def read_image(
self,
itype,
image_id,
frame='example', # int, 'all', 'example'(0)
#'all_labeled' --> only if training
read_targets=False,
use_cache=None,
verbose=False):
'''Custom read_image function for this model.
'''
start_time_L1 = time.time()
targets = None
# Read image.
vidD = self.Data.load_vidDATA(itype, image_id)
# Read annotations
df = self.Data.annotations
mini_df = df[df.video_id == image_id]
mini_df = mini_df[np.logical_not(np.isnan(mini_df.fish_number))]
nb_frames = len(mini_df)
# Create frames list
if frame == 'all':
frames = range(len(vidD.vi))
elif frame == 'example':
frames = [
0,
]
elif frame == 'all_labeled' and nb_frames > 0:
frames = mini_df.frame.values.tolist()
else:
frames = [
int(frame),
]
# Start df
cols = {}
cols['itype'] = itype
cols['image_id'] = image_id
# Ax, no-fish prediction
Model = self.PREV_Model[0]
pred = Model.get_predictions(itype,
image_id,
return_imgs=False,
use_cache=use_cache,
verbose=verbose)
pred = pred[:, 0]
cols['frame'] = range(pred.shape[0])
cols['frames_left'] = pred.shape[0] - np.arange(pred.shape[0])
cols['AxPred'] = pred
cols['AxM3'] = ld.mov_avg(pred, 1, 1)
cols['AxM5'] = ld.mov_avg(pred, 2, 2)
cols['AxM3i1'] = ld.mov_avg(pred, 3, 1)
cols['AxM1i3'] = ld.mov_avg(pred, 1, 3)
cols['AxM11'] = ld.mov_avg(pred, 5, 5)
cols['AxM7i3'] = ld.mov_avg(pred, 7, 3)
cols['AxM3i7'] = ld.mov_avg(pred, 3, 7)
cols['AxM5max'] = ld.mov_func(pred, 2, 2, np.max)
cols['AxM5min'] = ld.mov_func(pred, 2, 2, np.min)
cols['AxM5std'] = ld.mov_func(pred, 2, 2, np.std)
cols['AxM0i5max'] = ld.mov_func(pred, 0, 5, np.max)
cols['AxM0i5min'] = ld.mov_func(pred, 0, 5, np.min)
cols['AxM0i5std'] = ld.mov_func(pred, 0, 5, np.std)
cols['AxM5i0max'] = ld.mov_func(pred, 5, 0, np.max)
cols['AxM5i0min'] = ld.mov_func(pred, 5, 0, np.min)
cols['AxM5i0std'] = ld.mov_func(pred, 5, 0, np.std)
# Dx, best frame prediction
Model = self.PREV_Model[1]
pred = Model.get_predictions(itype,
image_id,
return_imgs=False,
use_cache=use_cache,
verbose=verbose)
pred = pred[:, 0]
cols['DxPred'] = pred
cols['DxP1'] = np.concatenate([np.repeat(pred[0], 1), pred[:-1]])
cols['DxP2'] = np.concatenate([np.repeat(pred[0], 2), pred[:-2]])
cols['DxP3'] = np.concatenate([np.repeat(pred[0], 3), pred[:-3]])
cols['DxN1'] = np.concatenate([pred[1:], np.repeat(pred[-1], 1)])
cols['DxN2'] = np.concatenate([pred[2:], np.repeat(pred[-1], 2)])
cols['DxN3'] = np.concatenate([pred[3:], np.repeat(pred[-1], 3)])
cols['DxM3'] = ld.mov_avg(pred, 1, 1)
cols['DxM5'] = ld.mov_avg(pred, 2, 2)
cols['DxM3i1'] = ld.mov_avg(pred, 3, 1)
cols['DxM1i3'] = ld.mov_avg(pred, 1, 3)
# Ex, 4 frames after best frame prediction
Model = self.PREV_Model[2]
pred = Model.get_predictions(itype,
image_id,
return_imgs=False,
use_cache=use_cache,
verbose=verbose)
pred = pred[:, 0]
cols['FxPred'] = pred
cols['FxP1'] = np.concatenate([np.repeat(pred[0], 1), pred[:-1]])
cols['FxP2'] = np.concatenate([np.repeat(pred[0], 2), pred[:-2]])
cols['FxP3'] = np.concatenate([np.repeat(pred[0], 3), pred[:-3]])
cols['FxP4'] = np.concatenate([np.repeat(pred[0], 4), pred[:-4]])
cols['FxP5'] = np.concatenate([np.repeat(pred[0], 5), pred[:-5]])
cols['FxP6'] = np.concatenate([np.repeat(pred[0], 6), pred[:-6]])
cols['FxM3'] = ld.mov_avg(pred, 1, 1)
cols['FxM5'] = ld.mov_avg(pred, 2, 2)
cols['FxM5i1'] = ld.mov_avg(pred, 5, 1)
cols['FxM1i5'] = ld.mov_avg(pred, 1, 5)
# Data Frame
dset_df = pd.DataFrame(cols)
init_dset = dset_df.loc[:,[s for s in dset_df.columns if s not in \
['itype', 'image_id', 'frames_left', 'target']]].copy()
tmp_dset = init_dset.copy()
tmp_dset['frame'] = tmp_dset['frame'].values - 5
tmp_dset.columns = [
s + '_p5' if s != 'frame' else s for s in tmp_dset.columns
]
dset_df = pd.merge(dset_df, tmp_dset, how='left', on='frame')
tmp_dset = init_dset.copy()
tmp_dset['frame'] = tmp_dset['frame'].values - 5
tmp_dset.columns = [
s + '_p10' if s != 'frame' else s for s in tmp_dset.columns
]
dset_df = pd.merge(dset_df, tmp_dset, how='left', on='frame')
tmp_dset = init_dset.copy()
tmp_dset['frame'] = tmp_dset['frame'].values + 5
tmp_dset.columns = [
s + '_n5' if s != 'frame' else s for s in tmp_dset.columns
]
dset_df = pd.merge(dset_df, tmp_dset, how='left', on='frame')
tmp_dset = init_dset.copy()
tmp_dset['frame'] = tmp_dset['frame'].values + 5
tmp_dset.columns = [
s + '_n10' if s != 'frame' else s for s in tmp_dset.columns
]
dset_df = pd.merge(dset_df, tmp_dset, how='left', on='frame')
# Add target
if read_targets:
targets = mini_df[['frame']]
targets = targets.assign(target=1)
if targets is None:
dset_df = dset_df.assign(target=0)
else:
dset_df = pd.merge(dset_df, targets, how='left', on='frame')
dset_df = dset_df.assign(
target=np.nan_to_num(dset_df.target.values))
# Convert infinites and nans
dset_df = dset_df.replace([np.inf, -np.inf, np.nan], 0)
# Filter frames
dset_df = dset_df.iloc[[s in frames for s in dset_df.frame], :]
if verbose:
print("Read image {} dataset in {:.2f} s".format(
image_id, (time.time() - start_time_L1) / 1))
return dset_df
def read_image(
self,
itype,
image_id,
frame='example', # int, 'all', 'example'(0)
#'all_labeled' --> only if training
#'all_train' --> only if training
read_targets=False,
use_cache=None,
verbose=False):
'''Custom read_image function for this model.
