def onmouse(event, x, y, flags, param):
#cur_vis = vis
if flags & cv2.EVENT_FLAG_LBUTTON:
vis = vis0.copy()
r = 8
m = (anorm(p1 - (x, y)) < r) | (anorm(p2 - (x, y)) < r)
idxs = np.where(m)[0]
for i in idxs:
status[i] = not status[i]
draw_keypoints(vis)
cv2.imshow(win, vis)
if flags & cv2.EVENT_RBUTTONDOWN:
vis = vis0.copy()
r = 8
m = (anorm(p1 - (x, y)) < r) | (anorm(p2 - (x, y)) < r)
idxs = np.where(m)[0]
kp1s, kp2s = [], []
for i in idxs:
(x1, y1), (x2, y2) = p1[i], p2[i]
col = (red, green)[status[i]]
cv2.line(vis, (x1, y1), (x2, y2), col)
kp1, kp2 = kp_pairs[i]
kp1s.append(kp1)
kp2s.append(kp2)
vis = cv2.drawKeypoints(vis, kp1s, flags=4, color=kp_color)
vis[:,w1:] = cv2.drawKeypoints(vis[:,w1:], kp2s, flags=4, color=kp_color)
cv2.imshow(win, vis)
def FASTDetection():
#initiate the FAST object with default values
fast = cv2.FastFeatureDetector()
#Find and draw out the key points
kp = fast.detect(img,None)
img2 = cv2.drawKeypoints(img, kp, color = (0, 255, 0))
#print all default params
print "Threshold: ", fast.getInt('threshold')
print "nonmaxSuppression: ", fast.getBool('nonmaxSuppression')
#print "neighborhood: ", fast.getInt('type')
print "Total Keypoints with nonmaxSuppression: ", len(kp)
cv2.imshow('dst',img2)
if cv2.waitKey(0) & 0xff == 27:
cv2.destroyAllWindows()
#Disable nomaxSuppression
fast.setBool('nonmaxSuppression',0)
kp = fast.detect(img, None)
img3 = cv2.drawKeypoints(img, kp, color = (0 , 0 ,255))
print "Total Keypoints with maxSuppression: ", len(kp)
cv2.imshow('dst',img3)
if cv2.waitKey(0) & 0xff == 27:
cv2.destroyAllWindows()
def match(img1, img2): sift = cv2.xfeatures2d.SIFT_create() kp1, des1 = sift.detectAndCompute(img1,None) kp1_img = cv2.drawKeypoints(img1, kp1, img1, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) kp2, des2 = sift.detectAndCompute(img2,None) kp2_img=cv2.drawKeypoints(img2,kp2,img2,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) bf = cv2.BFMatcher() matches = bf.knnMatch(des1,des2, k=2) good = [] src = [] dst = [] for m,n in matches: if m.distance < 0.7*n.distance: good.append([m]) src_pts = np.float32([ kp1[m[0].queryIdx].pt for m in good ]).reshape(-1,1,2) des_cor = [kp2[m[0].trainIdx].pt for m in good] dst_pts = np.float32(des_cor).reshape(-1,1,2) centroid = findCentroid(des_cor) img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good, img2, flags=2) M, mask = cv2.findHomography(src_pts, dst_pts) return (matches, kp1_img, kp2_img, img3, M, centroid)
def find_features():
print ("Position the board and press SPACE to continue")
k = 0xFF & cv2.waitKey(1)
while k != ord(' '):
status, img1 = cap.read()
k = 0xFF & cv2.waitKey(1)
status, img1 = cap.read()
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
#img1 = img1[80:160, 80:240]
cv2.imshow("Video", img1)
kp1, des1 = orb_f(img1)
img = cv2.drawKeypoints(img1,kp1,dummy,color=(0,255,0), flags=0)
cv2.imshow("Video", img)
print ('Please move the patern and press SPACE when you are ready,'
'or press ESC to cancel the calibration')
k = 0
while k != ord(' '):
status, img2 = cap.read()
k = 0xFF & cv2.waitKey(1)
if k == 27:
return [], []
status, img2 = cap.read()
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
cv2.imshow("Video", img2)
kp2, des2 = orb_f(img2)
img = cv2.drawKeypoints(img2,kp2,dummy,color=(0,255,0), flags=0)
cv2.imshow("Video", img)
print ("Press SPACE to continue")
k = 0xFF & cv2.waitKey(0)
print ("Done!")
return kp1, des1, img1, kp2, des2, img2
def do_fast(img, file_true, file_false):
# Initiate FAST object with default values
fast = cv2.FastFeatureDetector(50)
# find and draw the keypoints
kp = fast.detect(img, None)
img2 = cv2.drawKeypoints(img, kp, color=(255, 0, 0))
# Print all default params
#print "Threshold: ", fast.getInt('threshold')
#print "nonmaxSuppression: ", fast.getBool('nonmaxSuppression')
#print "neighborhood: ", fast.getInt('type')
#print "Total Keypoints with nonmaxSuppression: ", len(kp)
cv2.imwrite(file_true, img2)
# Disable nonmaxSuppression
fast.setBool('nonmaxSuppression',0)
kp = fast.detect(img,None)
print "Total Keypoints without nonmaxSuppression: ", len(kp)
img3 = cv2.drawKeypoints(img, kp, color=(255,0,0))
cv2.imwrite(file_false, img3)
def fast():
img = cv2.imread('test.jpg',0)
# Initiate FAST object with default values
fast = cv2.FastFeatureDetector()
# find and draw the keypoints
kp = fast.detect(img,None)
img2 = cv2.drawKeypoints(img, kp, color=(255,0,0))
# Print all default params
print "Threshold: ", fast.getInt('threshold')
print "nonmaxSuppression: ", fast.getBool('nonmaxSuppression')
# print "neighborhood: ", fast.getInt('type')
print "Total Keypoints with nonmaxSuppression: ", len(kp)
# Disable nonmaxSuppression
fast.setBool('nonmaxSuppression',0)
kp = fast.detect(img,None)
print "Total Keypoints without nonmaxSuppression: ", len(kp)
img3 = cv2.drawKeypoints(img, kp, color=(255,0,0))
cv2.imwrite('fast_false.png',img3)
cv2.imshow('img2', img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
def drawMatchesAndHomography(img1, img2, lkp1, lkp2, status, H, num=10):
imgm1=cv2.drawKeypoints(img1, lkp1, color=(0,0,255))
imgm2=cv2.drawKeypoints(img2, lkp2, color=(0,0,255))
w1=imgm1.shape[1]
#
siz1=(img1.shape[1],img1.shape[0])
arrROI=np.array([(0,0,1), (siz1[0],0,1), (siz1[0],siz1[1],1), (0,siz1[1],1), (0,0,1)])
prjROI=np.transpose(np.dot(H,np.transpose(arrROI)))
prjROI=prjROI/np.matlib.repmat(prjROI[:,2],3,1).transpose()
# img2n=imgm2.copy()
for ii in xrange(arrROI.shape[0]-1):
p1=(int(prjROI[ii+0,0] + 0*siz1[0]), int(prjROI[ii+0,1]))
p2=(int(prjROI[ii+1,0] + 0*siz1[0]), int(prjROI[ii+1,1]))
cv2.line(imgm2, p1,p2, color=(0,255,0), thickness=3)
#
totimg=np.concatenate((imgm1.copy(),imgm2.copy()), axis=1).copy()
cnt=0
cntb=0
for pp1,pp2 in zip(lkp1,lkp2):
if status[cnt]==1:
p1=(int(pp1.pt[0]), int(pp1.pt[1]))
p2=(int(pp2.pt[0])+w1, int(pp2.pt[1]))
cv2.line(totimg,p1,p2,(0,255,0))
if cntb>num:
break
cntb+=1
cnt+=1
return totimg
def sift_space(fileList): image_points = [] no_images = len(fileList) no_sift = 300 numpoints = no_images*no_sift X = np.zeros((numpoints,128)) i = 0 # sift = cv2.xfeatures2d.SIFT_create(nfeatures=no_sift) sift = cv2.SIFT(nfeatures=no_sift) no_im = 0 for fil in fileList: print fil im = cv2.imread(fil); gray= cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) (kps, descs) = sift.detectAndCompute(gray, None) print len(kps) no_sift = min(300, len(kps)) # print no_sift image_points.append([i,i+no_sift]) no_im += 1 X[i:i+no_sift] = descs[0:no_sift] i += no_sift img = im cv2.drawKeypoints(im,kps[0:no_sift],img) cv2.imwrite(kres_dir+fil[28:-4]+'2.jpg',img) # print X[0:i] # print image_points return [X[0:i],image_points]
def mergeImages(img1, img2, AlignMethod='', Jacobian='', **kwargs):
(kp1Matches, kp2Matches) = Alignment2D.ExtractFeatures(img1, img2, **kwargs)
Transform = Alignment2D.AlignImages(kp1Matches, kp2Matches, AlignMethod, Jacobian)
#Overlay the two images, showing the detected feature.
