def processFolder(datasetPath): stats = Stats(datasetPath) props = SaliencyProps() sal = sal_instance(SaliencyMethods.REGION_CONTRAST,props); for category in getDirectories(datasetPath): stats.addCategories(category) categoryPath = os.path.join(datasetPath,category) for image in getDirectories(categoryPath): stats.update(category,image,compareWithGroundtruth(sal,categoryPath,image)); stats.writeCategoryResult(category) stats.writeOverallResults()
def processFolder(datasetPath):
stats = Stats(datasetPath)
props = SaliencyProps()
sal = sal_instance(SaliencyMethods.REGION_CONTRAST, props)
for category in getDirectories(datasetPath):
stats.addCategories(category)
categoryPath = os.path.join(datasetPath, category)
for image in getDirectories(categoryPath):
stats.update(category, image,
compareWithGroundtruth(sal, categoryPath, image))
stats.writeCategoryResult(category)
stats.writeOverallResults()
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='Event tracking algorithm using saliency and bg subtraction')
parser.add_argument("input",nargs='?',help = "input path",default="../examples/videos/sample_video1.avi");
parser.add_argument("output",nargs='?',help = "output path",default="test_results/final.avi");
parser.add_argument("--bg",nargs='?',help = "background subtraction method 1-FD, 2-ES, 3-MG default-2",default=1,type=int);
parser.add_argument("--sal",nargs='?',help = "saliency method 1-CF, 2-CA, 3-RC, 4-SD default-3",default=3,type=int);
parser.add_argument("--smoothner",nargs='?',help = "smoothing method 1-Eigen, 2-GMM, 3-SSL, default-2",default=2,type=int);
parser.add_argument("--write",nargs='?',help = "write path default-Dont write",default="test_results/final.avi",type=str);
parser.add_argument("--extract",nargs='?',help = "extract path default-Dont extract",default="test_results/final.feats",type=str);
parser.add_argument("--num_prev_frames",nargs='?',help = "num prev frames default-3",default=2,type=int);
parser.add_argument("--num_blocks",nargs='?',help = "num blocks default-6",default=6,type=int);
parser.add_argument("--rsz_shape",nargs='+',help = "overlap default-[80 60]",default=[80,60],type=int);
parser.add_argument("--write_gray",nargs ='?',help = "Write gray value default-0",default=0,type=int);
parser.add_argument("--write_bgsub",nargs='?',help = "Write bgsub default-0",default=0,type=int);
parser.add_argument("--window",nargs='?',help = "window size default-5",default=5,type=int);
parser.add_argument("--overlap",nargs='?',help = "overlap default-2",default=2,type=int);
args = parser.parse_args()
inp = args.input; out = args.output
vidreader = VideoReader(inp)
sal = sal_instance(args.sal,SaliencyProps())
bg = get_instance(args.bg);
smoothner = smooth_instance(feats,args.smoothner);
start = time.time();
process(vidreader,sal,bg,smoothner,args.num_prev_frames,args.num_blocks,args.write,args.extract,
args.write_gray==1,args.write_bgsub==1,args.window,args.overlap,args.rsz_shape);
print "Event Tracker ...",inp,"[DONE] in",(time.time()-start),"seconds";
