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data_frame.py
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data_frame.py
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from tools import distortions
import copy
import utils
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
class DataFrame(object):
"""Minimal pd.DataFrame analog for handling n-dimensional numpy matrices with additional
support for shuffling, batching, and train/test splitting.
Args:
columns: List of names corresponding to the matrices in data.
data: List of n-dimensional data matrices ordered in correspondence with columns.
All matrices must have the same leading dimension. Data can also be fed a list of
instances of np.memmap, in which case RAM usage can be limited to the size of a
single batch.
"""
def __init__(self, columns, data, warp=True):
assert len(columns) == len(data), 'columns length does not match data length'
lengths = [mat.shape[0] for mat in data]
assert len(set(lengths)) == 1, 'all matrices in data must have same first dimension'
self.length = lengths[0]
self.columns = columns
self.data = data
self.dict = dict(zip(self.columns, self.data))
self.idx = np.arange(self.length)
self.warp = warp
def to_numpy(self):
pd.DataFrame.to_numpy(self)
def shapes(self):
return pd.Series(dict(zip(self.columns, [mat.shape for mat in self.data])))
def dtypes(self):
return pd.Series(dict(zip(self.columns, [mat.dtype for mat in self.data])))
def shuffle(self):
np.random.shuffle(self.idx)
def train_test_split(self, train_size, random_state=np.random.randint(1000), stratify=None):
train_idx, test_idx = train_test_split(
self.idx,
train_size=train_size,
random_state=random_state,
stratify=stratify
)
train_df = DataFrame(copy.copy(self.columns), [mat[train_idx] for mat in self.data])
test_df = DataFrame(copy.copy(self.columns), [mat[test_idx] for mat in self.data])
return train_df, test_df
def batch_generator(self, batch_size, shuffle=True, num_epochs=10000, allow_smaller_final_batch=False):
epoch_num = 0
while epoch_num < num_epochs:
if shuffle:
self.shuffle()
for i in range(0, self.length + 1, batch_size): # loop through all items using batch_size step
batch_idx = self.idx[i: i + batch_size]
if not allow_smaller_final_batch and len(batch_idx) != batch_size:
break
if self.warp:
data = [mat[batch_idx].copy() for mat in self.data]
coords_batch = data[0] # list, ['x', 'x_len', 'c', 'c_len', 'text']
lens_batch = data[1]
for ii, item in enumerate(coords_batch):
length = lens_batch[ii]
#utils.plot_from_synth_format(item)
gt, xmin, ymin, factor = utils.convert_synth_offsets_to_gt(item[:length], return_all=True)
gt[:,0:2] = distortions.warp_points(gt*61)/61
gt[:, 0:2] *= factor
gt[:, 1] += ymin # min_y = 0
gt[:, 0] += xmin # min_x = 0
coords_batch[ii][:length] = utils.convert_gts_to_synth_format(gt, adjustments=True)
utils.plot_from_synth_format(coords_batch[ii])
continue
yield DataFrame(
columns=copy.copy(self.columns),
data=data
)
epoch_num += 1
def iterrows(self):
for i in self.idx:
yield self[i]
def mask(self, mask):
return DataFrame(copy.copy(self.columns), [mat[mask] for mat in self.data])
def concat(self, other_df):
mats = []
for column in self.columns:
mats.append(np.concatenate([self[column], other_df[column]], axis=0))
return DataFrame(copy.copy(self.columns), mats)
def items(self):
return self.dict.items()
def __iter__(self):
return self.dict.items().__iter__()
def __len__(self):
return self.length
def __getitem__(self, key):
if isinstance(key, str):
return self.dict[key]
elif isinstance(key, int):
return pd.Series(dict(zip(self.columns, [mat[self.idx[key]] for mat in self.data])))
def __setitem__(self, key, value):
assert value.shape[0] == len(self), 'matrix first dimension does not match'
if key not in self.columns:
self.columns.append(key)
self.data.append(value)
self.dict[key] = value
def test():
gt = [[0, 1, 1],
[2, 5, 0],
[8, 9, 0],
[20, 21, 1],
[12, 13, 1],
[16, 17, 0],
[32, 33, 1],
[28, 29, 0],
[24, 15, 0]]
gt = np.asarray(gt).astype(np.float64)
# gt[:, 1] -= np.min(gt[:, 1]) # min_y = 0
# gt[:, 0] -= np.min(gt[:, 0]) # min_x = 0
#gt[:, :2] = gt[:, :2]/np.max(gt[:, 1])
offsets = utils.convert_gts_to_synth_format(gt, adjustments=True)
gt2 = utils.convert_synth_offsets_to_gt(offsets)
offsets2 = utils.convert_gts_to_synth_format(gt2, adjustments=False)
gt3 = utils.convert_synth_offsets_to_gt(offsets2)
np.set_printoptions(suppress=True)
print(gt)
print(gt2)
print(gt3)
print(offsets)
print(offsets2)
if __name__=='__main__':
# Draw a thing
ROOT = utils.get_project_root()
original = ROOT / f"data/processed/original_mine"
file = "x.npy"
np.load(original / file, allow_pickle=True)