print('Error opening image')
return -1
## [basic_method]
def main(argv):
- filename = "../../../../data/lena.jpg"
+ filename = 'lena.jpg'
img_codec = cv.IMREAD_COLOR
if argv:
if len(argv) >= 2 and sys.argv[2] == "G":
img_codec = cv.IMREAD_GRAYSCALE
- src = cv.imread(filename, img_codec)
+ src = cv.imread(cv.samples.findFile(filename), img_codec)
if src is None:
print("Can't open image [" + filename + "]")
print("Usage:")
- print("mat_mask_operations.py [image_path -- default ../../../../data/lena.jpg] [G -- grayscale]")
+ print("mat_mask_operations.py [image_path -- default lena.jpg] [G -- grayscale]")
return -1
cv.namedWindow("Input", cv.WINDOW_AUTOSIZE)
## [load]
parser = argparse.ArgumentParser(description='Code for AKAZE local features matching tutorial.')
-parser.add_argument('--input1', help='Path to input image 1.', default='../data/graf1.png')
-parser.add_argument('--input2', help='Path to input image 2.', default='../data/graf3.png')
-parser.add_argument('--homography', help='Path to the homography matrix.', default='../data/H1to3p.xml')
+parser.add_argument('--input1', help='Path to input image 1.', default='graf1.png')
+parser.add_argument('--input2', help='Path to input image 2.', default='graf3.png')
+parser.add_argument('--homography', help='Path to the homography matrix.', default='H1to3p.xml')
args = parser.parse_args()
-img1 = cv.imread(args.input1, cv.IMREAD_GRAYSCALE)
-img2 = cv.imread(args.input2, cv.IMREAD_GRAYSCALE)
+img1 = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
+img2 = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
print('Could not open or find the images!')
exit(0)
-fs = cv.FileStorage(args.homography, cv.FILE_STORAGE_READ)
+fs = cv.FileStorage(cv.samples.findFile(args.homography), cv.FILE_STORAGE_READ)
homography = fs.getFirstTopLevelNode().mat()
## [load]
import argparse
parser = argparse.ArgumentParser(description='Code for Feature Detection tutorial.')
-parser.add_argument('--input1', help='Path to input image 1.', default='../data/box.png')
-parser.add_argument('--input2', help='Path to input image 2.', default='../data/box_in_scene.png')
+parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
+parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()
-img1 = cv.imread(args.input1, cv.IMREAD_GRAYSCALE)
-img2 = cv.imread(args.input2, cv.IMREAD_GRAYSCALE)
+img1 = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
+img2 = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
print('Could not open or find the images!')
exit(0)
import argparse
parser = argparse.ArgumentParser(description='Code for Feature Detection tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/box.png')
+parser.add_argument('--input', help='Path to input image.', default='box.png')
args = parser.parse_args()
-src = cv.imread(args.input, cv.IMREAD_GRAYSCALE)
+src = cv.imread(cv.samples.findFile(args.input), cv.IMREAD_GRAYSCALE)
if src is None:
print('Could not open or find the image:', args.input)
exit(0)
import argparse
parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
-parser.add_argument('--input1', help='Path to input image 1.', default='../data/box.png')
-parser.add_argument('--input2', help='Path to input image 2.', default='../data/box_in_scene.png')
+parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
+parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()
-img1 = cv.imread(args.input1, cv.IMREAD_GRAYSCALE)
-img2 = cv.imread(args.input2, cv.IMREAD_GRAYSCALE)
+img1 = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
+img2 = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
print('Could not open or find the images!')
exit(0)
import argparse
parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
-parser.add_argument('--input1', help='Path to input image 1.', default='../data/box.png')
-parser.add_argument('--input2', help='Path to input image 2.', default='../data/box_in_scene.png')
+parser.add_argument('--input1', help='Path to input image 1.', default='box.png')
+parser.add_argument('--input2', help='Path to input image 2.', default='box_in_scene.png')
args = parser.parse_args()
-img_object = cv.imread(args.input1, cv.IMREAD_GRAYSCALE)
-img_scene = cv.imread(args.input2, cv.IMREAD_GRAYSCALE)
+img_object = cv.imread(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
+img_scene = cv.imread(cv.samples.findFile(args.input2), cv.IMREAD_GRAYSCALE)
if img_object is None or img_scene is None:
print('Could not open or find the images!')
exit(0)
## [on_trackbar]
parser = argparse.ArgumentParser(description='Code for Adding a Trackbar to our applications tutorial.')
