How to use load_test method in avocado

Best Python code snippet using avocado_python

extra_img.py

Source:extra_img.py

...14from scipy.misc import imresize15WIDTH, HEIGHT = 224, 22416def load_image(path):17    return imresize(imread(path), (HEIGHT, WIDTH))18def load_test(base):19    paths = glob.glob('{}*.png'.format(base))20    print('Reading images...')21    for i, path in tqdm(enumerate(paths), total=len(paths)):22        datagen = ImageDataGenerator(23                rotation_range=20,24                width_shift_range=0.2,25                height_shift_range=0.2,26                shear_range=0.1,27                zoom_range=0.1,28                horizontal_flip=False,29                fill_mode='nearest')30        id = os.path.basename(path)31        img = load_image(path)32        x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)33        x = x.reshape((1,) + x.shape)  # this is a Numpy array with shape (1, 3, 150, 150)34        # the .flow() command below generates batches of randomly transformed images35        # and saves the results to the `preview/` directory36        i = 037	if (base == 'database/0/'):38            dir = 'train/0'39        elif(base == 'database/1/'):40            dir = 'train/1'41        elif(base == 'database/2/'):42            dir = 'train/2'43        elif(base == 'database/3/'):44            dir = 'train/3'45        elif(base == 'database/4/'):46            dir = 'train/4'47        elif(base == 'database/5/'):48            dir = 'train/5'49        elif(base == 'database/6/'):50            dir = 'train/6'51        elif(base == 'database/7/'):52            dir = 'train/7'53        elif(base == 'database/8/'):54            dir = 'train/8'55        elif(base == 'database/9/'):56            dir = 'train/9'57        elif(base == 'database/a/'):58            dir = 'train/10'59        elif(base == 'database/b/'):60            dir = 'train/11'61        elif(base == 'database/c/'):62            dir = 'train/12'63        elif(base == 'database/d/'):64            dir = 'train/13'65        elif(base == 'database/e/'):66            dir = 'train/14'67        elif(base == 'database/f/'):68            dir = 'train/15'69        elif(base == 'database/g/'):70            dir = 'train/16'71        elif(base == 'database/h/'):72            dir = 'train/17'73        elif(base == 'database/i/'):74            dir = 'train/18'75        elif(base == 'database/j/'):76            dir = 'train/19'77        elif(base == 'database/k/'):78            dir = 'train/20'79        elif(base == 'database/l/'):80            dir = 'train/21'81        elif(base == 'database/m/'):82            dir = 'train/22'83        elif(base == 'database/n/'):84            dir = 'train/23'85        elif(base == 'database/o/'):86            dir = 'train/24'87        elif(base == 'database/p/'):88            dir = 'train/25'89        elif(base == 'database/q/'):90            dir = 'train/26'91        elif(base == 'database/r/'):92            dir = 'train/27'93        elif(base == 'database/s/'):94            dir = 'train/28'95        elif(base == 'database/t/'):96            dir = 'train/29'97        elif(base == 'database/u/'):98            dir = 'train/30'99        elif(base == 'database/v/'):100            dir = 'train/31'101        elif(base == 'database/w/'):102            dir = 'train/32'103        elif(base == 'database/x/'):104            dir = 'train/33'105        elif(base == 'database/y/'):106            dir = 'train/34'107        elif(base == 'database/z/'):108            dir = 'train/35'109        # print (dir)110        for batch in datagen.flow(x, batch_size=1,111                                  save_to_dir=dir, save_prefix='gesture', save_format='jpg'):112            i += 1113            if i > 5:114                break  # otherwise the generator would loop indefinitely115load_test('database/0/')116load_test('database/1/')117load_test('database/2/')118load_test('database/3/')119load_test('database/4/')120load_test('database/5/')121load_test('database/6/')122load_test('database/7/')123load_test('database/8/')124load_test('database/9/')125load_test('database/a/')126load_test('database/b/')127load_test('database/c/')128load_test('database/d/')129load_test('database/e/')130load_test('database/f/')131load_test('database/g/')132load_test('database/h/')133load_test('database/i/')134load_test('database/j/')135load_test('database/k/')136load_test('database/l/')137load_test('database/m/')138load_test('database/n/')139load_test('database/o/')140load_test('database/p/')141load_test('database/q/')142load_test('database/r/')143load_test('database/s/')144load_test('database/t/')145load_test('database/u/')146load_test('database/v/')147load_test('database/w/')148load_test('database/x/')149load_test('database/y/')...

split_data.py

Source:split_data.py

1import json2import shutil34path = "E:/Dataset/manipulated_sequences/FaceShifter/c40/videos/"5path_dict = []67# with open("D:/PycharmProjects/Make_dataset/test.json",'r') as load_test:8#     load_dict = json.load(load_test)9#     print(load_dict)10#     print(len(load_dict))11#     for i in range(len(load_dict)):12#         # print(load_dict[i])13#         path_temp = path + str(load_dict[i][0]) + "_" + str(load_dict[i][1]) + ".mp4"14#         path_dict.append(path_temp)15#     print(path_dict)1617# with open("D:/PycharmProjects/Make_dataset/train.json",'r') as load_test:18#     load_dict = json.load(load_test)19#     print(load_dict)20#     print(len(load_dict))21#     for i in range(len(load_dict)):22#         # print(load_dict[i])23#         path_temp = path + str(load_dict[i][0]) + "_" + str(load_dict[i][1]) + ".mp4"24#         path_dict.append(path_temp)25#     print(path_dict)262728with open("D:/PycharmProjects/Make_dataset/val.json",'r') as load_test:29    load_dict = json.load(load_test)30    print(load_dict)31    print(len(load_dict))32    for i in range(len(load_dict)):33        # print(load_dict[i])34        path_temp = path + str(load_dict[i][1]) + "_" + str(load_dict[i][0]) + ".mp4"35        path_dict.append(path_temp)36    print(path_dict)37383940filepath = "E:/Dataset/manipulated_sequences/FaceShifter/c40/val"41for files in path_dict:
...

SpikeForecast.py

Source:SpikeForecast.py

1from xgboost import XGBClassifier2import matplotlib.pyplot as plt3from sklearn.preprocessing import StandardScaler4from sklearn.model_selection import GridSearchCV5import numpy6def spike_forecast(load_train, spike_train, load_test):7    scaler1 = StandardScaler()8    load_train = numpy.reshape(load_train, [-1, 1])9    load_test = numpy.reshape(load_test, [-1, 1])10    spike_train = numpy.reshape(spike_train, [-1, 1])11    scaler1.fit(load_train)12    load_train = scaler1.transform(load_train)13    load_test = scaler1.transform(load_test)14    print(load_train[0:24])15    print(spike_train[0:24])16    clf = XGBClassifier()17    clf.fit(load_train, spike_train.ravel())18    spike_fore = clf.predict(load_test)...

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