def prepare_imgset():
## parameters
cs_val_dir = '/mnt/phoenix_fastdir/dataset/cityscapes/leftImg8bit_sequence/val'
imgset_dir = '/mnt/phoenix_fastdir/dataset/cityscapes/leftImg8bit_sequence/imgset'
set_names = ['frankfurt', 'lindau', 'munster']
imgset_val_dir = os.path.join(imgset_dir, 'val')
create_dir(imgset_val_dir)
name_suffix = 'leftImg8bit'
im_ext = '.png'
for set_name in set_names:
imgset_val_path = os.path.join(imgset_val_dir, set_name + '.txt')
arr = os.listdir(os.path.join(cs_val_dir, set_name))
arr = np.sort(arr)
print arr
print imgset_val_path
with open(imgset_val_path, 'w') as f1:
for row_id in xrange(0, len(arr)):
im_name = arr[row_id][:-4]
f1.write('{:s}\n'.format(im_name))
f1.close
def generate_ppmimgs(seq_data_dir, seq_names, dest_ppmimgs_dir):
if not os.path.exists(dest_ppmimgs_dir):
create_dir(dest_ppmimgs_dir)
for seq_name in seq_names:
seq_folder_dir = os.path.join(seq_data_dir, seq_name)
seq_img1_dir = os.path.join(seq_folder_dir, 'img1')
rename_create_ppmimgs(seq_img1_dir, dest_ppmimgs_dir, seq_name)
def prepare_imgset():
## parameters
data_root_dir=cscfg.DATA.ROOT_DIR
set_branch=cscfg.DATA.BRANCH ## validation set
sub_set_dir=os.path.join(data_root_dir,set_branch)
imgset_dir=cscfg.DATA.IMGSET_DIR
im_ext=cscfg.DATA.IM_EXT
if set_branch=='val':
set_names=['frankfurt','lindau','munster']
imgset_dir=os.path.join(imgset_dir,set_branch)
create_dir(imgset_dir)
for set_name in set_names:
print set_name
imgset_path=os.path.join(imgset_dir,set_name+'.txt')
arr=os.listdir(os.path.join(sub_set_dir,set_name))
arr=np.sort(arr)
print arr
print imgset_path
with open(imgset_path, 'w') as f1:
for row_id in xrange(0, len(arr)):
im_name=arr[row_id][:-4]
f1.write('{:s}\n'. format(im_name))
f1.close
def get_vis_dir():
res_dir = mcfg.PROPOSAL.RES_DIR
res_dir = res_dir.replace('Continious', 'PerPerson')
create_recur_dirs(res_dir)
vis_gt_dir = os.path.join(res_dir, 'vis_gt_all')
create_dir(vis_gt_dir)
return vis_gt_dir
def motdevkit_16_to_17():
src_dir='/mnt/phoenix_fastdir/dataset/MOTdevkit2016/MOT2016'
dest_dir='/mnt/phoenix_fastdir/dataset/MOTdevkit2017/MOT2017'
im_ext='.jpg'
##imgsets
src_imgsets_dir=os.path.join(src_dir,'ImageSets/Main')
dest_imgsets_dir=os.path.join(dest_dir,'ImageSets/Main')
imgset_name='val.txt'
src_imgsets_path=os.path.join(src_imgsets_dir,imgset_name)
dest_imgsets_path=os.path.join(dest_imgsets_dir,imgset_name)
##jpg images
src_jpg_dir=os.path.join(src_dir,'JPEGImages')
dest_jpg_dir=os.path.join(dest_dir,'JPEGImages')
create_dir(dest_jpg_dir)
##batch imgsets
