def normCrossCorrelation(img1, img2, pt0, pt1, status, winsize, method=cv2.cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
(Dev Zone)
Calculates normalized cross correlation for every point.
**PARAMETERS**
img1 - Image 1.
img2 - Image 2.
pt0 - vector of points of img1
pt1 - vector of points of img2
status - Switch which point pairs should be calculated.
if status[i] == 1 => match[i] is calculated.
else match[i] = 0.0
winsize- Size of quadratic area around the point
which is compared.
method - Specifies the way how image regions are compared. see cv2.matchTemplate
**RETURNS**
match - Output: Array will contain ncc values.
0.0 if not calculated.
"""
nPts = len(pt0)
match = np.zeros(nPts)
for i in np.argwhere(status):
i = i[0]
patch1 = cv2.getRectSubPix(img1,(winsize,winsize),tuple(pt0[i]))
patch2 = cv2.getRectSubPix(img2,(winsize,winsize),tuple(pt1[i]))
match[i] = cv2.matchTemplate(patch1,patch2,method)
return match
def normCrossCorrelation(img1,
img2,
pt0,
pt1,
status,
winsize,
method=cv2.cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
(Dev Zone)
Calculates normalized cross correlation for every point.
**PARAMETERS**
img1 - Image 1.
img2 - Image 2.
pt0 - vector of points of img1
pt1 - vector of points of img2
status - Switch which point pairs should be calculated.
if status[i] == 1 => match[i] is calculated.
else match[i] = 0.0
winsize- Size of quadratic area around the point
which is compared.
method - Specifies the way how image regions are compared. see cv2.matchTemplate
**RETURNS**
match - Output: Array will contain ncc values.
0.0 if not calculated.
"""
nPts = len(pt0)
match = np.zeros(nPts)
for i in np.argwhere(status):
i = i[0]
patch1 = cv2.getRectSubPix(img1, (winsize, winsize), tuple(pt0[i]))
patch2 = cv2.getRectSubPix(img2, (winsize, winsize), tuple(pt1[i]))
match[i] = cv2.matchTemplate(patch1, patch2, method)
return match
def lktrack(img1,
img2,
ptsI,
nPtsI,
winsize_ncc=10,
win_size_lk=4,
method=cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
(Dev Zone)
Lucas-Kanede Tracker with pyramids
**PARAMETERS**
img1 - Previous image or image containing the known bounding box (Numpy array)
img2 - Current image
ptsI - Points to track from the first image
Format ptsI[0] - x1, ptsI[1] - y1, ptsI[2] - x2, ..
nPtsI - Number of points to track from the first image
winsize_ncc - size of the search window at each pyramid level in LK tracker (in int)
method - Paramete specifying the comparison method for normalized cross correlation
(see http://opencv.itseez.com/modules/imgproc/doc/object_detection.html?highlight=matchtemplate#cv2.matchTemplate)
**RETURNS**
fb - forward-backward confidence value. (corresponds to euclidean distance between).
ncc - normCrossCorrelation values
status - Indicates positive tracks. 1 = PosTrack 0 = NegTrack
ptsJ - Calculated Points of second image
"""
template_pt = []
target_pt = []
fb_pt = []
ptsJ = [-1] * len(ptsI)
for i in range(nPtsI):
template_pt.append((ptsI[2 * i], ptsI[2 * i + 1]))
target_pt.append((ptsI[2 * i], ptsI[2 * i + 1]))
fb_pt.append((ptsI[2 * i], ptsI[2 * i + 1]))
template_pt = np.asarray(template_pt, dtype="float32")
target_pt = np.asarray(target_pt, dtype="float32")
fb_pt = np.asarray(fb_pt, dtype="float32")
target_pt, status, track_error = cv2.calcOpticalFlowPyrLK(
img1,
img2,
template_pt,
target_pt,
winSize=(win_size_lk, win_size_lk),
flags=cv.OPTFLOW_USE_INITIAL_FLOW,
criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
fb_pt, status_bt, track_error_bt = cv2.calcOpticalFlowPyrLK(
img2,
img1,
target_pt,
fb_pt,
winSize=(win_size_lk, win_size_lk),
flags=cv.OPTFLOW_USE_INITIAL_FLOW,
criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
status = status & status_bt
ncc = normCrossCorrelation(img1, img2, template_pt, target_pt, status,
winsize_ncc, method)
fb = euclideanDistance(template_pt, target_pt)
newfb = -1 * np.ones(len(fb))
newncc = -1 * np.ones(len(ncc))
for i in np.argwhere(status):
i = i[0]
ptsJ[2 * i] = target_pt[i][0]
ptsJ[2 * i + 1] = target_pt[i][1]
newfb[i] = fb[i]
newncc[i] = ncc[i]
return newfb, newncc, status, ptsJ
def lktrack(img1, img2, ptsI, nPtsI, winsize_ncc=10, win_size_lk=4, method=cv2.cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
(Dev Zone)
Lucas-Kanede Tracker with pyramids
**PARAMETERS**
img1 - Previous image or image containing the known bounding box (Numpy array)
img2 - Current image
ptsI - Points to track from the first image
Format ptsI[0] - x1, ptsI[1] - y1, ptsI[2] - x2, ..
nPtsI - Number of points to track from the first image
winsize_ncc - size of the search window at each pyramid level in LK tracker (in int)
method - Paramete specifying the comparison method for normalized cross correlation
(see http://opencv.itseez.com/modules/imgproc/doc/object_detection.html?highlight=matchtemplate#cv2.matchTemplate)
**RETURNS**
fb - forward-backward confidence value. (corresponds to euclidean distance between).
ncc - normCrossCorrelation values
status - Indicates positive tracks. 1 = PosTrack 0 = NegTrack
ptsJ - Calculated Points of second image
"""
template_pt = []
target_pt = []
fb_pt = []
ptsJ = [-1]*len(ptsI)
for i in range(nPtsI):
template_pt.append((ptsI[2*i],ptsI[2*i+1]))
target_pt.append((ptsI[2*i],ptsI[2*i+1]))
fb_pt.append((ptsI[2*i],ptsI[2*i+1]))
template_pt = np.asarray(template_pt,dtype="float32")
target_pt = np.asarray(target_pt,dtype="float32")
fb_pt = np.asarray(fb_pt,dtype="float32")
target_pt, status, track_error = cv2.calcOpticalFlowPyrLK(img1, img2, template_pt, target_pt,
winSize=(win_size_lk, win_size_lk), flags = cv2.OPTFLOW_USE_INITIAL_FLOW,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
fb_pt, status_bt, track_error_bt = cv2.calcOpticalFlowPyrLK(img2,img1, target_pt,fb_pt,
winSize = (win_size_lk,win_size_lk),flags = cv2.OPTFLOW_USE_INITIAL_FLOW,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
status = status & status_bt
ncc = normCrossCorrelation(img1, img2, template_pt, target_pt, status, winsize_ncc, method)
fb = euclideanDistance(template_pt, target_pt)
newfb = -1*np.ones(len(fb))
newncc = -1*np.ones(len(ncc))
for i in np.argwhere(status):
i = i[0]
ptsJ[2 * i] = target_pt[i][0]
ptsJ[2 * i + 1] = target_pt[i][1]
newfb[i] = fb[i]
newncc[i] = ncc[i]
return newfb, newncc, status, ptsJ