from skimage.feature import canny from pylab import cm from dnn_predict import get_instance as dnn_instance from io_module.video_reader import VideoReader from io_module.video_writer import VideoWriter; from io_module.block_reader import BlockReader from saliency import SaliencyProps,SaliencyMethods,get_instance as sal_instance from bg_sub import get_instance as bg_instance,BGMethods from tracker import get_instance as tracker_instance,TrackerMethods from smoothing import get_instance as smooth_instance,SmoothMethods from features.pixellete import allFeats as feats from features.color import GRAY from utils import normalize sal = sal_instance(SaliencyMethods.REGION_CONTRAST,SaliencyProps()) bg = bg_instance(BGMethods.FRAME_DIFFERENCING); smoothner = smooth_instance(feats,SmoothMethods.GMM_BASED); tracker = tracker_instance(TrackerMethods.MIXTURE_BASED); KERNEL = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3)); def __morphologicalOps__(mask): #_mask = binary_fill_holes(mask) _mask = cv2.medianBlur(np.uint8(mask),3) _mask = cv2.morphologyEx(_mask, cv2.MORPH_CLOSE,KERNEL) _mask = binary_fill_holes(_mask) _mask = remove_small_objects(_mask,min_size=128,connectivity=2) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5)) _mask = cv2.dilate(np.uint8(_mask),kernel,iterations = 1) return _mask;
type=int)
parser.add_argument("--write_bgsub",
nargs='?',
help="Write bgsub default-0",
default=0,
type=int)
parser.add_argument("--window",
nargs='?',
help="window size default-5",
default=5,
type=int)
parser.add_argument("--overlap",
nargs='?',
help="overlap default-2",
default=2,
type=int)
args = parser.parse_args()
inp = args.input
out = args.output
vidreader = VideoReader(inp)
sal = sal_instance(args.sal, SaliencyProps())
bg = get_instance(args.bg)
smoothner = smooth_instance(feats, args.smoothner)
start = time.time()
process(vidreader, sal, bg, smoothner, args.num_prev_frames,
args.num_blocks, args.write, args.extract, args.write_gray == 1,
args.write_bgsub == 1, args.window, args.overlap, args.rsz_shape)
print "Event Tracker ...", inp, "[DONE] in", (time.time() -
start), "seconds"
def process(vidreader, vidwriter):
vidwriter.build()
bg_es = get_instance(BGMethods.FRAME_DIFFERENCING, vidreader.read_next)
bg_es.setShape((vidreader.height, vidreader.width))
sal_rc = sal_instance(SaliencyMethods.REGION_CONTRAST, SaliencyProps())
zero = np.zeros((vidreader.height, vidreader.width), np.float32)
zero3 = np.zeros((vidreader.height, 3 * vidreader.width), np.float32)
frame_idx = 0
N = vidreader.frames
prev_saliency = np.zeros((vidreader.height, vidreader.width), np.float32)
#kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
prev_rect = np.array([
vidreader.width / 4, vidreader.height / 4, vidreader.width / 2,
vidreader.height / 2
],
dtype=np.float32)
kernel = np.ones((5, 5), np.uint8)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
while (True):
frame_idx += 1
mask_es = bg_es.process()
mask_es = cv2.medianBlur(mask_es, 5)
mask_es = cv2.medianBlur(mask_es, 3)
print 'Proceesing ... {0}%\r'.format((frame_idx * 100 / N)),
if bg_es.isFinish():
break
sal_rc.process(bg_es.cur_frame)
#mask_lbls = np.unique(mask_es * sal_rc.cut_labels)[1:]
#if len(mask_lbls)>0:
