def ssc_main(dataSource, VideoIndex):
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource, VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource, VideoIndex)
clsObj = trjClusteringFromAdj()
existingFiles = sorted(glob.glob(DataPathobj.sscpath + '*.mat'))
existingFileNames = []
for jj in range(len(existingFiles)):
existingFileNames.append(int(existingFiles[jj][-7:-4]))
for matidx in range(len(clsObj.adjmatfiles)):
if (matidx + 1) in existingFileNames:
print "alredy processed ", str(matidx + 1)
continue
else:
print "clustering trj based on adj truncation ", matidx
clsObj.trjclustering(matidx)
if isSave:
clsObj.saveLabel(matidx)
if isVisualize:
clsObj.visLabel(matidx)
def trjcluster_func_SBS_main(dataSource,VideoIndex):
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource,VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource,VideoIndex)
adjObj = adjacencyMatrix()
existingFiles = sorted(glob.glob(DataPathobj.adjpath+'*.mat'))
existingFileNames = []
for jj in range(len(existingFiles)):
existingFileNames.append(int(existingFiles[jj][-7:-4]))
# pdb.set_trace()
for matidx in range(len(adjObj.matfiles)):
if (matidx+1) in existingFileNames:
print "alredy processed ", str(matidx+1)
continue
print "building adj mtx ....", matidx
adjObj.prepare_input_data(matidx)
"""First cluster using just direction Information"""
adjObj.directionGroup()
adjObj.adjConstruct(matidx)
print "saving adj..."
adjObj.saveADJ(matidx)
""" visualization, see if connected components make sense"""
def dic_main(dataSource, VideoIndex):
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource, VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource, VideoIndex)
global subSampRate
# subSampRate = int(np.round(DataPathobj.cap.get(cv2.cv.CV_CAP_PROP_FPS)/Parameterobj.targetFPS))
subSampRate = int(30.0 / Parameterobj.targetFPS)
existingFiles = sorted(glob.glob(DataPathobj.dicpath + '*final_vcxtrj*.p'))
existingFileNames = []
for jj in range(len(existingFiles)):
existingFileNames.append(int(
existingFiles[jj][-5:-2])) # files starts from 0
if len(existingFileNames) > 0:
print "alredy processed from 0 to ", str(max(existingFileNames))
start_frame_idx = max(existingFileNames) * 3600 + 3600
# print "processing", str(15)
# start_frame_idx = (15-1)*3600+3600
else:
start_frame_idx = 0
print "start_frame_idx: ", start_frame_idx
trj2dicObj = trj2dic()
VCobj = VirtualCenter()
videoReadingObj = videoReading(DataPathobj.video, subSampRate)
trj2dicObj.get_XYT_inDic(start_frame_idx, videoReadingObj, VCobj)
def trjcluster_func_SBS_main(dataSource, VideoIndex):
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource, VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource, VideoIndex)
adjObj = adjacencyMatrix()
existingFiles = sorted(glob.glob(DataPathobj.adjpath + '*.mat'))
existingFileNames = []
for jj in range(len(existingFiles)):
existingFileNames.append(int(existingFiles[jj][-7:-4]))
# pdb.set_trace()
for matidx in range(len(adjObj.matfiles)):
if (matidx + 1) in existingFileNames:
print "alredy processed ", str(matidx + 1)
continue
print "building adj mtx ....", matidx
adjObj.prepare_input_data(matidx)
"""First cluster using just direction Information"""
adjObj.directionGroup()
adjObj.adjConstruct(matidx)
print "saving adj..."
