I'm running opencv 2.4.1 using python bindings and am having difficulty calculating the optical flow.
Specifically this section of code:
#calculate the opticalflow
if prev_saturation_thresh_img==None:
prev_saturation_thresh_img=saturation_img
if i >=0:
prev_img=prev_saturation_thresh_img
next_img=saturation_thresh_img
p1, st, err = cv2.calcOpticalFlowPyrLK(prev_img,next_img,tracks_np,**lk_params)
Returns the error:
<unknown> is not a numpy array
So then I try to convert the images to numpy arrays:
prev_img=prev_saturation_thresh_img
next_img=saturation_thresh_img
Now I have a new error:
<unknown> data type = 17 is not supported
In a last-ditch effort I convert the images to cvmat (from iplimage) before converting it to a numpy array, just to see what happens
error: ..\..\..\OpenCV-2.4.1\modules\video\src\lkpyramid.cpp:607: error: (-215) nextPtsMat.checkVector(2, CV_32F, true) == npoints
So now I'm stuck. Below is the code in it's entirety for reference
import cv
import cv2
import numpy as np
class Target:
def __init__(self):
self.capture = cv.CaptureFromFile("raw_gait_cropped.avi")
def run(self):
#initiate font
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 3, 8)
#instantiate images
img_size=cv.GetSize(cv.QueryFrame(self.capture))
hsv_img=cv.CreateImage(img_size,8,3)
saturation_img=cv.CreateImage(img_size,8,1)
saturation_thresh_img=cv.CreateImage(img_size,8,1)
prev_saturation_thresh_img=None
#create params for GoodFeaturesToTrack and calcOpticalFlowPyrLK
gftt_params = dict( cornerCount=11,
qualityLevel=0.2,
minDistance=5,
mask=None,
useHarris=True
)
lk_params = dict( winSize = (15, 15),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03),
flags = cv2.OPTFLOW_USE_INITIAL_FLOW,
minEigThreshold=1
)
tracks=[]
writer=cv.CreateVideoWriter("angle_tracking.avi",cv.CV_FOURCC('M','J','P','G'),30,cv.GetSize(hsv_img),1)
i=0
while True:
#grab a frame from the video capture
img=cv.QueryFrame(self.capture)
#break the loop when the video is over
if img == None:
break
#convert the image to HSV
cv.CvtColor(img,hsv_img,cv.CV_BGR2HSV)
#Get Saturation channel
cv.MixChannels([hsv_img],[saturation_img],[(1,0)])
#Apply threshold to saturation channel
cv.InRangeS(saturation_img,145,255,saturation_thresh_img)
#locate initial features to track
if i==0:
eig_image=temp_image = cv.CreateMat(img.height, img.width, cv.CV_32FC1)
for (x,y) in cv.GoodFeaturesToTrack(saturation_thresh_img, eig_image, temp_image, **gftt_params):
tracks.append([(x,y)])
cv.Circle(saturation_thresh_img,(int(x),int(y)),5,(255,255,255),-1,cv.CV_AA,0)
tracks_np=np.float32(tracks).reshape(-1,2)
print tracks
#calculate the opticalflow
if prev_saturation_thresh_img==None:
prev_saturation_thresh_img=saturation_img
if i >=0:
prev_img=prev_saturation_thresh_img
next_img=saturation_thresh_img
p1, st, err = cv2.calcOpticalFlowPyrLK(prev_img,next_img,tracks_np,**lk_params)
prev_saturation_thresh_img=saturation_img
i=i+1
print i
#display frames to users
cv.ShowImage("Raw Video",img)
cv.ShowImage("Saturation Channel",saturation_img)
cv.ShowImage("Saturation Thresholded",saturation_thresh_img)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if c == 27 or c == 10:
break
#close all windows once video is done
cv.DestroyAllWindows()
if __name__=="__main__":
t = Target()
t.run()
OpenCV can be very picky about the data formats it accepts. The following code extract works for me:
prev = cv.LoadImage('images/'+file_list[0])
prev = np.asarray(prev[:,:])
prev_gs = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
current = cv.LoadImage('images/'+file)
current = np.asarray(current[:,:])
current_gs = cv2.cvtColor(current, cv2.COLOR_BGR2GRAY)
features, status, track_error = cv2.calcOpticalFlowPyrLK(prev_gs, current_gs, good_features, None,
**lk_params)
Note the [:,:] when converting from images to numpy arrays, I have found that they are required.
I hope that this may solve your problem.