From e783ffa2cc358ad60dcd3126ddb6c4e9e063495e Mon Sep 17 00:00:00 2001 From: kanopo Date: Fri, 29 Nov 2024 16:19:05 +0100 Subject: [PATCH] init --- LICENSE | 21 + README.md | 10 + .../.gitignore | 2 + .../poetry.lock | 1314 ++++++++++ .../pyproject.toml | 24 + .../src/load_dataset.py | 340 +++ .../src/main.py | 789 ++++++ .../src/model.py | 124 + .../src/predict.py | 0 .../src/training.py | 171 ++ .../src/validation.py | 58 + .../combined_fetus_mother_series.png | Bin 0 -> 403941 bytes mean_mouth_distance/fetus_series.png | Bin 0 -> 176943 bytes mean_mouth_distance/main.ipynb | 169 ++ mean_mouth_distance/mother_series.png | Bin 0 -> 304943 bytes mean_mouth_distance/poetry.lock | 2235 +++++++++++++++++ mean_mouth_distance/pyproject.toml | 18 + 17 files changed, 5275 insertions(+) create mode 100755 LICENSE create mode 100755 README.md create mode 100755 fetus-event-detection-classification/.gitignore create mode 100755 fetus-event-detection-classification/poetry.lock create mode 100755 fetus-event-detection-classification/pyproject.toml create mode 100755 fetus-event-detection-classification/src/load_dataset.py create mode 100755 fetus-event-detection-classification/src/main.py create mode 100755 fetus-event-detection-classification/src/model.py create mode 100755 fetus-event-detection-classification/src/predict.py create mode 100755 fetus-event-detection-classification/src/training.py create mode 100755 fetus-event-detection-classification/src/validation.py create mode 100644 mean_mouth_distance/combined_fetus_mother_series.png create mode 100644 mean_mouth_distance/fetus_series.png create mode 100644 mean_mouth_distance/main.ipynb create mode 100644 mean_mouth_distance/mother_series.png create mode 100644 mean_mouth_distance/poetry.lock create mode 100644 mean_mouth_distance/pyproject.toml diff --git a/LICENSE b/LICENSE new file mode 100755 index 0000000..b7c7fa5 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Dmitri Ollari Ischimji + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100755 index 0000000..f120c67 --- /dev/null +++ b/README.md @@ -0,0 +1,10 @@ +# Womb Wise + +Progetto di ricerca svolto con L'ospedale di Parma per una tesi. +Ho svolto la parte di machine learning. +Il compito principale era capire se dei video di feti contenevano o meno determinate azioni come sbadigli. +Essendo un movimento complesso e prolungato nel tempo ho dovuto approfondire il discorso delle time series. + +Ho provveduto ad eliminare i dati di input e output per evitare problematiche legate alla privacy. + +Kanopo diff --git a/fetus-event-detection-classification/.gitignore b/fetus-event-detection-classification/.gitignore new file mode 100755 index 0000000..b976baa --- 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"0.1.0" +description = "" +authors = ["kanopo "] +license = "MIT" +readme = "README.md" +package-mode = false + +[tool.poetry.dependencies] +python = "^3.12" +pandas = "^2.2.2" +matplotlib = "^3.9.1" +torch = "^2.3.1" +seaborn = "^0.13.2" +imblearn = "^0.0" +imbalanced-learn = "^0.12.3" +torchmetrics = "^1.4.0.post0" +tqdm = "^4.66.4" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/fetus-event-detection-classification/src/load_dataset.py b/fetus-event-detection-classification/src/load_dataset.py new file mode 100755 index 0000000..be1914b --- /dev/null +++ b/fetus-event-detection-classification/src/load_dataset.py @@ -0,0 +1,340 @@ +import os +import warnings +import pandas as pd +import numpy as np +import re +from typing import List, Dict + +warnings.simplefilter(action="ignore", category=FutureWarning) + + +def remove_dot_files(files: list) -> list: + return [file for file in files if not file.startswith(".")] + + +def get_dataset(path: str, data_type: str) -> pd.DataFrame: + path_base_dir = os.path.expanduser(path) + dirs = [] + tmp = os.listdir(path_base_dir) + tmp = remove_dot_files(tmp) + dirs.extend(tmp) + + dataset = pd.DataFrame( + columns=[ + "image_name", # string value + "leftLip_x", # float value + "leftLip_y", # float value + "rightLip_x", # float value + "rightLip_y", # float value + "topMidInner_x", # float value + "topMidInner_y", # float value + "bottomMidInner_x", # float value + "bottomMidInner_y", # float value + "nose_x", # float value + "nose_y", # float value + "test", + "frame", + "label", + # "isFetus", + ] + ) + + for root, dirs, _ in os.walk(path_base_dir): + + for d in dirs: + splitted = root.split("/") + path = os.path.join(root, d) + fetus_name = splitted[-2] + fetus_action = splitted[-3] + + if data_type == "baseline": + if re.search(r"baseline", d) or re.search(r"Baseline", d): + for f in remove_dot_files(os.listdir(path)): + if ".png" in f and fetus_action.lower() == data_type: + pass + + elif ".csv" in f and fetus_action.lower() == data_type: + p = os.path.join(path, f) + data = pd.read_csv(p) + data.columns = data.iloc[0] + data = data.drop([0, 1]) + data = data.iloc[:, 2:] + + data.columns = [ + "image_name", + "leftLip_x", + "leftLip_y", + "rightLip_x", + "rightLip_y", + "topMidInner_x", + "topMidInner_y", + "bottomMidInner_x", + "bottomMidInner_y", + "nose_x", + "nose_y", + ] + + data = data.dropna() + data["test"] = fetus_name.split("_")[1] + data["frame"] = p.split("/")[-2].split("_")[-1] + data["label"] = data_type + + image_name = data["image_name"].apply( + lambda x: x.split(".")