base class name resolution
fixed find_obj.py
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@ -16,6 +16,7 @@ endforeach(mp)
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ocv_list_filterout(candidate_deps "^opencv_cud(a|ev)")
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ocv_list_filterout(candidate_deps "^opencv_matlab$")
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ocv_list_filterout(candidate_deps "^opencv_ts$")
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ocv_list_filterout(candidate_deps "^opencv_adas$")
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ocv_add_module(${MODULE_NAME} BINDINGS OPTIONAL ${candidate_deps})
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@ -35,6 +36,7 @@ ocv_list_filterout(opencv_hdrs ".h$")
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ocv_list_filterout(opencv_hdrs "cuda")
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ocv_list_filterout(opencv_hdrs "cudev")
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ocv_list_filterout(opencv_hdrs "opencv2/objdetect/detection_based_tracker.hpp")
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ocv_list_filterout(opencv_hdrs "opencv2/ximgproc/structured_edge_detection.hpp")
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set(cv2_generated_hdrs
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"${CMAKE_CURRENT_BINARY_DIR}/pyopencv_generated_include.h"
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@ -267,7 +267,7 @@ class ClassInfo(object):
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#return sys.exit(-1)
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if self.bases and self.bases[0].startswith("cv::"):
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self.bases[0] = self.bases[0][4:]
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if self.bases and self.bases[0] == "cv::Algorithm":
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if self.bases and self.bases[0] == "Algorithm":
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self.isalgorithm = True
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for m in decl[2]:
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if m.startswith("="):
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@ -752,8 +752,19 @@ class PythonWrapperGenerator(object):
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% (classinfo.name, classinfo.cname))
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sys.exit(-1)
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self.classes[classinfo.name] = classinfo
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if classinfo.bases and not classinfo.isalgorithm:
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classinfo.isalgorithm = self.classes[classinfo.bases[0].replace("::", "_")].isalgorithm
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if classinfo.bases:
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chunks = classinfo.bases[0].split('::')
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base = '_'.join(chunks)
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while base not in self.classes and len(chunks)>1:
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del chunks[-2]
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base = '_'.join(chunks)
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if base not in self.classes:
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print("Generator error: unable to resolve base %s for %s"
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% (classinfo.bases[0], classinfo.name))
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sys.exit(-1)
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classinfo.bases[0] = "::".join(chunks)
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classinfo.isalgorithm |= self.classes[base].isalgorithm
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def split_decl_name(self, name):
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chunks = name.split('.')
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@ -4,7 +4,7 @@
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Feature-based image matching sample.
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USAGE
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find_obj.py [--feature=<sift|surf|orb|brisk>[-flann]] [ <image1> <image2> ]
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find_obj.py [--feature=<sift|surf|orb|akaze|brisk>[-flann]] [ <image1> <image2> ]
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--feature - Feature to use. Can be sift, surf, orb or brisk. Append '-flann'
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to feature name to use Flann-based matcher instead bruteforce.
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@ -23,14 +23,17 @@ FLANN_INDEX_LSH = 6
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def init_feature(name):
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chunks = name.split('-')
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if chunks[0] == 'sift':
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detector = cv2.SIFT()
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detector = cv2.xfeatures2d.SIFT()
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norm = cv2.NORM_L2
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elif chunks[0] == 'surf':
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detector = cv2.SURF(800)
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detector = cv2.xfeatures2d.SURF(800)
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norm = cv2.NORM_L2
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elif chunks[0] == 'orb':
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detector = cv2.ORB(400)
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norm = cv2.NORM_HAMMING
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elif chunks[0] == 'akaze':
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detector = cv2.AKAZE()
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norm = cv2.NORM_HAMMING
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elif chunks[0] == 'brisk':
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detector = cv2.BRISK()
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norm = cv2.NORM_HAMMING
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@ -136,8 +139,8 @@ if __name__ == '__main__':
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try:
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fn1, fn2 = args
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except:
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fn1 = '../c/box.png'
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fn2 = '../c/box_in_scene.png'
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fn1 = '../cpp/box.png'
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fn2 = '../cpp/box_in_scene.png'
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img1 = cv2.imread(fn1, 0)
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img2 = cv2.imread(fn2, 0)
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