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train.py
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train.py
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import numpy as np
import os
import pandas as pd
from keras.engine.saving import load_model
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from fall_model import baseline_model
from fall_model import lstm_model
select_data = []
vector = []
BODY_PARTS = {"Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14}
n_frames = 50
label_count = 0
def extract_frames(frames, labels):
# n_frames = 10 * int(len(frames) / 10)
n_frames = len(frames)
idx = np.round(np.linspace(0, len(frames) - 1, n_frames)).astype(int)
new_frames = frames[idx]
global label_count
new_labels = labels[idx + label_count]
label_count += len(frames)
return new_frames, new_labels
seq_len = 10
def create_dataset(frames, labels):
x = []
y = []
for i in range(len(frames) - seq_len + 1):
one_set_x = frames[i: i + seq_len, ]
one_y = labels[i + seq_len - 1]
x.append(one_set_x)
y.append(one_y)
x = np.array(x)
y = np.array(y)
return x, y
def select(x):
for s in x:
select_data.append((s[0], s[1], s[2], s[5], s[8], s[9], s[11], s[12]))
# select_data.append((s[0], s[1], s[8], s[9], s[11], s[12]))
return np.array(select_data)
def merge(x1, x2):
x3 = np.vstack((x1, x2))
print(x3)
def load_baseline_data():
npy_path = "./output/npy"
filenames = os.listdir(npy_path)
filenames.sort()
x = np.load(os.path.join(npy_path, filenames[0]))
label_data = pd.read_csv("./dataset/UR/urfall-cam0-falls.csv")
labels = label_data["label"]
y = to_categorical(labels, num_classes=3)
for i in range(1, len(filenames)):
file = os.path.splitext(filenames[i])
filename, file_type = file
if file_type == ".npy":
new_x = np.load(os.path.join(npy_path, filenames[i]), allow_pickle=True)
x = np.vstack((x, new_x))
print(x.shape)
print(y.shape)
# exit(0)
se = select(x)
x = se.reshape((-1, se.shape[1] * se.shape[2]))
# x = se.reshape((-1, 1, se.shape[1] * se.shape[2]))
# x = x.reshape((-1, x.shape[1], x.shape[2] * x.shape[3]))
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2021)
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
return x_train, y_train, x_test, y_test
def load_data():
npy_path = "./output/processed_npy"
filenames = os.listdir(npy_path)
filenames.sort()
label_data = pd.read_csv("./dataset/UR/urfall-cam0-falls.csv")
labels = label_data["label"]
y = to_categorical(labels, num_classes=3)
labels = y
one_data = np.load(os.path.join(npy_path, filenames[0]))
new_x, new_y = extract_frames(one_data, labels)
print(one_data.shape)
# x = new_x
# y = new_y
x, y = create_dataset(new_x, new_y)
for i in range(1, len(filenames)):
file = os.path.splitext(filenames[i])
filename, file_type = file
if file_type == ".npy":
one_data = np.load(os.path.join(npy_path, filenames[i]), allow_pickle=True)
print(one_data.shape)
new_x, new_y = extract_frames(one_data, labels)
new_x, new_y = create_dataset(new_x, new_y)
x = np.vstack((x, new_x))
y = np.vstack((y, new_y))
print(x.shape)
print(y.shape)
# exit(0)
# se = select(x)
# x = se.reshape((-1, se.shape[1] * se.shape[2]))
# x = se.reshape((-1, 1, se.shape[1] * se.shape[2]))
x = x.reshape((-1, x.shape[1], x.shape[2] * x.shape[3]))
one_test = x[x.shape[0] - 2]
print(y[x.shape[0] - 2])
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2021)
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
return x_train, y_train, x_test, y_test, one_test
if __name__ == '__main__':
# x_train, y_train, x_test, y_test = load_baseline_data()
# baseline_model(x_train, y_train, x_test, y_test)
x_train, y_train, x_test, y_test, test = load_data()
lstm_model(x_train, y_train, x_test, y_test)
#
# model = load_model('./models/my_trained_model/fall_model.h5')
# test = test.reshape(1, test.shape[0], test.shape[1])
#
# pred = model.predict(test)
# print(test, pred)