[0].split("img")[1] + ) + + image_name = image_name.apply(lambda x: x.zfill(4)) + + data["image_name"] = image_name + + data = calculate_distance(data) + + dataset = pd.concat([dataset, data]) + + elif data_type == "yawn": + if re.search(r"yawn", d) or re.search(r"Yawn", d): + for f in remove_dot_files(os.listdir(path)): + if ".png" in f and fetus_action.lower() == data_type: + pass + + elif ".csv" in f and fetus_action.lower() == data_type: + p = os.path.join(path, f) + data = pd.read_csv(p) + data.columns = data.iloc[0] + data = data.drop([0, 1]) + data = data.iloc[:, 2:] + + data.columns = [ + "image_name", + "leftLip_x", + "leftLip_y", + "rightLip_x", + "rightLip_y", + "topMidInner_x", + "topMidInner_y", + "bottomMidInner_x", + "bottomMidInner_y", + "nose_x", + "nose_y", + ] + + data = data.dropna() + data["test"] = fetus_name.split("_")[1] + data["frame"] = p.split("/")[-2].split("_")[-1] + data["label"] = data_type + + image_name = data["image_name"].apply( + lambda x: x.split(".")[0].split("img")[1] + ) + + image_name = image_name.apply(lambda x: x.zfill(4)) + + data["image_name"] = image_name + data = calculate_distance(data) + + dataset = pd.concat([dataset, data]) + elif data_type == "opcl": + if re.search(r"opcl", d) or re.search(r"Opcl", d): + for f in remove_dot_files(os.listdir(path)): + if ".png" in f and fetus_action.lower() == data_type: + pass + + elif ".csv" in f and fetus_action.lower() == data_type: + p = os.path.join(path, f) + data = pd.read_csv(p) + data.columns = data.iloc[0] + data = data.drop([0, 1]) + data = data.iloc[:, 2:] + + data.columns = [ + "image_name", + "leftLip_x", + "leftLip_y", + "rightLip_x", + "rightLip_y", + "topMidInner_x", + "topMidInner_y", + "bottomMidInner_x", + "bottomMidInner_y", + "nose_x", + "nose_y", + ] + + data = data.dropna() + data["test"] = fetus_name.split("_")[1] + data["frame"] = p.split("/")[-2].split("_")[-1] + data["label"] = data_type + + image_name = data["image_name"].apply( + lambda x: x.split(".")[0].split("img")[1] + ) + + image_name = image_name.apply(lambda x: x.zfill(4)) + + data["image_name"] = image_name + data = calculate_distance(data) + + dataset = pd.concat([dataset, data]) + + dataset = dataset.dropna() + dataset = dataset.reset_index(drop=True) + + return dataset + + +def euclidean_distance(x1, y1, x2, y2) -> float: + x1 = float(x1) + y1 = float(y1) + x2 = float(x2) + y2 = float(y2) + + return np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + + +def calculate_distance(dataset: pd.DataFrame) -> pd.DataFrame: + dataset["top_bottom_distance"] = dataset.apply( + lambda x: euclidean_distance( + x["topMidInner_x"], + x["topMidInner_y"], + x["bottomMidInner_x"], + x["bottomMidInner_y"], + ), + axis=1, + ) + return dataset + + +def split_dataset( + dataset: pd.DataFrame, classes, train_optimal_size, test_optimal_size, series_length +) -> List[Dict[np.ndarray, int]]: + dataset["label"] = dataset["label"].apply(lambda x: classes.index(x)) + + total_dataset: np.ndarray = [] + + grouped = dataset.groupby(by=["fetus", "frame", "label"]) + for name, group in grouped: + df = group.reset_index(drop=True, inplace=False) + # remove a col + label = df.pop("label") + frame = df.pop("frame") + fetus = df.pop("fetus") + distance = df.pop("top_bottom_distance") + + df = df.sort_values(by="image_name") + df.pop("image_name") + df = df.reset_index(drop=True, inplace=False) + x = df.values + # x = distance.values + y = label.values[0] + + if x.shape[0] < series_length: + missing_cols = series_length - x.shape[0] + if len(x.shape) == 1: + new_matrix = np.full((missing_cols), x[0]) + else: + new_matrix = np.zeros((missing_cols, x.shape[1])) + x = np.concatenate((x, new_matrix), axis=0) + + if x.shape[0] > series_length: + if len(x.shape) == 1: + x = x[:series_length] + else: + x = x[:series_length, :] + + total_dataset.append({"x": x, "y": y}) + + # if y.shape[0] < series_length: + # missing_cols = series_length - y.shape[0] + # new_matrix = np.full((missing_cols), y[0]) + # y = np.concatenate((y, new_matrix), axis=0) + # + # if y.shape[0] > series_length: + # y = y[:series_length] + + total_yawn_count = 0 + total_baseline_count = 0 + total_opcl_count = 0 + for i in range(len(total_dataset)): + if total_dataset[i]["y"] == 0: + total_baseline_count += 1 + + if total_dataset[i]["y"] == 1: + total_opcl_count += 1 + + if total_dataset[i]["y"] == 2: + total_yawn_count += 1 + + train: List[Dict[np.ndarray, int]] = [] + test: List[Dict[np.ndarray, int]] = [] + # val: List[Dict[np.ndarray, int]] = [] + + train_yawn_optimal_size = int(total_yawn_count * train_optimal_size) + test_yawn_optimal_size = int(total_yawn_count * test_optimal_size) + + train_baseline_optimal_size = int(total_baseline_count * train_optimal_size) + test_baseline_optimal_size = int(total_baseline_count * test_optimal_size) + + train_opcl_optimal_size = int(total_opcl_count * train_optimal_size) + test_opcl_optimal_size = int(total_opcl_count * test_optimal_size) + + train_yawn: List[Dict[np.ndarray, int]] = [] + test_yawn: List[Dict[np.ndarray, int]] = [] + # val_yawn: List[Dict[np.ndarray, int]] = [] + + train_baseline: List[Dict[np.ndarray, int]] = [] + test_baseline: List[Dict[np.ndarray, int]] = [] + # val_baseline: List[Dict[np.ndarray, int]] = [] + + train_opcl: List[Dict[np.ndarray, int]] = [] + test_opcl: List[Dict[np.ndarray, int]] = [] + # val_opcl: List[Dict[np.ndarray, int]] = [] + + for data in total_dataset: + if data["y"] == 2: + # yawn + if len(train_yawn) < train_yawn_optimal_size: + train_yawn.append({"data": data["x"], "label": data["y"]}) + else: + test_yawn.append({"data": data["x"], "label": data["y"]}) + # elif ( + # len(val_yawn) + # < total_yawn_count - train_yawn_optimal_size - test_yawn_optimal_size + # ): + # val_yawn.append({"data": data["x"], "label": data["y"]}) + elif