def load_zhicheng_Params_TT():
'''
loading tracked target information in zhicheng datasets, including parameters and tracked targets tbbs and blobs.
:return:
'''
FilePath = 'F:\\code\\Python\\visual_radar_tracker\\images\\zhicheng20130808_22.28\\Res_MultiKCFs_AutoParams\\'
ParamFiles = {
'ALice': 'ALice_Params.txt',
'Billy': 'Billy_Params.txt',
'Camen': 'Camen_Params.txt',
'Dolphin': 'Dolphin_Params.txt',
'Ellen': 'Ellen_Params.txt'
}
TtTbbsFiles = {
'ALice': 'ALice_tbbs_%02d_option.txt',
'Billy': 'Billy_tbbs_%02d_option.txt',
'Camen': 'Camen_tbbs_%02d_option.txt',
'Dolphin': 'Dolphin_tbbs_%02d_option.txt',
'Ellen': 'Ellen_tbbs_%02d_option.txt'
}
TtBlobsFiles = {
'ALice': 'ALice_blobs_%02d_option.txt',
'Billy': 'Billy_blobs_%02d_option.txt',
'Camen': 'Camen_blobs_%02d_option.txt',
'Dolphin': 'Dolphin_blobs_%02d_option.txt',
'Ellen': 'Ellen_blobs_%02d_option.txt'
}
ParamsTT = {}
#according the parameters' file's lines to decide howmany tbbs and blob file included.
for key in ParamFiles:
obj_file = FilePath + ParamFiles[key]
with open(obj_file, 'r') as obj:
obj_lines = obj.readlines()
for i, line in enumerate(obj_lines):
#tbbs or blob ttfiles's index is correlated with the line number of parameters file
tbbsFileName = (FilePath + TtTbbsFiles[key] % i)
blobsFileName = (FilePath + TtBlobsFiles[key] % i)
print(tbbsFileName)
tbbsDict = uti.get_obj_dict(tbbsFileName)
print(blobsFileName)
tblobsDict = uti.get_obj_dict(blobsFileName)
if tbbsDict == {} or tblobsDict == {}:
print('Empty tt file is found in %s_%d, be carefull !!!!' %
(key, i))
ptKey = '%s %d' % (key, i)
ParamsTT.update({
ptKey: {
'Params': line,
'TbbsDict': tbbsDict,
'tblobsDict': tblobsDict
}
})
return ParamsTT
def show_precision(tracker_name):
'''
show the scores of the given tracker
:return:
'''
print('\n%s\'s Tracking Results' % tracker_name)
ObjNames = []
GtInfo = load_zhicheng_GT()
for key in GtInfo:
ObjNames.append(key)
ObjNames.sort()
res_path = '/Users/yizhou/code/MKCF_MAC_Python3.6/results/Res_%s/' % tracker_name
# scores_table, contain 3 rows, for cle, pre_cle, pre_iou
# the last column is for average.
scores_table = np.zeros((3, 5))
# containing 5 objects' tracked bounding box in KfResData
KfResData = {}
for i, key in enumerate(ObjNames):
fname = res_path + key + '_%s_Tbbs.txt' % tracker_name
ttData = uti.get_obj_dict(fname)
KfResData.update({key: ttData})
GtData = GtInfo[key]['GtData']
(evalue_reports, cle, IoU, pre_cle, pre_iou,
fids) = show_tbb_precision(ttData, GtData, bplot=False)
print(key)
print(evalue_reports)
scores_table[0, i] = np.mean(cle)
scores_table[1, i] = np.mean(pre_cle)
scores_table[2, i] = np.mean(pre_iou)
return scores_table
def show_precision_MKCF():
'''
Show the tracking results with Extended Object Tracking based on Random Matrix, and draw the precision curve.
