labels in generate dataset should be int now
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92ab342a65
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5f270c41f2
@ -1,44 +0,0 @@
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import pandas as pd
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from pathlib import Path
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import numpy as np
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import os
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from IPython import embed
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def define_train_test_img_names(bbox, test_size = 0.2):
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np.random.seed(42)
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unique_imgs = np.asarray(pd.unique(bbox['image']))
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np.random.shuffle(unique_imgs)
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test_img = sorted(unique_imgs[:int(len(unique_imgs) * test_size)])
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train_img = sorted(unique_imgs[int(len(unique_imgs) * test_size):])
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return test_img, train_img
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def split_data_df_in_test_train_df(bbox, test_img, train_img):
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cols = list(bbox.keys())
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test_bbox = pd.DataFrame(columns=cols)
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train_bbox = pd.DataFrame(columns=cols)
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for img_name in test_img:
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tmp_df = bbox[bbox['image'] == img_name]
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test_bbox = pd.concat([test_bbox, tmp_df], ignore_index=True)
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for img_name in train_img:
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tmp_df = bbox[bbox['image'] == img_name]
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train_bbox = pd.concat([train_bbox, tmp_df], ignore_index=True)
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return train_bbox, test_bbox, cols
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def main(path):
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bbox = pd.read_csv(path/'bbox_dataset.csv', sep=',', index_col=0)
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test_img, train_img = define_train_test_img_names(bbox)
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train_bbox, test_bbox, cols = split_data_df_in_test_train_df(bbox, test_img, train_img)
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train_bbox.to_csv(path/'bbox_train.csv', columns=cols, sep=',')
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test_bbox.to_csv(path/'bbox_test.csv', columns=cols, sep=',')
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if __name__ == '__main__':
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main(Path('./dataset'))
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@ -78,7 +78,7 @@ def load_trial_data(folder):
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return fish_freq, rise_idx, rise_size, fish_baseline_freq, fish_baseline_freq_time
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def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, dataset_folder):
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def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, pic_save_folder):
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f_res, t_res = freq[1] - freq[0], times[1] - times[0]
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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')
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@ -90,14 +90,14 @@ def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1,
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ax.axis(False)
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# plt.savefig(os.path.join(dataset_folder, fig_title), dpi=IMG_DPI)
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plt.savefig(Path(DATA_DIR)/fig_title, dpi=IMG_DPI)
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plt.savefig(Path(pic_save_folder)/fig_title, dpi=IMG_DPI)
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plt.close()
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return fig_title, (IMG_SIZE[0]*IMG_DPI, IMG_SIZE[1]*IMG_DPI)
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return fig_title
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def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq_time, fish_baseline_freq, pic_save_str,
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bbox_df, cols, width, height, t0, t1, f0, f1):
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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):
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times_v_idx0, times_v_idx1 = np.argmin(np.abs(times_v - t0)), np.argmin(np.abs(times_v - t1))
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@ -161,13 +161,6 @@ def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq
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if len(left_time_bound) == 0:
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continue
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# x0 = np.array((left_time_bound - t0) / (t1 - t0) * width, dtype=int)
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# x1 = np.array((right_time_bound - t0) / (t1 - t0) * width, dtype=int)
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#
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# y0 = np.array((1 - (upper_freq_bound - f0) / (f1 - f0)) * height, dtype=int)
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# y1 = np.array((1 - (lower_freq_bound - f0) / (f1 - f0)) * height, dtype=int)
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rel_x0 = np.array((left_time_bound - t0) / (t1 - t0), dtype=float)
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rel_x1 = np.array((right_time_bound - t0) / (t1 - t0), dtype=float)
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@ -184,15 +177,8 @@ def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq
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all_width.extend(rel_width)
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all_height.extend(rel_height)
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# bbox = np.array([[pic_save_str for i in range(len(left_time_bound))],
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# left_time_bound,
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# right_time_bound,
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# lower_freq_bound,
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# upper_freq_bound,
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# x0, y0, x1, y1])
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bbox_yolo_style = np.array([
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np.ones(len(all_x_center)),
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np.ones(len(all_x_center), dtype=int),
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all_x_center,
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all_y_center,
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all_width,
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@ -205,16 +191,19 @@ def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq
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def main(args):
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folders = list(f.parent for f in Path(args.folder).rglob('fill_times.npy'))
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pic_save_folder = DATA_DIR if not args.inference else (Path('data') / Path(args.folder).name)
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if len(folders) == 0:
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print('no datasets containing fill_times.npy found')
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if not args.inference:
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print('generate training dataset only for files with detected rises')
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folders = [folder for folder in folders if (folder / 'analysis' / 'rise_idx.npy').exists()]
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cols = ['image', 't0', 't1', 'f0', 'f1', 'x0', 'y0', 'x1', 'y1']
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bbox_df = pd.DataFrame(columns=cols)
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else:
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print('generate inference dataset ... only image output')
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bbox_df = {}
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if not (Path('data') / Path(args.folder).name).exists():
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(Path('data') / Path(args.folder).name).mkdir(parents=True, exist_ok=True)
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for enu, folder in enumerate(folders):