rows,cols,colours = img1.shape
Canvas1 = np.zeros((rows * 2, cols * 2, colours) , img1.dtype)
Canvas2 = np.copy(Canvas1)
finalRows, finalCols, colours = Canvas1.shape
tx = cols/2; # Translate to the center of the canvas
ty = rows/2;
M = np.float32([[1,0,tx],[0,1,ty],[0,0,1]])
img3 = cv2.drawKeypoints(img1, kp1Matches,color=(0,0,255))
cv2.warpPerspective(img3, M, (finalCols, finalRows), Canvas1)
finalTransform = np.dot(M, Transform) ; # Translate to the center of the canvas
img2 = cv2.drawKeypoints(img2, kp2Matches,color=(255,0,0))
cv2.warpPerspective(img2, finalTransform, (finalCols, finalRows), Canvas2, borderMode=cv2.BORDER_TRANSPARENT)
alpha = 0.5
beta = (1.0 - alpha)
cv2.addWeighted(Canvas1, alpha, Canvas2, beta, 0.0, Canvas1)
return Canvas1
def main():
args = sys.argv[1:]
sujeto1=args[0]
sujeto2=args[1]
im=Imagen("image-"+sujeto1+".jpg")
im1=Imagen("image-"+sujeto2+".jpg")
gray = cv2.cvtColor(im.imagen, cv2.COLOR_BGR2GRAY)
gray1 = cv2.cvtColor(im1.imagen, cv2.COLOR_BGR2GRAY)
orb=cv2.ORB()
vis = im.imagen.copy()
vis1 = im1.imagen.copy()
#
rects=f.detectCara(gray)
x1c, y1c, x2c, y2c=rects[0]
subrects,vis_roi=f.detectMouth(x1c,(y1c-y2c)*2/3,x2c,y2c,gray,vis)
x1, y1, x2, y2=subrects[0]
keypoints=orb.detect(im.imagen)
zona=(x1c+x1,y1c+y1,x2c+x2,y2c+y2)
#
rects=f.detectCara(gray)
x1, y1, x2, y2=rects[0]
subrects,vis_roi=f.detectMouth(x1,(y1-y2)*2/3,x2,y2,gray1,vis1)
x1, y1, x2, y2=subrects[0]
keypoints1=orb.detect(im1.imagen)
zona2=(x1c+x1,y1c+y1,x2c+x2,y2c+y2)
cv2.drawKeypoints(im.imagen,keypoints,im.imagen)
cv2.drawKeypoints(im1.imagen,keypoints1,im1.imagen)
c=coincidencias(keypoints1,keypoints,zona,zona2)
if c>10:
print True
else:
print False
def drawrelation(self):
if self.flags & cv2.EVENT_FLAG_LBUTTON:
x,y = self.rx, self.ry
cur_vis = self.vis0.copy() # actual visualization
r = self.thick + 8 # proximity to keypoint
m = (ar.anorm(self.rp1 - (x, y)) < r) | (ar.anorm(self.rp2 - (x, y)) < r)
idxs = np.where(m)[0] # get indexes near pointer
kp1s, kp2s = [], []
for i in idxs: # for all keypints near pointer
(rx1, ry1), (rx2, ry2) = self.rp1[i], self.rp2[i] # my keypoint
col = (self.badcolor, self.goodcolor)[status[i]] # choosing False=red,True=green
cv2.line(cur_vis, (rx1,ry1), (rx2,ry2), col, self.thick) # drawing line
# keypoints to show on event
kp1, kp2 = self.kp_pairs2[i]
kp1s.append(kp1)
kp2s.append(kp2)
# drawing keypoints near pointer for imgf and imgb
cur_vis = cv2.drawKeypoints(cur_vis, kp1s, flags=4, color=self.kpcolor)
cur_vis = cv2.drawKeypoints(cur_vis, kp2s, flags=4, color=self.kpcolor)
self.rimg = cur_vis
else:
self.rimg = self.vis
if self.y is not None and self.x is not None:
self.builtinplot(self.sample[self.y,self.x])
def identify_blobs(image,processed,size):
identified = image.copy()
#BLOB DETECTION ...
params = cv2.SimpleBlobDetector_Params()
params.minDistBetweenBlobs = 0
params.filterByColor = True
params.blobColor = 255
params.filterByArea = True
params.minArea = circleArea(size) * 0.3
params.maxArea = circleArea(size) * 2.0
params.filterByCircularity = False
params.filterByConvexity = True
params.minConvexity = 0.5
params.filterByInertia = False
detector = cv2.SimpleBlobDetector_create(params)
labels = detector.detect(processed)
cv2.drawKeypoints(identified,labels,identified,color=(255,0,0),flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
return len(labels), identified
def main():
org_image = cv2.imread("../data/house.tiff", 1)
'''
SURF is better than SIFT and computes and detects feature fast,
but unfortunately both are paid.
Alternative, we have ORB by OpenCV. Free. OSS.
PARAM: nfeatures : Number of features to be detected.
Default value is around 100.
