alot of work. dataset and loader not created
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6
custom_utils.py
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6
custom_utils.py
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@ -0,0 +1,6 @@
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def collate_fn(batch):
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"""
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To handle the data loading as different images may have different number
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of objects and to handle varying size tensors as well.
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"""
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return tuple(zip(*batch))
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@ -9,6 +9,7 @@ import torchvision.transforms as T
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from pathlib import Path
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import pandas as pd
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from tqdm.auto import tqdm
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@ -55,7 +56,9 @@ def load_data(folder):
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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
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def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, t_res, f_res):
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fig_title = (f'{Path(folder).name}__{t0:.0f}s-{t1:.0f}s__{f0:4.0f}-{f1:4.0f}Hz').replace(' ', '0')
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size = (7, 7)
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dpi = 256
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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')
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fig = plt.figure(figsize=(7, 7), num=fig_title)
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gs = gridspec.GridSpec(1, 2, width_ratios=(8, 1), wspace=0) # , bottom=0, left=0, right=1, top=1
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gs2 = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1) #
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@ -64,9 +67,72 @@ def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1,
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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('train', fig_title + '.png'), dpi=256)
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plt.savefig(os.path.join('train', 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) & (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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dt_bbox = right_time_bound - left_time_bound
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df_bbox = upper_freq_bound - lower_freq_bound
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left_time_bound -= dt_bbox * 0.1
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right_time_bound += dt_bbox * 0.1
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lower_freq_bound -= df_bbox * 0.1
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upper_freq_bound += df_bbox * 0.1
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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, x1, y0, y1])
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# test_s = ['a', 'a', 'a', 'a']
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tmp_df = pd.DataFrame(
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# index= [pic_save_str for i in range(len(left_time_bound))],
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# index= test_s,
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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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# bbox_df.append(tmp_df)
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return bbox_df
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def main(args):
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min_freq = 200
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@ -76,109 +142,129 @@ def main(args):
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d_time = 60*15
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time_overlap = 60*5
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freq, times, spec, EODf_v, ident_v, idx_v, times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq, fish_baseline_freq_time = (
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load_data(args.folder))
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f_res, t_res = freq[1] - freq[0], times[1] - times[0]
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unique_ids = np.unique(ident_v[~np.isnan(ident_v)])
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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) * (times[-1] // d_time))
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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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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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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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if not os.path.exists(os.path.join('train', 'bbox_dataset.csv')):
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cols = ['image', 't0', 't1', 'f0', 'f1', 'x0', 'x1', 'y0', 'y1']
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bbox_df = pd.DataFrame(columns=cols)
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else:
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bbox_df = pd.read_csv(os.path.join('train', '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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# ToDo: make sure not same file twice
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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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folders = [args.folders]
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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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freq, times, spec, EODf_v, ident_v, idx_v, times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq, fish_baseline_freq_time = (
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load_data(folder))
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f_res, t_res = freq[1] - freq[0], times[1] - times[0]
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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) * (times[-1] // d_time))
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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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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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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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if not args.dev:
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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, t_res, f_res)
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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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else:
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fig_title = (f'{Path(args.folder).name}__{t0:.0f}s-{t1:.0f}s__{f0:4.0f}-{f1:4.0f}Hz').replace(' ', '0')
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fig = plt.figure(figsize=(10, 7), num=fig_title)
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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
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ax = fig.add_subplot(gs[0, 0])
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cax = fig.add_subplot(gs[0, 1])
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im = ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower',
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extent=(times[t_idx0], times[t_idx1] + t_res, freq[f_idx0], freq[f_idx1] + f_res))
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fig.colorbar(im, cax=cax, orientation='vertical')
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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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ax.plot(times_v[times_v_idx0:times_v_idx1], fish_freq[id_idx][times_v_idx0:times_v_idx1], marker='.', color='k', markersize=4)
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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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ax.plot(times_v[rise_idx_oi], fish_freq[id_idx][rise_idx_oi], 'o', color='tab:red')
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if len(rise_idx_oi) > 0:
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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) & (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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dt_bbox = right_time_bound - left_time_bound
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df_bbox = upper_freq_bound - lower_freq_bound
