def main(argv):
inputfile=''
k=10
frame_width=1
frame_height=2000
grayscale = False
interval = 1
try:
opts, args = getopt.getopt(argv,"mi:k:w:h:gf:",["ifile="])
except getopt.GetoptError:
print('barcode.py -i <inputFile> [-k <kmeans>] [-f <frameInterval>] [-w <frameWidth>] [-h <frameHeight] [-g] [-m]')
sys.exit(2)
for opt, arg in opts:
if opt == '-m':
print('barcode.py -i <inputFile> [-f <frameInterval>] [-k <kmeans>] [-w <frameWidth>] [-h <frameHeight] [-g] [-m]')
sys.exit()
elif opt in ("-i", "-input"):
inputfile = arg
elif opt in ("-k", "-kmeans"):
k = int(arg)
elif opt in ("-w", "-width"):
frame_width = int(arg)
elif opt in ("-h", "-height"):
frame_height = int(arg)
elif opt == "-g":
grayscale = True
elif opt == "-f":
interval = int(arg)
barcode.generate(inputfile, k, frame_width, frame_height, grayscale, interval)
def test_generate_animal_name():
element = generate(Version.ANIMAL)
assert element != None
assert len(element) == 2
name, adj = generate(Version.ANIMAL)
assert name != None
assert len(name) >= 3
assert adj != None
assert len(adj) >= 3
def generate_json():
req_type = request.args.get("type")
gender = request.args.get("gender")
version_enum = to_version_enum(req_type)
gender_enum = to_gender_enum(gender)
first, second = generate(version=version_enum, gender=gender_enum)
logger.info(f"first is: {first}, second is: {second}")
timestamp = strftime("%Y-%m-%d %H:%M:%S", gmtime())
json_string = {
"first": f"{cleanUp(first.lower())}",
"second": f"{cleanUp(second.lower())}",
"timestamp": timestamp
}
response = jsonify(json_string)
logger.info(f"Returning {json_string}")
response.headers.add('Access-Control-Allow-Origin', '*')
return response
def show_reference(subj, hemi):
s, w = generate(subj, hemi, 5)
v = cortex.Vertex.empty(subj)
obj = v.left if hemi == "lh" else v.right
pts, polys = cortex.db.get_surf(subj, "inflated", hemi)
surface = cortex.polyutils.Surface(pts, polys)
for i in range(5):
seam = s[i]
wall = w[i]
for j in range(len(seam) - 1):
path = cortex.polyutils.Surface.geodesic_path(
surface, seam[j], seam[j + 1])
obj[path] = i + 1
for j in range(len(wall) - 1):
path = cortex.polyutils.Surface.geodesic_path(
surface, wall[j], wall[j + 1])
obj[path] = i + 1
return v
def cer_c(c):
(a, r, o) = generate('./data/delfin.jpeg', 'maresteAmbele', 'programareDinamica', 50)
add_picture_to_canvas(c, a, 'Cerinta (c) Figura 3.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (c) Figura 3.2: Marit 50 pixeli pe ambele directii cu imresize.')
add_picture_to_canvas(c, o, 'Cerinta (c) Figura 3.3: Marit 50 pixeli pe ambele directii cu content-aware seam carving. (algoritm programareDinamica)')
def cer_b(c):
(a, r, o) = generate('./data/praga.jpg', 'miscoreazaInaltime', 'greedy', 200)
add_picture_to_canvas(c, a, 'Cerinta (b) Figura 2.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (b) Figura 2.2: Micsorat 100 pixeli in inaltime cu imresize.')
add_picture_to_canvas(c, o, 'Cerinta (b) Figura 2.3: Micsorat 100 pixeli in inaltime cu content-aware seam carving. (algoritm greedy)')
def cer_a(c):
(a, r, o) = generate('./data/castel.jpg', 'micsoreazaLatime', 'programareDinamica', 50)
add_picture_to_canvas(c, a, 'Cerinta (a) Figura 1.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (a) Figura 1.2: Micsorat 50 pixeli in latime cu imresize.')