# mask = sum([sal_rc.cut_labels==lbl for lbl in mask_lbls])
# mask = np.uint8(mask*255)
#else:
# mask = zero
#out_frame = sal_rc.ncut*mask[:,:,None]
#frame = np.uint8(sal_rc.ncut * mask[:,:,None]);
#mask_es = np.uint8(mask_es*255);
#cv2.accumulateWeighted(sal_rc.saliency,prev_saliency,0.7,None)
#saliency_prob = bg_es.variation + prev_saliency;
#_,mask = cv2.threshold(prev_mask,0.8 * np.max(prev_mask),255,cv2.THRESH_BINARY)
#mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
#mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
#bgdModel = np.zeros((1,65),np.float64); fgdModel = np.zeros((1,65),np.float64)
saliency_prob = bg_es.variation + sal_rc.saliency
saliency_prob = normalize(saliency_prob)
_, mask = cv2.threshold(saliency_prob, 0.6, 1, cv2.THRESH_BINARY)
#mask = cv2.medianBlur(mask,3)
pixels = np.where(mask == 1)
_max = np.max(pixels, 1)
_min = np.min(pixels, 1)
rect = np.array(
[_min[1], _min[0], _max[1] - _min[1], _max[0] - _min[0]],
dtype=np.float32)
#out_frame = np.uint8(bg_es.cur_frame.copy());
#cv2.accumulateWeighted(rect,prev_rect,0.3,None);
rect = tuple(rect)
mask_cut = np.zeros(bg_es.cur_frame.shape[:2], np.uint8)
#mask_cut[_min[1]:_max[1]-_min[1],_min[0]:_max[0]-_min[0]]=3
mask_cut[saliency_prob >= 0.2] = 2
mask_cut[saliency_prob >= 0.6] = 3
mask_cut[saliency_prob >= 0.8] = 1
#_,mask_cut = cv2.threshold(saliency_prob,0.8,1,cv2.THRESH_BINARY)
#mask_cut = np.uint8(mask_cut)
#count = np.array([np.sum((mask_cut%2)==lbl) for lbl in [0,1]]);
#Laplcian features
#_frame = np.uint8(cv2.Laplacian(bg_es.cur_frame,3))
gray = cv2.cvtColor(bg_es.cur_frame, cv2.COLOR_RGB2GRAY)
#eigen = cv2.cornerEigenValsAndVecs(gray,15,3);
#eigen = eigen.reshape(gray.shape[0], gray.shape[1], 3, 2)
#texture_mag = normalize(np.sqrt(eigen[:,:,0,0]**2 + eigen[:,:,0,1]**2))*255
#texture_dir1 = normalize(np.arctan2(eigen[:,:,1,1],eigen[:,:,1,0]))*255
#texture_dir2 = normalize(np.arctan2(eigen[:,:,2,1],eigen[:,:,2,0]))*255
t_frame = bg_es.cur_frame #np.uint8(np.dstack((gray,gray,gray)));
regions = segmentation.slic(t_frame,
50,
20,
convert2lab=True,
multichannel=True)
_frame = color.label2rgb(regions, bg_es.cur_frame, kind='avg')
#_frame = np.uint8(np.dstack((gray,gray,texture_mag)));
#print 'texture flow',texture_flow.shape
max_iter = 4
#_frame = np.uint8(np.dstack((luminescence,texture_flow,laplacian)));
#if np.all(count>4):
cv2.grabCut(_frame, mask_cut, rect, bgdModel, fgdModel, 1,
cv2.GC_INIT_WITH_MASK)
#else:
#cv2.grabCut(bg_es.cur_frame,mask_cut,rect,bgdModel,fgdModel,1,cv2.GC_INIT_WITH_RECT)
for _iter in range(max_iter):
mask = np.uint8(np.where((mask_cut == 2) | (mask_cut == 0), 0, 1))
mask_erode = cv2.erode(mask, kernel, 1)
mask_dilate = cv2.dilate(mask, kernel, 1)
mask_cut = np.uint8(3 * mask_dilate - 2 * mask_erode)
if np.unique(mask_cut).__len__() == 1:
break
cv2.grabCut(_frame, mask_cut, rect, bgdModel, fgdModel, 1,
cv2.GC_INIT_WITH_MASK)
#out_frame = cv2.cvtColor(stacked*255,cv2.COLOR_GRAY2RGB);
#_,mask = cv2.threshold(out_frame[:,:,0],0.8*255,255,cv2.THRESH_BINARY)
mask = np.float32(np.where((mask_cut == 2) | (mask_cut == 0), 0, 1))
mask[mask_cut == 3] = 0.8
