def run(img_path, cfg):
'''Wrapper over OpenCV SURF.
Parameters
----------
img_path (str): Path to images.
cfg: (Namespace): Configuration.
'''
# Init opencv feature extractor
if cfg.method_dict['config_common']['keypoint'].lower() == 'surf-def':
feature = cv2.xfeatures2d.SURF_create()
elif cfg.method_dict['config_common']['keypoint'].lower() == 'surf-lowth':
feature = cv2.xfeatures2d.SURF_create(hessianThreshold=0)
else:
raise RuntimeError('Unknown local feature type')
# Load Image
img, _ = load_image(img_path, use_color_image=False, crop_center=False)
# Compute features
kp, desc = feature.detectAndCompute(img, None)
# Convert opencv keypoints into our benchmark format
kp, desc = convert_opencv_kp_desc(
kp, desc, cfg.method_dict['config_common']['num_keypoints'])
result = {}
result['kp'] = [p[0:2] for p in kp]
result['scale'] = [p[2] for p in kp]
result['angle'] = [p[3] for p in kp]
result['score'] = [p[4] for p in kp]
result['descs'] = desc
return result
def run(img_path, cfg):
'''Wrapper over OpenCV ORB.
Parameters
----------
img_path (str): Path to images.
cfg: (Namespace): Configuration.
'''
# Init opencv feature extractor
feature = cv2.ORB_create(
nfeatures=cfg.method_dict['config_common']['num_keypoints'])
# Load Image
img, _ = load_image(img_path, use_color_image=False, crop_center=False)
# Compute features
kp, desc = feature.detectAndCompute(img, None)
# Convert opencv keypoints into our benchmark format
kp, desc = convert_opencv_kp_desc(
kp, desc, cfg.method_dict['config_common']['num_keypoints'])
result = {}
result['kp'] = [p[0:2] for p in kp]
result['descs'] = desc
return result
def run(img_path, cfg):
'''Wrapper over OpenCV SIFT.
Parameters
----------
img_path (str): Path to images.
cfg: (Namespace): Configuration.
Valid keypoint methods: "sift-def" (standard detection threshold)
and "sift-lowth" (lowered detection threshold to extract 8000 features).
Optional suffixes: "-clahe" (applies CLAHE over the image).
Valid descriptors methods: "sift" and "rootsift".
Optional suffixes: "-clahe" (applies CLAHE over the image), "upright"
(sets keypoint orientations to 0, removing duplicates).
'''
# Parse options
kp_name = cfg.method_dict['config_common']['keypoint'].lower()
desc_name = cfg.method_dict['config_common']['descriptor'].lower()
num_kp = cfg.method_dict['config_common']['num_keypoints']
# Do a strict name check to prevent mistakes (e.g. due to flag order)
if kp_name == 'sift-def':
use_lower_det_th = False
use_clahe_det = False
elif kp_name == 'sift-lowth':
use_lower_det_th = True
use_clahe_det = False
elif kp_name == 'sift-def-clahe':
use_lower_det_th = False
use_clahe_det = True
elif kp_name == 'sift-lowth-clahe':
use_lower_det_th = True
use_clahe_det = True
else:
raise ValueError('Unknown detector')
if desc_name == 'sift':
use_rootsift = False
use_clahe_desc = False
use_upright = False
elif desc_name == 'rootsift':
use_rootsift = True
use_clahe_desc = False
use_upright = False
elif desc_name == 'sift-clahe':
use_rootsift = False
use_clahe_desc = True
use_upright = False
elif desc_name == 'rootsift-clahe':
use_rootsift = True
use_clahe_desc = True
use_upright = False
elif desc_name == 'sift-upright':
use_rootsift = False
use_clahe_desc = False
use_upright = True
elif desc_name == 'rootsift-upright':
use_rootsift = True
use_clahe_desc = False
use_upright = True
elif desc_name == 'sift-clahe-upright':
use_rootsift = False
use_clahe_desc = True
use_upright = True
elif desc_name == 'rootsift-clahe-upright':
use_rootsift = True
use_clahe_desc = True
use_upright = True
else:
raise ValueError('Unknown descriptor')
# print('Extracting SIFT features with'
# ' use_lower_det_th={},'.format(use_lower_det_th),
# ' use_clahe_det={},'.format(use_clahe_det),
# ' use_rootsift={},'.format(use_rootsift),
# ' use_clahe_desc={},'.format(use_clahe_desc),
# ' use_upright={}'.format(use_upright))
# Initialize feature extractor
if use_lower_det_th:
feature = cv2.xfeatures2d.SIFT_create(contrastThreshold=-10000,
edgeThreshold=-10000)
else:
feature = cv2.xfeatures2d.SIFT_create()
# Load image, for detection
if use_clahe_det:
img_det, _ = load_image(img_path,
use_color_image=True,
crop_center=False)
img_det = l_clahe(img_det)
else:
img_det, _ = load_image(img_path,
use_color_image=False,
crop_center=False)
# Load image, for descriptor extraction
if use_clahe_desc:
img_desc, _ = load_image(img_path,
use_color_image=True,
crop_center=False)
img_desc = l_clahe(img_desc)
else:
img_desc, _ = load_image(img_path,
use_color_image=False,
crop_center=False)
# Get keypoints
kp = feature.detect(img_det, None)
# Compute descriptors
if use_upright:
unique_kp = []
for i, x in enumerate(kp):
if i > 0:
if x.response == kp[i - 1].response:
continue
x.angle = 0
unique_kp.append(x)
unique_kp, unique_desc = feature.compute(img_desc, unique_kp, None)
top_resps = np.array([x.response for x in unique_kp])
idxs = np.argsort(top_resps)[::-1]
kp = np.array(unique_kp)[idxs[:min(len(unique_kp), num_kp)]]
desc = unique_desc[idxs[:min(len(unique_kp), num_kp)]]
else:
kp, desc = feature.compute(img_desc, kp, None)
# Use root-SIFT
if use_rootsift:
desc /= desc.sum(axis=1, keepdims=True) + 1e-8
desc = np.sqrt(desc)
# Convert opencv keypoints into our format
kp, desc = convert_opencv_kp_desc(kp, desc, num_kp)
result = {}
result['kp'] = [p[0:2] for p in kp]
result['scale'] = [p[2] for p in kp]
result['angle'] = [p[3] for p in kp]
result['score'] = [p[4] for p in kp]
result['descs'] = desc
return result