AML_Task4/mainleonie.py at master · sebastianwindeck/AML_Task4 · GitHub

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import os

import numpy as np

from helpers.io import inputter_csv_file, inputter_videos_from_folder, outputter
from helpers.preprocessing import preprocessing, max_time, cropping, gaussian_filtering, edge_filter, min_time
from models.conv2Dclassifier import to2D

from models.Sequential_Conv3D import evaluate_sequential

# from helpers.output import output_generator

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

dir_path = os.path.dirname(os.path.realpath(__file__))

PREPROCESSING = False
# Preprocessing parameter
RESOLUTION = 1.0


if PREPROCESSING:
    # Input folders

    train_folder = os.path.join(dir_path, "data/train/")
    test_folder = os.path.join(dir_path, "data/test/")

    # Create tf records in super folder for train records outside git-repository

    tf_record_dir = os.path.join(dir_path, 'tf_records')
    os.makedirs(tf_record_dir, exist_ok=True)

    tf_record_train = os.path.join(tf_record_dir, 'train' + '.tfrecords')
    tf_record_test = os.path.join(tf_record_dir, 'test' + '.tfrecords')

    x_train = inputter_videos_from_folder(train_folder)
    y_train = inputter_csv_file(dir_path, 'data/train_target.csv')

    x_test = inputter_videos_from_folder(test_folder)

    max_time_steps = np.max((max_time(x_train), max_time(x_test)))

    min_time_steps = np.min((min_time(x_train), min_time(x_test)))


    x_train = preprocessing(x_train, max_time_steps, normalizing=True,

                            scaling=False, resolution=RESOLUTION, cut_time=True, length = min_time_steps-10, crop=0, filter='no', binary=False)
    x_test = preprocessing(x_test, max_time_steps, normalizing=True,
                           scaling=False, resolution=RESOLUTION, cut_time=True, length= min_time_steps-10, crop=0, filter='no', binary=False)


else:

    y_train = np.load('data/numpy/y_train.npy')
    print("Loaded: y_train with shape {}".format(y_train.shape))
    x_train = np.load('data/numpy/x_train.npy')
    print("Loaded: x_train with shape {}".format(x_train.shape))
    x_test = np.load('data/numpy/x_test.npy')
    print("Loaded: x_test with shape {}".format(x_test.shape))


#y = evaluate_sequential(x_train,
                    #    y_train,
                       # x_test)

#outputter(y)

#Idee:


a_train = np.zeros((158 * x_train.shape[1], x_train.shape[2]*x_train.shape[3]))
b = np.zeros((158 * x_train.shape[1]))
for i in range(x_train.shape[0]):
    for j in range(x_train.shape[1]):
        index = x_train.shape[1] * i + j
        a_train[index, :] = np.ndarray.flatten(x_train[i, j, :, :, 0])
        b[index] = y_train[i]


a_test = np.zeros((x_test.shape[0] * x_test.shape[1], x_test.shape[2]* x_test.shape[3]))

for i in range(x_test.shape[0]):
    for j in range(x_test.shape[1]):
        index = x_test.shape[1] * i + j
        a_test[index, :] = np.ndarray.flatten(x_test[i, j, :, :, 0])

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

print(x_test.shape)

print(a_test.shape)

clf = RandomForestClassifier(n_estimators=100, max_depth=2,random_state=0)
clf.fit(a_train, b)
b_test = clf.predict(a_test)

outputter(b_test)

print("output.")