'''
sift = cv2.xfeatures2d.SIFT_create()
surf = cv2.xfeatures2d.SURF_create()
orb = cv2.ORB_create(nfeatures=1000)
kp_sift, decep_sift = sift.detectAndCompute(org_image, None)
kp_surf, decep_sift = surf.detectAndCompute(org_image, None)
kp_orb, decep_sift = orb.detectAndCompute(org_image, None)
org_image_sift = cv2.drawKeypoints(org_image, kp_sift, None)
org_image_surf = cv2.drawKeypoints(org_image, kp_surf, None)
org_image_orb = cv2.drawKeypoints(org_image, kp_orb, None)
cv2.imshow("SIFT Features Detected", org_image_sift)
cv2.imshow("SURF Features Detected", org_image_surf)
cv2.imshow("ORB Features Detected", org_image_orb)
cv2.waitKey(0)
cv2.destroyAllWindows()
def blob_process(cv_image): hsv_cv_image = convert_bgr2hsv(cv_image) yellow_mask_hsv = cv2.inRange(hsv_cv_image, YELLOW_MIN_HSV, YELLOW_MAX_HSV) red_mask_hsv = cv2.inRange(hsv_cv_image, RED_MIN_HSV, RED_MAX_HSV) green_mask_hsv = cv2.inRange(hsv_cv_image, GREEN_MIN_HSV, GREEN_MAX_HSV) blue_mask_hsv = cv2.inRange(hsv_cv_image, BLUE_MIN_HSV, BLUE_MAX_HSV) yellow_masked_img = cv2.bitwise_and(cv_image, cv_image, mask=yellow_mask_hsv) red_masked_img = cv2.bitwise_and(cv_image, cv_image, mask=red_mask_hsv) green_masked_img = cv2.bitwise_and(cv_image, cv_image, mask=green_mask_hsv) blue_masked_img = cv2.bitwise_and(cv_image, cv_image, mask=blue_mask_hsv) yr_or_img = cv2.bitwise_or(yellow_masked_img, red_masked_img) bg_or_img = cv2.bitwise_or(green_masked_img, blue_masked_img) final_or_img = cv2.bitwise_or(yr_or_img, bg_or_img) #final_or_img = cv2.bitwise_or(yr_or_img, green_masked_img) #kernel = np.ones((5,5),np.uint8) kernel = np.array([[0,1,1,1,0],[1,1,1,1,1],[1,1,1,1,1],[1,1,1,1,1],[0,1,1,1,0]],np.uint8) eroded_masked_img = cv2.erode(final_or_img, kernel, iterations = 1) dilatated_masked_img = cv2.dilate(eroded_masked_img,kernel,iterations = 2) #ilatated_masked_img = cv2.dilate(cv_image,kernel,iterations = 1) #bilateral = cv2.bilateralFilter(cv_image, 2,25,25) return dilatated_masked_img #return bilateral bouy_detector = cv2.SimpleBlobDetector() keypoints = bouy_detector.detect(processed_cv_image) cv2.drawKeypoints(processed_cv_image, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) return processed_cv_image
def showImg(self, frame, keypoints, lines, contours):
if not self.args.nodisplay:
if keypoints:
frame = cv2.drawKeypoints(frame, [keypoints[0]],
np.array([]),
(0,0,255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
if len(keypoints) > 1:
frame = cv2.drawKeypoints(frame, keypoints[1:],
np.array([]),
(255,205,25),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
if lines != None:
for l in lines[0]:
cv2.line(frame, (l[0], l[1]), (l[2], l[3]), (20,255,255))
if contours != None:
contours0,hier = contours
cindex = self.values[3] # if -1, all are drawn
maxlevel = self.values[4]
if len(contours0) <= cindex:
self.putNotice("reset contour id")
values[3] = -1
cindex = -1
cv2.drawContours(frame, contours0, cindex,
(128,255,255),
thickness=1,
lineType=cv2.CV_AA,
hierarchy=hier,
maxLevel=maxlevel)
cv2.imshow("img", frame)
def onmouse(event, x, y, flags, param):
cur_vis = vis
if flags & cv2.EVENT_FLAG_LBUTTON:
cur_vis = vis0.copy()
r = 8
m = (anorm(np.array(p1) - (x, y)) < r) | (anorm(np.array(p2) - (x, y)) < r)
idxs = np.where(m)[0]
kp1s, kp2s = [], []
for i in idxs:
(x1, y1), (x2, y2) = p1[i], p2[i]
col = (red, green)[status[i]]
cv2.line(cur_vis, (x1, y1), (x2, y2), col)
kp1, kp2 = kp_pairs[i]
kp1s.append(kp1)
kp2s.append(kp2)
cur_vis = cv2.drawKeypoints(cur_vis, kp1s, None, flags=4, color=kp_color)
cur_vis[:,w1:] = cv2.drawKeypoints(cur_vis[:,w1:], kp2s, None, flags=4, color=kp_color)
cv2.imshow(win, cur_vis)
'''
b_channel, g_channel, r_channel = cv2.split(cur_vis)
alpha_channel = np.ones((cur_vis.shape[0], cur_vis.shape[1]),dtype=np.uint8) #creating a dummy alpha channel image.
alpha_channel.fill(255)
img_RGBA = cv2.merge((b_channel, g_channel, r_channel, alpha_channel))
'''
send1.send('image', cur_vis)
def SURF(img2, debug):
# Initiate SURF detector
surf = cv2.xfeatures2d.SURF_create()
# find the keypoints and descriptors with SURF
kp1, des1 = surf.detectAndCompute(img1,None)
kp2, des2 = surf.detectAndCompute(img2,None)
#draw the keypoints
cv2.drawKeypoints(img1,kp1,None,(255,0,0),4)
# BFMatcher (Brute Force Matcher) Iniitialize with default params
bf = cv2.BFMatcher()
#do brute force matching with k nearest neighbors
matches = bf.knnMatch(des1,des2, k=2)
# Apply distance ratio test
good = []
for m,n in matches:
if m.distance < 0.7*n.distance:
good.append(m)
if len(good)>10:
src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,flags=2)
if img3!= None and debug:
plt.imshow(img3, 'gray'),plt.show()
print "Number of Features: ", len(good)
return good
def two_a(a_img, a_Ix, a_Iy, a_corners, a_R, b_img, b_Ix, b_Iy, b_corners, b_R, a_run="foo", _size=5.0, _octave=0):
a_angle = gradient_angle(a_Ix, a_Iy)
a_kps = get_keypoints(a_corners, a_R, a_angle, _size, _octave)
# TODO: Draw keypoints on transA
b_angle = gradient_angle(b_Ix, b_Iy)
b_kps = get_keypoints(b_corners, b_R, b_angle, _size, _octave)
# TODO: Similarly, find keypoints for transB and draw them
# TODO: Combine transA and transB images (with keypoints drawn) using make_image_pair() and write to file
# make_image_pair(imgA, imgB)
# print a_img.shape
a_img_match = cv2.drawKeypoints(cv2.cvtColor(a_img.copy().astype(np.uint8), cv2.COLOR_GRAY2BGR), a_kps,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
b_img_match = cv2.drawKeypoints(cv2.cvtColor(b_img.copy().astype(np.uint8), cv2.COLOR_GRAY2BGR), b_kps,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
combined_img = make_image_pair(a_img_match, b_img_match)
if a_run == "transA":
write_image(combined_img, "ps5-2-a-1.png")
elif a_run == "simA":
write_image(combined_img, "ps5-2-a-2.png")
# TODO: Ditto for (simA, simB) pair
return a_kps, b_kps
def displayKeypoints(matchDict):
if s.DISPLAY() :
m = utils.Bunch(matchDict)
leftKpImg = cv2.drawKeypoints(m.left,m.leftKp,None,(255,0,0),4)
rightKpImg = cv2.drawKeypoints(m.right,m.rightKp,None,(255,0,0),4)
img3 = arragePairs(leftKpImg,rightKpImg)
cv2.imshow('Keypoints',img3)
def detect_bouys(processed_cv_image):
bouy_detector = cv2.SimpleBlobDetector()
keypoints = bouy_detector.detect(processed_cv_image)
cv2.drawKeypoints(
processed_cv_image, keypoints, np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
)
return processed_cv_image
def test_cv2_flann():
"""
Ignore:
[name for name in dir(cv2) if 'create' in name.lower()]
[name for name in dir(cv2) if 'stereo' in name.lower()]
ut.grab_zipped_url('https://priithon.googlecode.com/archive/a6117f5e81ec00abcfb037f0f9da2937bb2ea47f.tar.gz', download_dir='.')