src_strs,dest_strs=batch_rename_strarr(src_imgsets_path,dest_imgsets_path)
##batch jpgimgs
##batch_rename_imgs(src_strs,dest_strs,src_jpg_dir,dest_jpg_dir,im_ext)
##'/Annotations'
##cur_dir='/mnt/phoenix_fastdir/dataset/MOTdevkit2017/MOT2017/Annotations'
bw_flow_dir='/mnt/phoenix_fastdir/experiments/opticalflow/MOT17/LDOF/MOT17-02/bw'
fw_flow_dir='/mnt/phoenix_fastdir/experiments/opticalflow/MOT17/LDOF/MOT17-02/fw'
batch_rename_func(fw_flow_dir)
def vis_link_imgs(im_names, src_dir1, src_dir2, dest_dir):
create_dir(dest_dir)
for im_name in im_names:
im_path1 = os.path.join(src_dir1, im_name + im_ext)
im_path2 = os.path.join(src_dir2, im_name + im_ext)
link_im_path = os.path.join(dest_dir, im_name + im_ext)
im1 = cv2.imread(im_path1)
im2 = cv2.imread(im_path2)
im_height = im1.shape[0]
im_width = im1.shape[1]
link_im = np.zeros((im_height * 2, im_width, 3), dtype=np.uint8)
link_im[0:im_height, :, :] = im1
link_im[im_height:im_height * 2, :, :] = im2
cv2.imwrite(link_im_path, link_im)
def vis_gt_per_person(gt_frm_ids,
gt_obj_ids,
gt_boxes,
gt_masks,
tight_gt_boxes=[]):
uniq_obj_ids = np.sort(np.unique(gt_obj_ids))
for obj_id in uniq_obj_ids:
row_indexes = np.where(gt_obj_ids == obj_id)[0]
label_frm_ids = gt_frm_ids[row_indexes]
label_boxes = tight_gt_boxes[row_indexes] ## tight box
label_boxes1 = gt_boxes[row_indexes] ## loose box
label_masks = gt_masks[row_indexes]
id_indexes = label_frm_ids - 1
label_ims = rgb_ims[id_indexes]
label_im_names = im_names[id_indexes]
label_dir = os.path.join(vis_gt_dir, str(obj_id))
create_dir(label_dir)
vis_box_per_person(label_dir, label_ims, label_im_names, label_boxes,
obj_id, label_masks, label_boxes1)
def save_vis_masks(ims,im_names,gt_masks_dir,gt_masks,corrup_masks_dir,corrup_masks):
# 5.2> save vis_gt_masks and corrup_masks
alpha_folder='alpha_masks'
color_folder='color_masks'
## alpha masks(gt)
alpha_gt_dir=os.path.join(gt_masks_dir,alpha_folder)
create_dir(alpha_gt_dir)
alpha_gt_masks=gt_masks*255.0
save_images(alpha_gt_masks,alpha_gt_dir,im_names,im_ext)
## alpha masks(corrup)
alpha_corrup_dir=os.path.join(corrup_masks_dir,alpha_folder)
create_dir(alpha_corrup_dir)
alpha_corrup_masks=corrup_masks*255.0
save_images(alpha_corrup_masks,alpha_corrup_dir,im_names,im_ext)
## color masks(gt)
color_gt_masks=np.zeros((ims.shape),dtype=np.uint8)
color=[0,0,255]
for m_id in xrange(len(gt_masks)):
im=ims[m_id]
mask=gt_masks[m_id]
color_mask=vis_im_mask(im,mask,color,True)
color_gt_masks[m_id]=color_mask
color_gt_dir=os.path.join(gt_masks_dir,color_folder)
create_dir(color_gt_dir)