adjObj.saveADJ(matidx)
""" visualization, see if connected components make sense"""
def fit_extrapolate_main(dataSource,VideoIndex): # define start and end regions #Canal video's dimensions: # """(528, 704, 3) # start :<=100, # end: >=500,""" import DataPathclass global DataPathobj DataPathobj = DataPathclass.DataPath(dataSource,VideoIndex) import parameterClass global Parameterobj Parameterobj = parameterClass.parameter(dataSource,VideoIndex) start_Y = 100; end_Y = 500; # matfilepath = '/Users/Chenge/Desktop/testklt/' matfilepath = DataPathobj.kltpath matfiles = sorted(glob.glob(matfilepath + '*.mat')) # matfiles = sorted(glob.glob(matfilepath + 'klt_*.mat')) # matfiles = sorted(glob.glob(matfilepath + 'sim*.mat')) start_position = 0 matfiles = matfiles[start_position:] existingFiles = sorted(glob.glob(DataPathobj.smoothpath+'*.mat')) existingFileNames = [] for jj in range(len(existingFiles)): existingFileNames.append(int(existingFiles[jj][-7:-4])) # for matidx,matfile in enumerate(matfiles): for matidx in range(len(matfiles)): if (matidx+1) in existingFileNames: print "alredy processed ", str(matidx+1) continue matfile = matfiles[matidx] # "if consecutive points are similar to each other, merge them, using one to represent" # didn't do this, smooth and resample instead print "reading data", matfile x,y,t,ptstrj = readData(matfile) print "get spatial and temporal smooth matrix" x_spatial_smooth_mtx,y_spatial_smooth_mtx,x_time_smooth_mtx,y_time_smooth_mtx, xspd_smooth_mtx,yspd_smooth_mtx = getSmoothMtx(x,y,t) """delete all-zero rows""" good_index_before_filtering = np.where(np.sum(x_spatial_smooth_mtx,1)!=0) x_spatial_smooth_mtx = x_spatial_smooth_mtx[good_index_before_filtering,:][0,:,:] y_spatial_smooth_mtx = y_spatial_smooth_mtx[good_index_before_filtering,:][0,:,:] x_time_smooth_mtx = x_time_smooth_mtx[good_index_before_filtering,:][0,:,:] y_time_smooth_mtx = y_time_smooth_mtx[good_index_before_filtering,:][0,:,:] xspd_smooth_mtx = xspd_smooth_mtx[good_index_before_filtering,:][0,:,:] yspd_smooth_mtx = yspd_smooth_mtx[good_index_before_filtering,:][0,:,:] t = t[good_index_before_filtering,:][0,:,:] # plotTrj(x_smooth_mtx,y_smooth_mtx) print "filtering out bad trajectories" goodTrj = filtering(x_spatial_smooth_mtx,y_spatial_smooth_mtx,xspd_smooth_mtx,yspd_smooth_mtx,t) # kmeansPolyCoeff(p3) # plotTrj(x_spatial_smooth_mtx,y_spatial_smooth_mtx,t,Trjchoice = goodTrj) print "saving=======!!" saveSmoothMat(x_time_smooth_mtx,y_time_smooth_mtx,xspd_smooth_mtx,yspd_smooth_mtx,goodTrj,ptstrj,matfile)
def ssc_main(dataSource,VideoIndex):
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource,VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource,VideoIndex)
clsObj = trjClusteringFromAdj()
existingFiles = sorted(glob.glob(DataPathobj.sscpath+'*.mat'))
existingFileNames = []
for jj in range(len(existingFiles)):
existingFileNames.append(int(existingFiles[jj][-7:-4]))
for matidx in range(len(clsObj.adjmatfiles)):
if (matidx+1) in existingFileNames:
print "alredy processed ", str(matidx+1)
continue
else:
print "clustering trj based on adj truncation ", matidx
clsObj.trjclustering(matidx)
if isSave:
clsObj.saveLabel(matidx)
if isVisualize:
clsObj.visLabel(matidx)
def unify_main(dataSource,VideoIndex):
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource,VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource,VideoIndex)
global useCC
useCC = False
if Parameterobj.useWarpped:
filePrefix = 'usewarpped'
else:
# filePrefix = 'Aug12'
# filePrefix = 'Aug10'
filePrefix = 'Aug15'
label_choice = Parameterobj.clustering_choice
if useCC:
matfilesAll = sorted(glob.glob(DataPathobj.adjpath +filePrefix+'*.mat'))
else:
matfilesAll = sorted(glob.glob(DataPathobj.sscpath +filePrefix+'*.mat'))
numTrunc = len(matfilesAll)
savename = ''
if numTrunc<=200:
if useCC:
savename = 'concomp'+savename
else:
savename = 'Complete_result'+savename
if Parameterobj.useWarpped:
savename = 'usewarpped_'+savename
unify_label(matfilesAll,savename,label_choice)
else:
for kk in range(0,numTrunc,25):
print "saved trunk",str(kk+1).zfill(3),'to' ,str(min(kk+25,numTrunc)).zfill(3)
matfiles = matfilesAll[kk:min(kk+25,numTrunc)]
savename = os.path.join(DataPathobj.unifiedLabelpath,'result_'+label_choice+str(kk+1).zfill(3)+'-'+str(min(kk+25,numTrunc)).zfill(3))
unify_label(matfiles,savename,label_choice)
def pair_main(dataSource, VideoIndex,folderName):
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource,VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource,VideoIndex)
# fps = 5
# subSampRate = 6
fps = Parameterobj.targetFPS
# cap = cv2.VideoCapture(DataPathobj.video)
# subSampRate = int(np.round(cap.get(cv2.cv.CV_CAP_PROP_FPS)))/Parameterobj.targetFPS
subSampRate = int(30)/Parameterobj.targetFPS
overlap_pair_threshold = 1*fps
# if len(glob.glob(os.path.join(DataPathobj.pairpath,'obj_pair2.p')))!=0 and len(glob.glob(os.path.join(DataPathobj.pairpath,'all_final_clusterSize.p')))!=0:
# print "already have obj_pair, loading...!"
# obj_pair2 = pickle.load(open(os.path.join(DataPathobj.pairpath,'obj_pair2.p'),'rb'))
# test_clusterSize = pickle.load( open( os.path.join(DataPathobj.pairpath,'all_final_clusterSize.p'), "rb" ) )
# else:
test_vctime,test_vcxtrj,test_vcytrj,test_clusterSize,image_list = prepare_data(isAfterWarpping,dataSource,folderName)
obj_pair = Trj_class_and_func_definitions.TrjObj(test_vcxtrj,test_vcytrj,test_vctime,subSampRate = subSampRate)
# badkeys = obj_pair.bad_IDs1+obj_pair.bad_IDs2+obj_pair.bad_IDs4
badkeys = obj_pair.bad_IDs1+obj_pair.bad_IDs2
for key in badkeys:
del test_vctime[key]
del test_vcxtrj[key]
del test_vcytrj[key]
clean_vctime = test_vctime
clean_vcxtrj = test_vcxtrj
clean_vcytrj = test_vcytrj
print "trj remaining: ", str(len(clean_vctime))
# rebuild this object using filtered data, should be no bad_IDs
obj_pair2 = Trj_class_and_func_definitions.TrjObj(clean_vcxtrj,clean_vcytrj,clean_vctime,subSampRate = subSampRate)
pickle.dump(obj_pair2,open(os.path.join(DataPathobj.pairpath,'obj_pair2.p'),'wb'))
pickle.dump(test_clusterSize, open( os.path.join(DataPathobj.pairpath,'all_final_clusterSize.p'), "wb" ) )
""" write clustered Trj infor(not-pairing): clusterID, virtual center X, vc Y, Y direction, X direction """
# obj2write = obj_pair2
# savename = os.path.join(DataPathobj.pairpath,'Trj_with_ID_frm.csv')
# writer = csv.writer(open(savename,'wb'))
# writer.writerow(['trj ID','frame','x','y','y direction','x direction'])
# temp = []
# for kk in range(np.size(obj2write.Trj_with_ID_frm,0)):
# temp = obj2write.Trj_with_ID_frm[kk]
# curkey = obj2write.Trj_with_ID_frm[kk][0]
# temp.append(obj2write.Ydir[curkey])
# temp.append(obj2write.Xdir[curkey])
# writer.writerow(temp)
# pickle.dump( obj_pair.Trj_with_ID_frm, open( "./mat/20150222_Mat/singleListTrj.p", "wb" ) )
# singleListTrj = pickle.load(open( "./mat/20150222_Mat/singleListTrj.p", "rb" ) )
#=======visualize the pair relationship==============================================
if isVisualize:
isVideo = True
if isVideo:
dataPath = DataPathobj.video
global cap
cap = cv2.VideoCapture(dataPath)
cap.set ( cv2.cv.CV_CAP_PROP_POS_FRAMES ,0)
status, firstfrm = cap.read()
framenum = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
else:
image_list = sorted(glob.glob('../DoT/[email protected]/[email protected]_2015-06-16_16h03min52s762ms/*.jpg')) # only 2000 pictures
firstfrm =cv2.imread(image_list[0])
framenum = int(len(image_list))
nrows = int(np.size(firstfrm,0))
ncols = int(np.size(firstfrm,1))
# plt.figure(1,figsize =[10,12])
# plt.figure()
# axL = plt.subplot(1,1,1)
# frame = np.zeros([nrows,ncols,3]).astype('uint8')
# im = plt.imshow(np.zeros([nrows,ncols,3]))
# plt.axis('off')
# color_choice = np.array([np.random.randint(0,255) for _ in range(3*int(max(obj_pair2.globalID)))]).reshape(int(max(obj_pair2.globalID)),3)
# colors = lambda: np.random.rand(50)
plt.figure('testing')
color_choice = np.array([np.random.randint(0,255) for _ in range(3*int(max(obj_pair2.globalID)))]).reshape(int(max(obj_pair2.globalID)),3)
savenameCooccur = os.path.join(DataPathobj.pairpath,'pair_relationship_overlap3s.csv')
global writerCooccur
writerCooccur = csv.writer(open(savenameCooccur,'wb'))
writerCooccur.writerow(['trj1 ID','frame','x','y','y direction','x direction','trj2 ID','frame','x','y','y direction','x direction'])
savename2 = os.path.join(DataPathobj.pairpath,'pairs_ID_overlap3s.csv')
global writer2
writer2 = csv.writer(open(savename2,'wb'))
writer2.writerow(['trj2 ID','trj2 ID'])
# global outputFile
# outputFile = open(savenameCooccur,'a')
obj_pair2loop = obj_pair2