:return:
'''
print('\nMKCF\'s Tracking Results')
ObjNames = []
GtInfo = load_zhicheng_GT()
for key in GtInfo:
ObjNames.append(key)
ObjNames.sort()
res_path = '/Users/yizhou/code/MKCF_MAC_Python3.6/results/Res_MKCF/'
scores_table = np.zeros(
(3, 5)) # scores_table, contain 3 rows, for cle, pre_cle, pre_iou
# containing 5 objects' tracked bounding box in KfResData
KfResData = {}
for i, key in enumerate(ObjNames):
fname = res_path + key + '_MKCF_Tbbs.txt'
ttData = uti.get_obj_dict(fname)
KfResData.update({key: ttData})
GtData = GtInfo[key]['GtData']
(evalue_reports, cle, IoU, pre_cle, pre_iou,
fids) = show_tbb_precision(ttData, GtData, bplot=False)
print(key)
print(evalue_reports)
scores_table[0, i] = cle
scores_table[1, i] = pre_cle
scores_table[2, i] = pre_iou
return scores_table
def load_zhicheng_GT():
'''
Set the path and GT files name, according start_id, end_id.
Convert the Gt file information into a dictionary data type [GtInfo]
:return GtInfo:
'''
FilePath = '../ground_truth/'
GtParam = {
'ALice': {
'FileName': 'zhicheng20130808_22.xx_60-400_polarGT_Alice.txt',
'StartId': 60,
'EndId': 400
},
'Billy': {
'FileName': 'zhicheng20130808_22.xx_2-215_polarGT_Billy.txt',
'StartId': 2,
'EndId': 226
},
'Camen': {
'FileName': 'zhicheng20130808_22.xx_2-408_polarGT_Camen.txt',
'StartId': 75,
'EndId': 335
},
#'Camen': {'FileName': 'zhicheng20130808_22.xx_2-408_polarGT_Camen.txt', 'StartId': 2, 'EndId': 408},
'Dolphin': {
'FileName': 'zhicheng20130808_22.xx_72-316_polarGT_Dolphin.txt',
'StartId': 73,
'EndId': 316
},
'Ellen': {
'FileName': 'zhicheng20130808_22.xx_129-408_polarGT_Ellen.txt',
'StartId': 129,
'EndId': 408
}
}
#将GtParam里面的文件路径参数赋予load_GT函数，分析出Gt的每帧位置信息，存入GtData
GtInfo = {}
for key in GtParam:
GtElem = GtParam[key]
fname = FilePath + GtElem['FileName']
GtData = uti.get_obj_dict(fname)
start_id = GtElem['StartId']
end_id = GtElem['EndId']
GtInfo[key] = {'GtData': GtData, 'StartId': start_id, 'EndId': end_id}
return GtInfo
def show_precision_KF():
'''
Show the tracking results with Kalman Filter, and draw the precision curve.
:return:
'''
print('\nKalman Filter\'s Tracking Results')
ObjNames = []
GtInfo = load_zhicheng_GT()
for key in GtInfo:
ObjNames.append(key)
ObjNames.sort()
res_path = '/Users/yizhou/code/MKCF_MAC_Python3.6/results/Res_KF/'
scores_table = np.zeros(
(3, 5)) #scores_table, contain 3 rows, for cle, pre_cle, pre_iou
#containing 5 objects' tracked bounding box in KfResData
KfResData = {}
for i, key in enumerate(ObjNames):
fname = res_path + key + '_KF_Tbbs.txt'
ttData = uti.get_obj_dict(fname)
KfResData.update({key: ttData})
#cle_fig = pylab.figure(figsize=(800/300, 600/300), dpi=300)
#cle_ax = cle_fig.add_subplot(111)
#pylab.xlabel('Frame', fontsize=20), pylab.ylabel('Location error(pixels)', fontsize=20)
#cle_ax.grid(True)
GtData = GtInfo[key]['GtData']
(evalue_reports, cle, IoU, pre_cle, pre_iou,
fids) = show_tbb_precision(ttData, GtData, bplot=False)
print(key)
print(evalue_reports)
#cle_ax.plot(fids, cle, label=key, linewidth=2.5, alpha=1)
#cle_ax.set_title(key)
scores_table[0, i] = cle
scores_table[1, i] = pre_cle
scores_table[2, i] = pre_iou
return scores_table
pylab.show()
def view_trackers():
tracker_names = ['KF', 'EOTRM', 'EOTGGIW', 'KCF', 'ASKCF', 'MKL', 'MKCF']
#colors[red, yellow, light blue, white, mud green, violite, light green]
tracker_colors= [(0,0,255),(0,255,255), (239,200,148), (255,255,255), (24,115,84), (227,5,198), (97,241,115) ]
target_names = ['ALice', 'Billy', 'Camen', 'Dolphin', 'Ellen']
res_path = '/Users/yizhou/code/MKCF_MAC_Python3.6/results/'
ResData = {}
#loading target's bounding box '*_tbbs.txt' file for 5 targets in 7 trackers.