"""
import cv2
from vtool.tests import dummy
import plottool as pt
import vtool as vt
img1 = vt.imread(ut.grab_test_imgpath('easy1.png'))
img2 = vt.imread(ut.grab_test_imgpath('easy2.png'))
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
disparity = stereo.compute(img1, img2)
pt.imshow(disparity)
pt.show()
#cv2.estima
flow = cv2.createOptFlow_DualTVL1()
img1, img2 = vt.convert_image_list_colorspace([img1, img2], 'gray', src_colorspace='bgr')
img2 = vt.resize(img2, img1.shape[0:2][::-1])
out = img1.copy()
flow.calc(img1, img2, out)
orb = cv2.ORB_create()
kp1, vecs1 = orb.detectAndCompute(img1, None)
kp2, vecs2 = orb.detectAndCompute(img2, None)
detector = cv2.FeatureDetector_create("SIFT")
descriptor = cv2.DescriptorExtractor_create("SIFT")
skp = detector.detect(img1)
skp, sd = descriptor.compute(img1, skp)
tkp = detector.detect(img2)
tkp, td = descriptor.compute(img2, tkp)
out = img1.copy()
cv2.drawKeypoints(img1, kp1, outImage=out)
pt.imshow(out)
vecs1 = dummy.testdata_dummy_sift(10)
vecs2 = dummy.testdata_dummy_sift(10) # NOQA
FLANN_INDEX_KDTREE = 0 # bug: flann enums are missing
#flann_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=4)
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50) # or pass empty dictionary
flann = cv2.FlannBasedMatcher(index_params, search_params) # NOQA
cv2.flann.Index(vecs1, index_params)
#cv2.FlannBasedMatcher(flann_params)
cv2.flann.Index(vecs1, flann_params) # NOQA
def showMatchingPoints(src_image,src_matches,dest_image,dest_matches):
src_kp_image = ocv.drawKeypoints(src_image, src_matches, color=(0,255,255))
dest_kp_image = ocv.drawKeypoints(dest_image, dest_matches, color=(0,255,255))
plot.subplot(2,1,1)
plot.imshow(src_kp_image)
plot.subplot(2,1,2)
plot.imshow(dest_kp_image)
plot.show()
def Detect_and_Draw():
img = cv2.imread('1.JPG', cv2.IMREAD_COLOR)
G_img = cv2.cvtColor(img, cv2.IMREAD_GRAYSCALE)
orb = cv2.ORB_create()
kp, des = orb.detectAndCompute(G_img, None)
cv2.drawKeypoints(img, kp, img, color=(255, 0, 0))
cv2.imshow('result', img)
cv2.waitKey(0)
def desenha_pontos_de_interesse_nas_imagens_em_tons_de_cinza(self):
"""Desenha pontos de interesse em cada uma das imagens coloridas"""
for index in range(self.numero_de_imagens):
cv2.drawKeypoints(self.imagens_em_tons_de_cinza[index],
self.pontos_de_interesse_das_imagens[index],
self.imagens_em_tons_de_cinza[index],
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
def find_image_position(origin='origin.png', query='query.png', outfile=None):
'''
find all image positions
@return None if not found else a tuple: (origin.shape, query.shape, postions)
might raise Exception
'''
img1 = cv2.imread(query, 0) # query image(small)
img2 = cv2.imread(origin, 0) # train image(big)
# Initiate SIFT detector
sift = cv2.SIFT()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
print len(kp1), len(kp2)
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
# flann
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
if m.distance < 0.7*n.distance:
good.append(m)
print len(kp1), len(kp2), 'good cnt:', len(good)
if len(good)*1.0/len(kp1) < 0.5:
#if len(good)<MIN_MATCH_COUNT:
print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
return img2.shape, img1.shape, []
queryPts = []
trainPts = []
for dm in good:
queryPts.append(kp1[dm.queryIdx])
trainPts.append(kp2[dm.trainIdx])
img3 = cv2.drawKeypoints(img1, queryPts)
cv2.imwrite('image/query.png', img3)
img3 = cv2.drawKeypoints(img2, trainPts)
point = _middlePoint(trainPts)
print 'position in', point
if outfile:
edge = 10
top_left = (point[0]-edge, point[1]-edge)
bottom_right = (point[0]+edge, point[1]+edge)
cv2.rectangle(img3, top_left, bottom_right, 255, 2)
cv2.imwrite(outfile, img3)
return img2.shape, img1.shape, [point]
def display_key_points(self):
im = cv2.imread(self._imagePath)
gray_im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
detector = cv2.FeatureDetector_create(self.model.key_point_type)
key_points = detector.detect(gray_im)
# descriptor_extractor = cv2.DescriptorExtractor_create(self.model.key_point_type)
# (key_points, descriptors) = descriptor_extractor.compute(gray_im, key_points)
cv2.drawKeypoints(gray_im, key_points, im)
cv2.imwrite("Image with keypoints.bmp", im)
def plot_features(im,kp,circle=False):
""" show image with features. input: im (image as array),
locs (row, col, scale, orientation of each feature) """
if circle:
img=cv2.drawKeypoints(im,kp,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
else:
img=cv2.drawKeypoints(im,kp)
imshow(img)
axis('off')
def _draw_keypoints(path, img, kps):
"""Export keypoints over the dataset to an image file."""
fmt = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
try:
img2 = cv2.drawKeypoints(img, kps, flags=fmt)
except TypeError:
img2 = np.empty(img.shape, dtype=np.uint8)
img2 = cv2.drawKeypoints(img, kps, img2, flags=fmt)
cv2.imwrite(path, img2)
def compute_sift_matches(im0g, im1g, y_th=3, good_ratio=0.75, verbose=False):
"""
Compute the SIFT matches given two images
:param im0: first image
:param im1: second image
:param y_th: allowed distance in y-direction of the matches
:param good_ratio: used to filter out low-response keypoints
:return: the sorted good matches (based on response)
"""
if int(cv2.__version__.split('.')[0])<3:
sift = cv2.SIFT(nOctaveLayers=7)
else:
sift = cv2.xfeatures2d.SIFT_create(nOctaveLayers=7)
kp0, des0 = sift.detectAndCompute(im0g, None)
kp1, des1 = sift.detectAndCompute(im1g, None)
bf_matcher = cv2.BFMatcher()
matches = bf_matcher.knnMatch(des0, des1, k=2)
# Apply ratio test
good = []
y_diffs = []
for m, n in matches:
if m.distance < good_ratio * n.distance:
y_diff = kp0[m.queryIdx].pt[1] - kp1[m.trainIdx].pt[1]
if np.abs(y_diff) < y_th:
y_diffs.append(y_diff)
good.append([m])
sorted_good = sorted(good, key=lambda x: kp0[x[0].queryIdx].response, reverse=False)
if verbose:
plt.figure(15)
im3 = cv2.drawKeypoints(im0g, kp0, im0g, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
plt.imshow(im3)
plt.title('im0 keypoints')
plt.pause(0.1)
plt.figure(17)
im4 = cv2.drawKeypoints(im1g, kp1, im1g, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
plt.imshow(im4)
plt.title('im1 keypoints')
plt.pause(0.1)
im5 = cv2.drawMatchesKnn(im0g, kp0, im1g, kp1, sorted_good, im4, flags=2)
plt.figure(16)
plt.imshow(im5)
plt.title('Found ' + str(len(sorted_good)) )
plt.pause(0.1)
return sorted_good, y_diffs, kp0, kp1
def save_sift_feature(img):
img = cv.imread(img)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 创建sift类
sift = cv.xfeatures2d.SIFT_create()
# 在图像中找到关键点
kp = sift.detect(gray, None)
img = cv.drawKeypoints(gray, kp, img)
# 计算每个点的sift
des = sift.compute(gray, kp)
# des[0] 关键点的list,des[1] 特征向量的矩阵
img = cv.drawKeypoints(gray, kp, img)
return img, des
import numpy as np
import os
import cv2
path = os.getcwd() + "\\vision\\"
print(path)
name = path + "000000.png"
img = cv2.imread(name)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Initiate STAR detector
orb = cv2.ORB_create()
# find the keypoints with ORB
kp = orb.detect(img_gray, None)
# compute the descriptors with ORB
kp, des = orb.compute(img, kp)
img2 = None
# draw only keypoints location,not size and orientation
img2 = cv2.drawKeypoints(img, kp, img2, color=(0, 0, 255), flags=0)
cv2.imshow('orb', img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
img1 = read_image(filename)
img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
harris_points1, sift_points1, sift_keypoints1, surf_points1, surf_keypoints1 = get_harris_sift_surf_points(img1_gray)