data["y"] == 0: + # baseline + if len(train_baseline) < train_baseline_optimal_size: + train_baseline.append({"data": data["x"], "label": data["y"]}) + else: + test_baseline.append({"data": data["x"], "label": data["y"]}) + # elif ( + # len(val_baseline) + # < total_baseline_count + # - train_baseline_optimal_size + # - test_baseline_optimal_size + # ): + # val_baseline.append({"data": data["x"], "label": data["y"]}) + elif data["y"] == 1: + # opcl + if len(train_opcl) < train_opcl_optimal_size: + train_opcl.append({"data": data["x"], "label": data["y"]}) + else: + test_opcl.append({"data": data["x"], "label": data["y"]}) + # elif ( + # len(val_opcl) + # < total_opcl_count - train_opcl_optimal_size - test_opcl_optimal_size + # ): + # val_opcl.append({"data": data["x"], "label": data["y"]}) + else: + print("[ERROR] Invalid class label during split") + break + + train += train_yawn + train_baseline + train_opcl + test += test_yawn + test_baseline + test_opcl + # val += val_yawn + val_baseline + val_opcl + + np.random.shuffle(train) + np.random.shuffle(test) + # np.random.shuffle(val) + + return train, test diff --git a/fetus-event-detection-classification/src/main.py b/fetus-event-detection-classification/src/main.py new file mode 100755 index 0000000..66bb327 --- /dev/null +++ b/fetus-event-detection-classification/src/main.py @@ -0,0 +1,789 @@ +#!/usr/bin/env python3.12 + +import os +import warnings +import pandas as pd +import numpy as np +from typing import List, Dict +from matplotlib import pyplot as plt +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import Dataset, DataLoader +import seaborn as sns +import argparse +from imblearn.over_sampling import RandomOverSampler +from imblearn.under_sampling import RandomUnderSampler +from collections import Counter +from sklearn.model_selection import StratifiedKFold +from sklearn.model_selection import train_test_split +from datetime import datetime + +from load_dataset import get_dataset +from model import SimpleLSTM +from training import training_loop +from validation import validation + +warnings.simplefilter(action="ignore", category=FutureWarning) + + +def setup_model_training( + input_size, + hidden_size, + num_layers, + num_classes, + sequence_length, + device, + lr, + weight_decay, + eps, +): + model = SimpleLSTM( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + num_classes=num_classes, + sequence_length=sequence_length, + device=device, + ) + + optimizer = optim.Adam( + model.parameters(), lr=lr, weight_decay=weight_decay, eps=eps + ) + + scheduler = optim.lr_scheduler.ReduceLROnPlateau( + optimizer, mode="min", factor=0.1, patience=25 + ) + + # criterion = nn.CrossEntropyLoss() + # criterion = nn.BCELoss() + criterion = nn.BCEWithLogitsLoss() + + return (model, optimizer, scheduler, criterion) + + +def create_loaders( + x, y, data, under=False, over=False, classes=["base", "yawn"], batch_size=2 +): + if under is True: + x, y = undersample(x, y, data) + + if over is True: + x, y = oversample(x, y, data) + + x_train, x_test, y_train, y_test = train_test_split( + x, y, test_size=TEST_OPTIMAL_SIZE, random_state=seed + ) + + train_dataset = FetusDataset( + [{"data": x, "label": y} for x, y in zip(x_train, y_train)], + train=True, + classes=len(classes), + ) + + test_dataset = FetusDataset( + [{"data": x, "label": y} for x, y in zip(x_test, y_test)], + train=True, + classes=len(classes), + ) + + train_loader = DataLoader( + train_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + ) + + test_loader = DataLoader( + test_dataset, + batch_size=batch_size, + shuffle=True, + drop_last=True, + ) + + return (train_loader, test_loader) + + +def get_device() -> torch.device: + if torch.backends.mps.is_built(): + return torch.device("mps") + + if torch.cuda.is_available(): + return torch.device("cuda") + + return torch.device("cpu") + + +def undersample(x: np.ndarray, y: np.ndarray, data): + print("Before undersampling") + print(Counter([d["label"] for d in data])) + + rus = RandomUnderSampler(random_state=seed, sampling_strategy="majority") + x = np.array([d["data"] for d in data]) + y = np.array([d["label"] for d in data]) + flat_x = np.array([x.flatten() for x in x]) + flat_y = np.array(y) + x, y = rus.fit_resample(flat_x, flat_y) + x = x.reshape(-1, SERIES_LENGTH, FEATURE_SIZE) + + print("After undersampling") + print(Counter(y)) + return x, y + + +def oversample(x: np.ndarray, y: np.ndarray, data): + print("Before oversampling") + print(Counter([d["label"] for d in data])) + + ros = RandomOverSampler(random_state=seed, sampling_strategy="all") + x = np.array([d["data"] for d in data]) + y = np.array([d["label"] for d in data]) + flat_x = np.array([x.flatten() for x in x]) + flat_y = np.array(y) + x, y = ros.fit_resample(flat_x, flat_y) + x = x.reshape(-1, SERIES_LENGTH, FEATURE_SIZE) + + print("After oversampling") + print(Counter(y)) + return x, y + + +class FetusDataset(Dataset): + def __init__( + self, data: List[Dict[np.ndarray, int]], train: bool = False, classes: int = 2 + ): + self.data = data + self.train = train + self.classes = classes + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + x = self.data[idx]["data"] + y = self.data[idx]["label"] + + # Conversione del tipo di dato + x = x.astype(np.float32) + y = np.eye(self.classes)[y] + + # Conversione in tensori + x = torch.tensor(x, dtype=torch.float32) + y = torch.tensor(y, dtype=torch.int32) + + # Gestione di valori NaN o infiniti + x = torch.nan_to_num( + x + ) # Sostituisce NaN con 0 e valori infiniti con numeri molto grandi o piccoli + + # Normalizzazione solo durante il training + if self.train: + mean = x.mean() + std = x.std() + + # Normalizzazione condizionale (solo se std > 0) + if std > 0: + x = (x - mean) / std + + return x, y + + +def createArgParser(): + parser = argparse.ArgumentParser(description="Womb Wise") + parser.add_argument( + "-rd", + "--reload-dataset", + action="store_true", + help="Reload the dataset", + ) + + # path to the dataset + parser.add_argument( + "-p", + "--path", + action="store", + help="Path to the dataset", + default="~/Documents/womb-wise/Data/", + ) + + # epoch + parser.add_argument( + "-e", + "--epochs", + action="store", + help="Number of epochs", + default=10, + ) + + parser.add_argument( + "-k", + "--kfold", + action="store", + help="Number of folds for kfold cross validation", + default=1, + ) + + parser.add_argument( + "-o", + "--oversampling", + action="store_true", + help="Apply oversampling", + ) + + parser.add_argument( + "-u", + "--undersampling", + action="store_true", + help="Apply undersampling", + ) + + parser.add_argument( + "-d", + "--dataset", + action="store", + default="all", + choices=["all", "fetus", "mother", "fetus-mother", "mother-fetus"], + help="Choose the dataset: all, fetus, mother or train with mother and test with fetus or viceversa", + ) + + args = parser.parse_args() + + print( + f""" + ARGS: + \n + reload-dataset: {args.reload_dataset} + path: {args.path} + epochs: {args.epochs} + kfold: {args.kfold} + oversampling: {args.oversampling} + undersampling: {args.undersampling} + dataset: {args.dataset} + """ + ) + return args + + +if __name__ == "__main__": + CLASSES = ["baseline", "opcl", "yawn"] + FEATURE_SIZE = 10 + SERIES_LENGTH = 60 + # SINGLE_FRAME_LENGTH = FEATURE_SIZE * SERIES_LENGTH + BATCH_SIZE = 4 + WEIGHT_DECAY = 1e-5 + LEARNING_RATE = 1e-3 + TEST_OPTIMAL_SIZE = 0.2 + HIDDEN_SIZE = 256 + DROP_OUT = 0.0 + NUM_LAYERS = 2 + EPS = 1e-7 + + # TEST_NAME = "0_k1_all" + # TEST_NAME = "1_k1_fetus" + # TEST_NAME = "2_k1_mother" + # TEST_NAME = "3_k1_mother_fetus" + # TEST_NAME = "4_k1_fetus_mother" + # TEST_NAME = "5_k5_all" + TEST_NAME = "6_k5_fetus" + # TEST_NAME = "7_k5_mother" + + if not os.path.exists("output/" + TEST_NAME): + os.makedirs("output/" + TEST_NAME) + + if not os.path.exists("output/" + TEST_NAME + "/weights"): + os.makedirs("output/" + TEST_NAME + "/weights") + + if not os.path.exists("output/" + TEST_NAME + "/confusion_matrix"): + os.makedirs("output/" + TEST_NAME + "/confusion_matrix") + + if not os.path.exists("output/" + TEST_NAME + "/metrics"): + os.makedirs("output/" + TEST_NAME + "/metrics") + + # fix the seed + seed = 42 + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + device = get_device() + args = createArgParser() + + PATH = args.path + EPOCHS = int(args.epochs) + K_FOLD = int(args.kfold) + OVER_SAMPLING = args.oversampling + UNDER_SAMPLING = args.undersampling + EARLY_STOPPING = True + DATASET_TYPE = args.dataset + + if os.path.exists("dataset.csv") and args.reload_dataset is False: + dataset = pd.read_csv("dataset.csv") + mother = pd.read_csv("mother.csv") + fetus = pd.read_csv("fetus.csv") + else: + baseline_fetus = get_dataset( + "~/Documents/kanopo/womb-wise/Data/Ultrasound_Scans/tracked_frames/", + "baseline", + ) + yawn_fetus = get_dataset( + "~/Documents/kanopo/womb-wise/Data/Ultrasound_Scans/tracked_frames/", + "yawn", + ) + opcl_fetus = get_dataset( + "~/Documents/kanopo/womb-wise/Data/Ultrasound_Scans/tracked_frames/", + "opcl", + ) + + fetus = pd.concat([baseline_fetus, yawn_fetus, opcl_fetus]) + + baseline_mother = get_dataset( + "~/Documents/kanopo/womb-wise/Data/Mothers_videos/Tracked/", + "baseline", + ) + yawn_mother = get_dataset( + "~/Documents/kanopo/womb-wise/Data/Mothers_videos/Tracked/", + "yawn", + ) + opcl_mother = get_dataset( + "~/Documents/kanopo/womb-wise/Data/Mothers_videos/Tracked/", + "opcl", + ) + + mother = pd.concat([baseline_mother, yawn_mother, opcl_mother]) + fetus["type"] = "fetus" + mother["type"] = "mother" + + fetus.to_csv("fetus.csv") + mother.to_csv("mother.csv") + + dataset = pd.concat([mother, fetus]) + dataset.to_csv("dataset.csv") + + + + mother = mother.drop(columns=["top_bottom_distance"]) + fetus = fetus.drop(columns=["top_bottom_distance"]) + dataset = dataset.drop(columns=["top_bottom_distance"]) + grouped_dataset = dataset.groupby(["label", "frame", "test", "type"]) + grouped_mother = mother.groupby(["label", "frame", "test"]) + grouped_fetus = fetus.groupby(["label", "frame", "test"]) + + data: List[Dict[np.ndarray, int]] = [] + mother_data: List[Dict[np.ndarray, int]] = [] + fetus_data: List[Dict[np.ndarray, int]] = [] + + for name, group in grouped_mother: + label = group["label"] + frame = group["frame"] + test = group["test"] + + group = group.drop(columns=["test", "frame", "label", "type"]) + + group.set_index("image_name", inplace=True) + + if group.columns[0] == "Unnamed: 0": + group = group.drop(columns=["Unnamed: 0"]) + + group = group.to_numpy() + + if group.shape[0] < SERIES_LENGTH: + group = np.vstack( + [group, np.zeros((SERIES_LENGTH - group.shape[0], FEATURE_SIZE))] + ) + + elif group.shape[0] > SERIES_LENGTH: + group = group[:SERIES_LENGTH] + + group = group.astype(np.float32) + + label = CLASSES.index(label.iat[0]) + mother_data.append( + { + "data": group, + "label": label, + } + ) + + for name, group in grouped_fetus: + label = group["label"] + frame = group["frame"] + test = group["test"] + + group = group.drop(columns=["test", "frame", "label", "type"]) + + group.set_index("image_name", inplace=True) + + if group.columns[0] == "Unnamed: 