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left_time_bound -= dt_bbox*0.1
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right_time_bound += dt_bbox*0.1
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lower_freq_bound -= df_bbox*0.1
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upper_freq_bound += df_bbox*0.1
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print(f'f0: {lower_freq_bound}')
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print(f'f1: {upper_freq_bound}')
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print(f't0: {left_time_bound}')
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print(f't1: {right_time_bound}')
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for enu in range(len(left_time_bound)):
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if np.isnan(right_time_bound[enu]):
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continue
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ax.add_patch(
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Rectangle((left_time_bound[enu], lower_freq_bound[enu]),
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(right_time_bound[enu] - left_time_bound[enu]),
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(upper_freq_bound[enu] - lower_freq_bound[enu]),
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fill=False, color="white", linewidth=2, zorder=10)
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)
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plt.show()
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if not args.dev:
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save_spec_pic(args.folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, t_res, f_res)
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else:
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fig_title = (f'{Path(args.folder).name}__{t0:.0f}s-{t1:.0f}s__{f0:4.0f}-{f1:4.0f}Hz').replace(' ', '0')
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fig = plt.figure(figsize=(10, 7), num=fig_title)
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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
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ax = fig.add_subplot(gs[0, 0])
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cax = fig.add_subplot(gs[0, 1])
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im = ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower',
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extent=(times[t_idx0], times[t_idx1] + t_res, freq[f_idx0], freq[f_idx1] + f_res))
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fig.colorbar(im, cax=cax, orientation='vertical')
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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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ax.plot(times_v[times_v_idx0:times_v_idx1], fish_freq[id_idx][times_v_idx0:times_v_idx1], marker='.', color='k', markersize=4)
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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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ax.plot(times_v[rise_idx_oi], fish_freq[id_idx][rise_idx_oi], 'o', color='tab:red')
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if len(rise_idx_oi) > 0:
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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) & (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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dt_bbox = right_time_bound - left_time_bound
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df_bbox = upper_freq_bound - lower_freq_bound
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left_time_bound -= dt_bbox*0.1
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right_time_bound += dt_bbox*0.1
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lower_freq_bound -= df_bbox*0.1
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upper_freq_bound += df_bbox*0.1
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print(f'f0: {lower_freq_bound}')
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print(f'f1: {upper_freq_bound}')
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print(f't0: {left_time_bound}')
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print(f't1: {right_time_bound}')
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for enu in range(len(left_time_bound)):
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if np.isnan(right_time_bound[enu]):
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continue
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ax.add_patch(
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Rectangle((left_time_bound[enu], lower_freq_bound[enu]),
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(right_time_bound[enu] - left_time_bound[enu]),
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(upper_freq_bound[enu] - lower_freq_bound[enu]),
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fill=False, color="white", linewidth=2, zorder=10)
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)
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plt.show()
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bbox_df.to_csv(os.path.join('train', '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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105
datasets.py
Normal file
105
datasets.py
Normal file
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import os
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import glob
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import torch
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||||
import torchvision
|
||||
import torchvision.transforms.functional as F
|
||||
from torch.utils.data import Dataset, DataLoader
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
from matplotlib.patches import Rectangle
|
||||
from pathlib import Path
|
||||
from tqdm.auto import tqdm
|
||||
from PIL import Image
|
||||
|
||||
from confic import (CLASSES, RESIZE_TO, TRAIN_DIR, BATCH_SIZE)
|
||||
from custom_utils import collate_fn
|
||||
|
||||
from IPython import embed
|
||||
|
||||
class CustomDataset(Dataset):
|
||||
def __init__(self, dir_path, use_idxs = None):
|
||||
self.dir_path = dir_path
|
||||
self.image_paths = glob.glob(f'{self.dir_path}/*.png')
|
||||
self.all_images = [img_path.split(os.path.sep)[-1] for img_path in self.image_paths]
|
||||
self.all_images = np.array(sorted(self.all_images), dtype=str)
|
||||
if hasattr(use_idxs, '__len__'):
|
||||
self.all_images = self.all_images[use_idxs]
|
||||
self.bbox_df = pd.read_csv(os.path.join(dir_path, 'bbox_dataset.csv'), sep=',', index_col=0)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
image_name = self.all_images[idx]
|
||||
image_path = os.path.join(self.dir_path, image_name)
|
||||
|
||||
img = Image.open(image_path)
|
||||
img_tensor = F.to_tensor(img.convert('RGB'))
|
||||
|
||||
Cbbox = self.bbox_df[self.bbox_df['image'] == image_name]
|
||||
|
||||
labels = np.ones(len(Cbbox), dtype=int)
|
||||
boxes = Cbbox.loc[:, ['x0', 'x1', 'y0', 'y1']].values
|
||||
|
||||
target = {}
|
||||
target["boxes"] = boxes
|
||||
target["labels"] = labels
|
||||
|
||||
return img_tensor, target
|
||||
|
||||
def __len__(self):
|
||||
return len(self.all_images)
|
||||
|
||||
def create_train_test_dataset(path, test_size=0.2):
|
||||
files = glob.glob(os.path.join(path, '*.png'))
|
||||
train_test_idx = np.arange(len(files), dtype=int)
|
||||
np.random.shuffle(train_test_idx)
|
||||
|
||||
train_idx = train_test_idx[int(test_size*len(train_test_idx)):]
|
||||
test_idx = train_test_idx[:int(test_size*len(train_test_idx))]
|
||||
|
||||
train_data = CustomDataset(path, use_idxs=train_idx)
|
||||
test_data = CustomDataset(path, use_idxs=test_idx)
|
||||
|
||||
return train_data, test_data
|
||||
|
||||
def create_train_loader(train_dataset, num_workers=0):
|
||||
train_loader = DataLoader(
|
||||
train_dataset,
|
||||
batch_size=BATCH_SIZE,
|
||||
shuffle=True,
|
||||
num_workers=num_workers,
|
||||
collate_fn=collate_fn
|
||||
)
|
||||
return train_loader
|
||||
def create_valid_loader(valid_dataset, num_workers=0):
|
||||
valid_loader = DataLoader(
|
||||
valid_dataset,
|
||||
batch_size=BATCH_SIZE,
|
||||
shuffle=False,
|
||||
num_workers=num_workers,
|
||||
collate_fn=collate_fn
|
||||
)
|
||||
return valid_loader
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
train_data, test_data = create_train_test_dataset(TRAIN_DIR)
|
||||
|
||||
train_loader = create_train_loader(train_data)
|
||||
test_loader = create_valid_loader(test_data)
|
||||
|
||||
for samples, targets in train_loader:
|
||||
for s, t in zip(samples, targets):
|
||||
fig, ax = plt.subplots()
|
||||
ax.imshow(s.permute(1, 2, 0), aspect='auto')
|
||||
for (x0, x1, y0, y1), l in zip(t['boxes'], t['labels']):
|
||||
print(x0, x1, y0, y1, l)
|
||||
ax.add_patch(
|
||||
Rectangle((x0, y0),
|
||||
(x1 - x0),
|
||||
(y1 - y0),
|
||||
fill=False, color="white", linewidth=2, zorder=10)
|
||||
)
|
||||
plt.show()
|
||||
Loading…
Reference in New Issue
Block a user