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print(f'DataSet generation from {folder} | {enu+1}/{len(folders)}')
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@ -259,52 +248,52 @@ def main(args):
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transformed = T.Normalize(mean=torch.mean(log_s), std=torch.std(log_s))
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s_trans = transformed(log_s.unsqueeze(0))
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pic_save_str, (width, height) = save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, args.dataset_folder)
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pic_save_str = save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, pic_save_folder)
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if not args.inference:
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bbox_yolo_style = bboxes_from_file(times_v, fish_freq, rise_idx, rise_size,
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fish_baseline_freq_time, fish_baseline_freq,
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pic_save_str, bbox_df, cols, width, height, t0, t1, f0, f1)
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pic_save_str,t0, t1, f0, f1)
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#######################################################################
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if False:
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if bbox_yolo_style.shape[0] >= 1:
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f_res, t_res = freq[1] - freq[0], times[1] - times[0]
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fig_title = (
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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(
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' ', '0')
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fig = plt.figure(figsize=IMG_SIZE, num=fig_title)
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gs = gridspec.GridSpec(1, 1, bottom=0.1, left=0.1, right=0.95, top=0.95) #
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ax = fig.add_subplot(gs[0, 0])
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ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower',
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extent=(times[t_idx0] / 3600, (times[t_idx1] + t_res) / 3600, freq[f_idx0], freq[f_idx1] + f_res))
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# ax.invert_yaxis()
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# ax.axis(False)
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for i in range(len(bbox_df)):
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# Cbbox = np.array(bbox_df.loc[i, ['x0', 'y0', 'x1', 'y1']].values, dtype=np.float32)
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Cbbox = bbox_df.loc[i, ['t0', 'f0', 't1', 'f1']]
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ax.add_patch(
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Rectangle((float(Cbbox['t0']) / 3600, float(Cbbox['f0'])),
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float(Cbbox['t1']) / 3600 - float(Cbbox['t0']) / 3600,
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float(Cbbox['f1']) - float(Cbbox['f0']),
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fill=False, color="white", linestyle='-', linewidth=2, zorder=10)
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)
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# print(bbox_yolo_style.T)
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for bbox in bbox_yolo_style:
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x0 = bbox[1] - bbox[3]/2 # x_center - width/2
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y0 = 1 - (bbox[2] + bbox[4]/2) # x_center - width/2
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w = bbox[3]
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h = bbox[4]
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ax.add_patch(
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Rectangle((x0, y0), w, h,
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fill=False, color="k", linestyle='--', linewidth=2, zorder=10,
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transform=ax.transAxes)
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)
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plt.show()
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# if False:
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# if bbox_yolo_style.shape[0] >= 1:
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# f_res, t_res = freq[1] - freq[0], times[1] - times[0]
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#
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# fig_title = (
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# 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(
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# ' ', '0')
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# fig = plt.figure(figsize=IMG_SIZE, num=fig_title)
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# gs = gridspec.GridSpec(1, 1, bottom=0.1, left=0.1, right=0.95, top=0.95) #
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# ax = fig.add_subplot(gs[0, 0])
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# ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower',
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# extent=(times[t_idx0] / 3600, (times[t_idx1] + t_res) / 3600, freq[f_idx0], freq[f_idx1] + f_res))
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# # ax.invert_yaxis()
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# # ax.axis(False)
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#
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# for i in range(len(bbox_df)):
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# # Cbbox = np.array(bbox_df.loc[i, ['x0', 'y0', 'x1', 'y1']].values, dtype=np.float32)
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# Cbbox = bbox_df.loc[i, ['t0', 'f0', 't1', 'f1']]
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# ax.add_patch(
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# Rectangle((float(Cbbox['t0']) / 3600, float(Cbbox['f0'])),
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# float(Cbbox['t1']) / 3600 - float(Cbbox['t0']) / 3600,
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# float(Cbbox['f1']) - float(Cbbox['f0']),
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# fill=False, color="white", linestyle='-', linewidth=2, zorder=10)
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# )
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#
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# # print(bbox_yolo_style.T)
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#
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# for bbox in bbox_yolo_style:
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# x0 = bbox[1] - bbox[3]/2 # x_center - width/2
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# y0 = 1 - (bbox[2] + bbox[4]/2) # x_center - width/2
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# w = bbox[3]
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# h = bbox[4]
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# ax.add_patch(
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# Rectangle((x0, y0), w, h,
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# fill=False, color="k", linestyle='--', linewidth=2, zorder=10,
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# transform=ax.transAxes)
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# )
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# plt.show()
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#######################################################################
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# if not args.inference:
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@ -314,11 +303,7 @@ def main(args):
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Evaluated electrode array recordings with multiple fish.')
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parser.add_argument('folder', type=str, help='single recording analysis', default='')
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parser.add_argument('-d', "--dataset_folder", type=str, help='designated datasef folder', default=DATA_DIR)
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parser.add_argument('-i', "--inference", action="store_true", help="generate inference dataset. Img only")
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parser.add_argument('-i', "--inference", action="store_true")
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args = parser.parse_args()
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if not Path(args.dataset_folder).exists():
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Path(args.dataset_folder).mkdir(parents=True, exist_ok=True)
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main(args)
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