OpenCV中如何正确使用CSRT Tracker跟踪物体
How to use the CSRT Tracker correctly to track objects in OpenCV
我尝试使用 OpenCv V4.5.1 中的 CSRT 跟踪器来跟踪视频序列中的面部，在 一些 视频的结尾我得到了这个我不明白为什么会发生错误！
- 我正在使用 AVDIAR dataset
- 能否请您告诉我如何使用 Viola-Jones 人脸检测器正确使用跟踪器？
Note: wen I used KCF Tracker things worked perfectly! tracker = cv2.TrackerKCF_create()
---> 12 tracker.init(frame, myBox)
error: OpenCV(4.5.1) C:\Users\appveyor\AppData\Local\Temp\pip-req-build-r2ue8w6k\opencv\modules\core\src\matrix.cpp:811:
error: (-215:Assertion failed)
0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows
in function 'cv::Mat::Mat'
我用来使用跟踪器的代码是：
def tracking(frame, bbox):
"""
Parameters:
@param: frame: nd-array frame from video sequence.
@param: bbox: bounding box
"""
[x0, y0, x1, y1] = bbox
myBox = (x0, y0, x1, y1)
tracker = cv2.TrackerCSRT_create()
# Initialize tracker with first frame and bounding box
tracker.init(frame, myBox)
# Update tracker
ok, box = tracker.update(frame)
if ok:
[x0, x1] = [x0, x1] if x1>x0 else [x1, x0]
[y0, y1] = [y0, y1] if y1>y0 else [y1, y0]
result = [x0, y0, x1, y1]
return result
print("tracking No result ", bbox)
return bbox
调用跟踪的函数是：
def violaJones(xmlPath, videoPath, verbose=False):
"""
Parameters:
@param: xmlPath: string, path to the Haar_Cascade xml file.
@param: videoPath: string, Video Full Path.
@param: verbose
"""
# print(videoPath)
cap = cv2.VideoCapture(videoPath)
bboxes = {}
nb_frame = 0
# Blue color in BGR
color = [(255, 128, 128),(128, 255, 128),(128, 128, 255), (128,255,255),(255,128,255), (255,255,128)]
# Line thickness of 2 px
thickness = 2
# max numer of people
maxID = 0
track = [None]*10
# Read until video is completed
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
if ret:
#
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gauss = cv2.GaussianBlur(gray, (3, 3), 0)
# gauss = skinThresh(frame)
# Cascade Classifier
face_cascade = cv2.CascadeClassifier(xmlPath)
detected_faces = face_cascade.detectMultiScale(gauss.astype(np.uint8), 1.2, 4)
id = 0
for (x0,y0,w,h) in detected_faces:
id += 1
maxID = maxID if maxID > id else id
x1 = x0 + w
y1 = y0 + h
# Tracking
track[id-1] = tracking(frame, [x0, y0, x1, y1])
for ID in range(maxID):
if track[ID] is not None:
bboxes[str(nb_frame)+str(ID+1)] = np.array(tracking(frame, track[ID]))
# Draw Annotations
if verbose:
[xx0, yy0, xx1, yy1] = track[ID]
cv2.rectangle(frame, (xx0,yy0),(xx1,yy1), color[ID], thickness)
# show
if verbose:
cv2.imshow("Viola_Jones", frame)
key = cv2.waitKey(1) & 0xFF
# Break the loop
else:
break
nb_frame +=1
# Closes all the frames
cv2.destroyAllWindows()
# When everything done, release the video capture object
cap.release()
return bboxes
错误前打印函数的最后值是：
x0
y0
x1
y1
Frame Shape
574
46
634
106
(450, 720, 3)
600
35
663
98
(450, 720, 3)
600
35
663
98
(450, 720, 3)
600
35
663
98
(450, 720, 3)
600
35
663
98
解决方案是为每个对象声明一个跟踪器，并如下启动该跟踪器一次：
追踪功能
def tracking(frame, bbox, tracker, Init):
"""
Parameters:
@param: frame: nd-array frame from video sequence.
@param: bbox: bounding box
@param: tracker: tracker from OpenCV such as KCF or CSRT
"""
[x0, y0, x1, y1] = bbox
myBox = (x0, y0, x1, y1)
if(not Init):
# Initialize tracker with first frame and bounding box
tracker.init(frame, myBox)
Init = True
# Update tracker
ok, box = tracker.update(frame)
if ok:
[x0, x1] = [x0, x1] if x1>x0 else [x1, x0]
[y0, y1] = [y0, y1] if y1>y0 else [y1, y0]
result = [x0, y0, x1, y1]
return result
return bbox
维奥拉-琼斯
def violaJones(xmlPath, videoPath, verbose=False):
"""
Parameters:
@param: xmlPath: string, path to the Haar_Cascade xml file.
