def compareWithGroundtruth(sal,datasetPath,imageName):
print "Processing "+imageName +" ....";
confusion = getNewConfusion();
srcImgPath = os.path.join(datasetPath,imageName,"src_color",imageName+".png")
image = cv2.imread(srcImgPath)
mask = getSalienyMap(sal,image)
#write results
mask_image = image*mask[:,:,None];
mkdirs(os.path.join(datasetPath,imageName,"results"));
file_name = os.path.join(datasetPath,imageName,"results",imageName+"_"+sal.method+".png")
cv2.imwrite(file_name,mask_image)
humanSegPath = os.path.join(datasetPath,imageName,"human_seg");
for bgImg in os.listdir(humanSegPath):
if bgImg.endswith(".png"):
bgMask = getMask(cv2.imread(os.path.join(humanSegPath,bgImg)))
updateConfusion(confusion,comparator(bgMask,mask))
print "Confusion : ",confusion;
return confusion;
def compareWithGroundtruth(sal, datasetPath, imageName):
print "Processing " + imageName + " ...."
confusion = getNewConfusion()
srcImgPath = os.path.join(datasetPath, imageName, "src_color",
imageName + ".png")
image = cv2.imread(srcImgPath)
mask = getSalienyMap(sal, image)
#write results
mask_image = image * mask[:, :, None]
mkdirs(os.path.join(datasetPath, imageName, "results"))
file_name = os.path.join(datasetPath, imageName, "results",
imageName + "_" + sal.method + ".png")
cv2.imwrite(file_name, mask_image)
humanSegPath = os.path.join(datasetPath, imageName, "human_seg")
for bgImg in os.listdir(humanSegPath):
if bgImg.endswith(".png"):
bgMask = getMask(cv2.imread(os.path.join(humanSegPath, bgImg)))
updateConfusion(confusion, comparator(bgMask, mask))
print "Confusion : ", confusion
return confusion
def perform_localization(vidreader,roi_mask,smoothner,fgMasks,bgMasks,gtMasks,
num_blocks=10,skip_frames = 0,last_frame=1000):
confusion = getNewConfusion();
start = time.time(); frameNewMasks = [];
frame_idx = skip_frames + 2*num_blocks; N = min(vidreader.frames,last_frame);
status = 'Localizing Video ' + vidreader.foldername +' ... {0}\r'
out_dir=os.path.dirname(vidreader.foldername)+os.sep+'output'+os.sep
create_folder_structure_if_not_exists(out_dir);
while(frame_idx +num_blocks < N):
print status.format((str(frame_idx*100/N)+'%')),
(num_frames,frames) = vidreader.read(frame_idx-2*num_blocks,2*num_blocks);
if num_frames > num_blocks:
start_idx = num_blocks/2; end_idx = min(3*num_blocks/2,num_frames);
s = frame_idx-2*num_blocks-skip_frames; e = s + num_frames;
newMasks = smoothner.process(frames,fgMasks[s:e],bgMasks[s:e],range(start_idx,end_idx));
#newMasks = __morphologicalOps__(newMasks);
groundTruthMasks = gtMasks[s+num_blocks/2:s+num_blocks/2+newMasks.__len__()];
_frames = frames[num_blocks/2:num_blocks/2+newMasks.__len__()];
tmp_idx= 0;
for (gt,my) in zip(groundTruthMasks,newMasks):
zero = np.zeros(gt.shape[:2],dtype=np.uint8)
my_mask = np.uint8(np.dstack((zero,zero,my*255)))
out = cv2.addWeighted(_frames[tmp_idx],0.6,my_mask,0.4,0);
path = out_dir+os.sep+'sal_'+str(frame_idx-2*num_blocks-skip_frames+tmp_idx)+'.png'
cv2.imwrite(path,out)
gt_mask = np.uint8(np.dstack((zero,zero,gt*255)));
out = cv2.addWeighted(_frames[tmp_idx],0.6,gt_mask,0.4,0);
path = out_dir+os.sep+'gt_'+str(frame_idx-2*num_blocks-skip_frames+tmp_idx)+'.png'
cv2.imwrite(path,out)
frame_confusion=comparator(gt,my,roi_mask);
updateConfusion(confusion,frame_confusion);
tmp_idx += 1
frame_idx += num_blocks
time_taken = time.time()-start;
print status.format('[Done] in '),time_taken," seconds"
return confusion;