image_with_points = img1.copy()
for x, y in harris_points1:
image_with_points[y, x] = [0, 0, 255]
image_rgb = cv2.cvtColor(image_with_points, cv2.COLOR_BGR2RGB)
plt.imshow(image_rgb)
plt.title("Image with Detected Corners Using Harris Algorithm")
plt.savefig('Images/harris_corners.png')
image_with_keypoints = cv2.drawKeypoints(img1, sift_keypoints1, None)
img_rgb = cv2.cvtColor(image_with_points, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)
plt.title("Image with Detected Corners Using SIFT algorithm")
plt.savefig('Images/sift_corners.png')
image_with_keypoints = cv2.drawKeypoints(img1, surf_keypoints1, None)
img_rgb = cv2.cvtColor(image_with_points, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)
plt.title("Image with Detected Corners Using SURF algorithm")
plt.savefig('Images/surf_corners.png')
noisy_repeatability = {'harris': [], 'sift': [], 'surf': []}
for i in range(len(noisy_image_files)):
img2 = read_image(noisy_image_files[i])
img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
def detect():
cap = cv2.VideoCapture('DJI_0005.mp4')
print("Target video captured")
while True:
ret, frame = cap.read()
if ret is True:
print("Processing the video")
#bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
#resized = cv2.resize(bgr, (640, 480)) # For a spesific resize operation
else:
break
def resize(image, scale_percent):
"""
Takes an image with a scaling percentage and returns resizing dimensions
"""
width = int(image.shape[1] * scale_percent / 100)
height = int(image.shape[0] * scale_percent / 100)
dimensions = (width, height)
return dimensions
dim = resize(frame, 100)
resized = cv2.resize(frame, dim)
#blur = cv2.GaussianBlur(resized, (15, 15), 2) # Making neighboring pixels become a little more uniform in color
hsv = cv2.cvtColor(
resized, cv2.COLOR_BGR2HSV) # Converting image to HSV colorspace
#bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
#lower_red = np.array([0, 100, 100]) # Lower limits for the red color
#upper_red = np.array([20, 255, 255]) # Upper limıts for the red color
# Mask specifications for red/orange/yellow colors 0 80 150 , 100 255 255
lower_red = np.array([0, 150, 150]) # Lower limits for the red color
upper_red = np.array([100, 255, 255]) # Upper limıts for the red color
red_mask = cv2.inRange(hsv, lower_red,
upper_red) # Creating a red color mask
red_masked = cv2.bitwise_and(resized, resized, mask=red_mask)
#cv2.imshow("Red Masked Image", red_masked)
# Mask specifications for dark blue/magenta/purple
lower_purple = np.array([170, 100,
100]) # Lower limits for the red color
upper_purple = np.array([200, 255,
255]) # Upper limıts for the red color
purple_mask = cv2.inRange(hsv, lower_purple, upper_purple)
purple_masked = cv2.bitwise_and(resized, resized, mask=purple_mask)
#cv2.imshow("Purple Masked Image", purple_masked)
#----------------------------------------------------------------------
_, vid = cap.read()
blended = cv2.addWeighted(red_masked, 1, purple_masked, 1, 0)
#cv2.imshow("blended", blended)
#----------------------------------------------------------------------
blacked = cv2.cvtColor(blended, cv2.COLOR_BGR2GRAY)
bw = cv2.adaptiveThreshold(blacked, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 115, -20)
# For removing small objects in the image, opening operation applied
# Opening operates erosion first and then dilation
opening_kernel = np.ones((3, 3), np.uint8)
#opening_kernel2 = np.ones((9,9),np.uint8)
opening = cv2.morphologyEx(bw, cv2.MORPH_OPEN, opening_kernel)
#cv2.imshow('Opening Operation',opening)
#opening2 = cv2.morphologyEx(bw, cv2.MORPH_OPEN, opening_kernel2)
#cv2.imshow('Opening Operation 2',opening2)
# For filling holes in the objects, dilation applied
dilation_kernel = np.ones((7, 7), np.uint8)
dilation = cv2.dilate(opening, dilation_kernel, iterations=2)
#cv2.imshow('Dilation Operation', dilation)
# Open here if you want to convert black<>white
#des = cv2.bitwise_not(dilation)
#cv2.imshow('bitwise not', des)
contours, _ = cv2.findContours(dilation, cv2.RETR_CCOMP,
cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
cv2.drawContours(dilation, [c], 0, 255, -1)
# Calculate moments for each contour
M = cv2.moments(c)
# Calculate x,y coordinate of center
c_X = int(M["m10"] / M["m00"])
c_Y = int(M["m01"] / M["m00"])
# Finding area for each contour
#resized = cv2.drawContours(resized, contours, c, (0,0,255), 1)
contour_area = cv2.contourArea(c)
if (contour_area > 2000):
contour_area = str(contour_area)
#cv2.circle(resized, (c_X, c_Y), 5, (255, 255, 255), -1) Putting circle to given coordinates
cv2.putText(resized, contour_area, (c_X, c_Y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
filled = dilation
#cv2.imshow('Filled', filled)
# This conversation required for blop detecion
filled = cv2.bitwise_not(filled)
# Setup SimpleBlobDetector parameters
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 10
params.maxThreshold = 200
# Filter by Area.
params.filterByArea = True
params.minArea = 20.0
params.maxArea = 8000.0
params.minDistBetweenBlobs = 15
# Filter by Circularity
params.filterByCircularity = False
params.minCircularity = 0.1
# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.87
# Filter by Inertia
params.filterByInertia = False
params.minInertiaRatio = 0.01
# Check OpenCV version and construct the detector
is_v2 = cv2.__version__.startswith("2.")
if is_v2:
detector = cv2.SimpleBlobDetector()
else:
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs
keypoints = detector.detect(filled)
# Draw detected blobs as red circles
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(
filled, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show blobs
cv2.imshow("Keypoints", im_with_keypoints)
# Display the image/video
cv2.imshow('Overlay Final Output', resized)
if cv2.waitKey(1) & 0xff == ord('e'):
break
cap.release()
cv2.destroyAllWindows()
# Set up the detector with default parameters.
# detector = cv2.SimpleBlobDetector() #for opencv2.0
is_cv3 = cv2.__version__.startswith("3.")
if is_cv3:
detector = cv2.SimpleBlobDetector_create()
else:
detector = cv2.SimpleBlobDetector()
# Detect blobs.
keypoints = detector.detect(image)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of
# the circle corresponds to the size of blob
blank = np.zeros((1, 1)) #creates a matrix of 1x1 with elements 0
blobs = cv2.drawKeypoints(image, keypoints, blank, (0, 255, 255),
cv2.DRAW_MATCHES_FLAGS_DEFAULT)
# Show keypoints
cv2.imshow("Blobs", blobs)
cv2.waitKey(0)
cv2.destroyAllWindows()
# The function **cv2.drawKeypoints** takes the following arguments:
#
# **cv2.drawKeypoints**(input image, keypoints, blank_output_array, color, flags)
#
# flags:
# - cv2.DRAW_MATCHES_FLAGS_DEFAULT
# - cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
# - cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG
# - cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS
def draw_keypoints(image, keypoints, label=None):
img = cv2.drawKeypoints(image, keypoints, None)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)
plt.title(label=label)
plt.show()
# exit simulation when 'q' key is pressed
if cv2.waitKey(25) & 0xFF == ord('q'):
flag = True
break
ret, view = vid.read()
hsv_view = cv2.cvtColor(view,cv2.COLOR_BGR2HSV)
thresh = cv2.inRange(hsv_view, np.array([lowH, lowS, lowV]), np.array([highH, highS, highV]))
cv2.imshow('threshold', thresh)
thresh = cv2.GaussianBlur(thresh, (5, 5), 0)
view = cv2.bitwise_and(view,view,mask=thresh)
keyPoints = detector.detect(view)