for ind1 in range(len(obj_pair2loop.globalID)-1):
for ind2 in range(ind1+1, min(len(obj_pair2loop.globalID),ind1+500)):
loopVehicleID1 = obj_pair2loop.globalID[ind1]
loopVehicleID2 = obj_pair2loop.globalID[ind2]
if (sum(test_clusterSize[loopVehicleID1])== len(test_clusterSize[loopVehicleID1]))\
or (sum(test_clusterSize[loopVehicleID2])== len(test_clusterSize[loopVehicleID2])):
# print "single trj as cluster!"
continue
# print "pairing: ",loopVehicleID1,' & ',loopVehicleID2
if isVisualize:
plt.cla()
axL = plt.subplot(1,1,1)
global im
im = plt.imshow(np.zeros([nrows,ncols,3]))
plt.axis('off')
visualize_threshold = fps*10 # only if a pair shared more than this many frames, show them
pair_givenID_vis(loopVehicleID1, loopVehicleID2, obj_pair2loop,color_choice, isWrite = isWrite, \
isVisualize = isVisualize, visualize_threshold = visualize_threshold,overlap_pair_threshold = overlap_pair_threshold)
"""use for signle testing in the end, show pairs given IDs"""
# plt.figure('testing2')
# axL = plt.subplot(1,1,1)
# im = plt.imshow(np.zeros([nrows,ncols,3]))
# plt.axis('off')
# isWrite = False
# isVisualize = True
# pair_givenID_vis(649, 703, obj_pair2loop,color_choice,isWrite, isVisualize ,visualize_threshold = 40)
"""what IDs each frame has"""
def fit_extrapolate_main(dataSource, VideoIndex):
# define start and end regions
#Canal video's dimensions:
# """(528, 704, 3)
# start :<=100,
# end: >=500,"""
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource, VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource, VideoIndex)
start_Y = 100
end_Y = 500
# matfilepath = '/Users/Chenge/Desktop/testklt/'
matfilepath = DataPathobj.kltpath
matfiles = sorted(glob.glob(matfilepath + '*.mat'))
# matfiles = sorted(glob.glob(matfilepath + 'klt_*.mat'))
# matfiles = sorted(glob.glob(matfilepath + 'sim*.mat'))
start_position = 0
matfiles = matfiles[start_position:]
existingFiles = sorted(glob.glob(DataPathobj.smoothpath + '*.mat'))
existingFileNames = []
for jj in range(len(existingFiles)):
existingFileNames.append(int(existingFiles[jj][-7:-4]))
# for matidx,matfile in enumerate(matfiles):
for matidx in range(len(matfiles)):
if (matidx + 1) in existingFileNames:
print "alredy processed ", str(matidx + 1)
continue
matfile = matfiles[matidx]
# "if consecutive points are similar to each other, merge them, using one to represent"
# didn't do this, smooth and resample instead
print "reading data", matfile
x, y, t, ptstrj = readData(matfile)
print "get spatial and temporal smooth matrix"
x_spatial_smooth_mtx, y_spatial_smooth_mtx, x_time_smooth_mtx, y_time_smooth_mtx, xspd_smooth_mtx, yspd_smooth_mtx = getSmoothMtx(
x, y, t)
"""delete all-zero rows"""
good_index_before_filtering = np.where(
np.sum(x_spatial_smooth_mtx, 1) != 0)
x_spatial_smooth_mtx = x_spatial_smooth_mtx[
good_index_before_filtering, :][0, :, :]
y_spatial_smooth_mtx = y_spatial_smooth_mtx[
good_index_before_filtering, :][0, :, :]
x_time_smooth_mtx = x_time_smooth_mtx[good_index_before_filtering, :][
0, :, :]
y_time_smooth_mtx = y_time_smooth_mtx[good_index_before_filtering, :][
0, :, :]
xspd_smooth_mtx = xspd_smooth_mtx[good_index_before_filtering, :][
0, :, :]
yspd_smooth_mtx = yspd_smooth_mtx[good_index_before_filtering, :][
0, :, :]
t = t[good_index_before_filtering, :][0, :, :]
# plotTrj(x_smooth_mtx,y_smooth_mtx)
print "filtering out bad trajectories"
goodTrj = filtering(x_spatial_smooth_mtx, y_spatial_smooth_mtx,
xspd_smooth_mtx, yspd_smooth_mtx, t)
# kmeansPolyCoeff(p3)
# plotTrj(x_spatial_smooth_mtx,y_spatial_smooth_mtx,t,Trjchoice = goodTrj)
print "saving=======!!"