-parser.add_argument('--input1', help='Path to the first input image.', default='../data/LinuxLogo.jpg')
-parser.add_argument('--input2', help='Path to the second input image.', default='../data/WindowsLogo.jpg')
+parser.add_argument('--input1', help='Path to the first input image.', default='LinuxLogo.jpg')
+parser.add_argument('--input2', help='Path to the second input image.', default='WindowsLogo.jpg')
args = parser.parse_args()
## [load]
# Read images ( both have to be of the same size and type )
-src1 = cv.imread(args.input1)
-src2 = cv.imread(args.input2)
+src1 = cv.imread(cv.samples.findFile(args.input1))
+src2 = cv.imread(cv.samples.findFile(args.input2))
## [load]
if src1 is None:
print('Could not open or find the image: ', args.input1)
* [ESC] -> Close program
""")
## [load]
- filename = argv[0] if len(argv) > 0 else "../data/chicky_512.png"
+ filename = argv[0] if len(argv) > 0 else 'chicky_512.png'
# Load the image
- src = cv.imread(filename)
+ src = cv.imread(cv.samples.findFile(filename))
# Check if image is loaded fine
if src is None:
cv.namedWindow(window_name, cv.WINDOW_AUTOSIZE)
# Load the source image
- imageName = argv[0] if len(argv) > 0 else "../data/lena.jpg"
+ imageName = argv[0] if len(argv) > 0 else 'lena.jpg'
global src
- src = cv.imread(imageName, 1)
+ src = cv.imread(cv.samples.findFile(imageName))
if src is None:
print ('Error opening image')
print ('Usage: smoothing.py [image_name -- default ../data/lena.jpg] \n')
# Read image given by user
## [basic-linear-transform-load]
parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/lena.jpg')
+parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
args = parser.parse_args()
-image = cv.imread(args.input)
+image = cv.imread(cv.samples.findFile(args.input))
if image is None:
print('Could not open or find the image: ', args.input)
exit(0)
gammaCorrection()
parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/lena.jpg')
+parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
args = parser.parse_args()
-img_original = cv.imread(args.input)
+img_original = cv.imread(cv.samples.findFile(args.input))
if img_original is None:
print('Could not open or find the image: ', args.input)
exit(0)
cv.imshow(title_dilatation_window, dilatation_dst)
parser = argparse.ArgumentParser(description='Code for Eroding and Dilating tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/LinuxLogo.jpg')
+parser.add_argument('--input', help='Path to input image.', default='LinuxLogo.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
import cv2 as cv
import numpy as np
-img = cv.imread('../data/sudoku.png')
+img = cv.imread(cv.samples.findFile('sudoku.png'))
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
edges = cv.Canny(gray,50,150,apertureSize = 3)
import cv2 as cv
import numpy as np
-img = cv.imread('../data/sudoku.png')
+img = cv.imread(cv.samples.findFile('sudoku.png'))
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
edges = cv.Canny(gray,50,150,apertureSize = 3)
lines = cv.HoughLinesP(edges,1,np.pi/180,100,minLineLength=100,maxLineGap=10)
cv.imshow(title_window, dst)
parser = argparse.ArgumentParser(description='Code for More Morphology Transformations tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/LinuxLogo.jpg')
+parser.add_argument('--input', help='Path to input image.', default='LinuxLogo.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
## [Threshold_Demo]
parser = argparse.ArgumentParser(description='Code for Basic Thresholding Operations tutorial.')
-parser.add_argument('--input', help='Path to input image.', default='../data/stuff.jpg')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
args = parser.parse_args()
## [load]
# Load an image
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
# Load image
parser = argparse.ArgumentParser(description='Code for Introduction to Principal Component Analysis (PCA) tutorial.\
This program demonstrates how to use OpenCV PCA to extract the orientation of an object.')
-parser.add_argument('--input', help='Path to input image.', default='../data/pca_test1.jpg')
+parser.add_argument('--input', help='Path to input image.', default='pca_test1.jpg')
args = parser.parse_args()
-src = cv.imread(args.input)
+src = cv.imread(cv.samples.findFile(args.input))
# Check if image is loaded successfully
if src is None:
print('Could not open or find the image: ', args.input)
cv.imshow('Capture - Face detection', frame)
parser = argparse.ArgumentParser(description='Code for Cascade Classifier tutorial.')