mask = cv2.medianBlur(np.uint8(mask), 5)
mask = cv2.medianBlur(mask, 3)
salProbColor = cv2.cvtColor(saliency_prob * 255, cv2.COLOR_GRAY2RGB)
_, salMask = cv2.threshold(sal_rc.saliency, 0.8, 1, cv2.THRESH_BINARY)
mask_frame = np.float32(np.dstack((zero, salMask * 255, zero)))
salProbColor = cv2.addWeighted(np.float32(salProbColor), 0.6,
mask_frame, 0.4, 0.0)
pixels = np.where(salMask == 1)
_max = np.max(pixels, 1)
_min = np.min(pixels, 1)
rect = np.array(
[_min[1], _min[0], _max[1] - _min[1], _max[0] - _min[0]],
dtype=np.float32)
cv2.accumulateWeighted(rect, prev_rect, 0.3, None)
rect = tuple(prev_rect)
cv2.rectangle(salProbColor, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (0, 0, 255))
mask_frame = np.float32(np.dstack((zero, mask * 255, zero)))
out_frame = cv2.addWeighted(np.float32(_frame), 0.6, mask_frame, 0.4,
0.0)
#out_frame = np.uint8( bg_es.cur_frame* mask[:,:,None])
frame = np.hstack((salProbColor, out_frame))
frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
vidwriter.write(np.uint8(frame))
vidreader.close()
vidwriter.close()
def process(vidreader,vidwriter):
vidwriter.build();
bg_es = get_instance(BGMethods.FRAME_DIFFERENCING,vidreader.read_next)
bg_es.setShape((vidreader.height,vidreader.width))
sal_rc = sal_instance(SaliencyMethods.REGION_CONTRAST,SaliencyProps())
zero = np.zeros((vidreader.height,vidreader.width),np.float32)
zero3 = np.zeros((vidreader.height,3*vidreader.width),np.float32)
frame_idx = 0; N = vidreader.frames;
prev_saliency = np.zeros((vidreader.height,vidreader.width),np.float32)
#kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
prev_rect = np.array([vidreader.width/4,vidreader.height/4,vidreader.width/2,vidreader.height/2],dtype=np.float32)
kernel = np.ones((5,5),np.uint8)
bgdModel = np.zeros((1,65),np.float64); fgdModel = np.zeros((1,65),np.float64)
while(True):
frame_idx += 1
mask_es = bg_es.process();
mask_es = cv2.medianBlur(mask_es,5)
mask_es = cv2.medianBlur(mask_es,3)
print 'Proceesing ... {0}%\r'.format((frame_idx*100/N)),
if bg_es.isFinish():
break;
sal_rc.process(bg_es.cur_frame);
#mask_lbls = np.unique(mask_es * sal_rc.cut_labels)[1:]
#if len(mask_lbls)>0:
# mask = sum([sal_rc.cut_labels==lbl for lbl in mask_lbls])
# mask = np.uint8(mask*255)
#else:
# mask = zero
#out_frame = sal_rc.ncut*mask[:,:,None]
#frame = np.uint8(sal_rc.ncut * mask[:,:,None]);
#mask_es = np.uint8(mask_es*255);
#cv2.accumulateWeighted(sal_rc.saliency,prev_saliency,0.7,None)
#saliency_prob = bg_es.variation + prev_saliency;
#_,mask = cv2.threshold(prev_mask,0.8 * np.max(prev_mask),255,cv2.THRESH_BINARY)
#mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
#mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
#bgdModel = np.zeros((1,65),np.float64); fgdModel = np.zeros((1,65),np.float64)
saliency_prob = bg_es.variation + sal_rc.saliency;
saliency_prob = normalize(saliency_prob)
_,mask = cv2.threshold(saliency_prob,0.6,1,cv2.THRESH_BINARY)
#mask = cv2.medianBlur(mask,3)
pixels = np.where(mask==1); _max = np.max(pixels,1); _min = np.min(pixels,1)
rect = np.array([_min[1],_min[0],_max[1]-_min[1],_max[0]-_min[0]],dtype=np.float32);
