diff --git a/data/train_test_split.py b/data/train_test_split.py
deleted file mode 100644
index 0f8f198..0000000
--- a/data/train_test_split.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import pandas as pd
-from pathlib import Path
-import numpy as np
-import os
-
-from IPython import embed
-
-def define_train_test_img_names(bbox, test_size = 0.2):
- np.random.seed(42)
- unique_imgs = np.asarray(pd.unique(bbox['image']))
- np.random.shuffle(unique_imgs)
-
- test_img = sorted(unique_imgs[:int(len(unique_imgs) * test_size)])
- train_img = sorted(unique_imgs[int(len(unique_imgs) * test_size):])
-
- return test_img, train_img
-
-def split_data_df_in_test_train_df(bbox, test_img, train_img):
- cols = list(bbox.keys())
-
- test_bbox = pd.DataFrame(columns=cols)
- train_bbox = pd.DataFrame(columns=cols)
-
- for img_name in test_img:
- tmp_df = bbox[bbox['image'] == img_name]
- test_bbox = pd.concat([test_bbox, tmp_df], ignore_index=True)
-
- for img_name in train_img:
- tmp_df = bbox[bbox['image'] == img_name]
- train_bbox = pd.concat([train_bbox, tmp_df], ignore_index=True)
-
- return train_bbox, test_bbox, cols
-def main(path):
- bbox = pd.read_csv(path/'bbox_dataset.csv', sep=',', index_col=0)
-
- test_img, train_img = define_train_test_img_names(bbox)
-
- train_bbox, test_bbox, cols = split_data_df_in_test_train_df(bbox, test_img, train_img)
-
- train_bbox.to_csv(path/'bbox_train.csv', columns=cols, sep=',')
- test_bbox.to_csv(path/'bbox_test.csv', columns=cols, sep=',')
-
-if __name__ == '__main__':
- main(Path('./dataset'))
\ No newline at end of file
diff --git a/generate_dataset.py b/generate_dataset.py
index bcd7e4f..6f282b7 100644
--- a/generate_dataset.py
+++ b/generate_dataset.py
@@ -78,7 +78,7 @@ def load_trial_data(folder):
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, dataset_folder):
+def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, pic_save_folder):
f_res, t_res = freq[1] - freq[0], times[1] - times[0]
fig_title = (f'{Path(folder).name}__{times[t_idx0]:5.0f}s-{times[t_idx1]:5.0f}s__{freq[f_idx0]:4.0f}-{freq[f_idx1]:4.0f}Hz.png').replace(' ', '0')
@@ -90,14 +90,14 @@ def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1,
ax.axis(False)
# plt.savefig(os.path.join(dataset_folder, fig_title), dpi=IMG_DPI)
- plt.savefig(Path(DATA_DIR)/fig_title, dpi=IMG_DPI)
+
+ plt.savefig(Path(pic_save_folder)/fig_title, dpi=IMG_DPI)
plt.close()
- return fig_title, (IMG_SIZE[0]*IMG_DPI, IMG_SIZE[1]*IMG_DPI)
+ return fig_title
-def 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):
+def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq_time, fish_baseline_freq, pic_save_str,t0, t1, f0, f1):
times_v_idx0, times_v_idx1 = np.argmin(np.abs(times_v - t0)), np.argmin(np.abs(times_v - t1))
@@ -161,13 +161,6 @@ def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq
if len(left_time_bound) == 0:
continue
- # x0 = np.array((left_time_bound - t0) / (t1 - t0) * width, dtype=int)
- # x1 = np.array((right_time_bound - t0) / (t1 - t0) * width, dtype=int)
- #
- # y0 = np.array((1 - (upper_freq_bound - f0) / (f1 - f0)) * height, dtype=int)
- # y1 = np.array((1 - (lower_freq_bound - f0) / (f1 - f0)) * height, dtype=int)
-
-
rel_x0 = np.array((left_time_bound - t0) / (t1 - t0), dtype=float)
rel_x1 = np.array((right_time_bound - t0) / (t1 - t0), dtype=float)
@@ -184,15 +177,8 @@ def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq
all_width.extend(rel_width)
all_height.extend(rel_height)
- # bbox = np.array([[pic_save_str for i in range(len(left_time_bound))],
- # left_time_bound,
- # right_time_bound,
- # lower_freq_bound,
- # upper_freq_bound,
- # x0, y0, x1, y1])
-
bbox_yolo_style = np.array([
- np.ones(len(all_x_center)),
+ np.ones(len(all_x_center), dtype=int),
all_x_center,
all_y_center,
all_width,
@@ -205,16 +191,19 @@ def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq
def main(args):
folders = list(f.parent for f in Path(args.folder).rglob('fill_times.npy'))
+ pic_save_folder = DATA_DIR if not args.inference else (Path('data') / Path(args.folder).name)
+
+ if len(folders) == 0:
+ print('no datasets containing fill_times.npy found')
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:
print('generate inference dataset ... only image output')
- bbox_df = {}
+ if not (Path('data') / Path(args.folder).name).exists():
+ (Path('data') / Path(args.folder).name).mkdir(parents=True, exist_ok=True)
for enu, folder in enumerate(folders):
print(f'DataSet generation from {folder} | {enu+1}/{len(folders)}')
@@ -259,52 +248,52 @@ def main(args):
transformed = T.Normalize(mean=torch.mean(log_s), std=torch.std(log_s))
s_trans = transformed(log_s.unsqueeze(0))
- pic_save_str, (width, height) = save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, args.dataset_folder)
+ pic_save_str = save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, pic_save_folder)
if not args.inference:
bbox_yolo_style = 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)
+ pic_save_str,t0, t1, f0, f1)
#######################################################################
- if False:
- if bbox_yolo_style.shape[0] >= 1:
- f_res, t_res = freq[1] - freq[0], times[1] - times[0]
-
- fig_title = (
- f'{Path(folder).name}__{times[t_idx0]:5.0f}s-{times[t_idx1]:5.0f}s__{freq[f_idx0]:4.0f}-{freq[f_idx1]:4.0f}Hz.png').replace(
- ' ', '0')
- fig = plt.figure(figsize=IMG_SIZE, num=fig_title)
- gs = gridspec.GridSpec(1, 1, bottom=0.1, left=0.1, right=0.95, top=0.95) #
- 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] + t_res) / 3600, freq[f_idx0], freq[f_idx1] + f_res))
- # ax.invert_yaxis()
- # ax.axis(False)
-
- for i in range(len(bbox_df)):
- # Cbbox = np.array(bbox_df.loc[i, ['x0', 'y0', 'x1', 'y1']].values, dtype=np.float32)
- Cbbox = bbox_df.loc[i, ['t0', 'f0', 't1', 'f1']]
- ax.add_patch(
- Rectangle((float(Cbbox['t0']) / 3600, float(Cbbox['f0'])),
- float(Cbbox['t1']) / 3600 - float(Cbbox['t0']) / 3600,
- float(Cbbox['f1']) - float(Cbbox['f0']),
- fill=False, color="white", linestyle='-', linewidth=2, zorder=10)
- )
-
- # print(bbox_yolo_style.T)
-
- for bbox in bbox_yolo_style:
- x0 = bbox[1] - bbox[3]/2 # x_center - width/2
- y0 = 1 - (bbox[2] + bbox[4]/2) # x_center - width/2
- w = bbox[3]
- h = bbox[4]
- ax.add_patch(
- Rectangle((x0, y0), w, h,
- fill=False, color="k", linestyle='--', linewidth=2, zorder=10,
- transform=ax.transAxes)
- )
- plt.show()
+ # if False:
+ # if bbox_yolo_style.shape[0] >= 1:
+ # f_res, t_res = freq[1] - freq[0], times[1] - times[0]
+ #
+ # fig_title = (
+ # f'{Path(folder).name}__{times[t_idx0]:5.0f}s-{times[t_idx1]:5.0f}s__{freq[f_idx0]:4.0f}-{freq[f_idx1]:4.0f}Hz.png').replace(
+ # ' ', '0')
+ # fig = plt.figure(figsize=IMG_SIZE, num=fig_title)
+ # gs = gridspec.GridSpec(1, 1, bottom=0.1, left=0.1, right=0.95, top=0.95) #
+ # 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] + t_res) / 3600, freq[f_idx0], freq[f_idx1] + f_res))
+ # # ax.invert_yaxis()
+ # # ax.axis(False)
+ #
+ # for i in range(len(bbox_df)):
+ # # Cbbox = np.array(bbox_df.loc[i, ['x0', 'y0', 'x1', 'y1']].values, dtype=np.float32)
+ # Cbbox = bbox_df.loc[i, ['t0', 'f0', 't1', 'f1']]
+ # ax.add_patch(
+ # Rectangle((float(Cbbox['t0']) / 3600, float(Cbbox['f0'])),
+ # float(Cbbox['t1']) / 3600 - float(Cbbox['t0']) / 3600,
+ # float(Cbbox['f1']) - float(Cbbox['f0']),
+ # fill=False, color="white", linestyle='-', linewidth=2, zorder=10)
+ # )
+ #
+ # # print(bbox_yolo_style.T)
+ #
+ # for bbox in bbox_yolo_style:
+ # x0 = bbox[1] - bbox[3]/2 # x_center - width/2
+ # y0 = 1 - (bbox[2] + bbox[4]/2) # x_center - width/2
+ # w = bbox[3]
+ # h = bbox[4]
+ # ax.add_patch(
+ # Rectangle((x0, y0), w, h,
+ # fill=False, color="k", linestyle='--', linewidth=2, zorder=10,
+ # transform=ax.transAxes)
+ # )
+ # plt.show()
#######################################################################
# if not args.inference:
@@ -314,11 +303,7 @@ def main(args):
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', "--dataset_folder", type=str, help='designated datasef folder', default=DATA_DIR)
- parser.add_argument('-i', "--inference", action="store_true", help="generate inference dataset. Img only")
+ parser.add_argument('-i', "--inference", action="store_true")
args = parser.parse_args()
- if not Path(args.dataset_folder).exists():
- Path(args.dataset_folder).mkdir(parents=True, exist_ok=True)
-
main(args)
\ No newline at end of file