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Code Packages Releases Wiki Activity

Merge branch 4.x

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This commit is contained in:
Alexander Alekhin
2022-02-22 19:33:07 +00:00
parent e5d648d2d0 92312fbc0c
commit 899b4d1452
256 changed files with 10503 additions and 5119 deletions
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samples/python/camera_calibration_show_extrinsics.py
+14 -1
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@@ -1,5 +1,18 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Plot camera calibration extrinsics.
usage:
camera_calibration_show_extrinsics.py [--calibration <input path>] [--cam_width] [--cam_height] [--scale_focal] [--patternCentric ]
default values:
--calibration : left_intrinsics.yml
--cam_width : 0.064/2
--cam_height : 0.048/2
--scale_focal : 40
--patternCentric : True
'''
# Python 2/3 compatibility
from __future__ import print_function
samples/python/common.py
+1 -1
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@@ -222,7 +222,7 @@ def mosaic(w, imgs):
pad = np.zeros_like(img0)
imgs = it.chain([img0], imgs)
rows = grouper(w, imgs, pad)
return np.vstack(map(np.hstack, rows))
return np.vstack(list(map(np.hstack, rows)))
def getsize(img):
h, w = img.shape[:2]
samples/python/digits.py
+1
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@@ -191,3 +191,4 @@ if __name__ == '__main__':
model.save('digits_svm.dat')
cv.waitKey(0)
cv.destroyAllWindows()
samples/python/digits_video.py
+1 -1
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@@ -29,7 +29,7 @@ def main():
src = sys.argv[1]
except:
src = 0
cap = video.create_capture(src)
cap = video.create_capture(src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('sudoku.png')))
classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn):
samples/python/facedetect.py
+3 -3
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@@ -39,13 +39,13 @@ def main():
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', "haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade', "haarcascades/haarcascade_eye.xml")
cascade = cv.CascadeClassifier(cv.samples.findFile(cascade_fn))
nested = cv.CascadeClassifier(cv.samples.findFile(nested_fn))
cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('samples/data/lena.jpg')))
cam = create_capture(video_src, fallback='synth:bg={}:noise=0.05'.format(cv.samples.findFile('lena.jpg')))
while True:
_ret, img = cam.read()
samples/python/qrcode.py
+2
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@@ -245,4 +245,6 @@ def main():
if __name__ == '__main__':
print(__doc__)
main()
cv.destroyAllWindows()
samples/python/stitching_detailed.py
+2 -2
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@@ -246,9 +246,9 @@ def get_matcher(args):
if matcher_type == "affine":
matcher = cv.detail_AffineBestOf2NearestMatcher(False, try_cuda, match_conf)
elif range_width == -1:
matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf)
matcher = cv.detail_BestOf2NearestMatcher(try_cuda, match_conf)
else:
matcher = cv.detail.BestOf2NearestRangeMatcher_create(range_width, try_cuda, match_conf)
matcher = cv.detail_BestOf2NearestRangeMatcher(range_width, try_cuda, match_conf)
return matcher
samples/python/text_skewness_correction.py
+6 -4
View File
@@ -15,7 +15,7 @@ import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--image", required=True, help="path to input image file")
parser.add_argument("-i", "--image", default="imageTextR.png", help="path to input image file")
args = vars(parser.parse_args())
# load the image from disk
@@ -37,9 +37,9 @@ def main():
coords = cv.findNonZero(thresh)
angle = cv.minAreaRect(coords)[-1]
# the `cv.minAreaRect` function returns values in the
# range [-90, 0) if the angle is less than -45 we need to add 90 to it
if angle < -45:
angle = (90 + angle)
# range [0, 90) if the angle is more than 45 we need to subtract 90 from it
if angle > 45:
angle = (angle - 90)
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
@@ -55,4 +55,6 @@ def main():
if __name__ == "__main__":
print(__doc__)
main()
cv.destroyAllWindows()
samples/python/tracker.py
+32 -36
View File
@@ -1,5 +1,4 @@
#!/usr/bin/env python
'''
Tracker demo
@@ -36,43 +35,49 @@ class App(object):
def __init__(self, args):
self.args = args
self.trackerAlgorithm = args.tracker_algo
self.tracker = self.createTracker()
def initializeTracker(self, image, trackerAlgorithm):
def createTracker(self):
if self.trackerAlgorithm == 'mil':
tracker = cv.TrackerMIL_create()
elif self.trackerAlgorithm == 'goturn':
params = cv.TrackerGOTURN_Params()
params.modelTxt = self.args.goturn
params.modelBin = self.args.goturn_model
tracker = cv.TrackerGOTURN_create(params)
elif self.trackerAlgorithm == 'dasiamrpn':
params = cv.TrackerDaSiamRPN_Params()
params.model = self.args.dasiamrpn_net
params.kernel_cls1 = self.args.dasiamrpn_kernel_cls1
params.kernel_r1 = self.args.dasiamrpn_kernel_r1
tracker = cv.TrackerDaSiamRPN_create(params)
else:
sys.exit("Tracker {} is not recognized. Please use one of three available: mil, goturn, dasiamrpn.".format(self.trackerAlgorithm))
return tracker
def initializeTracker(self, image):
while True:
if trackerAlgorithm == 'mil':
tracker = cv.TrackerMIL_create()
elif trackerAlgorithm == 'goturn':
params = cv.TrackerGOTURN_Params()
params.modelTxt = self.args.goturn
params.modelBin = self.args.goturn_model
tracker = cv.TrackerGOTURN_create(params)
elif trackerAlgorithm == 'dasiamrpn':
params = cv.TrackerDaSiamRPN_Params()
params.model = self.args.dasiamrpn_net
params.kernel_cls1 = self.args.dasiamrpn_kernel_cls1
params.kernel_r1 = self.args.dasiamrpn_kernel_r1
tracker = cv.TrackerDaSiamRPN_create(params)
else:
sys.exit("Tracker {} is not recognized. Please use one of three available: mil, goturn, dasiamrpn.".format(trackerAlgorithm))
print('==> Select object ROI for tracker ...')
