def _get_feature(self, img, feature_type=0):
'''
Get features from give image.\n
img: np.array\n
feature_type: int, 0:ORB, 1:SIFT, 2:FAST+ORB descriptor\n
return: keypoints, descriptor
'''
img = self._gaussian_filter(img)
if feature_type == 0:
# ORB
orb = cv.ORB_create()
kp, des = orb.detectAndCompute(img, None)
return kp, des
elif feature_type == 1:
# SIFT
sift = cv.xfeatures2d.SIFT_create()
kp, des = sift.detectAndCompute(img, None)
return kp, des
elif feature_type == 2:
# FAST corners + SIFT descriptors
fast = cv.FastFeatureDetector_create()
sift = cv.xfeatures2d.SIFT_create()
kp = fast.detect(img)
kp, des = sift.compute(img, kp)
return kp, des
else:
print('Wrong feature type code!')
return None
def fast_corner_detect(image):
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
gray = cv.GaussianBlur(gray, (9, 9), 25) # sigma
fast = cv.FastFeatureDetector_create()
kp = fast.detect(gray, None)
image = cv.drawKeypoints(image, kp, None, color=(255, 0, 0))
cv.imshow('image for fast', image)
def fast_detection(image):
fast = cv2.FastFeatureDetector_create()
kp = fast.detect(image, None)
fast.setNonmaxSuppression(0)
image_with_keypoints = cv2.drawKeypoints(image,
kp,
None,
color=(255, 0, 0))
return image_with_keypoints
def FAST(self):
img = cv2.imread(os.path.join(root, '..', 'static', 'photos', session['org_img']))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
fast = cv2.FastFeatureDetector_create(threshold=25)
# find and draw the keypoints
kp = fast.detect(gray, None)
gray = cv2.drawKeypoints(gray, kp, None, color=(255, 0, 0))
filename = str(randint(1000000000, 9999999999)) + session['org_img']
cv2.imwrite(os.path.join(root, '..', 'static', 'photos', filename), gray)
session['corner_img'] = filename
import numpy as np
from cv2 import cv2
img = cv2.imread('resource/block_test.png', 0)
# Initiate FAST object with default values
fast = cv2.FastFeatureDetector_create()
# find and draw the keypoints
kp = fast.detect(img, None)
img2 = None
img2 = cv2.drawKeypoints(img, kp, img2, color=(255, 0, 0))
# Print all default params
# print "Threshold: ", fast.('threshold')
# print "nonmaxSuppression: ", fast.getBool('nonmaxSuppression')
# print "neighborhood: ", fast.getInt('type')
# print "Total Keypoints with nonmaxSuppression: ", len(kp)
# Disable nonmaxSuppression
# fast.setBool('nonmaxSuppression',0)
fast = cv2.FastFeatureDetector_create(nonmaxSuppression=0)
kp = fast.detect(img)
print "Total Keypoints without nonmaxSuppression: ", len(kp)
img3 = None
img3 = cv2.drawKeypoints(img, kp, img3, color=(255, 0, 0))
cv2.imshow('fast', img2)
cv2.imshow('fast nonmaxSuppression', img3)
print("[INFO] computing Precision!")
correct_matches = int(
input("Please enter the number of correct matches: "))
incorrect_matches = 10 - correct_matches
coresponding_matches = len(matches[:10])
thePrecision[0, 1] = correct_matches / (correct_matches + incorrect_matches)
print("[INFO] BRISK was done...!")
# BRIEF ################################################################
print("[INFO] Starting BRIEF!")
# Making a instance of class FAST for feature detecting
FAST = cv2.FastFeatureDetector_create()
FAST.setThreshold(80)
print("[INFO] stage 1: extracting features!")
kp1 = FAST.detect(img1, None)
kp2 = FAST.detect(img2, None)
# showing keypoints in images
img11 = cv2.drawKeypoints(img1,
kp1,
None,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
img22 = cv2.drawKeypoints(img2,
kp2,
None,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
return matrix
#Calibration parameters for algorithm
STEPSIZE = 4
FPS_MULT = 1 #For very high FPS videos
SCALE = 1
FRAME_SIZE = (800, 600)
CMAT = ReadCameraMat(CMAT_GOPRO)
FPS = 30
if __name__ == '__main__':
cap = cv2.VideoCapture(vid_path4) #Change video path for different video
fast = cv2.FastFeatureDetector_create(threshold=60,
nonmaxSuppression=True,
type=1) #Feature Detector
frame_counter = 1
trajectory_map = np.zeros((800, 800, 3), dtype=np.uint8)
featureList = ft.FeatureList([]) #List of actively Tracked Features
roadFeatureList = ft.FeatureList([])
# Initializations
print(CMAT)
update_flag = True
camera_tf = np.eye(3)
trans_f = [0, 0, 0]
trans_f1 = trans_f
trans_f_prev = trans_f
trans = np.array([[0], [0], [0]])