Python parseData Examples

Example #1

File: depthmap.py Project: torresxavier/cgm-ml

def process(plt, dir, depth, rgb):

    #extract depthmap
    with zipfile.ZipFile(dir + '/depth/' + depth, 'r') as zip_ref:
        zip_ref.extractall('.')
    utils.parseData('data')

    #read rgb data
    global im_array
    if rgb:
        width = utils.getWidth()
        height = utils.getHeight()
        pil_im = Image.open(dir + '/rgb/' + rgb)
        pil_im = pil_im.resize((width, height), Image.ANTIALIAS)
        im_array = np.asarray(pil_im)
    else:
        im_array = 0

    #parse calibration
    global calibration
    calibration = utils.parseCalibration(dir + '/camera_calibration.txt')

Example #2

def main():
    df = parseData(sys.argv[1])
    #print(df)
    df = processData(df)
    #print(df)
    drawFig(df, 'time')

Example #3

platform = 'android'

# Keyword (separated by either blanks or by the following logic commands: OR/AND/NOT
keyword = 'diabetes OR mellitus'

# App categories (separated by commas)
categories = 'MEDICAL, LIFESTYLE, EDUCATION, HEALTH_AND_FITNESS'

# Token of 42Matters
token = '6dc3eba38263b374e06986f69c876c3ea6cb2f9f'

# App's metadata language (all languages if argument is in blank)
langs = 'en'

con = connection.Connection()
con.set_categories(categories)
con.set_keyword(keyword)
con.set_langs(langs)
con.set_os(platform)
con.set_token(token)

data = con.get_data()

utils = utils.Utils()
result = utils.parseData(data)

csv_file_name = 'result.csv'
csv_export = csvExport.csvExport()
csv_export.set_file_name(csv_file_name)
csv_export.write_to_csv(result)

Example #4

                             args.init_lr,
                             betas=(args.beta_1, args.beta_2))
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
                                                 milestones=args.milestones,
                                                 gamma=args.lr_decay,
                                                 last_epoch=-1)
if args.masked_loss:
    print('using mask')
    criterion = utils.Criterion_mask(args)
else:
    criterion = utils.Criterion(args)
model.train()
for epoch in range(1, args.epochs + 1):
    print('---- Start Training Epoch %d: %d batches ----' %
          (epoch, len(train_loader)))
    scheduler.step()
    for i, sample in enumerate(train_loader):
        data = utils.parseData(args, sample, 'train')
        input = [data['input']]
        if args.in_light:
            input.append(data['l'])
        output = model(input)
        optimizer.zero_grad()
        loss = criterion.forward(output, data['tar'])
        criterion.backward()
        optimizer.step()
    print("Loss in epoch %d: %.3f" % (epoch, loss))

torch.save(model, './TrainedModels/model_new.pth.tar')
print("saved the model")

Example #5

File: eval.py Project: chinmay0301/PS-FCN-Chinmay

test_set = DiLiGenT_main(args, 'test')
test_loader = torch.utils.data.DataLoader(test_set,
                                          batch_size=args.test_batch,
                                          num_workers=args.workers,
                                          pin_memory=args.cuda,
                                          shuffle=False)

model.eval()
print('---- Testing for %d images - DiLiGent Dataset ----' %
      (len(test_loader)))

err_mean = 0
with torch.no_grad():
    for i, sample in enumerate(test_loader):
        data = utils.parseData(args, sample, 'test')
        input = [data['input']]
        if args.in_light:
            input.append(data['l'])
        output = model(input)
        acc = utils.errorPred(data['tar'].data, output.data, data['m'].data)
        err_mean = err_mean + acc
        print('error: %.3f' % (acc))
        result = (output.data + 1) / 2
        result_masked = result * data['m'].data.expand_as(output.data)

        save_path = './Results/' + 'img8_mask_%d.png' % (i + 1)
        tv.utils.save_image(result_masked, save_path)
        print('saved image %d' % (i + 1))

print('------------ mean error: %.3f ------------' %

Example #6

#import src.api.utils as utils

import config
import utils

app = Flask(__name__)

logger = utils.setup_logger()


@app.route('/')
def hello_world():
    return 'Running Correctly!'


if __name__ == '__main__':

    if config.STARTUP["DOWNLOAD"]:
        utils.downloadAllData()

    if config.STARTUP["EXTRACT"]:
        utils.extractData()

    if config.STARTUP["PARSE"]:
        data = utils.parseData()

        if config.STARTUP["REBUILD_DB"]:
            utils.buildDB(data)

    #app.run(debug=config.DEBUG, host = config.HOST)
    app.run(host=config.HOST)