save_images(color_gt_masks,color_gt_dir,im_names,im_ext)
## here is small bug(need to debug)
## color masks(corrup)
color_corrup_masks=np.zeros((ims.shape),dtype=np.uint8)
color=[0,0,255]
for m_id in xrange(len(corrup_masks)):
im=ims[m_id]
mask=corrup_masks[m_id]
color_mask=vis_im_mask(im,mask,color,True)
color_corrup_masks[m_id]=color_mask
color_corrup_dir=os.path.join(corrup_masks_dir,color_folder)
create_dir(color_corrup_dir)
save_images(color_corrup_masks,color_corrup_dir,im_names,im_ext)
def get_match_res_dir(obj_id):
### create gt_masks_dir, corrup_masks_dir and pick_masks_dir for later use
print '=========================get match res dir====================='
# seg_dir
seg_dir=mcfg.EXPR.SEG_DIR
dataset=mcfg.DATA.DATASET
seg_algr=mcfg.EXPR.SEG_ALGR
seg_dir=os.path.join(seg_dir,dataset,seg_algr)
# random_corrup_dir
rand_corrup_dir=os.path.join(seg_dir,'random_corrup')
create_dir(rand_corrup_dir)
##match_boxes_dir
match_boxes_dir=os.path.join(rand_corrup_dir,'match_boxes')
create_dir(match_boxes_dir)
match_boxes_dir=os.path.join(match_boxes_dir,str(obj_id))
create_dir(match_boxes_dir)
return match_boxes_dir
def get_annotations_dir(data_dir):
annots_dir = os.path.join(data_dir, annots_folder)
create_dir(annots_dir)
return annots_dir
def create_masks_dirs(obj_id):
##print '=========================get maks dir====================='
# seg_dir
seg_dir=mcfg.EXPR.SEG_DIR
dataset=mcfg.DATA.DATASET
seg_algr=mcfg.EXPR.SEG_ALGR
seg_dir=os.path.join(seg_dir,dataset,seg_algr)
# random_corrup_dir
rand_corrup_dir=os.path.join(seg_dir,'random_corrup')
create_dir(rand_corrup_dir)
# sub masks dirs
gt_masks_dir=os.path.join(rand_corrup_dir,'gt_masks')
corrup_masks_dir=os.path.join(rand_corrup_dir,'corrup_masks')
pick_masks_dir=os.path.join(rand_corrup_dir,'pick_masks')
create_dir(gt_masks_dir)
create_dir(corrup_masks_dir)
create_dir(pick_masks_dir)
## sub masks dirs with obj_ids
gt_masks_dir=os.path.join(gt_masks_dir,str(obj_id))
create_dir(gt_masks_dir)
corrup_masks_dir=os.path.join(corrup_masks_dir,str(obj_id))
create_dir(corrup_masks_dir)
# pick_masks_dir=os.path.join(pick_masks_dir,str(obj_id))
# create_dir(pick_masks_dir)
return gt_masks_dir,corrup_masks_dir,pick_masks_dir
def get_ref_dir():
track_mask_dir = io.get_track_mask_info(seq_name)
refine_res_dir = os.path.join(track_mask_dir, 'res')
create_dir(refine_res_dir)
return refine_res_dir
def online_pick_iteration(track_mask_dir, label, frm_ids, init_masks,
init_boxes, cur_ims, cur_fw_flows, cur_bw_flows,
shift_vec):
print 'online pick iteration...'