'''
start_time_L1 = time.time()
targets = None
# Read image.
vidD = self.Data.load_vidDATA(itype, image_id)
# Read annotations
df = self.Data.annotations
mini_df = df[df.video_id == image_id]
mini_df = mini_df[np.logical_not(np.isnan(mini_df.fish_number))]
nb_frames = len(mini_df)
# Create frames list
if frame == 'all':
frames = range(len(vidD.vi))
elif frame == 'example':
frames = [
0,
]
elif frame == 'all_labeled' and nb_frames > 0:
frames = mini_df.frame.values.tolist()
elif frame == 'all_train' and nb_frames > 0:
i_frames = mini_df.frame.values.tolist()
frames = [[
s,
] for s in i_frames]
frames = [s for ss in frames for s in ss]
frames = np.unique(np.clip(frames, 0, len(vidD.vi) - 1))
else:
frames = [
int(frame),
]
# Start df
cols = {}
cols['itype'] = itype
cols['image_id'] = image_id
# Bx, fish_length
i_model = -1
bx_models = 2
for i in range(bx_models):
i_model += 1
Model = self.PREV_Model[i_model]
pred = Model.get_predictions(itype,
image_id,
return_imgs=False,
use_cache=use_cache,
verbose=verbose)
pred = pred[:, 0]
cols['Bx{}_{}Pred'.format(i_model, i)] = pred
cols['Bx{}_{}M3'.format(i_model, i)] = ld.mov_avg(pred, 1, 1)
cols['Bx{}_{}M5'.format(i_model, i)] = ld.mov_avg(pred, 2, 2)
cols['Bx{}_{}M11'.format(i_model, i)] = ld.mov_avg(pred, 5, 5)
cols['Bx{}_{}M0i3max'.format(i_model, i)] = ld.mov_func(
pred, 0, 3, np.nanmax)
cols['Bx{}_{}M3i0max'.format(i_model, i)] = ld.mov_func(
pred, 3, 0, np.nanmax)
cols['Bx{}_{}M0i5max'.format(i_model, i)] = ld.mov_func(
pred, 0, 5, np.nanmax)
cols['Bx{}_{}M5i0max'.format(i_model, i)] = ld.mov_func(
pred, 5, 0, np.nanmax)
cols['Bx{}_{}M0i11max'.format(i_model, i)] = ld.mov_func(
pred, 0, 11, np.nanmax)
cols['Bx{}_{}M11i0max'.format(i_model, i)] = ld.mov_func(
pred, 11, 0, np.nanmax)
# Bx, fish_bbox
i_model += 1
Model = self.PREV_Model[i_model]
pred = Model.get_predictions(itype,
image_id,
return_imgs=False,
use_cache=use_cache,
verbose=verbose)
pred = pred.iloc[:, 4:].values
for i in range(pred.shape[1]):
cols['Bx{}_{}Pred'.format(i_model, i)] = pred[:, i]
cols['Bx{}_{}M3'.format(i_model, i)] = ld.mov_avg(pred[:, i], 1, 1)
cols['Bx{}_{}M5'.format(i_model, i)] = ld.mov_avg(pred[:, i], 2, 2)
cols['Bx{}_{}M11'.format(i_model,
i)] = ld.mov_avg(pred[:, i], 5, 5)
# Data Frame
dset_df = pd.DataFrame(cols)
# Convert infinites and nans
dset_df = dset_df.replace([np.inf, -np.inf, np.nan], 0)
# Add fetaures
dset_df = dset_df.assign(frame=np.arange(len(dset_df)))
dset_df = dset_df.assign(sample_weight=1 / float(len(dset_df)))
dset_df = dset_df.assign(frames_left=len(dset_df) -
dset_df.frame.values)
# Add target
if read_targets:
targets = mini_df[[
'frame',
] + [
'length',
]]
if targets is not None:
dset_df = pd.merge(dset_df, targets, how='left', on='frame')
# Filter frames
dset_df = dset_df.iloc[[s in frames for s in dset_df.frame], :]
if verbose:
print("Read image {} dataset in {:.2f} s".format(
image_id, (time.time() - start_time_L1) / 1))
return dset_df