0": + group = group.drop(columns=["Unnamed: 0"]) + + group = group.to_numpy() + + if group.shape[0] < SERIES_LENGTH: + group = np.vstack( + [group, np.zeros((SERIES_LENGTH - group.shape[0], FEATURE_SIZE))] + ) + + elif group.shape[0] > SERIES_LENGTH: + group = group[:SERIES_LENGTH] + + group = group.astype(np.float32) + + label = CLASSES.index(label.iat[0]) + fetus_data.append( + { + "data": group, + "label": label, + } + ) + + for name, group in grouped_dataset: + label = group["label"] + frame = group["frame"] + test = group["test"] + data_type = group["type"] + + group = group.drop(columns=["test", "frame", "label", "type"]) + + group.set_index("image_name", inplace=True) + + if group.columns[0] == "Unnamed: 0": + group = group.drop(columns=["Unnamed: 0"]) + + group = group.to_numpy() + + if group.shape[0] < SERIES_LENGTH: + group = np.vstack( + [group, np.zeros((SERIES_LENGTH - group.shape[0], FEATURE_SIZE))] + ) + + elif group.shape[0] > SERIES_LENGTH: + group = group[:SERIES_LENGTH] + + group = group.astype(np.float32) + + label = CLASSES.index(label.iat[0]) + data.append( + { + "data": group, + "label": label, + } + ) + + if K_FOLD == 1: + + x_all = [d["data"] for d in data] + y_all = [d["label"] for d in data] + + x_mother = [d["data"] for d in mother_data] + y_mother = [d["label"] for d in mother_data] + + x_fetus = [d["data"] for d in fetus_data] + y_fetus = [d["label"] for d in fetus_data] + + (train_loader_all, test_loader_all) = create_loaders( + x_all, + y_all, + data, + over=OVER_SAMPLING, + under=UNDER_SAMPLING, + classes=CLASSES, + batch_size=BATCH_SIZE, + ) + + (train_loader_mother, test_loader_mother) = create_loaders( + x_mother, + y_mother, + mother_data, + over=OVER_SAMPLING, + under=UNDER_SAMPLING, + classes=CLASSES, + batch_size=BATCH_SIZE, + ) + + (train_loader_fetus, test_loader_fetus) = create_loaders( + x_fetus, + y_fetus, + fetus_data, + over=OVER_SAMPLING, + under=UNDER_SAMPLING, + classes=CLASSES, + batch_size=BATCH_SIZE, + ) + + (model, optimizer, scheduler, criterion) = setup_model_training( + input_size=FEATURE_SIZE, + hidden_size=HIDDEN_SIZE, + num_layers=NUM_LAYERS, + num_classes=len(CLASSES), + sequence_length=SERIES_LENGTH, + device=device, + lr=LEARNING_RATE, + weight_decay=WEIGHT_DECAY, + eps=EPS, + ) + + if DATASET_TYPE == "all": + train_loader = train_loader_all + test_loader = test_loader_all + + elif DATASET_TYPE == "fetus": + train_loader = train_loader_fetus + test_loader = test_loader_fetus + + elif DATASET_TYPE == "mother": + train_loader = train_loader_mother + test_loader = test_loader_mother + + elif DATASET_TYPE == "fetus-mother": + train_loader = train_loader_fetus + test_loader = test_loader_mother + + elif DATASET_TYPE == "mother-fetus": + train_loader = train_loader_mother + test_loader = test_loader_fetus + + else: + Exception("Invalid dataset type") + + trained_model = training_loop( + model=model, + train_loader=train_loader, + test_loader=test_loader, + optimizer=optimizer, + scheduler=scheduler, + criterion=criterion, + device=device, + epochs=EPOCHS, + early_stopping=EARLY_STOPPING, + log_dir="output/" + TEST_NAME + "/metrics", + ) + + loss, conf_matrix, classification_rep = validation( + trained_model, + test_loader, + criterion, + device, + ) + + # save classification report to a file + df = pd.DataFrame(classification_rep).transpose() + df.to_csv("output/" + TEST_NAME + "/metrics/classification_report.csv") + + torch.save( + trained_model.state_dict(), + "output/" + TEST_NAME + "/weights/model.pth", + ) + + plt.figure(figsize=(19.20, 10.80)) + plt.title("Confusion Matrix") + sns.heatmap( + conf_matrix, + annot=True, + fmt=".2f", + xticklabels=["Baseline", "Opcl", "Yawn"], + yticklabels=["Baseline", "Opcl", "Yawn"], + cmap="viridis", + ) + + plt.xlabel("Predicted") + plt.ylabel("Actual") + + plt.savefig("output/" + TEST_NAME + "/confusion_matrix/confusion_matrix.png") + + plt.figure(figsize=(19.20, 10.80)) + plt.title("Confusion Matrix Percentage") + conf_matrix_percent = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis] * 100 + sns.heatmap( + conf_matrix_percent, + annot=True, + fmt=".2f", + xticklabels=["Baseline", "Opcl", "Yawn"], + yticklabels=["Baseline", "Opcl", "Yawn"], + cmap="viridis", + ) + + plt.xlabel("Predicted") + plt.ylabel("Actual") + + plt.savefig("output/" + TEST_NAME + "/confusion_matrix/confusion_matrix_percentage.png") + + else: + x_all = [d["data"] for d in data] + y_all = [d["label"] for d in data] + + x_mother = [d["data"] for d in mother_data] + y_mother = [d["label"] for d in mother_data] + + x_fetus = [d["data"] for d in fetus_data] + y_fetus = [d["label"] for d in fetus_data] + + (train_loader_all, test_loader_all) = create_loaders( + x_all, + y_all, + data, + over=OVER_SAMPLING, + under=UNDER_SAMPLING, + classes=CLASSES, + batch_size=BATCH_SIZE, + ) + + (train_loader_mother, test_loader_mother) = create_loaders( + x_mother, + y_mother, + mother_data, + over=OVER_SAMPLING, + under=UNDER_SAMPLING, + classes=CLASSES, + batch_size=BATCH_SIZE, + ) + + (train_loader_fetus, test_loader_fetus) = create_loaders( + x_fetus, + y_fetus, + fetus_data, + over=OVER_SAMPLING, + under=UNDER_SAMPLING, + classes=CLASSES, + batch_size=BATCH_SIZE, + ) + + (model, optimizer, scheduler, criterion) = setup_model_training( + input_size=FEATURE_SIZE, + hidden_size=HIDDEN_SIZE, + num_layers=NUM_LAYERS, + num_classes=len(CLASSES), + sequence_length=SERIES_LENGTH, + device=device, + lr=LEARNING_RATE, + weight_decay=WEIGHT_DECAY, + eps=EPS, + ) + + kf = StratifiedKFold(n_splits=K_FOLD, shuffle=True, random_state=seed) + model_index = 0 + + x = None + y = None + + if DATASET_TYPE == "all": + x = [d["data"] for d in data] + y = [d["label"] for d in data] + elif DATASET_TYPE == "fetus": + x = [d["data"] for d in fetus_data] + y = [d["label"] for d in fetus_data] + elif DATASET_TYPE == "mother": + x = [d["data"] for d in mother_data] + y = [d["label"] for d in mother_data] + + # TODO: HOW TO HANDLE THIS CASES? for the mixed training and validation + else: + Exception("Invalid dataset type") + + for train_index, test_index in kf.split(X=x, y=y): + train_data = [data[i] for i in train_index] + test_data = [data[i] for i in test_index] + + data = train_data + test_data + + x = [d["data"] for d in train_data] + y = [d["label"] for d in train_data] + + (train_loader, test_loader) = create_loaders( + x, + y, + data, + over=OVER_SAMPLING, + under=UNDER_SAMPLING, + classes=CLASSES, + batch_size=BATCH_SIZE, + ) + + (model, optimizer, scheduler, criterion) = setup_model_training( + input_size=FEATURE_SIZE, + hidden_size=HIDDEN_SIZE, + num_layers=NUM_LAYERS, + num_classes=len(CLASSES), + sequence_length=SERIES_LENGTH, + device=device, + lr=LEARNING_RATE, + weight_decay=WEIGHT_DECAY, + eps=EPS, + ) + + trained_model = training_loop( + model=model, + train_loader=train_loader, + test_loader=test_loader, + optimizer=optimizer, + scheduler=scheduler, + criterion=criterion, + device=device, + epochs=EPOCHS, + early_stopping=EARLY_STOPPING, + log_dir="output/" + TEST_NAME + "/metrics/" + f"{model_index}", + ) + + loss, conf_matrix, classification_rep = validation( + trained_model, + test_loader, + criterion, + device, + ) + + # save classification report to a file + df = pd.DataFrame(classification_rep).transpose() + df.to_csv( + "output/" + + TEST_NAME + + "/metrics/classification_report_" + + str(model_index) + + ".csv" + ) + + torch.save( + trained_model.state_dict(), + "output/" + TEST_NAME + "/weights/model_" + str(model_index) + ".pth", + ) + plt.figure(figsize=(19.20, 10.80)) + plt.title("Confusion Matrix") + sns.heatmap( + conf_matrix, + annot=True, + fmt=".2f", + xticklabels=["Baseline", "Opcl", "Yawn"], + yticklabels=["Baseline", "Opcl", "Yawn"], + cmap="viridis", + ) + + plt.xlabel("Predicted") + plt.ylabel("Actual") + plt.savefig( + "output/" + + TEST_NAME + + "/confusion_matrix/confusion_matrix_" + + str(model_index) + + ".png" + ) + conf_matrix_percent = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis] * 100 + plt.figure(figsize=(19.20, 10.80)) + plt.title("Confusion Matrix Percentage") + sns.heatmap( + conf_matrix_percent, + annot=True, + fmt=".2f", + xticklabels=["Baseline", "Opcl", "Yawn"], + yticklabels=["Baseline", "Opcl", "Yawn"], + cmap="viridis", + ) + + plt.xlabel("Predicted") + plt.ylabel("Actual") + plt.savefig( + "output/" + + TEST_NAME + + "/confusion_matrix/confusion_matrix_percentage_" + + str(model_index) + + ".png" + ) + model_index += 1 diff --git a/fetus-event-detection-classification/src/model.py b/fetus-event-detection-classification/src/model.py new file mode 100755 index 0000000..6636a74 --- /dev/null +++ b/fetus-event-detection-classification/src/model.py @@ -0,0 +1,124 @@ +import torch +import torch.nn as nn + + +class LSTM(nn.Module): + def __init__( + self, input_size, batch_size, hidden_size, drop_out, num_classes, num_layers + ): + super(LSTM, self).__init__() + + self.input_size = input_size + self.batch_size = batch_size + self.hidden_size = hidden_size + self.drop_out = drop_out + self.num_classes = num_classes + self.num_layers = num_layers + + self.lstm = nn.LSTM( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + dropout=drop_out, + batch_first=True, + ) + + self.fc = nn.Linear(hidden_size, num_classes) + + def forward(self, x): + x = x.view(x.size(0), -1) + x = x.unsqueeze(1) + # out, (hn, cn) = self.lstm(x) + # out = self.fc(hn[-1]) + # return out + + out, hidden = self.lstm(x) + out = self.fc(out[:, -1, :]) + return out + + +class GRU(nn.Module): + def __init__( + self, input_size, batch_size, hidden_size, drop_out, num_classes, num_layers + ): + super(GRU, self).__init__() + self.input_size = input_size + self.batch_size = batch_size + self.hidden_size = hidden_size + self.drop_out = drop_out + self.num_classes = num_classes + self.gru = nn.GRU( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + dropout=drop_out, + batch_first=True, + ) + self.fc = nn.Linear(hidden_size, num_classes) + + def forward(self, x): + x = x.view(x.size(0), -1) + out, _ = self.gru(x) + out = self.fc(out) + return out + + +class SimpleGRU(nn.Module): + def __init__( + self, + input_size, + hidden_size, + num_layers, + num_classes, + sequence_length, + device, + ): + super(SimpleGRU, self).__init__() + self.hidden_size = hidden_size + self.num_layers = num_layers + + self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True) + self.fc1 = nn.Linear(hidden_size * sequence_length, num_classes) + self.device = device + + def forward(self, x): + h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(self.device) + + out, _ = self.gru(x, h0) + out = out.reshape(out.shape[0], -1) + out = self.fc1(out) + return out + + +class SimpleLSTM(nn.Module): + def __init__( + self, + input_size, + hidden_size, + num_layers, + sequence_length, + num_classes, + device, + ): + super(SimpleLSTM, self).