saveSmoothMat(x_time_smooth_mtx, y_time_smooth_mtx, xspd_smooth_mtx,
yspd_smooth_mtx, goodTrj, ptstrj, matfile)
def pair_main(dataSource, VideoIndex, folderName):
import DataPathclass
global DataPathobj
DataPathobj = DataPathclass.DataPath(dataSource, VideoIndex)
import parameterClass
global Parameterobj
Parameterobj = parameterClass.parameter(dataSource, VideoIndex)
# fps = 5
# subSampRate = 6
fps = Parameterobj.targetFPS
# cap = cv2.VideoCapture(DataPathobj.video)
# subSampRate = int(np.round(cap.get(cv2.cv.CV_CAP_PROP_FPS)))/Parameterobj.targetFPS
subSampRate = int(30) / Parameterobj.targetFPS
overlap_pair_threshold = 1 * fps
# if len(glob.glob(os.path.join(DataPathobj.pairpath,'obj_pair2.p')))!=0 and len(glob.glob(os.path.join(DataPathobj.pairpath,'all_final_clusterSize.p')))!=0:
# print "already have obj_pair, loading...!"
# obj_pair2 = pickle.load(open(os.path.join(DataPathobj.pairpath,'obj_pair2.p'),'rb'))
# test_clusterSize = pickle.load( open( os.path.join(DataPathobj.pairpath,'all_final_clusterSize.p'), "rb" ) )
# else:
test_vctime, test_vcxtrj, test_vcytrj, test_clusterSize, image_list = prepare_data(
isAfterWarpping, dataSource, folderName)
obj_pair = Trj_class_and_func_definitions.TrjObj(test_vcxtrj,
test_vcytrj,
test_vctime,
subSampRate=subSampRate)
# badkeys = obj_pair.bad_IDs1+obj_pair.bad_IDs2+obj_pair.bad_IDs4
badkeys = obj_pair.bad_IDs1 + obj_pair.bad_IDs2
for key in badkeys:
del test_vctime[key]
del test_vcxtrj[key]
del test_vcytrj[key]
clean_vctime = test_vctime
clean_vcxtrj = test_vcxtrj
clean_vcytrj = test_vcytrj
print "trj remaining: ", str(len(clean_vctime))
# rebuild this object using filtered data, should be no bad_IDs
obj_pair2 = Trj_class_and_func_definitions.TrjObj(clean_vcxtrj,
clean_vcytrj,
clean_vctime,
subSampRate=subSampRate)
pickle.dump(obj_pair2,
open(os.path.join(DataPathobj.pairpath, 'obj_pair2.p'), 'wb'))
pickle.dump(
test_clusterSize,
open(os.path.join(DataPathobj.pairpath, 'all_final_clusterSize.p'),
"wb"))
""" write clustered Trj infor(not-pairing): clusterID, virtual center X, vc Y, Y direction, X direction """
# obj2write = obj_pair2
# savename = os.path.join(DataPathobj.pairpath,'Trj_with_ID_frm.csv')
# writer = csv.writer(open(savename,'wb'))
# writer.writerow(['trj ID','frame','x','y','y direction','x direction'])
# temp = []
# for kk in range(np.size(obj2write.Trj_with_ID_frm,0)):
# temp = obj2write.Trj_with_ID_frm[kk]
# curkey = obj2write.Trj_with_ID_frm[kk][0]
# temp.append(obj2write.Ydir[curkey])
# temp.append(obj2write.Xdir[curkey])
# writer.writerow(temp)
# pickle.dump( obj_pair.Trj_with_ID_frm, open( "./mat/20150222_Mat/singleListTrj.p", "wb" ) )
# singleListTrj = pickle.load(open( "./mat/20150222_Mat/singleListTrj.p", "rb" ) )
#=======visualize the pair relationship==============================================
if isVisualize:
isVideo = True
if isVideo:
