def show_collection(char):
ori = ochre.get_letter(char)
img = ochre.get_letter(char)
for img_ in ochre.collection.collection(ori):
img = np.concatenate((img, img_), axis=1)
plt.imshow(img)
plt.show()
def main(sentence, collection=True):
print(sentence)
box = lambda idx: (0, idx * 14, 14, (idx + 1) * 14)
sentence_img = ochre.get_sentence(sentence)
letters = sorted(set(sentence.replace(" ", "")))
if collection:
ts = {
c: list(ochre.collection.collection(ochre.get_letter(c)))
for c in letters
}
else:
ts = {c: [ochre.get_letter(c)] for c in letters}
nn = ochre.training.train(ts)
prediction = [
ochre.training.predict(nn, sentence_img, box=box(idx))
for idx in range(len(sentence))
]
print("".join(prediction))
for idx, c in enumerate(sentence):
if c == " ":
print(f"Skipping space at idx={idx}")
continue
letter = ochre.training.predict(nn, sentence_img, box=box(idx))
cor = "ok" if letter == c else "fail"
print(f"{letter} == {c} {cor}")
def _get_nn(letters, n_copies=1, collection=False):
if not collection:
ts = {c: [ochre.get_letter(c) for _ in range(n_copies)] for c in letters}
else:
for c in letters:
ts = {
c: list(ochre.collection.collection(ochre.get_letter(c)))
for c in letters
}
nn = ochre.training.train(ts)
return nn
def test_training():
letters = ["a", "b", "c", "x", "y", "z"]
nn = _get_nn(letters)
for letter in letters:
c = ochre.get_letter(letter)
assert ochre.training.predict(nn, c) == letter
def test_mulitple_training():
letters = ["a", "b", "c", "x", "y", "z"]
nn = _get_nn(letters, n_copies=1)
for letter in letters:
c = ochre.get_letter(letter)
vec = ochre.training.predict_vector(nn, c)
assert ochre.training.predict(nn, c) == letter, vec
def test_collection_training():
letters = "abcdefghijklxyz"
nn = _get_nn(letters, n_copies=1, collection=True)
for letter in letters:
c = ochre.get_letter(letter)
vec = ochre.training.predict_vector(nn, c)
assert ochre.training.predict(nn, c) == letter, vec
def train_collection(chars):
ts = {
c: list(ochre.collection.collection(ochre.get_letter(c), num=1))
for c in chars
}
nn = ochre.training.train(ts)
def test_letter():
x = ochre.get_letter("x")
assert isinstance(x, np.ndarray)