## para0: max_bbox
iter_num = mcfg.FTRACK.ITER_NUM
seq_name = mcfg.DATA.SEQNAME
im_ext = mcfg.DATA.IMGEXT
mask_im_ext = mcfg.MASK.BIN_IM_EXT
track_lab_mask_dir = os.path.join(track_mask_dir, str(label))
create_dir(track_lab_mask_dir)
if debug_flag:
print 'track_mask_dir:', track_mask_dir
print 'track_lab_mask_dir:', track_lab_mask_dir
print 'iter_num:', iter_num
iou_arr = np.zeros(iter_num)
cur_masks = init_masks
cur_boxes = init_boxes
conv_flag = False
iter_id = 0 ## counter
ave_iou = 0.0
old_ave_iou = 0.0
while iter_id < iter_num and conv_flag == False:
#for iter_id in xrange(iter_num):
old_ave_iou = ave_iou
picked_masks = []
picked_boxes = []
## ---------------------------------------create dirs----------------------------------------------------------------------
## iter dir
iter_name = 'iter' + str(iter_id)
iter_label_prop_path = os.path.join(
track_lab_mask_dir,
iter_name + '.txt') ##picked dets(boxes) file path
## picked masks dir
iter_label_dir = os.path.join(track_lab_mask_dir, iter_name)
create_dir(iter_label_dir)
## vis for debuging
vis_iter_name = 'vis_iter' + str(iter_id)
vis_iter_label_dir = os.path.join(track_lab_mask_dir, vis_iter_name)
create_dir(vis_iter_label_dir)
## new added, for converage
## converage .txt
iter_conv_name = 'iter_conv'
iter_conv_label_prop_path = os.path.join(
track_lab_mask_dir,
iter_conv_name + '.txt') ##picked dets(boxes) file path
# # converage masks dir
iter_conv_label_dir = os.path.join(track_lab_mask_dir, iter_conv_name)
create_dir(iter_conv_label_dir)
## pick masks in different cases
if len(cur_ims) > 2:
prev_boxes, next_boxes, fw_masks, bw_masks = cal_per_label(
cur_ims, cur_fw_flows, cur_bw_flows, frm_ids, cur_boxes,
cur_masks)
picked_bboxes, picked_masks, y_labels, ave_iou = lpsolve_pick_mask(
cur_ims, frm_ids, prev_boxes, cur_boxes, next_boxes, fw_masks,
cur_masks, bw_masks, shift_vec)
if vis_picked_flag:
vis_picked_masks(vis_iter_label_dir, cur_ims, frm_ids,
y_labels, picked_masks, picked_bboxes,
fw_masks, prev_boxes, cur_masks, cur_boxes,
bw_masks, next_boxes)
else: ## just one image
picked_masks = cur_masks
picked_bboxes = cur_boxes
iou_arr[iter_id] = ave_iou
## judge if it's converaged
if ave_iou == old_ave_iou:
conv_flag = True
else:
cur_masks = picked_masks
cur_boxes = picked_bboxes
if save_masks_flag:
save_picked_masks(iter_label_dir, seq_name, picked_masks,
frm_ids) ## save binary masks(for later using)
if save_dets_flag:
save_picked_proposals(iter_label_prop_path, frm_ids, label,
picked_bboxes) ## save det proposals
print 'iou_arr:', iou_arr
## save conv results
if conv_flag == True or iter_id == iter_num - 1:
if save_masks_flag:
save_picked_masks(
iter_conv_label_dir, seq_name, cur_masks,
frm_ids) ## save binary masks(for later using)
if save_dets_flag:
save_picked_proposals(iter_conv_label_prop_path, frm_ids,
label, cur_boxes) ## save det proposals
iter_id += 1 ## loop
if __name__ == '__main__':
get_video_demo()
## MOT
dataset = 'MOT17'
det_res_dir = mcfg.DETCTION.RES_DIR
det_res_dir = os.path.join(det_res_dir, dataset)
det_algr = mcfg.DETCTION.ALG
det_im_dir = os.path.join(det_res_dir, det_algr)
video_fps = mcfg.DEMO.VIDEO_FPS
##in local disk(transfer to phoenix later)
data_dir = mcfg.DATA.DATA_DIR