dataPath = DataPathobj.video
global cap
cap = cv2.VideoCapture(dataPath)
cap.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, 0)
status, firstfrm = cap.read()
framenum = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
else:
image_list = sorted(
glob.glob(
'../DoT/[email protected]/[email protected]_2015-06-16_16h03min52s762ms/*.jpg'
)) # only 2000 pictures
firstfrm = cv2.imread(image_list[0])
framenum = int(len(image_list))
nrows = int(np.size(firstfrm, 0))
ncols = int(np.size(firstfrm, 1))
# plt.figure(1,figsize =[10,12])
# plt.figure()
# axL = plt.subplot(1,1,1)
# frame = np.zeros([nrows,ncols,3]).astype('uint8')
# im = plt.imshow(np.zeros([nrows,ncols,3]))
# plt.axis('off')
# color_choice = np.array([np.random.randint(0,255) for _ in range(3*int(max(obj_pair2.globalID)))]).reshape(int(max(obj_pair2.globalID)),3)
# colors = lambda: np.random.rand(50)
plt.figure('testing')
color_choice = np.array([
np.random.randint(0, 255)
for _ in range(3 * int(max(obj_pair2.globalID)))
]).reshape(int(max(obj_pair2.globalID)), 3)
savenameCooccur = os.path.join(DataPathobj.pairpath,
'pair_relationship_overlap3s.csv')
global writerCooccur
writerCooccur = csv.writer(open(savenameCooccur, 'wb'))
writerCooccur.writerow([
'trj1 ID', 'frame', 'x', 'y', 'y direction', 'x direction', 'trj2 ID',
'frame', 'x', 'y', 'y direction', 'x direction'
])
savename2 = os.path.join(DataPathobj.pairpath, 'pairs_ID_overlap3s.csv')
global writer2
writer2 = csv.writer(open(savename2, 'wb'))
writer2.writerow(['trj2 ID', 'trj2 ID'])
# global outputFile
# outputFile = open(savenameCooccur,'a')
obj_pair2loop = obj_pair2
for ind1 in range(len(obj_pair2loop.globalID) - 1):
for ind2 in range(ind1 + 1, min(len(obj_pair2loop.globalID),
ind1 + 500)):
loopVehicleID1 = obj_pair2loop.globalID[ind1]
loopVehicleID2 = obj_pair2loop.globalID[ind2]
if (sum(test_clusterSize[loopVehicleID1])== len(test_clusterSize[loopVehicleID1]))\
or (sum(test_clusterSize[loopVehicleID2])== len(test_clusterSize[loopVehicleID2])):
# print "single trj as cluster!"
continue
# print "pairing: ",loopVehicleID1,' & ',loopVehicleID2
if isVisualize:
plt.cla()
axL = plt.subplot(1, 1, 1)
global im
im = plt.imshow(np.zeros([nrows, ncols, 3]))
plt.axis('off')
visualize_threshold = fps * 10 # only if a pair shared more than this many frames, show them
pair_givenID_vis(loopVehicleID1, loopVehicleID2, obj_pair2loop,color_choice, isWrite = isWrite, \
isVisualize = isVisualize, visualize_threshold = visualize_threshold,overlap_pair_threshold = overlap_pair_threshold)
"""use for signle testing in the end, show pairs given IDs"""
# plt.figure('testing2')
# axL = plt.subplot(1,1,1)
# im = plt.imshow(np.zeros([nrows,ncols,3]))
# plt.axis('off')
# isWrite = False
# isVisualize = True
# pair_givenID_vis(649, 703, obj_pair2loop,color_choice,isWrite, isVisualize ,visualize_threshold = 40)
"""what IDs each frame has"""