if len(keyPoints) > 0:
for i in range(0,len(keyPoints)):
x = '%.1f' % keyPoints[i].pt[0]
y = '%.1f' % keyPoints[i].pt[1]
print("Blob detected at (" + str(x)+ " , "+ str(y) + ")")
im_with_keypoints = cv2.drawKeypoints(view, keyPoints,np.array([]),(255,0,0),cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow('keypoints', im_with_keypoints)
# FAST
# https://www.edwardrosten.com/work/rosten_2006_machine.pdf
# http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV1011/AV1FeaturefromAcceleratedSegmentTest.pdf
# Create FAST Detector object
fast = cv2.FastFeatureDetector_create()
# Obtain Key points, by default non max suppression is On
# to turn off set fast.setBool('nonmaxSuppression', False)
keypoints = fast.detect(gray, None)
print "Number of keypoints Detected: ", len(keypoints)
out_im = image.copy()
# Draw rich keypoints on input image
image = cv2.drawKeypoints(image,
keypoints,
out_im, (0, 255, 0),
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow('Feature Method - FAST', image)
cv2.waitKey()
cv2.destroyAllWindows()
# # # BRIEF
# # http://cvlabwww.epfl.ch/~lepetit/papers/calonder_pami11.pdf
#
# image = cv2.imread('../images/input.jpg')
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#
# # Create FAST detector object
# fast = cv2.FastFeatureDetector_create()
#
import cv2
import numpy as np
img = cv2.imread("../1.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d.SURF_create()
kp = sift.detect(gray, None)
cv2.drawKeypoints(gray, kp, img)
cv2.imwrite("surf_keypoints.jpg", img)
# -*- coding: utf-8 -*-
import cv2
import numpy as np
img = cv2.imread('/home/mahdi/9.jpg')
gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
sift = cv2.SIFT()
kp = sift.detect(gray,None)
img=cv2.drawKeypoints(gray,kp)
cv2.imwrite('./images/sift_keypoints.jpg',img)
# extract normal SIFT descriptors
extractor = cv2.DescriptorExtractor_create("SIFT")
(kps_sift, descs_sift) = extractor.compute(image, kps)
print "SIFT: kps=%d, descriptors=%s " % (len(kps_sift), descs_sift.shape)
# extract RootSIFT descriptors
rs = RootSIFT()
(kps_rootsift, descs_rootsift) = rs.compute(image, kps)
print "RootSIFT: kps=%d, descriptors=%s " % (len(kps_rootsift),
descs_rootsift.shape)
pylab.figure()
rgbImage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
img_SIFT = cv2.drawKeypoints(rgbImage, kps_sift, None, (255, 0, 255), 4)
pylab.gray()
pylab.subplot(2, 1, 1)
pylab.imshow(img_SIFT)
pylab.axis('off')
img_rootsift = cv2.drawKeypoints(rgbImage, kps_rootsift, None, (255, 0, 255),
4)
pylab.gray()
pylab.subplot(2, 1, 2)
pylab.imshow(img_rootsift)
pylab.gray()
pylab.axis('off')
pylab.show()
# ScoreType = cv2.ORB_FAST_SCORE
ORB = cv2.ORB(nfeatures=FeaturePointsNum,
scaleFactor=PyramidScale,
nlevels=PyramidLevel,
edgeThreshold=EdgeThresh,
firstLevel=FirstLevel,
WTA_K=OrientedBRIEFPointNum,
scoreType=ScoreType,
patchSize=PatchSize)
# -------------- detect features --------------- #
ModelKeyPoints = ORB.detect(ModelGrayImg, mask=None)
ModelKeyPoints, ModelDescriptions = ORB.compute(ModelGrayImg,
ModelKeyPoints)
ModelShowImg = cv2.drawKeypoints(image=ModelImg,
keypoints=ModelKeyPoints,
color=RED)
QueryKeyPoints = ORB.detect(QueryGrayImg, mask=None)
QueryKeyPoints, QueryDescriptions = ORB.compute(QueryGrayImg,
QueryKeyPoints)
QueryShowImg = cv2.drawKeypoints(image=QueryImg,
keypoints=QueryKeyPoints,
color=RED)
cv2.imshow('ModelShowImg', ModelShowImg)
cv2.imshow('QueryShowImg', QueryShowImg)
# -------------- matching --------------- #
# use crossCheck instead of ratio check
CrossCheckFlag = True
print("# kps: {}, descriptors: {}".format(len(kps), descs.shape))
# kps: 274, descriptors: (274, 128)
surf = cv2.xfeatures2d.SURF_create()
(kps, descs) = surf.detectAndCompute(gray, None)
print("# kps: {}, descriptors: {}".format(len(kps), descs.shape))
# kps: 393, descriptors: (393, 64)
surf.setHessianThreshold(2000)
print surf.getHessianThreshold()
(kp, des) = surf.detectAndCompute(gray, None)
print("# kps: {}, descriptors: {}".format(len(kp), des.shape))
img2 = cv2.drawKeypoints(image, kp, None, (255, 0, 0), 4)
cv2.imshow('image', img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Check upright flag, if it False, set it to True
print surf.getUpright()
surf.setUpright(True)
print surf.getUpright()
# Recompute the feature points and draw it
kp = surf.detect(gray, None)
img2 = cv2.drawKeypoints(image, kp, None, (255, 0, 0), 4)
cv2.imshow('image', img2)
cv2.waitKey(0)
def detect_blob(img, mask_params):
LED_list = []
l_b = mask_params[0]
u_b = mask_params[1]
l_g = mask_params[2]
u_g = mask_params[3]
l_r = mask_params[4]
u_r = mask_params[5]
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 10
params.maxThreshold = 255
# Filter by Area.
params.filterByArea = True
params.minArea = 100
# All to be adjusted to the real test image
# Filter by Circularity
params.filterByCircularity = False
params.minCircularity = 0.1
# Filter by Convexity
params.filterByConvexity = False
params.minConvexity = 0.1
# Filter by Inertia
params.filterByInertia = False
params.minInertiaRatio = 0.1
# Create a detector with the parameters
# the if statements are in case its runs on an older version of OpenCV since the function name changes in previous versions
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
# using these bounds on the received image to genberate a Blue mask
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask_b = cv2.inRange(img_hsv, l_b,
u_b) # mask created using threshold values
res_b = cv2.bitwise_and(img, img, mask=mask_b)
gray_res_b = cv2.cvtColor(res_b, cv2.COLOR_BGR2GRAY)
#gray_res_b= cv2.equalizeHist(gray_res_b) HISTOGRAM EQUALIZATION LEADS TO ARTIFACTS , BUT HELPS IF CONTRAST FROM BACK GROUND IS LOW
#gray_res_b = cv2.blur(gray_res_b, (5, 5)) MAKES FOR WEAKER EDGES DECIDED LATER IF YOU ANT TO KEEP BOTH FOR ALL 3 COLORS
cv2.imshow("masked image_b", 255 - gray_res_b)
# Detect blobs.
keypoints = detector.detect(255 - gray_res_b)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(
img, keypoints, np.array([]), (0, 0, 0),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
for i in range(0, len(keypoints)):
cv2.circle(im_with_keypoints,
(round(keypoints[i].pt[0]), round(keypoints[i].pt[1])), 1,
(0, 0, 0), 3)
LED_list.append(
[round(keypoints[i].pt[0]),
round(keypoints[i].pt[1]), "b"])
cv2.imshow("Keypoints", im_with_keypoints)
# Repeating for green
mask_g = cv2.inRange(img_hsv, l_g,
u_g) # mask created using threshold values
res_g = cv2.bitwise_and(img, img, mask=mask_g)
gray_res_g = cv2.cvtColor(res_g, cv2.COLOR_BGR2GRAY)
#gray_res_g = cv2.equalizeHist(gray_res_g)
#gray_res_g = cv2.blur(gray_res_g, (5, 5))
cv2.imshow("masked image_g", 255 - gray_res_g)
# Detect blobs.
keypoints = detector.detect(255 - gray_res_g)
# Draw detected blobs as black circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(
im_with_keypoints, keypoints, np.array([]), (0, 0, 0),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
for i in range(0, len(keypoints)):
cv2.circle(im_with_keypoints,
(round(keypoints[i].pt[0]), round(keypoints[i].pt[1])), 1,
(0, 0, 0), 3)
LED_list.append(
[round(keypoints[i].pt[0]),
round(keypoints[i].pt[1]), "g"])
cv2.imshow("Keypoints", im_with_keypoints)
# Repeating for Red
mask_r = cv2.inRange(img_hsv, l_r,
u_r) # mask created using threshold values
res_r = cv2.bitwise_and(img, img, mask=mask_r)
gray_res_r = cv2.cvtColor(res_r, cv2.COLOR_BGR2GRAY)
#gray_res_r = cv2.equalizeHist(gray_res_r)
#gray_res_r = cv2.blur(gray_res_r, (5, 5))
cv2.imshow("masked image_r", 255 - gray_res_r)