for tracker_name in tracker_names:
TrackerResData = {}
for target_name in target_names:
ttData = {}
fname = res_path + 'Res_' + tracker_name + '/' + target_name + '_' + tracker_name + '_Tbbs.txt'
ttData = uti.get_obj_dict(fname)
TrackerResData.update({target_name: ttData})
ResData.update({tracker_name: TrackerResData})
print('size of the results data in bytes: %d'% ResData.__sizeof__())
image_path = '/Users/yizhou/code/MKCF_MAC_Python3.6/sequences/gray/'
video_path = '/Users/yizhou/code/MKCF_MAC_Python3.6/results/video/'
# video = cv2.VideoWriter(video_path + '7trackers_5targets.mp4', cv2.VideoWriter_fourcc('a', 'v', 'c', '1'), 30,
# (2048, 600))
video = cv2.VideoWriter(video_path + '7trackers_5targets.mp4', cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), 30,
(2048, 600))
img_files = os.listdir(image_path)
img_files.sort()
for file in img_files:
img_file = os.path.join(image_path, file)
if img_file.endswith('.png'):
img = cv2.imread(img_file)
id = int(file.split('.')[0])
if id > 409:
break
frame_id = 'frame %d' % id
for i,tracker_name in enumerate(tracker_names):
cv2.putText(img, frame_id, org=(20, 50),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,fontScale=1, color=(245,255,118),#show the tracker's color in rectangle and label.
thickness=2, lineType=cv2.LINE_AA)
img = cv2.rectangle(img, (30, 60+i*40), (80, 90+i*40), tracker_colors[i], 2)
cv2.putText(img, tracker_name, org=(80, 90+i*40),
fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=tracker_colors[i],
thickness=2, lineType=cv2.LINE_AA)
for target_name in target_names:
if frame_id in ResData[tracker_name][target_name]:
tbb = ResData[tracker_name][target_name][frame_id]['BoundingBox']
if len(tbb)>0:
tp = (tbb[0], tbb[1])
br = (int(tbb[0]+tbb[2]), int(tbb[1]+tbb[3]))
img = cv2.rectangle(img, tp, br, tracker_colors[i], 2)
if(tracker_name == 'MKCF'):
cv2.putText(img, target_name, org=tp,
fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(245,255,118),
thickness=2, lineType=cv2.LINE_AA)
video.write(img)
cv2.imshow("Image", img)
cv2.waitKey(1)
video.release()
cv2.destroyAllWindows()
def draw_precision_curve_MKCF():
'''
Draw the 5 precision results on a single plot
:return:
'''
ObjNames = []
GtInfo = load_zhicheng_GT()
for key in GtInfo:
ObjNames.append(key)
ObjNames.sort()
res_path = 'F:\\code\\Python\\visual_radar_tracker\\images\\zhicheng20130808_22.28\\Res_MKCF\\'
trackers = ['3', '5', '8', '10', '15']
line_width = [1, 1.2, 1.5, 2, 2.5]
line_style = ['o', '+', '--', '-.', '-']
alpha_vals = [0.3, 1, 1, 1, 1]
cle_fig = pylab.figure()
cle_ax = cle_fig.add_subplot(111)
pylab.xlabel('Frame ids',
fontsize=15), pylab.ylabel('Location error(pixels)',
fontsize=15)
precle_fig = pylab.figure()
precle_ax = precle_fig.add_subplot(111)
pylab.xlabel('Center Location error threshold',
fontsize=15), pylab.ylabel('Precision', fontsize=15)
iou_fig = pylab.figure()
iou_ax = iou_fig.add_subplot(111)
pylab.xlabel('Frame ids'), pylab.ylabel('Intersection Rate')
preiou_fig = pylab.figure()
preiou_ax = preiou_fig.add_subplot(111)
pylab.xlabel('Intersection over union threshold'), pylab.ylabel(
'Precision')
metric_matrix = np.zeros((5, 5, 3), dtype=np.float32)
KfResData = {}
for j, key in enumerate(ObjNames):
for i, tname in enumerate(trackers):
fname = res_path + '20180323_votepsr=10\\20180323_M=' + tname + '\\' + key + '_MKCF_Tbbs.txt'
ttData = uti.get_obj_dict(fname)
KfResData.update({key: ttData})
GtData = GtInfo[key]['GtData']
(evalue_reports, cle, IoU, pre_cle, pre_iou,
fids) = show_tbb_precision(ttData, GtData, bplot=False)
