本文整理汇总了Python中cv2.NORM_L2属性的典型用法代码示例。如果您正苦于以下问题:Python cv2.NORM_L2属性的具体用法?Python cv2.NORM_L2怎么用?Python cv2.NORM_L2使用的例子?那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类cv2
的用法示例。
在下文中一共展示了cv2.NORM_L2属性的13个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __rotate_image_size_corrected
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def __rotate_image_size_corrected(image, angle):
# Calculate max size for the rotated template and image offset
image_size_height, image_size_width = image.shape
image_center_x = image_size_width // 2
image_center_y = image_size_height // 2
# Create rotation matrix
rotation_matrix = cv2.getRotationMatrix2D((image_center_x, image_center_y), -angle, 1)
# Apply offset
new_image_size = int(math.ceil(cv2.norm((image_size_height, image_size_width), normType=cv2.NORM_L2)))
rotation_matrix[0, 2] += (new_image_size - image_size_width) / 2
rotation_matrix[1, 2] += (new_image_size - image_size_height) / 2
# Apply rotation to the template
image_rotated = cv2.warpAffine(image, rotation_matrix, (new_image_size, new_image_size))
return image_rotated
示例2: filter_matrix_corners_homography
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def filter_matrix_corners_homography(pts, max, matrix) -> (float, List):
'''
Compute the images of the image corners and of its center (i.e. the points you get when you apply the homography to those corners and center),
and verify that they make sense, i.e. are they inside the image canvas (if you expect them to be)? Are they well separated from each other?
Return a distance and a list of the transformed points
'''
# Transform the 4 corners thanks to the transformation matrix calculated
transformed_pts = cv2.perspectiveTransform(pts, matrix)
# Compute the difference between original and modified position of points
dist = round(cv2.norm(pts - transformed_pts, cv2.NORM_L2) / max, 10) # sqrt((X1-X2)²+(Y1-Y2)²+...)
# Totally an heuristic (geometry based):
if dist < 0.20:
return dist, transformed_pts
else:
return 1, transformed_pts
示例3: filter_matrix_corners_affine
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def filter_matrix_corners_affine(pts, max, matrix) -> (float, List):
'''
Compute the images of the image corners and of its center (i.e. the points you get when you apply the homography to those corners and center),
and verify that they make sense, i.e. are they inside the image canvas (if you expect them to be)? Are they well separated from each other?
Return a distance and a list of the transformed points
'''
# Make affine transformation
add_row = np.array([[0, 0, 1]])
affine_matrix = np.concatenate((matrix, add_row), axis=0)
transformed_pts_affine = cv2.perspectiveTransform(pts, affine_matrix)
# Affine distance
tmp_dist_affine = round(cv2.norm(pts - transformed_pts_affine, cv2.NORM_L2) / max, 10) # sqrt((X1-X2)²+(Y1-Y2)²+...)
# Totally an heuristic (geometry based):
if tmp_dist_affine < 0.20:
return tmp_dist_affine, transformed_pts_affine
else:
return 1, transformed_pts_affine
示例4: init_feature
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.SURF(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
示例5: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def __init__(self, norm_type=cv2.NORM_HAMMING, cross_check = False, ratio_test=kRatioTest, type = FeatureMatcherTypes.FLANN):
super().__init__(norm_type=norm_type, cross_check=cross_check, ratio_test=ratio_test, type=type)
if norm_type == cv2.NORM_HAMMING:
# FLANN parameters for binary descriptors
FLANN_INDEX_LSH = 6
self.index_params= dict(algorithm = FLANN_INDEX_LSH, # Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) # 2
if norm_type == cv2.NORM_L2:
# FLANN parameters for float descriptors
FLANN_INDEX_KDTREE = 1
self.index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 4)
self.search_params = dict(checks=32) # or pass empty dictionary
self.matcher = cv2.FlannBasedMatcher(self.index_params, self.search_params)
self.matcher_name = 'FlannFeatureMatcher'
示例6: matching
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def matching(desc1, desc2, do_ratio_test=False, cross_check=True):
if desc1.dtype == np.bool and desc2.dtype == np.bool:
desc1, desc2 = np.packbits(desc1, axis=1), np.packbits(desc2, axis=1)
norm = cv2.NORM_HAMMING
else:
desc1, desc2 = np.float32(desc1), np.float32(desc2)
norm = cv2.NORM_L2
if do_ratio_test:
matches = []
matcher = cv2.BFMatcher(norm)
for m, n in matcher.knnMatch(desc1, desc2, k=2):
m.distance = 1.0 if (n.distance == 0) else m.distance / n.distance
matches.append(m)
else:
matcher = cv2.BFMatcher(norm, crossCheck=cross_check)
matches = matcher.match(desc1, desc2)
return matches_cv2np(matches)
示例7: init_feature
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.SURF(400)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
示例8: init_feature
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.xfeatures2d.SIFT_create()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.xfeatures2d.SURF_create(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB_create(400)
norm = cv2.NORM_HAMMING
elif chunks[0] == 'akaze':
detector = cv2.AKAZE_create()
norm = cv2.NORM_HAMMING
elif chunks[0] == 'brisk':
detector = cv2.BRISK_create()
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
示例9: compute_matrix_pictures_corners
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def compute_matrix_pictures_corners():
