def estimateOpticalFlowTVL1(dataset_path, showResult, Backward):
print 'Optical Flow computation with Farneback method'
frames_list, gt_list, frames_path, gt_path = readDataset(dataset_path)
nFrames = len(frames_list)
for idx in range(0, nFrames, 2):
print ' --> Analyzing sequence ',frames_list[idx],' and ',frames_list[idx+1],'...'
# print 'Evaluating frame '+ pr_name
frame_dir_0 = os.path.join(frames_path, frames_list[idx])
frame_dir_1 = os.path.join(frames_path, frames_list[idx+1])
if Backward:
os.system('./IPOL_TVL1/tvl1flow ' + frame_dir_1 + ' ' + frame_dir_0 + ' IPOL_TVL1/flow.flo')
#toExplore_img = cv2.imread(frame_dir_0, 0)
curr_img = cv2.imread(frame_dir_1, 0)
else:
os.system('./IPOL_TVL1/tvl1flow ' + frame_dir_0 + ' ' + frame_dir_1 + ' IPOL_TVL1/flow.flo')
curr_img = cv2.imread(frame_dir_0, 0)
#toExplore_img = cv2.imread(frame_dir_1, 0)
data2D = readFLO('./IPOL_TVL1/flow.flo')
OF_image = np.zeros([data2D.shape[0], data2D.shape[1], 3])
OF_image[:,:,0:2] = data2D
if showResult:
showOpticalFlowArrows(OF_image, curr_img, frames_list[idx], saveResult = True )
showOpticalFlowHSVBlockMatching(OF_image, frames_list[idx], visualization='HS', saveResult=True)
np.save('results/OpticalFlow/files/OF_'+frames_list[idx], OF_image)
return
def estimateOpticalFlow(dataset_path, blockSize, areaOfSearch, Backward, showResult):
print 'Optical Flow computation with BlockSize = ',blockSize, ' and AreaOfSearch = ',areaOfSearch
frames_list, gt_list, frames_path, gt_path = readDataset(dataset_path)
nFrames = len(frames_list)
for idx in range(0, nFrames, 2):
print ' --> Analyzing sequence ',frames_list[idx],' and ',frames_list[idx+1],'...'
# print 'Evaluating frame '+ pr_name
frame_dir_0 = os.path.join(frames_path, frames_list[idx])
frame_dir_1 = os.path.join(frames_path, frames_list[idx+1])
if Backward:
toExplore_img = cv2.imread(frame_dir_0, 0)
curr_img = cv2.imread(frame_dir_1, 0)
else:
curr_img = cv2.imread(frame_dir_0, 0)
toExplore_img = cv2.imread(frame_dir_1, 0)
OF_image = calcOpticalFlowBM(curr_img, toExplore_img, blockSize, areaOfSearch)
if showResult:
showOpticalFlowArrows(OF_image, curr_img, frames_list[idx], saveResult=True)
showOpticalFlowHSVBlockMatching(OF_image, frames_list[idx], visualization='HS', saveResult=True)
np.save('results/OpticalFlow/files/OF_'+frames_list[idx], OF_image)
return
def estimateOpticalFlowFarneback(dataset_path, showResult, Backward):
print 'Optical Flow computation with Farneback method'
frames_list, gt_list, frames_path, gt_path = readDataset(dataset_path)
nFrames = len(frames_list)
for idx in range(0, nFrames, 2):
print ' --> Analyzing sequence ',frames_list[idx],' and ',frames_list[idx+1],'...'
# print 'Evaluating frame '+ pr_name
frame_dir_0 = os.path.join(frames_path, frames_list[idx])
frame_dir_1 = os.path.join(frames_path, frames_list[idx+1])
if Backward:
toExplore_img = cv2.imread(frame_dir_0, 0)
curr_img = cv2.imread(frame_dir_1, 0)
else:
curr_img = cv2.imread(frame_dir_0, 0)
toExplore_img = cv2.imread(frame_dir_1, 0)
#OF_image = cv2.calcOpticalFlowFarneback(curr_img, toExplore_img, pyr_scale=0.5, levels=5, winsize=15, iterations=9, poly_n=7, poly_sigma=1.5, flags=0)
#OF_image = cv2.calcOpticalFlowFarneback(curr_img, toExplore_img, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=5, poly_sigma=1.2, flags=0)
OF_image = cv2.calcOpticalFlowFarneback(curr_img, toExplore_img, pyr_scale=0.5, levels=5, winsize=20, iterations=10, poly_n=7, poly_sigma=1.1, flags=0)
if showResult:
showOpticalFlowArrows(OF_image, curr_img, frames_list[idx], saveResult = True )
showOpticalFlowHSVBlockMatching(OF_image, frames_list[idx], visualization='HS', saveResult=True)
np.save('results/OpticalFlow/files/OF_'+frames_list[idx], OF_image)
return