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import numpy as np
import pandas as pd
from collections import defaultdict
from params import (
CLASSES_SCS,
SEVERITIES,
CLASSES_CROP,
LEVELS,
)
def get_coords(row):
"""
Extracts coordinates from a row of data.
Args:
row (pandas Series): Row containing 'instance_number', 'x', 'y', and 'frames' columns.
Returns:
np.array: Coordinates.
"""
coords = []
for i, x, y in zip(row["instance_number"], row["x"], row["y"]):
coords.append([row["frames"].index(i), x, y])
return np.array(coords)
def prepare_data(data_path="../input/"):
"""
Prepares data for training by loading and processing CSV files.
Args:
data_path (str, optional): The path to the input data directory. Defaults to "../input/".
Returns:
pd.DataFrame: A DataFrame containing the prepared data with additional columns for
orientation, weighting, image paths, and coordinates.
"""
df = pd.read_csv(data_path + "train_series_descriptions.csv")
df["orient"] = df["series_description"].apply(lambda x: x.split()[0])
df["weighting"] = df["series_description"].apply(lambda x: x.split()[1])
df["img_path"] = df["study_id"].astype(str) + "_" + df["series_id"].astype(str)
df["img_path"] = data_path + "npy2/" + df["img_path"] + ".npy"
labels = pd.read_csv(data_path + "train_label_coordinates.csv")
labels = labels.groupby(["study_id", "series_id"]).agg(list).reset_index()
frames = pd.read_csv("../input/df_frames.csv")
frames["frames"] = frames["frames"].apply(eval)
labels = labels.merge(frames, how="left")
labels["coords"] = labels.apply(get_coords, axis=1)
df = df.merge(
labels[["study_id", "series_id", "condition", "level", "coords"]], how="left"
)
return df
def get_target(row):
"""
Extracts the target value from a row based on the condition and level.
Args:
row (pandas Series): A row containing 'level', 'condition', and relevant target columns.
Returns:
float or np.nan: The target value if found, otherwise np.nan.
"""
la, lb = row.level.lower().split("/")
c = re.sub(" ", "_", row.condition.lower())
try:
return row[f"{c}_{la}_{lb}"]
except KeyError:
return np.nan
def prepare_data_scs(data_path="../input/", crop_folder=None, explode=True):
"""
Prepares data for the SCS task by loading and processing CSV files.
Args:
data_path (str, optional): The path to the input data directory. Defaults to "../input/".
crop_folder (str, optional): Path to the folder containing cropped images. Defaults to None.
explode (bool, optional): Whether to explode the DataFrame. Defaults to True.
Returns:
pd.DataFrame: A DataFrame containing the prepared data.
"""
df = prepare_data(data_path)
df = df[df["weighting"] == "T2/STIR"].reset_index(drop=True)
df_train = pd.read_csv(data_path + "train.csv")
df_train = df_train[["study_id"] + CLASSES_SCS]
for c in df_train.columns[1:]:
df_train[c] = df_train[c].map(dict(zip(SEVERITIES, [0, 1, 2]))).fillna(-1)
df_train = df_train.astype(int)
df = df.merge(df_train, on="study_id", how="left")
if explode:
df = (
df.explode(["condition", "level", "coords"]).dropna().reset_index(drop=True)
)
df["target"] = df.apply(get_target, axis=1)
df.drop(CLASSES_SCS, axis=1, inplace=True)
if crop_folder is not None:
df["img_path"] = (
df["study_id"].astype(str) + "_" + df["series_id"].astype(str)
)
lvl = df["level"].apply(lambda x: re.sub("/", "_", x.lower()))
df["img_path"] = crop_folder + df["img_path"] + "_" + lvl + ".npy"
try:
df_coords_crops = pd.read_csv(crop_folder + "df_injury_coords.csv")
df = df.merge(
df_coords_crops, on=["study_id", "series_id", "level"], how="left"
)
df["side"] = df["side"].fillna("Center")
except FileNotFoundError:
df["side"] = "Center"
pass
return df
def prepare_data_lvl2(data_path="../input/"):
"""
Prepares data for the Level 2 task by loading and processing CSV files.
Args:
data_path (str, optional): The path to the input data directory. Defaults to "../input/".
Returns:
pd.DataFrame: A DataFrame containing the prepared data with additional columns for targets.
"""
df = pd.read_csv(data_path + "train_series_descriptions.csv")
df = df.groupby("study_id").agg(list)
df_train = pd.read_csv(data_path + "train.csv")
for c in df_train.columns[1:]:
df_train[c] = df_train[c].map(dict(zip(SEVERITIES, [0, 1, 2]))).fillna(-1)
df_train = df_train.astype(int)
df = df.merge(df_train, on="study_id", how="left")
return df
def get_target_crop(row):
"""
Extracts the target values for cropping from a row based on the condition and level.
Args:
row (pandas Series): A row from a DataFrame containing 'level' and relevant target columns.
Returns:
list or np.nan: A list of target values if found, otherwise np.nan.
"""
la, lb = row.level.lower().split("/")
try:
labels = []
for c in CLASSES_CROP:
labels.append(row[f"{c}_{la}_{lb}"])
return labels
except KeyError:
return np.nan
def simplify_coords(x):
"""
Simplifies the coordinates by calculating the median of the x-coordinates for each side.
