gnerate dataset now capable of only producing images
This commit is contained in:
parent
a6641accaf
commit
2a0a1a2766
@ -22,8 +22,7 @@ from IPython import embed
|
||||
from matplotlib.patches import Rectangle
|
||||
from matplotlib.collections import PatchCollection
|
||||
|
||||
|
||||
def load_data(folder):
|
||||
def load_spec_data(folder):
|
||||
fill_freqs, fill_times, fill_spec = [], [], []
|
||||
|
||||
if os.path.exists(os.path.join(folder, 'fill_spec.npy')):
|
||||
@ -40,12 +39,19 @@ def load_data(folder):
|
||||
fill_spec = np.memmap(os.path.join(folder, 'fine_spec.npy'), dtype='float', mode='r',
|
||||
shape=(fill_spec_shape[0], fill_spec_shape[1]), order='F')
|
||||
|
||||
return fill_freqs, fill_times, fill_spec
|
||||
|
||||
def load_tracking_data(folder):
|
||||
base_path = Path(folder)
|
||||
EODf_v = np.load(base_path / 'fund_v.npy')
|
||||
ident_v = np.load(base_path / 'ident_v.npy')
|
||||
idx_v = np.load(base_path / 'idx_v.npy')
|
||||
times_v = np.load(base_path / 'times.npy')
|
||||
|
||||
return EODf_v, ident_v, idx_v, times_v
|
||||
|
||||
def load_trial_data(folder):
|
||||
base_path = Path(folder)
|
||||
fish_freq = np.load(base_path / 'analysis' / 'fish_freq.npy')
|
||||
rise_idx = np.load(base_path / 'analysis' / 'rise_idx.npy')
|
||||
rise_size = np.load(base_path / 'analysis' / 'rise_size.npy')
|
||||
@ -53,21 +59,22 @@ def load_data(folder):
|
||||
fish_baseline_freq = np.load(base_path / 'analysis' / 'baseline_freqs.npy')
|
||||
fish_baseline_freq_time = np.load(base_path / 'analysis' / 'baseline_freq_times.npy')
|
||||
|
||||
return fill_freqs, fill_times, fill_spec, EODf_v, ident_v, idx_v, times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq, fish_baseline_freq_time
|
||||
return fish_freq, rise_idx, rise_size, fish_baseline_freq, fish_baseline_freq_time
|
||||
|
||||
def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, t_res, f_res):
|
||||
def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, dataset_folder):
|
||||
size = (7, 7)
|
||||
dpi = 256
|
||||
f_res, t_res = freq[1] - freq[0], times[1] - times[0]
|
||||
|
||||
fig_title = (f'{Path(folder).name}__{times[t_idx0]:.0f}s-{times[t_idx1]:.0f}s__{freq[f_idx0]:4.0f}-{freq[f_idx1]:4.0f}Hz.png').replace(' ', '0')
|
||||
fig = plt.figure(figsize=(7, 7), num=fig_title)
|
||||
gs = gridspec.GridSpec(1, 2, width_ratios=(8, 1), wspace=0) # , bottom=0, left=0, right=1, top=1
|
||||
gs2 = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1) #
|
||||
ax = fig.add_subplot(gs2[0, 0])
|
||||
im = ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower',
|
||||
extent=(times[t_idx0] / 3600, times[t_idx1] / 3600 + t_res, freq[f_idx0], freq[f_idx1] + f_res))
|
||||
gs = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1) #
|
||||
ax = fig.add_subplot(gs[0, 0])
|
||||
ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower',
|
||||
extent=(times[t_idx0] / 3600, times[t_idx1] / 3600 + t_res, freq[f_idx0], freq[f_idx1] + f_res))
|
||||
ax.axis(False)
|
||||
|
||||
plt.savefig(os.path.join('dataset', fig_title), dpi=256)
|
||||
plt.savefig(os.path.join(dataset_folder, fig_title), dpi=256)
