def process_image():
numbers = list(map(int, open('numbers.txt').read().split(' ')))
o, _ = get_image('numbers.png')
slices = [(s, 'green', n) for ((_, s), n) in zip(get_digits_fake(o), numbers)]
ani = make_animation(o, slices, 300)
# plt.show()
return ani
def process_image():
numbers = list(map(int, open('numbers.txt').read().split(' ')))
o, _ = get_image('numbers.png')
slices = [(s, 'green', n)
for ((_, s), n) in zip(get_digits_fake(o), numbers)]
ani = make_animation(o, slices, 300)
# plt.show()
return ani
def post_something():
meme_path = request.args.get('meme')
result_dict = {}
if (not request.data):
return "No data was sent !"
# getting the image from client
image_name = image_processing.get_image(request)
# check if the file less than 1 MB
if os.stat(image_name).st_size > 1000000:
# reduce the size of the received image and delete the old one
new_image_name = image_processing.get_image_less_than_1MB(image_name)
os.remove(image_name)
else:
new_image_name = image_name
predicted_label = predict_meme_class(new_image_name)
return jsonify(predicted_label)
def convert(image_file, text_file=None):
img = ip.get_image(image_file)
lines = []
for line in ip.get_lines(img):
words = []
for word in ip.get_words(img, line):
chars = []
for char in ip.get_chars(img, word):
c = convert_char(img, char)
chars.append(c)
words.append(''.join(chars))
lines.append(' '.join(words))
if text_file:
f = open(text_file, 'w')
f.write('\n'.join(lines))
f.close()
else:
print '\n'.join(lines)
def get_image_by_name(image_name, folder):
return image_processing.get_image(image_name, folder)
from digits_classifiers import get_predictor
from image_processing import get_digits, get_image
from utils import make_animation
import matplotlib.pylab as plt
predictor = get_predictor('Logistic regression')
original, img = get_image('numbers.png')
predictions = []
for digit, slice_ in get_digits(img):
p = predictor.predict(digit)[0]
predictions.append((slice_, 'green', p))
ani = make_animation(original, predictions, interval=300)
plt.show()
