reworking the data structure. dataset generator is now adapted... dataloader fn are next
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*.png
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*.txt
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@ -26,9 +26,10 @@ DELTA_TIME = 60*10
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TIME_OVERLAP = 60*1
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# output parameters
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DATA_DIR = 'data/dataset'
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DATA_DIR = 'data/images'
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LABEL_DIR = 'data/labels'
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OUTDIR = 'model_outputs'
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INFERENCE_OUTDIR = 'inference_outputs'
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for required_folders in [DATA_DIR, OUTDIR, INFERENCE_OUTDIR]:
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for required_folders in [DATA_DIR, OUTDIR, INFERENCE_OUTDIR, LABEL_DIR]:
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if not pathlib.Path(required_folders).exists():
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pathlib.Path(required_folders).mkdir(parents=True, exist_ok=True)
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@ -1,262 +0,0 @@
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import itertools
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import sys
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import os
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import argparse
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import torch
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from torch import nn
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import torch.nn.functional as F
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import torchvision.transforms as T
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import pandas as pd
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from pathlib import Path
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from tqdm.auto import tqdm
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def load_spec_data(folder: str):
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"""
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Load spectrogram of a given electrode-grid recording generated with the wavetracker package. The spectrograms may
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be to large to load in total, thats why memmory mapping is used (numpy.memmap).
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Parameters
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----------
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folder: str
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Folder where fine spec numpy files generated for grid recordings with the wavetracker package can be found.
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Returns
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-------
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fill_freqs: ndarray
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Freuqencies corresponding to 1st dimension of the spectrogram.
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fill_times: ndarray
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Times corresponding to the 2nd dimenstion if the spectrigram.
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fill_spec: ndarray
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Spectrigram of the recording refered to in the input folder.
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"""
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fill_freqs, fill_times, fill_spec = [], [], []
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if os.path.exists(os.path.join(folder, 'fill_spec.npy')):
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fill_freqs = np.load(os.path.join(folder, 'fill_freqs.npy'))
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fill_times = np.load(os.path.join(folder, 'fill_times.npy'))
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fill_spec_shape = np.load(os.path.join(folder, 'fill_spec_shape.npy'))
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fill_spec = np.memmap(os.path.join(folder, 'fill_spec.npy'), dtype='float', mode='r',
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shape=(fill_spec_shape[0], fill_spec_shape[1]), order='F')
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elif os.path.exists(os.path.join(folder, 'fine_spec.npy')):
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fill_freqs = np.load(os.path.join(folder, 'fine_freqs.npy'))
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fill_times = np.load(os.path.join(folder, 'fine_times.npy'))
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fill_spec_shape = np.load(os.path.join(folder, 'fine_spec_shape.npy'))
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fill_spec = np.memmap(os.path.join(folder, 'fine_spec.npy'), dtype='float', mode='r',
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shape=(fill_spec_shape[0], fill_spec_shape[1]), order='F')
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return fill_freqs, fill_times, fill_spec
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def load_tracking_data(folder):
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base_path = Path(folder)
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EODf_v = np.load(base_path / 'fund_v.npy')
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ident_v = np.load(base_path / 'ident_v.npy')
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idx_v = np.load(base_path / 'idx_v.npy')
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times_v = np.load(base_path / 'times.npy')
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return EODf_v, ident_v, idx_v, times_v
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def load_trial_data(folder):
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base_path = Path(folder)
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fish_freq = np.load(base_path / 'analysis' / 'fish_freq.npy')
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rise_idx = np.load(base_path / 'analysis' / 'rise_idx.npy')
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rise_size = np.load(base_path / 'analysis' / 'rise_size.npy')
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fish_baseline_freq = np.load(base_path / 'analysis' / 'baseline_freqs.npy')
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fish_baseline_freq_time = np.load(base_path / 'analysis' / 'baseline_freq_times.npy')
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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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size = (7, 7)
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dpi = 256
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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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fig = plt.figure(figsize=(7, 7), num=fig_title)
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gs = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1) #
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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] / 3600 + t_res, freq[f_idx0], freq[f_idx1] + f_res))
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ax.axis(False)
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plt.savefig(os.path.join(dataset_folder, fig_title), dpi=256)
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plt.close()
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return fig_title, (size[0]*dpi, size[1]*dpi)
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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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times_v_idx0, times_v_idx1 = np.argmin(np.abs(times_v - t0)), np.argmin(np.abs(times_v - t1))
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for id_idx in range(len(fish_freq)):
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rise_idx_oi = np.array(rise_idx[id_idx][
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(rise_idx[id_idx] >= times_v_idx0) &
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(rise_idx[id_idx] <= times_v_idx1) &
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(rise_size[id_idx] >= 10)], dtype=int)
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rise_size_oi = rise_size[id_idx][(rise_idx[id_idx] >= times_v_idx0) &
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(rise_idx[id_idx] <= times_v_idx1) &
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(rise_size[id_idx] >= 10)]
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if len(rise_idx_oi) == 0:
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continue
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closest_baseline_idx = list(map(lambda x: np.argmin(np.abs(fish_baseline_freq_time - x)), times_v[rise_idx_oi]))
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closest_baseline_freq = fish_baseline_freq[id_idx][closest_baseline_idx]
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upper_freq_bound = closest_baseline_freq + rise_size_oi
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lower_freq_bound = closest_baseline_freq
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left_time_bound = times_v[rise_idx_oi]
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right_time_bound = np.zeros_like(left_time_bound)
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for enu, Ct_oi in enumerate(times_v[rise_idx_oi]):
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Crise_size = rise_size_oi[enu]
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Cblf = closest_baseline_freq[enu]
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rise_end_t = times_v[(times_v > Ct_oi) &
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(fish_freq[id_idx] < Cblf + Crise_size * 0.37)]
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if len(rise_end_t) == 0:
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right_time_bound[enu] = np.nan
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else:
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right_time_bound[enu] = rise_end_t[0]
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mask = (~np.isnan(right_time_bound) & ((right_time_bound - left_time_bound) > 1.))