imgset_path = mcfg.DATA.IMGSET = 'MOTdevkit2016/MOT2016/ImageSets/Main/val.txt'
imgset_path = os.path.join(data_dir, imgset_path)
im_ext = mcfg.DATA.IMGEXT = '.jpg'
video_demo_dir = os.path.join(det_res_dir, 'video_demo')
if not os.path.exists(video_demo_dir):
create_dir(video_demo_dir)
with open(imgset_path) as f:
im_names = [x.strip() for x in f.readlines()]
f.close
im_names = im_names
##video_demo_dir=os.path.join('/mnt/phoenix_fastdir/experiments/detection/MOT','video_demo')
im_seq_to_video(det_algr, det_im_dir, im_names, im_ext, video_demo_dir,
video_fps)
def vis_proposals_multi_instances(proposal_algr_dir, det_proposals_folder, im,
im_name, bboxes, masks, labels):
print '==============================vis_proposals==========================================='
vis_im = im.copy()
im_width = im.shape[1]
im_height = im.shape[0]
cmap = color_map() ## color map
base_im_name = im_name
vis_det_dir = os.path.join(proposal_algr_dir, det_proposals_folder)
vis_seg_dir = vis_det_dir ## det & seg in the same folder
create_dir(vis_det_dir)
print '==========================vis proposals================================='
print 'im_name:', im_name
print 'im_width:', im_width
print 'im_height:', im_height
print 'len(masks):', len(masks)
print 'bboxes.shape:', bboxes.shape
print 'seg_im_ext:', seg_im_ext
print 'vis_det_dir:', vis_det_dir
vis_im_seg = vis_im
for b_id in xrange(
0,
len(bboxes)): ## here obj_id is actually the index(of the array)
bbox = bboxes[b_id]
x1 = int(round(bbox[0]))
y1 = int(round(bbox[1]))
x2 = int(round(bbox[2]))
y2 = int(round(bbox[3]))
x1 = np.min((im_width - 1, np.max((0, x1))))
y1 = np.min((im_height - 1, np.max((0, y1))))
x2 = np.min((im_width - 1, np.max((0, x2))))
y2 = np.min((im_height - 1, np.max((0, y2))))
w = x2 - x1 + 1
h = y2 - y1 + 1
##object id and assign color
label = labels[b_id]
color = cmap[label]
colo = np.array((int(color[0]), int(color[1]), int(color[2])))
# ##'----------------------------------Mask part------------------------------------------------'
# # mask = masks[b_id, :, :] #mask.shape=(28,28)
# # mask = cv2.resize(mask, (int(w), int(h)), interpolation=cv2.INTER_LINEAR) #bilinear interpolation
# mask[mask >= thresh_mask] = 1
# mask[mask < thresh_mask] = 0
mask = masks[b_id]
###===============================per-person visualization=======================================
if vis_mask_flag:
label_id_fg = (mask >= thresh_mask)
label_mask_t = np.zeros((h, w, 3), dtype=np.uint8)
label_im_box = im[y1:y2 + 1, x1:x2 + 1, :]
label_mask_t[:, :, :] = vis_im_seg[
y1:y2 + 1,
x1:x2 + 1, :] # copy the proposal region from the color image
label_mask_t[label_id_fg, 0] = colo[0]
label_mask_t[label_id_fg, 1] = colo[1]
label_mask_t[label_id_fg, 2] = colo[2]
cv2.addWeighted(label_mask_t, alpha, label_im_box, 1 - alpha, 0,
label_mask_t)
vis_im_seg[y1:y2 + 1, x1:x2 + 1, :] = label_mask_t
##cv2.imwrite(vis_label_mask_path,label_mask_t)
##cv2.imwrite(vis_label_mask_path,vis_im_seg)
##cv2.imshow('label_mask',label_mask_t)
##cv2.waitKey(-1)
cv2.rectangle(vis_im, (x1, y1), (x2, y2), (colo), mid_line_width)
font = cv2.FONT_HERSHEY_SIMPLEX
vis_im_path = os.path.join(vis_det_dir, im_name + im_ext)
print 'vis_im_path:', vis_im_path
##cv2.imshow('det_proposal', vis_im)