__init__() + + self.hidden_size = hidden_size + self.num_layers = num_layers + + self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) + self.fc1 = nn.Linear(hidden_size * sequence_length, num_classes) + self.device = device + + def forward(self, x): + + # h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(self.device) + # c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(self.device) + + if len(x.shape) == 2: + x = x.unsqueeze(2) + + # out, _ = self.lstm(x, (h0, c0)) + out, _ = self.lstm(x) + out = out.reshape(out.size(0), -1) + out = self.fc1(out) + return out diff --git a/fetus-event-detection-classification/src/predict.py b/fetus-event-detection-classification/src/predict.py new file mode 100755 index 0000000..e69de29 diff --git a/fetus-event-detection-classification/src/training.py b/fetus-event-detection-classification/src/training.py new file mode 100755 index 0000000..564583c --- /dev/null +++ b/fetus-event-detection-classification/src/training.py @@ -0,0 +1,171 @@ +import torch +from torchmetrics import Accuracy, Precision, Recall, F1Score +import tqdm +from tqdm import tqdm +import pandas as pd +import os +from sklearn.metrics import classification_report + + +class EarlyStopping: + def __init__(self, tolerance=10, min_delta=0): + + self.tolerance = tolerance + self.min_delta = min_delta + self.counter = 0 + self.early_stop = False + + def __call__(self, train_loss, validation_loss): + if (validation_loss - train_loss) > self.min_delta: + self.counter += 1 + if self.counter >= self.tolerance: + self.early_stop = True + + +def training_loop( + model, + train_loader, + test_loader, + optimizer, + scheduler, + criterion, + device, + epochs, + early_stopping=False, + log_dir=None, +): + + if log_dir is not None: + logs: pd.DataFrame = pd.DataFrame( + columns=[ + "train_loss", + "test_loss", + "train_accuracy", + "test_accuracy", + "train_precision", + "test_precision", + "train_recall", + "test_recall", + "train_f1", + "test_f1", + ] + ) + + # move to device + model = model.to(device) + + # metrics + accuracy = Accuracy(task="multiclass", num_classes=3).to(device) + precision = Precision(task="multiclass", num_classes=3).to(device) + recall = Recall(task="multiclass", num_classes=3).to(device) + f1 = F1Score(task="multiclass", num_classes=3).to(device) + + t = tqdm(range(epochs)) + + early_stopping = EarlyStopping(tolerance=50, min_delta=0) + for epochs in t: + + model.train() + train_loss = 0 + train_accuracy = 0 + train_precision = 0 + train_recall = 0 + train_f1 = 0 + + for x, y in train_loader: + x = x.to(device) + y = y.to(device) + + optimizer.zero_grad() + y_pred = model(x) + y = y.squeeze(1) + y = y.float() + y_pred = y_pred.squeeze(1) + y_pred = y_pred.float() + loss = criterion(y_pred, y) + + loss.backward() + optimizer.step() + train_loss += loss.item() + + y_pred = torch.argmax(y_pred, dim=1) + y = torch.argmax(y, dim=1) + + train_accuracy += accuracy(y_pred, y) + train_precision += precision(y_pred, y) + train_recall += recall(y_pred, y) + train_f1 += f1(y_pred, y) + + train_loss /= len(train_loader) + train_accuracy /= len(train_loader) + train_precision /= len(train_loader) + train_recall /= len(train_loader) + train_f1 /= len(train_loader) + + model.eval() + test_loss = 0 + test_accuracy = 0 + test_precision = 0 + test_recall = 0 + test_f1 = 0 + + for x, y in test_loader: + x = x.to(device) + y = y.to(device) + + optimizer.zero_grad() + y_pred = model(x) + y = y.squeeze(1) + y = y.float() + y_pred = y_pred.squeeze(1) + y_pred = y_pred.float() + loss = criterion(y_pred, y) + test_loss += loss.item() + + y_pred = torch.argmax(y_pred, dim=1) + y = torch.argmax(y, dim=1) + + test_accuracy += accuracy(y_pred, y) + test_precision += precision(y_pred, y) + test_recall += recall(y_pred, y) + test_f1 += f1(y_pred, y) + + test_loss /= len(test_loader) + test_accuracy /= len(test_loader) + test_precision /= len(test_loader) + test_recall /= len(test_loader) + test_f1 /= len(test_loader) + + if log_dir is not None: + new_line = { + "train_loss": train_loss, + "test_loss": test_loss, + "train_accuracy": train_accuracy.item(), + "test_accuracy": test_accuracy.item(), + "train_precision": train_precision.item(), + "test_precision": test_precision.item(), + "train_recall": train_recall.item(), + "test_recall": test_recall.item(), + "train_f1": train_f1.item(), + "test_f1": test_f1.item(), + } + + logs = pd.concat([logs, pd.DataFrame([new_line])]) + + scheduler.step(train_loss) + t.set_description( + f"Epoch: {epochs + 1}, Train Loss: {train_loss}, Test Loss: {test_loss}" + ) + + early_stopping(train_loss, test_loss) + if early_stopping.early_stop: + print("Early stopping") + break + + if log_dir is not None: + if not os.path.exists(log_dir): + os.makedirs(log_dir) + + logs.to_csv(log_dir + "/logs.csv", index=False) + + return model diff --git a/fetus-event-detection-classification/src/validation.py b/fetus-event-detection-classification/src/validation.py new file mode 100755 index 0000000..9419a65 --- /dev/null +++ b/fetus-event-detection-classification/src/validation.py @@ -0,0 +1,58 @@ +import torch +import torch.nn as nn +import torch.optim as optim +import torchmetrics +from torchmetrics import Accuracy, Precision, Recall, F1Score +import tqdm +from tqdm import tqdm +from sklearn.metrics import classification_report, confusion_matrix + + +def validation( + model, + val_loader, + criterion, + device, +): + # move to device + model = model.to(device) + + # metrics + + losses = [] + predictions = [] + ground_truth = [] + + with torch.no_grad(): + for x, y in val_loader: + x = x.to(device) + y = y.to(device) + y_pred = model(x) + y = y.squeeze(1) + y = y.float() + y_pred = y_pred.squeeze(1) + y_pred = y_pred.float() + loss = criterion(y_pred, y) + losses.append(loss.item()) + predictions.append(torch.argmax(y_pred, dim=1)) + ground_truth.append(torch.argmax(y, dim=1)) + + loss = 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z>Wl0u?;)7cy?gi0b}6%X0*asdIB{m=D({=+bS2fVTWQffAi{63E#%7|s%*K+$`XZG%R literal 0 HcmV?d00001 diff --git a/mean_mouth_distance/main.ipynb b/mean_mouth_distance/main.ipynb new file mode 100644 index 0000000..302d667 --- /dev/null +++ b/mean_mouth_distance/main.