def preprocess_and_sanity_check(png_files):
hasWarning = False
for fname in png_files:
if not fname.endswith(".png"):
print ("Warning: %s is not a PNG file. Deleting it from the frames list" % fname)
hasWarning = True
png_files.remove(fname)
png_files = sorted(png_files, key=frameid_from_filename)
prev_idx = 0
for fname in png_files:
# check if the index starts with one, and is free of gap (e.g. free of jumping from i to i+2)
while prev_idx + 1 != frameid_from_filename(fname)[1]:
print ("Warning: there is a gap between frames. Missing frame ID: %d" % (prev_idx+1))
hasWarning = True
prev_idx += 1
prev_idx += 1
if hasWarning:
print "There are warnings. Sleeping for 2 sec..."
time.sleep(2)
return png_files
print "Reading optical flow images..."
val_fid = []
png_files = []
with open(LABELS_FILE_VAL, 'r') as f:
for line in f:
line = line.strip()
if line.startswith("#") or line == "":
continue # skip comments or empty lines
fname, lbl, x, y, w = line.split(' ')
file_name = splitext(fname)[0]
if file_name[-2] == '_' and file_name[
-1] == '1': # skip the first frame, because they don't have optical flow
continue
else:
png_files.append(fname)
val_fid.append(frameid_from_filename(fname))
N = len(png_files)
val_predict = np.empty((N, RESIZE_SHAPE[0], RESIZE_SHAPE[1], 1),
dtype=np.float32)
def read_thread(PID):
d = os.path.dirname(LABELS_FILE_VAL)
for i in range(PID, N, num_thread):
img = misc.imread(os.path.join(d + '/optical_flow', png_files[i]))
SUM = img.sum()
img = img.astype(
np.float32
) / SUM # normalize the bottom up saliency map to a probability distribution
val_predict[i, :, :, 0] = img
sys.exit(0)
png_tar = sys.argv[1]
tar = tarfile.open(png_tar, 'r')
#asc_path = sys.argv[1].split(".")[0] + ".asc"
asc_path = sys.argv[2]
result_path = sys.argv[3]
dataset_dir = os.path.dirname(png_tar)+'/'
png_files = tar.getnames()
png_files = preprocess_and_sanity_check(png_files)
print "\nYou can control the replay using keyboard. Try pressing space/up/down/left/right."
print "For all available keys, see event_handler_func() code.\n"
print "Uncompressing PNG tar file into memory (/dev/shm/)..."
tar.extractall("/scratch/cluster/zharucs/dataset_gaze/")
UTIDhash = frameid_from_filename(png_files[2])[0]
# init pygame and other stuffs
RESIZE_SHAPE = (84,84,1)
w, h = 160*V.xSCALE, 210*V.ySCALE
pygame.init()
pygame.display.set_mode((w, h), RESIZABLE | DOUBLEBUF | RLEACCEL, 32)
screen = pygame.display.get_surface()
print "Reading gaze data in ASC file into memory..."
frameid2pos, _, _, _, _ = read_gaze_data_asc_file(asc_path)
predicts = read_result_data(result_path, RESIZE_SHAPE)
dw = drawgc_wrapper('original')
dw_pred = drawgc_wrapper('predict')
ds.target_fps = 60
ds.total_frame = len(png_files)
def use_spec_file():
print ("Reading dataset specification file...")
spec_file, dataset_name, output_path = sys.argv[2], sys.argv[3], sys.argv[4]
lines = []
with open(spec_file,'r') as f:
for line in f:
if line.strip().startswith("#") or line == "":
continue # skip comments or empty lines
lines.append(line)
spec = '\n'.join(lines)
import ast
spec = ast.literal_eval(spec)
# some simple sanity checks
assert isinstance(spec,list)
for fname in [e['TAR'] for e in spec] + [e['ASC'] for e in spec]:
if not os.path.exists(fname):
raise IOError("No such file: %s" % fname)
print ("Untaring files in parallel...")
png_files_each=[None]*len(spec)
def untar_thread(PID):
png_files_each[PID] = untar(spec[PID]['TAR'], output_path)
untar_work=ForkJoiner(num_thread=len(spec), target=untar_thread)
print ("Reading asc files while untaring...")