cv2.imwrite(vis_im_path, vis_im) #complete annotation
def get_mot_gt_path(mot_data_dir):
dets_gt_dir = os.path.join(mot_data_dir, 'gt')
create_dir(dets_gt_dir)
dets_gt_path = os.path.join(dets_gt_dir, 'gt.txt')
return dets_gt_path
def get_bmfsets_path(imgsets_dir, set_name):
imgsets_main_dir = os.path.join(imgsets_dir, 'Bmf')
create_dir(imgsets_main_dir)
bmfsets_path = os.path.join(imgsets_main_dir, set_name + bmfsets_file_ext)
return bmfsets_path
def get_ppmimgs_dir(data_dir):
ppmimgs_dir = os.path.join(data_dir, ppmimgs_folder)
create_dir(ppmimgs_dir)
return ppmimgs_dir
def get_imgsets_path(imgsets_dir, set_prop):
imgsets_main_dir = os.path.join(imgsets_dir, 'Main')
create_dir(imgsets_main_dir)
imgsets_path = os.path.join(imgsets_main_dir, set_prop + imgsets_file_ext)
return imgsets_path
def get_vis_dir_info(vis_folder):
track_mask_dir=os.path.join(seg_res_dir,track_mask_folder)
vis_mask_dir=os.path.join(track_mask_dir,vis_folder)
create_dir(vis_mask_dir)
return vis_mask_dir
def get_jpgimgs_dir(data_dir):
jpgimgs_dir = os.path.join(data_dir, jpgimgs_folder)
create_dir(jpgimgs_dir)
return jpgimgs_dir
def get_data_dir(devkit_dir, dataset_name):
data_dir = os.path.join(devkit_dir, dataset_name)
create_dir(data_dir)
return data_dir
def get_imgnet_devkit_dir(f_year):
imgnet_devkit_dir = os.path.join(data_dir, upper_mot + 'devkit' + f_year)
create_dir(imgnet_devkit_dir)
return imgnet_devkit_dir
jpgdir_path = os.path.join(data_dir, jpgdir)
##(2) point tracking
track_res_dir = mcfg.PTRACK.RES_DIR
frame_interval = mcfg.PTRACK.FRAME_INTER
sample_rate = mcfg.PTRACK.SAMPLE_RATE
sub_dir_name = str(frame_interval) + 'frm_' + str(sample_rate) + 'pt'
point_mat_path = os.path.join(track_res_dir, sub_dir_name, 'pt_mat.mat')
label_mat_path = os.path.join(track_res_dir, sub_dir_name, 'lab_mat.mat')
##(3) proposals
prop_res_dir = mcfg.PROPOSAL.RES_DIR
## result directory
vis_dir = os.path.join(track_res_dir, sub_dir_name, 'per_person')
create_dir(vis_dir)
##at present, mainly focus on large objects
large_obj_dir = os.path.join(vis_dir, 'large_person')
small_obj_dir = os.path.join(vis_dir, 'small_person')
create_dir(large_obj_dir)
create_dir(small_obj_dir)
size_thre = mcfg.PTRACK.OBJECT_SIZE_THRESHOLD
set_name, shift_num, end_frame, im_names = load_seq_info(imgset_path)
rgb_ims = load_color_images(jpgdir_path, im_names, im_ext)
prop_arr = load_proposals(prop_res_dir, set_name)
im_width = rgb_ims[0].shape[1]
def get_imgsets_dir(data_dir):
imgsets_dir = os.path.join(data_dir, imgsets_folder)
create_dir(imgsets_dir)
return imgsets_dir
im=cv2.imread(im_path1)
cv2.imwrite(im_path2,im)
print 'convert jpg to ppm...'
if __name__ == '__main__':
data_dir=mcfg.DATA.DATA_DIR
imgset=mcfg.DATA.IMGSET_TRAIN
jpg_dir=mcfg.DATA.JPGDIR
ppm_dir='MOTdevkit2017/MOT2017/PPMImages'
im_ext=mcfg.DATA.IMGEXT
imgset=os.path.join(data_dir,imgset)
jpg_dir=os.path.join(data_dir,jpg_dir)
ppm_dir=os.path.join(data_dir,ppm_dir)
create_dir(ppm_dir)
im_names=load_txt_to_strarr(imgset)
im_names=im_names[:600] ##MOT17-02
##print im_names
print 'im_names:', im_names
'''
set_name='MOT17-02'
bmf_dir='/home/uni/Lab/projects/C++/trackingCPU'
bmf_path=os.path.join(bmf_dir,set_name+'.bmf')
tmp_arr=[]
for im_name in im_names:
im_name=im_name+'.ppm'