|
||||
plt.close()
|
||||
|
||||
return fig_title, (size[0]*dpi, size[1]*dpi)
|
||||
@ -148,45 +155,8 @@ def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq
|
||||
bbox_df = pd.concat([bbox_df, tmp_df], ignore_index=True)
|
||||
return bbox_df
|
||||
|
||||
def main(args):
|
||||
def development_fn():
|
||||
fig_title = (f'{Path(args.folder).name}__{t0:.0f}s-{t1:.0f}s__{f0:4.0f}-{f1:4.0f}Hz').replace(' ', '0')
|
||||
fig = plt.figure(figsize=(7, 7), num=fig_title)
|
||||
gs = gridspec.GridSpec(1, 2, width_ratios=(8, 1), wspace=0, left=0.1, bottom=0.1, right=0.9,
|
||||
top=0.95) # , bottom=0, left=0, right=1, top=1
|
||||
ax = fig.add_subplot(gs[0, 0])
|
||||
cax = fig.add_subplot(gs[0, 1])
|
||||
im = ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower',
|
||||
extent=(times[t_idx0], times[t_idx1] + t_res, freq[f_idx0], freq[f_idx1] + f_res))
|
||||
fig.colorbar(im, cax=cax, orientation='vertical')
|
||||
|
||||
|
||||
cols = ['image', 't0', 't1', 'f0', 'f1', 'x0', 'y0', 'x1', 'y1']
|
||||
dev_df = pd.DataFrame(columns=cols)
|
||||
|
||||
dev_df = bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq_time, fish_baseline_freq,
|
||||
fig_title, dev_df, cols, (7*256), (7*256), t0, t1, f0, f1)
|
||||
|
||||
# embed()
|
||||
# quit()
|
||||
time_freq_bbox = torch.as_tensor(dev_df.loc[:, ['t0', 'f0', 't1', 'f1']].values.astype(np.float32))
|
||||
|
||||
for bbox in time_freq_bbox:
|
||||
Ct0, Cf0, Ct1, Cf1 = bbox
|
||||
ax.add_patch(
|
||||
Rectangle((Ct0, Cf0), Ct1-Ct0, Cf1-Cf0, fill=False, color="white", linewidth=2, zorder=10)
|
||||
)
|
||||
# for enu in range(len(left_time_bound)):
|
||||
# if np.isnan(right_time_bound[enu]):
|
||||
# continue
|
||||
# ax.add_patch(
|
||||
# Rectangle((left_time_bound[enu], lower_freq_bound[enu]),
|
||||
# (right_time_bound[enu] - left_time_bound[enu]),
|
||||
# (upper_freq_bound[enu] - lower_freq_bound[enu]),
|
||||
# fill=False, color="white", linewidth=2, zorder=10)
|
||||
# )
|
||||
plt.show()
|
||||
|
||||
def main(args):
|
||||
# Hyperparameter
|
||||
min_freq = 200
|
||||
max_freq = 1500
|
||||
@ -195,38 +165,30 @@ def main(args):
|
||||
d_time = 60*10
|
||||
time_overlap = 60*1
|
||||
|
||||
# init dataframe if not existent so far
|
||||
eval_files = []
|
||||
if not os.path.exists(os.path.join('dataset', 'bbox_dataset.csv')):
|
||||
folders = list(f.parent for f in Path(args.folder).rglob('fill_times.npy'))
|
||||
|
||||
if not args.inference:
|
||||
print('generate training dataset only for files with detected rises')
|
||||
folders = [folder for folder in folders if (folder / 'analysis' / 'rise_idx.npy').exists()]
|
||||
cols = ['image', 't0', 't1', 'f0', 'f1', 'x0', 'y0', 'x1', 'y1']
|
||||
bbox_df = pd.DataFrame(columns=cols)
|
||||
|
||||
# else load datafile ... and check for already regarded files (eval_files)
|
||||
else:
|
||||
bbox_df = pd.read_csv(os.path.join('dataset', 'bbox_dataset.csv'), sep=',', index_col=0)
|
||||
cols = list(bbox_df.keys())
|
||||
# ToDo: make sure not same file twice
|
||||
for f in pd.unique(bbox_df['image']):
|
||||
eval_files.append(f.split('__')[0])
