ds_name = 'OpenEDS_{}_{}'.format(dirCond, ds_num)
print('Opening the {} folder'.format(dirCond))
# Read UID
path2json = os.path.join(
PATH_OPENEDS,
'OpenEDS_{}_userID_mapping_to_images.json'.format(dirCond))
im2UID = json.load(open(path2json, 'r'))
PATH_IMAGES = os.path.join(PATH_DIR, dirCond, 'images')
PATH_LABELS = os.path.join(PATH_DIR, dirCond, 'labels')
PATH_FITS = os.path.join(PATH_DIR, dirCond, 'fits')
listIm = os.listdir(PATH_IMAGES)
Data, keydict = generateEmptyStorage(name='OpenEDS', subset=dirCond)
i = 0
if not noDisp:
fig, plts = plt.subplots(1, 1)
for pData in im2UID:
# Image number and UID for each person
listIm = pData['semantic_segmenation_images']
pid = int(pData['id'].replace('U', '')) - 111
for imName_full in listIm:
imName, _ = os.path.splitext(imName_full)
# Do not save images without a proper ellipse and iris fit
# Load image, label map and fits
I = cv2.imread(os.path.join(PATH_IMAGES, imName_full), 0)
for name in list_ds:
# Ignore the first row and column.
# Columns: [index, p_x, p_y]
opts = glob.glob(os.path.join(PATH_DIR, name, '*.avi'))
for Path2vid in opts:
# Read pupil info
loc, fName = os.path.split(Path2vid)
fName = os.path.splitext(fName)[0]
Path2text = os.path.join(loc, fName + '.txt')
PupilData = np.array(readFormattedText(Path2text, 0))
VidObj = cv2.VideoCapture(Path2vid)
ds_name = ds_name = 'LPW_{}_{}_{}'.format(name, fName, ds_num)
# Generate empty dictionaries
Data, keydict = generateEmptyStorage(name='LPW',
subset='LPW_{}'.format(name))
if not noDisp:
fig, plts = plt.subplots(1, 1)
fr_num = 0
while (VidObj.isOpened()):
ret, I = VidObj.read()
if ret == True:
I = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)
Data['Images'].append(I)
keydict['resolution'].append(I.shape)
keydict['archive'].append(ds_name)
pupil_loc = PupilData[fr_num, :]
keydict['pupil_loc'].append(pupil_loc)
Data['pupil_loc'].append(pupil_loc)
Data['Info'].append(str(fr_num))
fr_num += 1
canvas.start_event_loop(interval)
return
def fix_pupil_loc(p, res):
# res: [H, W]
p[0] = 0.5 * p[0]
p[1] = res[0] - 0.5 * p[1]
return p
for name in list_ds:
# Ignore the first row and column.
# Columns: [index, p_x, p_y]
Data, keydict = generateEmptyStorage(name='PupilNet', subset=name)
ds_name = 'PupilNet_{}_{}'.format(name, ds_num)
PupilData = np.genfromtxt(os.path.join(PATH_DIR, name + '.txt'),
delimiter=' ')[1:, 1:]
listFiles = glob.glob(os.path.join(PATH_DIR, name, '*.png'))
imNames = list(map(os.path.basename, listFiles))
imNames = list(map(os.path.splitext, imNames))
imNames, _ = list(zip(*imNames))
imNames = np.array(list(map(int, imNames)))
# Create an empty dictionary as per agreed structure
Data['pupil_loc'] = PupilData[:, 1:] * sc
keydict['pupil_loc'] = PupilData[:, 1:] * sc
for name in list_ds:
# Ignore the first row and column.
# Columns: [index, p_x, p_y]
# Read pupil data from the published dataset
PupilData = np.genfromtxt(os.path.join(PATH_DIR, name + '.txt'),
delimiter=' ')[1:, 1:]
listFiles = glob.glob(os.path.join(PATH_DIR, name, '*.png'))
imNames = list(map(os.path.basename, listFiles))
imNames = list(map(os.path.splitext, imNames))
imNames, _ = list(zip(*imNames))
imNames = np.array(list(map(int, imNames)))
# Generate an empty data container
Data, keydict = generateEmptyStorage(name='Fuhl', subset=name)
ds_name = keydict['dataset'] + '_' + keydict['subset'] + '_' + str(ds_num)
if not noDisp:
fig, plts = plt.subplots(1, 1)
for i in range(0, PupilData.shape[0]):
iNum = PupilData[i, 0]
# Assert unique image names
loc = imNames == iNum
assert sum(loc) == 1, "Error. Only one file should have that number"
loc = np.where(loc)[0]
path2im = listFiles[int(loc)]
imStr = os.path.split(path2im)[1]
list_ds = [ele for ele in os.listdir(PATH_DIR) if os.path.isdir(os.path.join(PATH_DIR, ele))]
list_ds.remove('3d')
for fName in list_ds:
warnings.filterwarnings("error")
PATH_IMAGES = os.path.join(PATH_DIR, fName, 'synthetic')
PATH_MASK_SKIN = os.path.join(PATH_DIR, fName, 'mask-withskin')
PATH_MASK_NOSKIN = os.path.join(PATH_DIR, fName, 'mask-withoutskin-noglasses')
imList = glob.glob(os.path.join(PATH_IMAGES, '*.tif'))
if not noDisp:
fig, plts = plt.subplots(1,1)
Data, keydict = generateEmptyStorage(name='riteyes_general', subset='riteyes_general_'+fName)
ds_name = 'riteyes_general_{}_{}'.format(fName, ds_num)
fr_num = 0
for ele in imList:
imName_withext = os.path.split(ele)[1]
imName = os.path.splitext(ele)[0]
path2im = os.path.join(PATH_IMAGES, imName_withext)
path2mask = os.path.join(PATH_MASK_SKIN, imName_withext)
path2mask_woskin = os.path.join(PATH_MASK_NOSKIN, imName_withext)
try:
I = np.asarray(Image.open(path2im).convert('L'))
maskIm = np.asarray(Image.open(path2mask).convert('RGB'))
maskIm_woskin = np.asarray(Image.open(path2mask_woskin).convert('RGB'))
for fName in list_ds:
warnings.filterwarnings("error")
ds_name = 'NVGaze' + '_' + fName + '_' + str(ds_num)
# Ignore the first row and column.
# Columns: [index, p_x, p_y]
ZipObj = zipfile.ZipFile(
os.path.join(PATH_DIR, fName, 'footage_image_data.zip'))
imList = [ele for ele in ZipObj.namelist() if 'type_img_frame' in ele]
shuffle(imList)
if not noDisp:
fig, plts = plt.subplots(1, 1)
Data, keydict = generateEmptyStorage(name='NVGaze', subset=fName)
fr_num = 0
for boo in imList[:500]:
# Read pupil info
imNum_str = re.findall('\d+', boo)[0]
str_imName = 'type_img_frame_{}.png'.format(imNum_str)
str_imName_Mask = 'type_maskWithSkin_frame_{}.png'.format(imNum_str)
str_imName_Mask_woSkin = 'type_maskWithoutSkin_frame_{}.png'.format(
imNum_str)
I = ZipObj.read(str_imName)
I = np.array(Image.open(io.BytesIO(I))).astype(np.double)
I = preProcessNV(I, np.uint8, 0.5)