@param: videoPath: string, Video Full Path.
@param: verbose, bolean to visualize the results.
"""
# print(videoPath)
cap = cv2.VideoCapture(videoPath)
bboxes = {}
nb_frame = 0
# Blue color in BGR
color = [(255, 128, 128),(128, 255, 128),(128, 128, 255), (128,255,255),(255,128,255), (255,255,128)]
# Line thickness of 2 px
thickness = 2
# max numer of people
maxID = 0
track = [None]*10
trackInit = [False]*5
tracker1 = cv2.TrackerKCF_create()
tracker2 = cv2.TrackerKCF_create()
tracker3 = cv2.TrackerKCF_create()
tracker4 = cv2.TrackerKCF_create()
tracker5 = cv2.TrackerKCF_create()
trackers = [tracker1, tracker2, tracker3, tracker4, tracker5]
# tracker1 = cv2.TrackerCSRT_create()
# tracker2 = cv2.TrackerKCF_create()
# Read until video is completed
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
if ret:
#
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# gauss = cv2.GaussianBlur(gray, (3, 3), 0)
gauss = skinThresh(frame)
# Cascade Classifier
face_cascade = cv2.CascadeClassifier(xmlPath)
detected_faces = face_cascade.detectMultiScale(gauss, 1.2, 4)
id = 0
for (x0,y0,w,h) in detected_faces:
id += 1
maxID = maxID if maxID > id else id
x1 = x0 + w
y1 = y0 + h
# Detection
track[id-1] = [x0, y0, x1, y1]
for ID in range(maxID):
#Tracking
if track[ID] is not None:
bboxes[str(nb_frame)+str(ID+1)] = np.array(tracking(frame, track[ID], trackers[ID], trackInit[ID]))
trackInit[ID] = True
# Draw Annotations
if verbose:
[xx0, yy0, xx1, yy1] = track[ID]
cv2.rectangle(frame, (xx0,yy0),(xx1,yy1), color[ID], thickness)
# show
if verbose:
cv2.imshow("Viola_Jones", frame)
key = cv2.waitKey(1) & 0xFF
# Break the loop
else:
break
nb_frame +=1
# When everything done, release the video capture object
cap.release()
# Closes all the frames
cv2.destroyAllWindows()
return bboxes
我尝试使用 OpenCv V4.5.1 中的 CSRT 跟踪器来跟踪视频序列中的面部，在 一些 视频的结尾我得到了这个我不明白为什么会发生错误！
- 我正在使用 AVDIAR dataset
- 能否请您告诉我如何使用 Viola-Jones 人脸检测器正确使用跟踪器？
Note: wen I used
KCFTracker things worked perfectly!tracker = cv2.TrackerKCF_create()
---> 12 tracker.init(frame, myBox)
error: OpenCV(4.5.1) C:\Users\appveyor\AppData\Local\Temp\pip-req-build-r2ue8w6k\opencv\modules\core\src\matrix.cpp:811:
error: (-215:Assertion failed)
0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows
in function 'cv::Mat::Mat'
我用来使用跟踪器的代码是：
def tracking(frame, bbox):
"""
Parameters:
@param: frame: nd-array frame from video sequence.
@param: bbox: bounding box
"""
[x0, y0, x1, y1] = bbox
myBox = (x0, y0, x1, y1)
tracker = cv2.TrackerCSRT_create()
# Initialize tracker with first frame and bounding box
tracker.init(frame, myBox)
# Update tracker
ok, box = tracker.update(frame)
if ok:
[x0, x1] = [x0, x1] if x1>x0 else [x1, x0]
[y0, y1] = [y0, y1] if y1>y0 else [y1, y0]
result = [x0, y0, x1, y1]
return result
print("tracking No result ", bbox)
return bbox
调用跟踪的函数是：
def violaJones(xmlPath, videoPath, verbose=False):
"""
Parameters:
@param: xmlPath: string, path to the Haar_Cascade xml file.
@param: videoPath: string, Video Full Path.