|
||||
|
||||
# find folders that have fine_specs...
|
||||
folders = list(f.parent for f in Path(args.folder).rglob('fill_times.npy'))
|
||||
|
||||
print('generate inference dataset ... only image output')
|
||||
bbox_df = {}
|
||||
|
||||
for enu, folder in enumerate(folders):
|
||||
print(f'DataSet generation from {folder} | {enu+1}/{len(folders)}')
|
||||
# check for those folders where rises are detected
|
||||
if not (folder/'analysis'/'rise_idx.npy').exists():
|
||||
continue
|
||||
|
||||
# embed()
|
||||
# quit()
|
||||
# ToDo: check if folder in eval_files ... is so: continue
|
||||
|
||||
freq, times, spec, EODf_v, ident_v, idx_v, times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq, fish_baseline_freq_time = (
|
||||
load_data(folder))
|
||||
f_res, t_res = freq[1] - freq[0], times[1] - times[0]
|
||||
# load different categories of data
|
||||
freq, times, spec = (
|
||||
load_spec_data(folder))
|
||||
EODf_v, ident_v, idx_v, times_v = (
|
||||
load_tracking_data(folder))
|
||||
if not args.inference:
|
||||
fish_freq, rise_idx, rise_size, fish_baseline_freq, fish_baseline_freq_time = (
|
||||
load_trial_data(folder))
|
||||
|
||||
# generate iterator for analysis window loop
|
||||
pic_base = tqdm(itertools.product(
|
||||
np.arange(0, times[-1], d_time),
|
||||
np.arange(min_freq, max_freq, d_freq)
|
||||
@ -247,30 +209,35 @@ def main(args):
|
||||
if len(present_freqs) == 0:
|
||||
continue
|
||||
|
||||
# get spec_idx for current spec snippet
|
||||
f_idx0, f_idx1 = np.argmin(np.abs(freq - f0)), np.argmin(np.abs(freq - f1))
|
||||
t_idx0, t_idx1 = np.argmin(np.abs(times - t0)), np.argmin(np.abs(times - t1))
|
||||
|
||||
# get spec snippet and create torch.tensfor from it
|
||||
s = torch.from_numpy(spec[f_idx0:f_idx1, t_idx0:t_idx1].copy()).type(torch.float32)
|
||||
log_s = torch.log10(s)
|
||||
transformed = T.Normalize(mean=torch.mean(log_s), std=torch.std(log_s))
|
||||
s_trans = transformed(log_s.unsqueeze(0))
|
||||
|
||||
if not args.dev:
|
||||
pic_save_str, (width, height) = save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, t_res, f_res)
|
||||
pic_save_str, (width, height) = save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, args.dataset_folder)
|
||||
|
||||
if not args.inference:
|
||||
bbox_df = bboxes_from_file(times_v, fish_freq, rise_idx, rise_size,
|
||||
fish_baseline_freq_time, fish_baseline_freq,
|
||||
pic_save_str, bbox_df, cols, width, height, t0, t1, f0, f1)
|
||||
else:
|
||||
development_fn()
|
||||
|
||||
if not args.dev:
|
||||
print('save')
|
||||
bbox_df.to_csv(os.path.join('dataset', 'bbox_dataset.csv'), columns=cols, sep=',')
|
||||
if not args.inference:
|
||||
print('save bboxes')
|
||||
bbox_df.to_csv(os.path.join(args.dataset_folder, 'bbox_dataset.csv'), columns=cols, sep=',')
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Evaluated electrode array recordings with multiple fish.')
|
||||
parser.add_argument('folder', type=str, help='single recording analysis', default='')
|
||||
parser.add_argument('-d', "--dev", action="store_true", help="developer mode; no data saved")
|
||||
parser.add_argument('-d', "--dataset_folder", type=str, help='designated datasef folder', default='dataset')
|
||||
parser.add_argument('-i', "--inference", action="store_true", help="generate inference dataset. Img only")
|
||||
args = parser.parse_args()
|
||||
|
||||
if not Path(args.dataset_folder).exists():
|
||||
Path(args.dataset_folder).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
main(args)
|
||||
Loading…
Reference in New Issue
Block a user