bbox = cv.selectROI('tracking', image)
print('ROI: {}'.format(bbox))
if bbox[2] <= 0 or bbox[3] <= 0:
sys.exit("ROI selection cancelled. Exiting...")
try:
tracker.init(image, bbox)
self.tracker.init(image, bbox)
except Exception as e:
print('Unable to initialize tracker with requested bounding box. Is there any object?')
print(e)
print('Try again ...')
continue
return tracker
return
def run(self):
videoPath = self.args.input
trackerAlgorithm = self.args.tracker_algo
camera = create_capture(videoPath, presets['cube'])
print('Using video: {}'.format(videoPath))
camera = create_capture(cv.samples.findFileOrKeep(videoPath), presets['cube'])
if not camera.isOpened():
sys.exit("Can't open video stream: {}".format(videoPath))
@@ -82,7 +87,7 @@ class App(object):
assert image is not None
cv.namedWindow('tracking')
tracker = self.initializeTracker(image, trackerAlgorithm)
self.initializeTracker(image)
print("==> Tracking is started. Press 'SPACE' to re-initialize tracker or 'ESC' for exit...")
@@ -92,7 +97,7 @@ class App(object):
print("Can't read frame")
break
ok, newbox = tracker.update(image)
ok, newbox = self.tracker.update(image)
#print(ok, newbox)
if ok:
@@ -101,7 +106,7 @@ class App(object):
cv.imshow("tracking", image)
k = cv.waitKey(1)
if k == 32: # SPACE
tracker = self.initializeTracker(image)
self.initializeTracker(image)
if k == 27: # ESC
break
@@ -112,22 +117,13 @@ if __name__ == '__main__':
print(__doc__)
parser = argparse.ArgumentParser(description="Run tracker")
parser.add_argument("--input", type=str, default="vtest.avi", help="Path to video source")
parser.add_argument("--tracker_algo", type=str, default="mil", help="One of three available tracking algorithms: mil, goturn, dasiamrpn")
parser.add_argument("--tracker_algo", type=str, default="mil", help="One of available tracking algorithms: mil, goturn, dasiamrpn")
parser.add_argument("--goturn", type=str, default="goturn.prototxt", help="Path to GOTURN architecture")
parser.add_argument("--goturn_model", type=str, default="goturn.caffemodel", help="Path to GOTERN model")
parser.add_argument("--dasiamrpn_net", type=str, default="dasiamrpn_model.onnx", help="Path to onnx model of DaSiamRPN net")
parser.add_argument("--dasiamrpn_kernel_r1", type=str, default="dasiamrpn_kernel_r1.onnx", help="Path to onnx model of DaSiamRPN kernel_r1")
parser.add_argument("--dasiamrpn_kernel_cls1", type=str, default="dasiamrpn_kernel_cls1.onnx", help="Path to onnx model of DaSiamRPN kernel_cls1")
parser.add_argument("--dasiamrpn_backend", type=int, default=0, help="Choose one of computation backends:\
0: automatically (by default),\
1: Halide language (http://halide-lang.org/),\
2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit),\
3: OpenCV implementation")
parser.add_argument("--dasiamrpn_target", type=int, default=0, help="Choose one of target computation devices:\
0: CPU target (by default),\
1: OpenCL,\
2: OpenCL fp16 (half-float precision),\
3: VPU")
args = parser.parse_args()
App(args).run()
cv.destroyAllWindows()