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left_time_bound = left_time_bound[mask]
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right_time_bound = right_time_bound[mask]
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lower_freq_bound = lower_freq_bound[mask]
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upper_freq_bound = upper_freq_bound[mask]
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left_time_bound -= 0.01 * (t1 - t0)
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right_time_bound += 0.05 * (t1 - t0)
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lower_freq_bound -= 0.01 * (f1 - f0)
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upper_freq_bound += 0.05 * (f1 - f0)
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mask2 = ((left_time_bound >= t0) &
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(right_time_bound <= t1) &
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(lower_freq_bound >= f0) &
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(upper_freq_bound <= f1)
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)
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left_time_bound = left_time_bound[mask2]
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right_time_bound = right_time_bound[mask2]
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lower_freq_bound = lower_freq_bound[mask2]
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upper_freq_bound = upper_freq_bound[mask2]
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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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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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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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tmp_df = pd.DataFrame(
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data=bbox.T,
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columns=cols
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)
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bbox_df = pd.concat([bbox_df, tmp_df], ignore_index=True)
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return bbox_df
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def main(args):
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# Hyperparameter
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min_freq = 200
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max_freq = 1500
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d_freq = 200
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freq_overlap = 25
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d_time = 60*10
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time_overlap = 60*1
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folders = list(f.parent for f in Path(args.folder).rglob('fill_times.npy'))
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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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# ToDo: Implement loading the old .csv file and upgrade it... how exactly I will determine after my vaccation
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# bbox_df = pd.read_csv(os.path.join('dataset', 'bbox_dataset.csv'), sep=',', index_col=0)
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# cols = list(bbox_df.keys())
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# eval_files = []
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# for f in pd.unique(bbox_df['image']):
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# eval_files.append(f.split('__')[0])
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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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for enu, folder in enumerate(folders):
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print(f'DataSet generation from {folder} | {enu+1}/{len(folders)}')
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# load different categories of data
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freq, times, spec = (
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load_spec_data(folder))
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EODf_v, ident_v, idx_v, times_v = (
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load_tracking_data(folder))
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if not args.inference:
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fish_freq, rise_idx, rise_size, fish_baseline_freq, fish_baseline_freq_time = (
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load_trial_data(folder))
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# generate iterator for analysis window loop
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pic_base = tqdm(itertools.product(
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np.arange(0, times[-1], d_time),
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np.arange(min_freq, max_freq, d_freq)
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),
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total=int((((max_freq-min_freq)//d_freq)+1) * ((times[-1] // d_time)+1))
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)
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for t0, f0 in pic_base:
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t1 = t0 + d_time + time_overlap
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f1 = f0 + d_freq + freq_overlap
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present_freqs = EODf_v[(~np.isnan(ident_v)) &
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(t0 <= times_v[idx_v]) &
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(times_v[idx_v] <= t1) &
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(EODf_v >= f0) &
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(EODf_v <= f1)]
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if len(present_freqs) == 0:
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continue
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# get spec_idx for current spec snippet
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f_idx0, f_idx1 = np.argmin(np.abs(freq - f0)), np.argmin(np.abs(freq - f1))
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t_idx0, t_idx1 = np.argmin(np.abs(times - t0)), np.argmin(np.abs(times - t1))
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# get spec snippet and create torch.tensfor from it
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s = torch.from_numpy(spec[f_idx0:f_idx1, t_idx0:t_idx1].copy()).type(torch.float32)
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log_s = torch.log10(s)
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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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if not args.inference:
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bbox_df = 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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if not args.inference:
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print('save bboxes')
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bbox_df.to_csv(os.path.join(args.dataset_folder, 'bbox_dataset.csv'), columns=cols, sep=',')
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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='dataset')
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parser.add_argument('-i', "--inference", action="store_true", help="generate inference dataset. Img only")
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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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