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "bb315604-cc62-46b7-8258-61a70f174386", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Load the dataset\n", + "dataset = pd.read_csv(\"./dataset.csv\")\n", + "\n", + "# Group by 'test', 'frame', 'label', and 'type'\n", + "grouped_data = dataset.groupby(['test', 'frame', 'label', 'type'])\n", + "\n", + "# Lists to store data based on type and label\n", + "fetus_baseline = []\n", + "fetus_opcl = []\n", + "fetus_yawn = []\n", + "mother_baseline = []\n", + "mother_opcl = []\n", + "mother_yawn = []\n", + "\n", + "# Process grouped data\n", + "for name, group in grouped_data:\n", + " group = group.drop(group.columns[0], axis=1) # Drop the first column\n", + " group = group.drop([\"leftLip_x\", \"leftLip_y\", \"rightLip_x\", \"rightLip_y\", \n", + " \"topMidInner_x\", \"topMidInner_y\", \"bottomMidInner_x\", \n", + " \"bottomMidInner_y\", \"nose_x\", \"nose_y\"], axis=1)\n", + " \n", + " # Get values from 'top_bottom_distance'\n", + " top_bottom_distance_values = group['top_bottom_distance'].values\n", + " \n", + " # Assign to appropriate lists based on label and type\n", + " label = name[2] # 'label' from groupby\n", + " type_ = name[3] # 'type' from groupby\n", + " \n", + " if type_ == 'fetus':\n", + " if label == 'baseline':\n", + " fetus_baseline.append(top_bottom_distance_values)\n", + " elif label == 'opcl':\n", + " fetus_opcl.append(top_bottom_distance_values)\n", + " elif label == 'yawn':\n", + " fetus_yawn.append(top_bottom_distance_values)\n", + " elif type_ == 'mother':\n", + " if label == 'baseline':\n", + " mother_baseline.append(top_bottom_distance_values)\n", + " elif label == 'opcl':\n", + " mother_opcl.append(top_bottom_distance_values)\n", + " elif label == 'yawn':\n", + " mother_yawn.append(top_bottom_distance_values)\n", + "\n", + "# Function to pad, sample, and smooth each array\n", + "def process_series(series_list, window_size=3):\n", + " max_length = max(len(series) for series in series_list)\n", + " padded_series = np.array([np.pad(series, (0, max_length - len(series)), mode='constant') for series in series_list])\n", + "\n", + " # Sampled series\n", + " sampled_series = []\n", + " sample_n = 1\n", + " for i in range(0, max_length, sample_n):\n", + " segment_values = padded_series[:, i:i + sample_n][padded_series[:, i:i + sample_n] != 0]\n", + " if len(segment_values) > 0:\n", + " sampled_point = np.random.choice(segment_values.flatten(), size=1)\n", + " sampled_series.append(sampled_point[0])\n", + " \n", + " sampled_series = np.array(sampled_series)\n", + "\n", + " # Apply moving average for smoothing\n", + " smooth_sampled_series = np.convolve(sampled_series, np.ones(window_size) / window_size, mode='valid')\n", + " \n", + " return smooth_sampled_series\n", + "\n", + "# Process all series\n", + "smooth_fetus_baseline = process_series(fetus_baseline)\n", + "smooth_fetus_opcl = process_series(fetus_opcl)\n", + "smooth_fetus_yawn = process_series(fetus_yawn)\n", + "smooth_mother_baseline = process_series(mother_baseline)\n", + "smooth_mother_opcl = process_series(mother_opcl)\n", + "smooth_mother_yawn = process_series(mother_yawn)\n", + "\n", + "# Function to plot and save images\n", + "def plot_and_save(series_dict, title, filename):\n", + " plt.figure(figsize=(19.20, 10.80))\n", + " for label, series in series_dict.items():\n", + " plt.plot(series, marker='x', label=label, linestyle='--')\n", + " \n", + " plt.title(title)\n", + " plt.xlabel('Time Points')\n", + " plt.ylabel('Values')\n", + " plt.legend()\n", + " plt.grid()\n", + " \n", + " # Save the plot\n", + " plt.savefig(filename)\n", + " plt.close() # Close the figure to save memory\n", + "\n", + "# Create plots for fetus and mother\n", + "fetus_series_dict = {\n", + " 'Smoothed Fetus Baseline': smooth_fetus_baseline,\n", + " 'Smoothed Fetus OPCL': smooth_fetus_opcl,\n", + " 'Smoothed Fetus Yawn': smooth_fetus_yawn,\n", + "}\n", + "\n", + "mother_series_dict = {\n", + " 'Smoothed Mother Baseline': smooth_mother_baseline,\n", + " 'Smoothed Mother OPCL': smooth_mother_opcl,\n", + " 'Smoothed Mother Yawn': smooth_mother_yawn,\n", + "}\n", + "\n", + "# Plot and save images\n", + "plot_and_save(fetus_series_dict, 'Smoothed Series for Fetus', 'fetus_series.png')\n", + "plot_and_save(mother_series_dict, 'Smoothed Series for Mother', 'mother_series.png')\n", + "\n", + "# Combine the plots into a single figure\n", + "plt.figure(figsize=(19.20, 10.80))\n", + "\n", + "# Plot all series together\n", + "for label, series in fetus_series_dict.items():\n", + " plt.plot(series, marker='x', label=label, linestyle='--')\n", + "for label, series in mother_series_dict.items():\n", + " plt.plot(series, marker='o', label=label, linestyle='--')\n", + "\n", + "plt.title('Combined Smoothed Series for Fetus and Mother')\n", + "plt.xlabel('Time Points')\n", + "plt.ylabel('Values')\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "# Save the combined plot\n", + "plt.savefig('combined_fetus_mother_series.png')\n", + "plt.close()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abd865fb-17e1-4608-a98a-32f65c5505bf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + 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