# the X_axis is the threashold and the y_axis is the precision.
prune_cle = cle.copy()
prune_cle[(prune_cle > 200)] = 200
cle_ax.plot(fids, prune_cle)
precle_ax.plot(pre_cle,
line_style[i],
label=tname + '[%2.3f]' % np.mean(pre_cle),
linewidth=2.5,
alpha=alpha_vals[i])
precle_ax.legend(loc="lower right",
fontsize=15) # set legend location
if key == 'ALice':
titl_str = 'Alice' #change the upper case to lower case
else:
titl_str = key
precle_ax.set_title(titl_str)
iou_ax.plot(fids, IoU)
preiou_ax.plot(np.arange(1, 0, -0.01).tolist(),
pre_iou,
line_style[i],
label=tname + '[%2.3f]' % np.mean(pre_iou),
linewidth=2.5,
alpha=alpha_vals[i])
preiou_ax.legend(loc="upper right",
fontsize=15) # set legend location
preiou_ax.set_title(titl_str)
pylab.show(block=False)
metric_matrix[i, j, 0] = np.mean(cle)
metric_matrix[i, j, 1] = np.mean(pre_cle)
metric_matrix[i, j, 2] = np.mean(pre_iou)
print(
'%s number of trackers %s of KMCF, mean cle %3.3f, mean pre_cle % 3.3f, mean pre_iou % 3.3f'
%
(key, tname, np.mean(cle), np.mean(pre_cle), np.mean(pre_iou)))
# cle_fig.savefig('F:\\code\\Python\\visual_radar_tracker\\images\\paper\\exp\\'+titl_str+'_cle.pdf', format='pdf',dpi = 1200, bbox_inches='tight')
# cle_fig.savefig('F:\\code\\Python\\visual_radar_tracker\\images\\paper\\exp\\'+titl_str+'_cle.png', format='png', dpi=1200, bbox_inches='tight')
# precle_fig.savefig('F:\\code\\Python\\visual_radar_tracker\\images\\paper\\exp\\'+titl_str+'_precle.pdf', format='pdf',dpi = 1200, bbox_inches='tight')
# precle_fig.savefig('F:\\code\\Python\\visual_radar_tracker\\images\\paper\\exp\\'+titl_str+'_precle.png', format='png', dpi=1200, bbox_inches='tight')
# iou_fig.savefig('F:\\code\\Python\\visual_radar_tracker\\images\\paper\\exp\\'+titl_str+'_iou.pdf', format='pdf',dpi = 1200, bbox_inches='tight')
# iou_fig.savefig('F:\\code\\Python\\visual_radar_tracker\\images\\paper\\exp\\'+titl_str+'_iou.png', format='png', dpi=1200, bbox_inches='tight')
# preiou_fig.savefig('F:\\code\\Python\\visual_radar_tracker\\images\\paper\\exp\\'+titl_str+'_preiou.pdf', format='pdf',dpi = 1200, bbox_inches='tight')
# preiou_fig.savefig('F:\\code\\Python\\visual_radar_tracker\\images\\paper\\exp\\'+titl_str+'_preiou.png', format='png', dpi=1200, bbox_inches='tight')
cle_ax.clear()
precle_ax.clear()
iou_ax.clear()
preiou_ax.clear()
for m, tname in enumerate(trackers):
mcle = metric_matrix[m, :, 0]
print('%s average mean of cle in five objects %f' %
(tname, np.mean(mcle)))