frameid2action_each=[None]*len(spec)
frameid2pos_each=[[]]*len(spec)
for i in range(len(spec)):
frameid2pos_each[i], frameid2action_each[i], _, _, _ = read_gaze_data_asc_file(spec[i]['ASC'])
print ("Concatenating asc file while untaring...")
asc_filename = output_path+'/'+dataset_name+'.asc'
with open(asc_filename, 'w') as f:
subprocess.call(['cat']+[entry['ASC'] for entry in spec], stdout=f)
print ("Waiting for untaring to finish...")
untar_work.join()
print ("Generating train/val label files...")
xy_str_train = []
xy_str_val = []
BAD_GAZE = (-1,-1)
RESIZE_SHAPE = (84,84)
for i in range(len(spec)):
# prepare xy_str[] --- all (example, label) strings
xy_str = []
for png in png_files_each[i]:
fid = frameid_from_filename(png)
if fid in frameid2action_each[i] and frameid2action_each[i][fid] != None:
# TODO(zhuode): Here, Luxin saves weight in label file, and outputs the last gaze/bad_gaze in label file, but it is not the best
# place to store such infomation, because the label file should not be designed to meet the need of training a specific model.
# the label file is used by all models, so we should guarantee it only stores info needed by all models.
# If a model needs such information, it should compute them in input_utils or store them in another file.
# An bad scenerio is when we have models that doesn't use the last gaze or the weight in label file, then such info in label file
# can only adds confusion.
if fid in frameid2pos_each[i] and frameid2pos_each[i][fid]:
weight = 1
# loop to find if there is bad gaze; if there is, then set weight to 0
for j in range(len(frameid2pos_each[i][fid])):
isbad, _, _ = rescale_and_clip_gaze_pos(frameid2pos_each[i][fid][j][0], frameid2pos_each[i][fid][j][1], RESIZE_SHAPE[0], RESIZE_SHAPE[1])
if isbad:
frameid2pos_each[i][fid] = [BAD_GAZE]
weight = 0
break
l = len(frameid2pos_each[i][fid])
xy_str.append('%s %d %f %f %f' % (png, frameid2action_each[i][fid], frameid2pos_each[i][fid][l-1][0], frameid2pos_each[i][fid][l-1][1], weight))
else:# if no gaze, set gaze to -1 and weight to 0
xy_str.append('%s %d %f %f %f' % (png, frameid2action_each[i][fid], BAD_GAZE[0], BAD_GAZE[1], 0))
else:
print ("Warning: Cannot find the label for frame ID %s. Skipping this frame." % str(fid))
# assign each xy_str to the train/val part of the dataset
def assign(range_list, target):
# sort the ranges using left bound as key (e.g. ["0.5-1", "0-0.2"] becomes ["0-0.2", "0.5-1"])
# A must, because Dataset_PastKFramesByTime in input_utils.py assert data_is_sorted_by_timestamp()
range_list=sorted(range_list, key=lambda x: float(x.split('-')[0]))
for range_ in range_list:
l, r = range_.split('-')
l, r = float(l), float(r)
target.extend(xy_str[int(l*len(xy_str)):int(r*len(xy_str))])
assign(spec[i]['TRAIN'], xy_str_train)
assign(spec[i]['VAL'], xy_str_val)
train_file_name = output_path + "/" + dataset_name + '-train.txt'
val_file_name = output_path + "/" + dataset_name + '-val.txt'
with open(train_file_name, 'w') as f:
f.write('# ' + '# '.join(lines) + '\n') # echo spec file content
f.write('\n'.join(xy_str_train))
f.write('\n')
with open(val_file_name, 'w') as f:
f.write('# ' + '# '.join(lines) + '\n') # echo spec file content
f.write('\n'.join(xy_str_val))
f.write('\n')
print ("\nDone. Outputs are:")
print (" %s" % asc_filename)
print (" %s (%d examples)" % (train_file_name, len(xy_str_train)))
print (" %s (%d examples)" % (val_file_name, len(xy_str_val)))
print ("For convenience, dataset specification is also prepended to train/val text file.")