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Most of the photos from this sample:link_to_sample_on_dropbox are not detected as containing asymmetric circles calibration pattern. It can see only IMG_0565.jpg IMG_0566.jpg IMG_0568.jpg IMG_0569.jpg. Videos from the same device (iPad 3) and of the same calibration pattern are detected fine. So, I wonder, what am I doing wrong? Maybe it is because of EXIF signature generated by iOS devices? Maybe camera orientation, while taking photos, matters?

Most of the photos from this sample:link_to_sample_on_dropbox are not detected as containing asymmetric circles calibration pattern. It can see only IMG_0565.jpg IMG_0566.jpg IMG_0568.jpg IMG_0569.jpg. Videos from the same device (iPad 3) and of the same calibration pattern are detected fine. So, I wonder, what am I doing wrong?

Most of the photos from this sample:link_to_sample_on_dropbox are not detected as containing asymmetric circles calibration pattern. It can see only IMG_0565.jpg IMG_0566.jpg IMG_0568.jpg IMG_0569.jpg. Videos from the same device (iPad 3) and of the same calibration pattern are detected fine. So, I wonder, what am I doing wrong?

Code for calibration script is taken unchanged from opencv-2.4.9/sample/cpp/calibration.cpp

Command line string: ./calibration -pt acircles -w 4 -h 11 imagelist.yaml

(also tried ./calibration -pt acircles -w 11 -h 4 imagelist.yaml)

imagelist.yaml is attached: imagelist.yaml.jpg (please remove suffix '.jpg')

My ideas: Maybe it is because of EXIF signature generated by iOS devices? Maybe camera orientation, while taking photos, matters?