print(mcle)
mprecle = metric_matrix[m, :, 1]
print('%s average mean of precle in five objects %f' %
(tname, np.mean(mprecle)))
print(mprecle)
mpreiou = metric_matrix[m, :, 2]
print('%s average mean of preiou in five objects %f' %
(tname, np.mean(mpreiou)))
print(mpreiou)
#pylab.show()
print('finish painting')
def draw_precision_curve():
'''
Draw the 5 precision results on a single plot
:return:
'''
ObjNames = []
GtInfo = load_zhicheng_GT()
for key in GtInfo:
ObjNames.append(key)
ObjNames.sort()
res_path = '/Users/yizhou/code/MKCF_MAC_Python3.6/results/'
trackers = [
'KF',
'EOTRM',
'EOTGGIW',
'KCF',
'ASKCF',
'MKL',
'MKCF',
]
line_width = [1, 1.2, 1.3, 1.5, 2, 2, 2.5]
line_style = ['o', '+', 'x', ':', '--', '-.', '-']
alpha_vals = [0.4, 1, 1, 1, 1, 1, 1]
params = {
'axes.labelsize': '20',
'xtick.labelsize': '15',
'ytick.labelsize': '15',
'lines.linewidth': 2,
'legend.fontsize': '16',
#'figure.figsize': '12, 9' # set figure size
}
pylab.rcParams.update(params) # set figure parameter
KfResData = {}
metric_matrix = np.zeros((7, 5, 3), dtype=np.float32)
label_font = 20
legend_font = 14
for j, key in enumerate(ObjNames):
cle_fig = pylab.figure(figsize=(9, 7), dpi=200)
cle_ax = cle_fig.add_subplot(111)
pylab.xlabel('Frame', fontsize=label_font), pylab.ylabel(
'Location error(pixels)', fontsize=label_font)
cle_ax.grid(True)
#figsize=(1200/200, 1000/200), dpi=200
precle_fig = pylab.figure(figsize=(9, 7), dpi=200)
precle_ax = precle_fig.add_subplot(111)
pylab.xlabel('Center location error threshold'), pylab.ylabel(
'Distance Precision')
precle_ax.grid(True)
# iou_fig = pylab.figure()
# iou_ax = iou_fig.add_subplot(111)
# pylab.xlabel('Frame'), pylab.ylabel('Intersection Rate')
# iou_ax.grid(True)
preiou_fig = pylab.figure(figsize=(9, 7), dpi=200)
preiou_ax = preiou_fig.add_subplot(111)
pylab.xlabel('Intersection over union threshold'), pylab.ylabel(
'Overlap Precision')
preiou_ax.grid(True)
for i, tname in enumerate(trackers):
fname = res_path + 'Res_' + tname + '/' + key + '_' + tname + '_Tbbs.txt'
ttData = uti.get_obj_dict(fname)
KfResData.update({key: ttData})
GtData = GtInfo[key]['GtData']
(evalue_reports, cle, IoU, pre_cle, pre_iou,
fids) = show_tbb_precision(ttData, GtData, bplot=False)
print(
'%s %s tracker, mean cle %f, mean pre_cle %f, mean pre_iou %f'
%
(key, tname, np.mean(cle), np.mean(pre_cle), np.mean(pre_iou)))
metric_matrix[i, j, 0] = np.mean(cle)
metric_matrix[i, j, 1] = np.mean(pre_cle)
metric_matrix[i, j, 2] = np.mean(pre_iou)