# Get the size of the current matching picture
# TODO : Store somewhere the shape of the uploaded picture ?
# h, w, d = pic1.image.shape
# TODO : For now, just take a random size picture
h, w, d = 1000, 1000, 3
# Get the position of the 4 corners of the current matching picture
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
max = 4 * cv2.norm(np.float32([[w, h]]), cv2.NORM_L2)
return pts, max
示例10: _calc_reprojection_error
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def _calc_reprojection_error(self,figure_size=(8,8),save_dir=None):
"""
Util function to Plot reprojection error
"""
reprojection_error = []
for i in range(len(self.calibration_df)):
imgpoints2, _ = cv2.projectPoints(self.calibration_df.obj_points[i], self.calibration_df.rvecs[i], self.calibration_df.tvecs[i], self.camera_matrix, self.dist_coefs)
temp_error = cv2.norm(self.calibration_df.img_points[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2)
reprojection_error.append(temp_error)
self.calibration_df['reprojection_error'] = pd.Series(reprojection_error)
avg_error = np.sum(np.array(reprojection_error))/len(self.calibration_df.obj_points)
x = [os.path.basename(p) for p in self.calibration_df.image_names]
y_mean = [avg_error]*len(self.calibration_df.image_names)
fig,ax = plt.subplots()
fig.set_figwidth(figure_size[0])
fig.set_figheight(figure_size[1])
# Plot the data
ax.scatter(x,reprojection_error,label='Reprojection error', marker='o') #plot before
# Plot the average line
ax.plot(x,y_mean, label='Mean Reprojection error', linestyle='--')
# Make a legend
ax.legend(loc='upper right')
for tick in ax.get_xticklabels():
tick.set_rotation(90)
# name x and y axis
ax.set_title("Reprojection_error plot")
ax.set_xlabel("Image_names")
ax.set_ylabel("Reprojection error in pixels")
if save_dir:
plt.savefig(os.path.join(save_dir,"reprojection_error.png"))
plt.show()
print("The Mean Reprojection Error in pixels is: {}".format(avg_error))
示例11: init_feature
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def init_feature(name):
chunks = name.split('-')
if chunks[0] == 'sift':
detector = cv2.xfeatures2d.SIFT()
norm = cv2.NORM_L2
elif chunks[0] == 'surf':
detector = cv2.xfeatures2d.SURF(800)
norm = cv2.NORM_L2
elif chunks[0] == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
elif chunks[0] == 'akaze':
detector = cv2.AKAZE()
norm = cv2.NORM_HAMMING
elif chunks[0] == 'brisk':
detector = cv2.BRISK()
norm = cv2.NORM_HAMMING
else:
return None, None
if 'flann' in chunks:
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
else:
matcher = cv2.BFMatcher(norm)
return detector, matcher
示例12: normalize_result
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def normalize_result(webcam, idcard):
diff_correy = cv2.norm(settings.COREY_MATRIX, idcard, cv2.NORM_L2)
diff_wilde = cv2.norm(settings.WILDE_MATRIX, idcard, cv2.NORM_L2)
diff_min = diff_correy if diff_correy < diff_wilde else diff_wilde
diff = cv2.norm(webcam, idcard, cv2.NORM_L2)
score = float(diff) / float(diff_min)
percentage = (1.28 - score * score * score) * 10000 / 128
return {
'percentage': percentage,
'score': score,
'message': utils.matching_message(score)
}
示例13: configure
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import NORM_L2 [as 别名]
def configure():
global detect_scale
global the_matcher
global max_distance
global min_pairs
detect_scale = detector_node.getFloat('scale')
detector_str = detector_node.getString('detector')
if detector_str == 'SIFT' or detector_str == 'SURF':
norm = cv2.NORM_L2
max_distance = 270.0
elif detector_str == 'ORB' or detector_str == 'Star':
norm = cv2.NORM_HAMMING
max_distance = 64
else:
log("Detector not specified or not known:", detector_str)
quit()
# work around a feature/bug: flann enums don't exist
FLANN_INDEX_KDTREE = 1
FLANN_INDEX_LSH = 6
if norm == cv2.NORM_L2:
flann_params = {
'algorithm': FLANN_INDEX_KDTREE,
'trees': 5
}
else:
flann_params = {
'algorithm': FLANN_INDEX_LSH,
'table_number': 6, # 12
'key_size': 12, # 20
'multi_probe_level': 1 #2
}
search_params = {
'checks': 100
}
the_matcher = cv2.FlannBasedMatcher(flann_params, search_params)
min_pairs = matcher_node.getFloat('min_pairs')
# Iterate through all the matches for the specified image and
# delete keypoints that don't satisfy the homography (or
# fundamental) relationship. Returns true if match set is clean, false
# if keypoints were removed.
#
# Notice: this tends to eliminate matches that aren't all on the
# same plane, so if the scene has a lot of depth, this could knock
# out a lot of good matches.