@param: verbose
"""
# print(videoPath)
cap = cv2.VideoCapture(videoPath)
bboxes = {}
nb_frame = 0
# Blue color in BGR
color = [(255, 128, 128),(128, 255, 128),(128, 128, 255), (128,255,255),(255,128,255), (255,255,128)]
# Line thickness of 2 px
thickness = 2
# max numer of people
maxID = 0
track = [None]*10
# Read until video is completed
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
if ret:
#
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gauss = cv2.GaussianBlur(gray, (3, 3), 0)
# gauss = skinThresh(frame)
# Cascade Classifier
face_cascade = cv2.CascadeClassifier(xmlPath)
detected_faces = face_cascade.detectMultiScale(gauss.astype(np.uint8), 1.2, 4)
id = 0
for (x0,y0,w,h) in detected_faces:
id += 1
maxID = maxID if maxID > id else id
x1 = x0 + w
y1 = y0 + h
# Tracking
track[id-1] = tracking(frame, [x0, y0, x1, y1])
for ID in range(maxID):
if track[ID] is not None:
bboxes[str(nb_frame)+str(ID+1)] = np.array(tracking(frame, track[ID]))
# Draw Annotations
if verbose:
[xx0, yy0, xx1, yy1] = track[ID]
cv2.rectangle(frame, (xx0,yy0),(xx1,yy1), color[ID], thickness)
# show
if verbose:
cv2.imshow("Viola_Jones", frame)
key = cv2.waitKey(1) & 0xFF
# Break the loop
else:
break
nb_frame +=1
# Closes all the frames
cv2.destroyAllWindows()
# When everything done, release the video capture object
cap.release()
return bboxes
错误前打印函数的最后值是：
| x0 | y0 | x1 | y1 | Frame Shape |
|---|---|---|---|---|
| 574 | 46 | 634 | 106 | (450, 720, 3) |
| 600 | 35 | 663 | 98 | (450, 720, 3) |
| 600 | 35 | 663 | 98 | (450, 720, 3) |
| 600 | 35 | 663 | 98 | (450, 720, 3) |
| 600 | 35 | 663 | 98 |
解决方案是为每个对象声明一个跟踪器，并如下启动该跟踪器一次：
追踪功能
def tracking(frame, bbox, tracker, Init):
"""
Parameters:
@param: frame: nd-array frame from video sequence.
@param: bbox: bounding box
@param: tracker: tracker from OpenCV such as KCF or CSRT
"""
[x0, y0, x1, y1] = bbox
myBox = (x0, y0, x1, y1)
if(not Init):
# Initialize tracker with first frame and bounding box
tracker.init(frame, myBox)
Init = True
# Update tracker
ok, box = tracker.update(frame)
if ok:
[x0, x1] = [x0, x1] if x1>x0 else [x1, x0]
[y0, y1] = [y0, y1] if y1>y0 else [y1, y0]
result = [x0, y0, x1, y1]
return result
return bbox
维奥拉-琼斯
def violaJones(xmlPath, videoPath, verbose=False):
"""
Parameters:
@param: xmlPath: string, path to the Haar_Cascade xml file.
@param: videoPath: string, Video Full Path.
@param: verbose, bolean to visualize the results.
"""
# print(videoPath)
cap = cv2.VideoCapture(videoPath)
bboxes = {}
nb_frame = 0
# Blue color in BGR
color = [(255, 128, 128),(128, 255, 128),(128, 128, 255), (128,255,255),(255,128,255), (255,255,128)]
# Line thickness of 2 px
thickness = 2
# max numer of people
maxID = 0
track = [None]*10
trackInit = [False]*5
tracker1 = cv2.TrackerKCF_create()
tracker2 = cv2.TrackerKCF_create()
tracker3 = cv2.TrackerKCF_create()
tracker4 = cv2.TrackerKCF_create()
tracker5 = cv2.TrackerKCF_create()
trackers = [tracker1, tracker2, tracker3, tracker4, tracker5]
# tracker1 = cv2.TrackerCSRT_create()
# tracker2 = cv2.TrackerKCF_create()
# Read until video is completed
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
if ret:
#
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# gauss = cv2.GaussianBlur(gray, (3, 3), 0)
gauss = skinThresh(frame)
# Cascade Classifier
face_cascade = cv2.CascadeClassifier(xmlPath)
detected_faces = face_cascade.detectMultiScale(gauss, 1.2, 4)
id = 0
for (x0,y0,w,h) in detected_faces:
id += 1
maxID = maxID if maxID > id else id
x1 = x0 + w
y1 = y0 + h
# Detection
track[id-1] = [x0, y0, x1, y1]
for ID in range(maxID):
#Tracking
if track[ID] is not None:
bboxes[str(nb_frame)+str(ID+1)] = np.array(tracking(frame, track[ID], trackers[ID], trackInit[ID]))
trackInit[ID] = True
# Draw Annotations
if verbose:
[xx0, yy0, xx1, yy1] = track[ID]
cv2.rectangle(frame, (xx0,yy0),(xx1,yy1), color[ID], thickness)
# show
if verbose:
cv2.imshow("Viola_Jones", frame)
key = cv2.waitKey(1) & 0xFF
# Break the loop
else:
break
nb_frame +=1
# When everything done, release the video capture object
cap.release()
# Closes all the frames
cv2.destroyAllWindows()
return bboxes