#out_frame = np.uint8(bg_es.cur_frame.copy());
#cv2.accumulateWeighted(rect,prev_rect,0.3,None);
rect = tuple(rect)
mask_cut = np.zeros(bg_es.cur_frame.shape[:2],np.uint8)
#mask_cut[_min[1]:_max[1]-_min[1],_min[0]:_max[0]-_min[0]]=3
mask_cut[saliency_prob >= 0.2] = 2
mask_cut[saliency_prob >= 0.6] = 3
mask_cut[saliency_prob >= 0.8] = 1
#_,mask_cut = cv2.threshold(saliency_prob,0.8,1,cv2.THRESH_BINARY)
#mask_cut = np.uint8(mask_cut)
#count = np.array([np.sum((mask_cut%2)==lbl) for lbl in [0,1]]);
#Laplcian features
#_frame = np.uint8(cv2.Laplacian(bg_es.cur_frame,3))
gray = cv2.cvtColor(bg_es.cur_frame,cv2.COLOR_RGB2GRAY)
#eigen = cv2.cornerEigenValsAndVecs(gray,15,3);
#eigen = eigen.reshape(gray.shape[0], gray.shape[1], 3, 2)
#texture_mag = normalize(np.sqrt(eigen[:,:,0,0]**2 + eigen[:,:,0,1]**2))*255
#texture_dir1 = normalize(np.arctan2(eigen[:,:,1,1],eigen[:,:,1,0]))*255
#texture_dir2 = normalize(np.arctan2(eigen[:,:,2,1],eigen[:,:,2,0]))*255
t_frame = bg_es.cur_frame#np.uint8(np.dstack((gray,gray,gray)));
regions = segmentation.slic(t_frame,50,20,convert2lab=True,multichannel=True)
_frame = color.label2rgb(regions,bg_es.cur_frame, kind='avg')
#_frame = np.uint8(np.dstack((gray,gray,texture_mag)));
#print 'texture flow',texture_flow.shape
max_iter = 4
#_frame = np.uint8(np.dstack((luminescence,texture_flow,laplacian)));
#if np.all(count>4):
cv2.grabCut(_frame,mask_cut,rect,bgdModel,fgdModel,1,cv2.GC_INIT_WITH_MASK)
#else:
#cv2.grabCut(bg_es.cur_frame,mask_cut,rect,bgdModel,fgdModel,1,cv2.GC_INIT_WITH_RECT)
for _iter in range(max_iter):
mask = np.uint8(np.where((mask_cut==2)|(mask_cut==0),0,1))
mask_erode = cv2.erode(mask,kernel,1)
mask_dilate = cv2.dilate(mask,kernel,1)
mask_cut = np.uint8(3 * mask_dilate - 2 * mask_erode)
if np.unique(mask_cut).__len__()==1:
break;
cv2.grabCut(_frame,mask_cut,rect,bgdModel,fgdModel,1,cv2.GC_INIT_WITH_MASK)
#out_frame = cv2.cvtColor(stacked*255,cv2.COLOR_GRAY2RGB);
#_,mask = cv2.threshold(out_frame[:,:,0],0.8*255,255,cv2.THRESH_BINARY)
mask = np.float32(np.where((mask_cut==2)|(mask_cut==0),0,1))
mask[mask_cut==3] = 0.8
mask = cv2.medianBlur(np.uint8(mask),5)
mask = cv2.medianBlur(mask,3)
salProbColor = cv2.cvtColor(saliency_prob*255,cv2.COLOR_GRAY2RGB);
_,salMask = cv2.threshold(sal_rc.saliency,0.8,1,cv2.THRESH_BINARY)
mask_frame = np.float32(np.dstack((zero,salMask*255,zero)))
salProbColor = cv2.addWeighted(np.float32(salProbColor),0.6,mask_frame,0.4,0.0);
pixels = np.where(salMask==1); _max = np.max(pixels,1); _min = np.min(pixels,1)
rect = np.array([_min[1],_min[0],_max[1]-_min[1],_max[0]-_min[0]],dtype=np.float32);
cv2.accumulateWeighted(rect,prev_rect,0.3,None);
rect = tuple(prev_rect)
cv2.rectangle(salProbColor, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (0,0,255))
mask_frame = np.float32(np.dstack((zero,mask*255,zero)))
out_frame = cv2.addWeighted(np.float32(_frame),0.6,mask_frame,0.4,0.0);
#out_frame = np.uint8( bg_es.cur_frame* mask[:,:,None])
frame = np.hstack((salProbColor,out_frame))
frame = cv2.resize(frame,(0,0),fx=0.25, fy=0.25)
vidwriter.write(np.uint8(frame));
vidreader.close()
vidwriter.close()