-parser.add_argument('--face_cascade', help='Path to face cascade.', default='../../data/haarcascades/haarcascade_frontalface_alt.xml')
-parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
+parser.add_argument('--face_cascade', help='Path to face cascade.', default='data/haarcascades/haarcascade_frontalface_alt.xml')
+parser.add_argument('--eyes_cascade', help='Path to eyes cascade.', default='data/haarcascades/haarcascade_eye_tree_eyeglasses.xml')
parser.add_argument('--camera', help='Camera devide number.', type=int, default=0)
args = parser.parse_args()
eyes_cascade = cv.CascadeClassifier()
#-- 1. Load the cascades
-if not face_cascade.load(face_cascade_name):
+if not face_cascade.load(cv.samples.findFile(face_cascade_name)):
print('--(!)Error loading face cascade')
exit(0)
-if not eyes_cascade.load(eyes_cascade_name):
+if not eyes_cascade.load(cv.samples.findFile(eyes_cascade_name)):
print('--(!)Error loading eyes cascade')
exit(0)
parser = argparse.ArgumentParser(description='This program shows how to use background subtraction methods provided by \
OpenCV. You can process both videos and images.')
-parser.add_argument('--input', type=str, help='Path to a video or a sequence of image.', default='../data/vtest.avi')
+parser.add_argument('--input', type=str, help='Path to a video or a sequence of image.', default='vtest.avi')
parser.add_argument('--algo', type=str, help='Background subtraction method (KNN, MOG2).', default='MOG2')
args = parser.parse_args()
## [create]
## [capture]
-capture = cv.VideoCapture(args.input)
+capture = cv.VideoCapture(cv.samples.findFileOrKeep(args.input))
if not capture.isOpened:
print('Unable to open: ' + args.input)
exit(0)
- synth:<params> for procedural video
Synth examples:
- synth:bg=../data/lena.jpg:noise=0.1
- synth:class=chess:bg=../data/lena.jpg:noise=0.1:size=640x480
+ synth:bg=lena.jpg:noise=0.1
+ synth:class=chess:bg=lena.jpg:noise=0.1:size=640x480
Keys:
ESC - exit
self.bg = None
self.frame_size = (640, 480)
if bg is not None:
- self.bg = cv.imread(bg, 1)
+ self.bg = cv.imread(cv.samples.findFile(bg))
h, w = self.bg.shape[:2]
self.frame_size = (w, h)
class Book(VideoSynthBase):
def __init__(self, **kw):
super(Book, self).__init__(**kw)
- backGr = cv.imread('../data/graf1.png')
- fgr = cv.imread('../data/box.png')
+ backGr = cv.imread(cv.samples.findFile('graf1.png'))
+ fgr = cv.imread(cv.samples.findFile('box.png'))
self.render = TestSceneRender(backGr, fgr, speed = 1)
def read(self, dst=None):
class Cube(VideoSynthBase):
def __init__(self, **kw):
super(Cube, self).__init__(**kw)
- self.render = TestSceneRender(cv.imread('../data/pca_test1.jpg'), deformation = True, speed = 1)
+ self.render = TestSceneRender(cv.imread(cv.samples.findFile('pca_test1.jpg')), deformation = True, speed = 1)
def read(self, dst=None):
noise = np.zeros(self.render.sceneBg.shape, np.int8)
presets = dict(
empty = 'synth:',
- lena = 'synth:bg=../data/lena.jpg:noise=0.1',
- chess = 'synth:class=chess:bg=../data/lena.jpg:noise=0.1:size=640x480',
- book = 'synth:class=book:bg=../data/graf1.png:noise=0.1:size=640x480',
- cube = 'synth:class=cube:bg=../data/pca_test1.jpg:noise=0.0:size=640x480'
+ lena = 'synth:bg=lena.jpg:noise=0.1',
+ chess = 'synth:class=chess:bg=lena.jpg:noise=0.1:size=640x480',
+ book = 'synth:class=book:bg=graf1.png:noise=0.1:size=640x480',
+ cube = 'synth:class=cube:bg=pca_test1.jpg:noise=0.0:size=640x480'
)
try:
fn = sys.argv[1]
except:
- fn = '../data/fruits.jpg'
+ fn = 'fruits.jpg'
print(__doc__)
- App(fn).run()
+ App(cv.samples.findFile(fn)).run()