Args:
x (pandas Series): A row from a DataFrame containing 'condition' and 'coords' columns.
Returns:
np.array: An array of simplified coordinates for the left, center, and right sides.
"""
coords = -1 * np.ones(3)
d = defaultdict(list)
if isinstance(x.condition, float):
return coords
for i, c in enumerate(x.condition):
side = c.split(" ")[0]
side = "Center" if side == "Spinal" else side
d[side].append(x.coords[i][0])
for k in d:
d[k] = int(np.median(d[k]))
coords[0] = d.get("Right", -1)
coords[1] = d.get("Center", -1)
coords[2] = d.get("Left", -1)
return coords
def prepare_data_crop(data_path, crop_folder=None, axial=False):
"""
Prepares data for the crop task by loading and processing CSV files.
Args:
data_path (str): The path to the input data directory.
crop_folder (str, optional): Path to the folder containing cropped images. Defaults to None.
axial (bool, optional): Whether to use axial orientation. Defaults to False.
Returns:
pd.DataFrame: A DataFrame containing the prepared data.
"""
df = prepare_data(data_path)
df["level"] = [
["L1/L2", "L2/L3", "L3/L4", "L4/L5", "L5/S1"] for _ in range(len(df))
]
df["side"] = "Center"
if axial:
df = df[df["orient"] == "Axial"].reset_index(drop=True)
else:
df = df[df["orient"] == "Sagittal"].reset_index(drop=True)
df["coords"] = df.apply(simplify_coords, axis=1)
df["coords"] = df["coords"].apply(
lambda x: int(np.mean(x[x > -1])) if x.max() > -1 else -1
)
df.drop("condition", axis=1, inplace=True)
df_train = pd.read_csv(data_path + "train.csv")
for c in df_train.columns[1:]:
df_train[c] = df_train[c].map(dict(zip(SEVERITIES, [0, 1, 2]))).fillna(-1)
df_train = df_train.astype(int)
df = df.merge(df_train, on="study_id", how="left")
df = df.explode("level").reset_index(drop=True)
df["target"] = df.apply(get_target_crop, axis=1)
df.drop(df_train.columns[1:], axis=1, inplace=True)
if crop_folder is not None:
df["img_path"] = df["study_id"].astype(str) + "_" + df["series_id"].astype(str)
lvl = df["level"].apply(lambda x: re.sub("/", "_", x.lower()))
df["img_path"] = crop_folder + df["img_path"] + "_" + lvl + ".npy"
return df
def get_coords_target(row, axial=False, relative=False):
"""
Extracts the target coordinates from a row based on the side or level.
Args:
row (pandas Series): A row from a DataFrame containing 'side', 'x', 'y',
'level', 'relative_x', and 'relative_y' columns.
axial (bool, optional): Whether to use axial orientation. Defaults to False.
relative (bool, optional): Whether to use relative coordinates. Defaults to False.
Returns:
np.array: An array of target coordinates.
"""
if axial:
target = np.zeros((2, 2), dtype=float) - 1
for s, side in enumerate(["Left", "Right"]):
i = row.side.index(side)
if relative:
target[s, 0] = row.relative_x[i]
target[s, 1] = row.relative_y[i]
else:
target[s, 0] = row.x[i]
target[s, 1] = row.y[i]
else:
target = np.zeros((5, 2), dtype=float) - 1
for lvl, level in enumerate(LEVELS):
if level in row.level:
i = row.level.index(level)
if relative:
target[lvl, 0] = row.relative_x[i]
target[lvl, 1] = row.relative_y[i]
else:
target[lvl, 0] = row.x[i]
target[lvl, 1] = row.y[i]
return target
def prepare_coords_data(data_path="../input/coords/", axial=False, use_ext=True):
"""
Prepares coordinate data by loading and processing CSV files.
Args:
data_path (str, optional): Path to the input data directory. Defaults to "../input/coords/".
axial (bool, optional): Whether to use axial orientation. Defaults to False.
use_ext (bool, optional): Whether to use external data. Defaults to True.
Returns:
pd.DataFrame: A DataFrame containing the prepared coordinate data.
"""
if axial:
df = pd.read_csv(data_path + "coords_ax.csv")
df = df.groupby(["study_id", "series_id", "img_path"]).agg(list).reset_index()
df["target"] = df.apply(
lambda x: get_coords_target(x, axial=True, relative=False), axis=1
).tolist()
df["target_rel"] = df.apply(
lambda x: get_coords_target(x, axial=True, relative=True), axis=1
).tolist()
else:
if use_ext:
df = pd.read_csv(data_path + "coords_pretrain.csv")
df["img_path"] = (
data_path + "data/processed_" + df["source"] + "_jpgs/" + df["filename"]
)
df = df.sort_values(["source", "filename", "level"], ignore_index=True)
df = df.rename(columns={"source": "study_id", "filename": "series_id"})
df = pd.concat(
[df, pd.read_csv(data_path + "coords_comp.csv")], ignore_index=True
)
else:
df = pd.read_csv(data_path + "coords_comp_3ch.csv")
df = df.groupby(["study_id", "series_id", "img_path"]).agg(list).reset_index()
df["target"] = df.apply(
lambda x: get_coords_target(x, relative=False), axis=1
).tolist()
df["target_rel"] = df.apply(
lambda x: get_coords_target(x, relative=True), axis=1
).tolist()
return df
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