# Detect blobs.
keypoints = detector.detect(255 - gray_res_r)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(
im_with_keypoints, keypoints, np.array([]), (0, 0, 0),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
for i in range(0, len(keypoints)):
cv2.circle(im_with_keypoints,
(round(keypoints[i].pt[0]), round(keypoints[i].pt[1])), 1,
(0, 0, 0), 3)
LED_list.append(
[round(keypoints[i].pt[0]),
round(keypoints[i].pt[1]), "r"])
cv2.imshow("Keypoints", im_with_keypoints)
# print(LED_list)
cv2.waitKey(0)
return LED_list
def key_show(self,img,key):
img = cv2.drawKeypoints(img, key, None,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
ORB busca la mayor velocidad de todas
'''
import cv2
import numpy as np
input_image = cv2.imread("/home/pi/Desktop/Milker_robot/image11.jpg")
gray_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
orb = cv2.ORB_create()
kp = orb.detect(gray_image, None)
kp, ds = orb.compute(gray_image, kp)
finalKP = input_image
finalKP = cv2.drawKeypoints(input_image,
kp,
color=(0, 255, 0),
flags=0,
outImage=finalKP)
cv2.imshow("ORB", finalKP)
cv2.waitKey()
cv2.destroyAllWindows()
# opevCV가 지원하는 특징점 검출 알고리즘 중 하나
# 4. Shift
#
# 이전과 다르게, 이미지 피라미드를 이용하여, 크기변화에 따른 특징검출 방식#
import cv2
import numpy as np
img = cv2.imread('/home/lkw/Downloads/cube.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# SIFT 추출기 생성
sift = cv2.xfeatures2d.SIFT_create()
# 키 포인트 검출과 서술자 계산
keypoints, descriptor = sift.detectAndCompute(gray, None)
print('keypoint:', len(keypoints), 'descriptor:', descriptor.shape)
print(descriptor)
# 키 포인트 그리기
img_draw = cv2.drawKeypoints(img,
keypoints,
None,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# 결과 출력
cv2.imshow('SIFT', img_draw)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.imshow('erosion',erosion)
cv2.imshow('closing',closing)
closing = cv2.bitwise_not(closing)
w = closing.shape[1] # y
h = closing.shape[0] # x
mask = np.zeros((h + 2, w + 2), np.uint8)
cv2.floodFill(closing, mask, (0, 0), 255);
cv2.floodFill(closing, mask, (0, 200), 255);
detector = cv2.SimpleBlobDetector_create(blob_params)
keypoints = detector.detect(closing)
img_with_keypoints = cv2.drawKeypoints(closing, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
number = 0
for i in keypoints[0:]:
number = number + 1
#print(number)
if number == 1:
one = one +1
elif number == 2:
two = two +1
# Set up the detector with default parameters.
detector = cv2.SimpleBlobDetector()
# Detect blobs.
keypoints = detector.detect(im)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
keypoints2=[]
for i in keypoints:
if i.size>20 and i.size<40:
#print i.size
keypoints2.append(i)
openposns.append(i.pt)
im_with_keypoints = cv2.drawKeypoints(im, keypoints2, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(1)
file = "test_image"+str(i)+".png"
# A nice feature of the imwrite method is that it will automatically choose the
# correct format based on the file extension you provide. Convenient!
#cv2.imwrite(file, im_with_keypoints)
# You'll want to release the camera, otherwise you won't be able to create a new
# capture object until your script exits
del(camera)
def euclideanDist(a,b):
mask_blue = cv.inRange(frame_hsv, lower_blue, upper_blue)
# Run the SimpleBlobDetector on the mask.
# The results are stored in a vector of 'KeyPoint' objects,
# which describe the location and size of the blobs.
#keypoints = detector.detect(mask)
keypoints_pink = detector.detect(mask_pink)
keypoints_green = detector.detect(mask_green)
keypoints_yellow = detector.detect(mask_yellow)
keypoints_blue = detector.detect(mask_blue)
# For each detected blob, draw a circle on the frame
#frame_with_keypoints = cv.drawKeypoints(frame, keypoints, None, color = (0, 255, 0), flags = cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
frame_with_keypoints_pink = cv.drawKeypoints(
frame,
keypoints_pink,
None,
color=(0, 255, 0),
flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
frame_with_keypoints_green = cv.drawKeypoints(
frame,
keypoints_green,
None,
color=(0, 255, 0),
flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
frame_with_keypoints_yellow = cv.drawKeypoints(
frame,
keypoints_yellow,
None,
color=(0, 255, 0),
flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
frame_with_keypoints_blue = cv.drawKeypoints(
import numpy as np
import cv2
import glob
for fname in glob.glob('*.jpg'):
I = cv2.imread(fname)
G = cv2.cvtColor(I,cv2.COLOR_BGR2GRAY)
#sift = cv2.FeatureDetector_create("SIFT") # opencv 2.x.x
sift = cv2.xfeatures2d.SIFT_create() # opencv 3.x.x
# use "sift = cv2.SIFT()" if the above fails
keypoints = sift.detect(G,None)
cv2.drawKeypoints(G,keypoints,I)
#cv2.drawKeypoints(G,keypoints,I, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# display keypoint properties
# for kp in keypoints:
# print '-'*40
# print 'location=(%.2f,%.2f)'%(kp.pt[0], kp.pt[1])
# print 'orientation angle=%1.1f'%kp.angle
# print 'scale=%f'%kp.size
cv2.putText(I,"Press 'q' to quit, any key for next image",(20,20), \
cv2.FONT_HERSHEY_SIMPLEX, .5,(255,0,0),1)
cv2.imshow('sift_keypoints',I)
import cv2
import numpy as np
from matplotlib import pyplot as plt
#filename = '/home/acp/work/ggp/cam_images/camera1/spot2_occupied.jpg'
filename = '/home/acp/work/ggp/cam_images/camera1/snap20160707185220.jpg'
img = cv2.imread(filename)
gray1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
detector = cv2.AKAZE_create()
(kps, descs) = detector.detectAndCompute(gray1, None)
img2 = cv2.drawKeypoints(img, kps, None, (0, 0, 255), 4)
plt.imshow(img2), plt.show()
cv2.imwrite('kaze_keypoints.jpg', img2)
def show_keypoints(image, keypoints, descriptors):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
img = cv2.drawKeypoints(gray, keypoints, image)
cv2.imshow("img", img)
cv2.waitKey(0)
do_motion_fade = False
if do_motion_fade:
motion_mask = motion2(new_frame, base)
else:
motion_mask = None
accum = overlay(new_frame, base, motion_mask)
final = accum
if args.draw_keypoints:
new_filtered = []
if affine_new != None:
affine_T = affine_new.T
for kp in filtered:
a = np.array( [kp.pt[0], kp.pt[1], 1.0] )
pt = np.dot(a, affine_T)
new_kp = cv2.KeyPoint(pt[0], pt[1], kp.size)
new_filtered.append(new_kp)
final = cv2.drawKeypoints(accum, new_filtered, color=(0,255,0), flags=0)
cv2.imshow('bgr', res1)
cv2.imshow('smooth', new_frame)
cv2.imshow('final', final)
#output.write(res1)
if args.write:
video_writer.writeFrame(final[:,:,::-1])
if 0xFF & cv2.waitKey(5) == 27:
break
cv2.destroyAllWindows()
