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main.py
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main.py
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from nn import NeuralNetwork
import argparse
import numpy as np
# from PIL import Image
import re
parser = argparse.ArgumentParser()
parser.add_argument("-V", "--verbose", help="increase output verbosity", action="store_true")
args = parser.parse_args()
verbose = False
if args.verbose:
verbose = True
def read_pgm(filename, plot=False, byteorder='>'):
"""Return image data from a raw PGM file as numpy array.
Format specification: http://netpbm.sourceforge.net/doc/pgm.html
Following code is inspired from https://stackoverflow.com/questions/7368739/numpy-and-16-bit-pgm
"""
if verbose:
print('reading image', filename)
with open(filename, 'rb') as f:
buffer = f.read()
try:
header, width, height, maxval = re.search(
b"(^P5\s(?:\s*#.*[\r\n])*"
b"(\d+)\s(?:\s*#.*[\r\n])*"
b"(\d+)\s(?:\s*#.*[\r\n])*"
b"(\d+)\s(?:\s*#.*[\r\n]\s)*)", buffer).groups()
# print (header, int(width), int(height), int(maxval))
except AttributeError:
raise ValueError("Not a raw PGM file: '%s'" % filename)
image = np.frombuffer(buffer,
dtype='u1' if int(maxval) < 256 else byteorder+'u2',
count=int(width)*int(height),
offset=len(header)
) #.reshape((int(height), int(width)))
# print(image)
# to save image
if plot:
im = Image.fromarray(image, 'L')
im.putdata(image)
im.save(filename + '.jpg')
im.show()
return image
def fetch_data(filename='downgesture_train.list'):
if verbose:
print('Reading ', filename)
data = []
labels = []
# files = []
with open(filename) as f:
for line in f:
if line:
# data.append(line.strip())
# print(line.strip())
line = line.strip()
# files.append(line)
data.append(read_pgm(line))
if "down" in line:
labels.append(1)
else:
labels.append(0)
if verbose:
# print(data)
# print(files)
print(labels)
print()
return data, labels
if __name__ == '__main__':
train_data, train_target = fetch_data('downgesture_train.list')
neuralnet = NeuralNetwork()
neuralnet.add_layer(size = 1, input_size = len(train_data[0]), type='input')
neuralnet.add_layer(size = 100)
neuralnet.add_layer(size = 1, type='output')
neuralnet.fit(data = train_data, target = train_target, eta = 0.1, verbose = verbose)
# if needed clean data
# fit a model
# train a model
test_data, test_target = fetch_data('downgesture_test.list')
predicted_target = neuralnet.predict(test_data)
accuracy = neuralnet.accuracy(test_target, predicted_target)
print("Accuracy:", accuracy)