# the X_axis is the threashold and the y_axis is the precision.
prune_cle = cle.copy()
prune_cle[(prune_cle > 200)] = 200
if tname == 'MKCF':
tname = 'MKCF' + '_10'
cle_ax.plot(fids,
prune_cle,
line_style[i],
label=tname,
linewidth=2.5,
alpha=alpha_vals[i])
if key == 'ALice':
titl_str = 'Alice' #change the upper case to lower case
else:
titl_str = key
cle_ax.set_ylim([0, 200])
cle_ax.set_title(titl_str, fontsize=label_font)
cle_ax.legend(loc="upper left", fontsize=legend_font)
#cle_ax.plot(fids, cle)
precle_ax.plot(pre_cle,
line_style[i],
label=tname + '[%2.3f]' % np.mean(pre_cle),
linewidth=2.5,
alpha=alpha_vals[i])
precle_ax.legend(loc="upper right",
fontsize=legend_font) # set legend location
if key == 'ALice':
titl_str = 'Alice' #change the upper case to lower case
else:
titl_str = key
precle_ax.set_title(titl_str, fontsize=label_font)
#iou_ax.plot(fids, IoU)
preiou_ax.plot(np.arange(1, 0, -0.01).tolist(),
pre_iou,
line_style[i],
label=tname + '[%2.3f]' % np.mean(pre_iou),
linewidth=2.5,
alpha=alpha_vals[i])
preiou_ax.legend(loc="upper right",
fontsize=legend_font) # set legend location
preiou_ax.set_title(titl_str, fontsize=label_font)
#pylab.show(block=True)
cle_fig.savefig('/Users/yizhou/code/MKCF_MAC_Python3.6/results/' +
titl_str + '_cle.pdf',
format='pdf',
dpi=200,
bbox_inches='tight')
cle_fig.savefig('/Users/yizhou/code/MKCF_MAC_Python3.6/results/' +
titl_str + '_cle.png',
format='png',
dpi=200,
bbox_inches='tight')
precle_fig.savefig('/Users/yizhou/code/MKCF_MAC_Python3.6/results/' +
titl_str + '_precle.pdf',
format='pdf',
dpi=200,
bbox_inches='tight')
precle_fig.savefig('/Users/yizhou/code/MKCF_MAC_Python3.6/results/' +
titl_str + '_precle.png',
format='png',
dpi=200,
bbox_inches='tight')
#iou_fig.savefig('/Users/yizhou/code/MKCF_MAC_Python3.6/results/'+titl_str+'_iou.pdf', format='pdf',dpi = 300, bbox_inches='tight')
#iou_fig.savefig('/Users/yizhou/code/MKCF_MAC_Python3.6/results/'+titl_str+'_iou.png', format='png', dpi=300, bbox_inches='tight')
preiou_fig.savefig('/Users/yizhou/code/MKCF_MAC_Python3.6/results/' +
titl_str + '_preiou.pdf',
format='pdf',
dpi=200,
bbox_inches='tight')
preiou_fig.savefig('/Users/yizhou/code/MKCF_MAC_Python3.6/results/' +
titl_str + '_preiou.png',
format='png',
dpi=200,
bbox_inches='tight')
#pylab.show(block=True)
cle_fig.clf()
precle_fig.clf()
#iou_fig.close()
preiou_fig.clf()
#pylab.show()
#print(metric_matrix)
for m, tname in enumerate(trackers):
mcle = metric_matrix[m, :, 0]
print('%s\t average mean of cle in five objects %3.3f' %
(tname, np.mean(mcle)))
#print (mcle)
for m, tname in enumerate(trackers):
mprecle = metric_matrix[m, :, 1]
print('%s\t average mean of precle in five objects %3.3f' %
(tname, np.mean(mprecle)))
#print (mprecle)
for m, tname in enumerate(trackers):
mpreiou = metric_matrix[m, :, 2]
print('%s\t average mean of preiou in five objects %3.3f' %
(tname, np.mean(mpreiou)))
#print (mpreiou)
print('cle of 7 trackers\n')
print(metric_matrix[:, :, 0])
print('precle of 7 trackers\n')
print(metric_matrix[:, :, 1])
print('preiou of 7 trackers\n')
print(metric_matrix[:, :, 2])
print('finish painting')