# between minCircularity (inclusive) and maxCircularity (exclusive).
params.filterByCircularity = True
params.minCircularity = current_min_circularity
params.maxCircularity = current_max_circularity
# Extracted blobs have this ratio between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).
params.filterByInertia = True
params.minInertiaRatio = current_min_inertia
params.maxInertiaRatio = current_max_inertia
# Extracted blobs have convexity (area / area of blob convex hull)
# between minConvexity (inclusive) and maxConvexity (exclusive).
params.filterByConvexity = True
params.minConvexity = current_min_convexity
params.maxConvexity = current_max_convexity
blob_detector = cv.SimpleBlobDetector_create(params)
# find the keypoints with SimpleBlobDetector
kp = blob_detector.detect(img, None)
# draw only keypoints location,not size and orientation
img2 = cv.drawKeypoints(img, kp, None, color=(0, 255, 0))
cv.imshow("SimpleBlobDetector", img2)
if cv.waitKey(10) & 0xFF == 27:
break
cv.destroyAllWindows()
#Importing Libraries
import cv2
import numpy as np
# Reading the Image
img = cv2.imread('aloeR.jpg')
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Applying the SIFT feature
sift = cv2.xfeatures2d_SIFT.create()
kp = sift.detect(grey, None)
# Highlighting the Key Points
img = cv2.drawKeypoints(grey, kp,img)
cv2.imshow("SIFT", img)
kp1, des1 = orb.compute(lena, kp1)
kp2 = orb.detect(lena2x, None)
kp2, des2 = orb.compute(lena2x, kp2)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)
result = cv2.drawMatches(lena, kp1, lena2x, kp2, matches[:20], None, flags=2)
lena = cv2.drawKeypoints(lena, kp1, None, color=(0, 255, 0), flags=0)
lena2x = cv2.drawKeypoints(lena2x, kp2, None, color=(0, 255, 0), flags=0)
cv2.imshow('img', lena)
cv2.imshow('img2', lena2x)
cv2.imshow('result', result)
while (1):
key = cv2.waitKey(5)
if key != 255:
print key
"""
while(1):
def image_circle(img_gray, tmpfig, channel, cutoff, sizemin, sizemax):
''' channel 0 is red channel, channel=1 is green channel; cutoff value maybed changed '''
#img_gray=cv2.imread("img_000000000_YFP_007.tif",-1)
gray2 = gaa(img_gray)
m = np.zeros(img_gray.size * 3)
m.shape = (img_gray.shape[0], img_gray.shape[1], 3)
gray2 = np.uint8(histeq2(img_gray, 255))
m[:, :, channel] = gray2
#m=np.uint8(m)
img = m.copy()
k5 = cv2.GaussianBlur(gray2, (7, 7), 0)
#(_, thresh) = cv2.threshold(k5, 45,255, cv2.THRESH_BINARY)
#sobelx8u = cv2.Sobel(k5,cv2.CV_8U,1,0,ksize=5)
#sobelx8u2 = cv2.Sobel((255-k5),cv2.CV_8U,1,0,ksize=5)
#save_img(m,"1.tif")
sobelx8u = cv2.Sobel(k5, cv2.CV_8U, 1, 0, ksize=3)
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 1
params.maxThreshold = 254
# Filter by Area.
params.filterByArea = True
params.minArea = sizemin
params.maxArea = sizemax
#Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.001
# Filter by color
params.filterByColor = True
params.blobColor = 255
# Filter by Convexity
params.filterByConvexity = 0
params.minConvexity = 0.01
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.05
# Create a detector with the parameters
detector = cv2.SimpleBlobDetector_create(params)
(_, thresh) = cv2.threshold(sobelx8u, 5, 1, cv2.THRESH_BINARY)
val = np.multiply(thresh, np.uint8(sobelx8u))
keypoints = detector.detect(val)
len(keypoints)
k = len(keypoints)
img3 = img.copy()
real_radius = []
realpos = []
real_keypoint = []
for i in range(k):
c = (np.uint(keypoints[i].pt[1]), np.uint(keypoints[i].pt[0]))
if max_near(thresh, c)[0] > 0 and max_near(
sobelx8u, c)[0] > 30 and max_near(gray2, c)[0] > 30:
img3 = cv2.circle(
img3, (c[1], c[0]), 15,
(0, abs(channel - 1) * 65535, abs(2 - channel) * 65535), 2)
real_radius.append(keypoints[i].size)
realpos.append(c)
real_keypoint.append(keypoints[i])
#cv2.imwrite('1.tif',img3)
len(real_keypoint)
im_with_keypoints = cv2.drawKeypoints(
gray2, real_keypoint, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
return img3, realpos, k, sobelx8u, real_radius, im_with_keypoints
def detect_screws(self, frame, disp):
grayIN = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# apply GuassianBlur to reduce noise. medianBlur is also added for smoothening, reducing noise.
gray = cv2.GaussianBlur(grayIN, (3, 3), 0)
gray = cv2.medianBlur(gray, 5)
ddepth = cv2.CV_64F
# Gradient X
grad_x = cv2.Sobel(gray, ddepth, 1, 0, 3)
# Gradient Y
grad_y = cv2.Sobel(gray, ddepth, 0, 1, 3)
# Total Gradient (approximate)
grad = cv2.magnitude(grad_x, grad_y)
grad = cv2.convertScaleAbs(grad)
# Adaptive Guassian Threshold is to detect sharp edges in the Image. For more information Google it.
gray = cv2.adaptiveThreshold(grad, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \
cv2.THRESH_BINARY, 11, 15)
gray = cv2.medianBlur(gray, 3)
kernel = np.ones((3, 3), np.uint8)
gray = cv2.erode(gray, kernel, iterations=4)
# Detect blobs.
keypoints = self._detector.detect(gray)
#gray_disp = cv2.drawKeypoints(gray, keypoints, np.array([]), (0, 0, 255),
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# cv2.imshow("keypoints", gray_disp)
# if cv2.waitKey(10) == 27:
# pass
listpoints = []
pointpts = np.empty((0, 2))
i = 0
guesses = []
# For each possible point classify
for point in keypoints:
# Select region around point and resize
rect = ((point.pt[0], point.pt[1]), (point.size, point.size), 0)
box_pts = np.int0(cv2.boxPoints(rect))
image = self.rot_crop(grayIN, rect, box_pts, self._size, self._size)
image = image.reshape(1, 28, 28, 1)
guess = self._model.predict_proba(image) # Use CNN to predict image contents
if int(np.argmax(guess[0])) != 4:
guesses.append(guess[0])
pointpts = np.append(pointpts, [[point.pt[0], point.pt[1]]], axis=0)
listpoints.append(point)
i = i + 1
i = 0
#screw_probs = np.zeros(4)
fourkeypoints = [None] * 4
tally = np.zeros(5)
if len(pointpts) > 0:
closestptsidx = self.find_four_closest(pointpts)
for p in range(0, len(closestptsidx)):
idx = closestptsidx[p]
#if np.max(guesses[idx]) > 0.999999:
guess = int(np.argmax(guesses[idx]))
tally[guess] = tally[guess] + 1
if guess == 4:
pass
else:
#screw_probs[p] = prob
fourkeypoints[p] = keypoints[idx]
if disp:
txt = f"{self._CATEGORIES[guess]}"
frame = cv2.drawKeypoints(frame, fourkeypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
frame = cv2.putText(frame, txt, (int(keypoints[idx].pt[0]), int(keypoints[idx].pt[1])),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
i = i + 1
# Display the resulting frame
if disp:
cv2.imshow("Circles", frame)
if cv2.waitKey(10) == 27:
pass
im_screw_probs = np.zeros((4, 5))
i = 0
for point in fourkeypoints:
if point is not None:
pointint = listpoints.index(point)
im_screw_probs[i, :] = guesses[pointint]
i = i+1
if time.time() - self._lasttime > 1:
print(tally)
self._lasttime = time.time()
return im_screw_probs, tally
params.filterByInertia = False
params.minInertiaRatio = 0.1
# Distance Between Blobs
params.minDistBetweenBlobs = 0.5
# Create a detector with the parameters
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs.
keypoints = detector.detect(im_thresh)
print(type(keypoints))
print('Total blobs:' + str(len(keypoints)))
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_thresh_with_keypoints = cv2.drawKeypoints(
gray, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imwrite('wings.jpg', equ)
# gray = cv2.GaussianBlur(gray,(9,9),0)
cv2.imshow('original', gray)
cv2.waitKey(0)
cv2.imshow('equalised', equ)
cv2.waitKey(0)
cv2.imshow("thresh", im_thresh)
cv2.waitKey(0)
cv2.imshow("keypoints", im_thresh_with_keypoints)
cv2.waitKey(0)
# When everything done, release the capture
cap.release()
import cv2 as cv
image = cv.imread("D:/images/dannis2.jpg");
cv.imshow("input", image)
# 创建GFTT特征检测器
gftt = cv.GFTTDetector_create(100, 0.01,1, 3, False, 0.04)
kp1 = gftt.detect(image,None)
result = cv.drawKeypoints(image, kp1, None, (0, 255, 0), cv.DrawMatchesFlags_DEFAULT)
cv.imshow("GFTT-Keypoint-Detect", result)
cv.waitKey(0)
cv.destroyAllWindows()