add_picture_to_canvas(c, o, 'Cerinta (a) Figura 1.3: Micsorat 50 pixeli in latime cu content-aware seam carving. (algoritm programareDinamica)')
def main():
TARGET_IMGS_DIR = './data/imaginiTest/'
CIFAR_DIR = './cifar-10-batches-py/'
COLLECTION_DIR = "./data/colectie/"
parser = argparse.ArgumentParser(description='Generate project 1 pdf.')
parser.add_argument('output', type=str, help='Where to store the generated pdf')
args = parser.parse_args()
target_path = Path(TARGET_IMGS_DIR)
c = canvas.Canvas(args.output)
c.setFont("Times-Roman", 17)
c.setPageSize((400, 700))
c.drawCentredString(200, 300, 'Proiect Vedere Artificiala #1')
c.drawCentredString(200, 250, 'Realizarea imaginilor mozaic')
c.drawCentredString(250, 500, 'Ionescu Teodor-Stelian, Grupa 331')
c.showPage()
imagelist = [f for f in target_path.iterdir() if not f.name.startswith('.')]
img_idx = [0]
# Cerinta a
comment = 'Cerinta (a) Figura {}: Mozaic {} din flori dreptunghiulare dispuse Grid, numarPieseMozaicOrizontala={}, criteriul distantei euclidiene dintre culorile medii.'
genfunc = lambda imgpath, size : generate(imgpath, COLLECTION_DIR, K_HORIZONTAL=size, K_RANDOM_PLACE=False, K_GRID_ALLOW_DUPLICATES=True, K_HEXAGONAL=False)
cerinta(imagelist, img_idx, comment, genfunc, c)
# Cerinta b
comment = 'Cerinta (b) Figura {}: Mozaic {} din flori dreptunghiulare dispuse Aleator, numarPieseMozaicOrizontala={}, criteriul distantei euclidiene dintre culorile medii.'
genfunc = lambda imgpath, size : generate(imgpath, COLLECTION_DIR, K_HORIZONTAL=size, K_RANDOM_PLACE=True, K_GRID_ALLOW_DUPLICATES=True, K_HEXAGONAL=False)
cerinta(imagelist, img_idx, comment, genfunc, c)
# Cerinta c
comment = 'Cerinta (c) Figura {}: Mozaic {} din flori dreptunghiulare dispuse Grid, numarPieseMozaicOrizontala={}, cu proprietatea ca nu exista doua piese adiacente identice.'
genfunc = lambda imgpath, size : generate(imgpath, COLLECTION_DIR, K_HORIZONTAL=size, K_RANDOM_PLACE=False, K_GRID_ALLOW_DUPLICATES=False, K_HEXAGONAL=False)
cerinta(imagelist, img_idx, comment, genfunc, c)
# Cerinta d
genfunc = lambda imgpath, size, collection : generate(imgpath, K_COLLECTION_DIR=collection, K_HORIZONTAL=size, K_RANDOM_PLACE=False, K_GRID_ALLOW_DUPLICATES=True, K_HEXAGONAL=False)
catchoice = ['frog', 'automobile', 'bird', 'cat', 'ship', 'truck']
comment = 'Cerinta (d) Figura {}: Mozaic {} compus din imagini din setul CIFAR cu eticheta {}'
num = 0
for imgpath in imagelist:
print(imgpath)
cat = catchoice[num]
num += 1
img_idx[0] += 1
label = comment.format(img_idx[0], imgpath.name.split('.')[0], cat)
img = imgresize(genfunc(imgpath, 30, './data/{}/'.format(cat)))
add_picture_to_canvas(img, label, c)
genfunc = lambda imgpath, size, collection : generate(imgpath, K_COLLECTION_DIR=collection, K_HORIZONTAL=size, K_RANDOM_PLACE=False, K_GRID_ALLOW_DUPLICATES=False, K_HEXAGONAL=True)
catchoice = ['dog', 'deer', 'airplane', 'horse', 'ship', 'cat']
comment = 'Cerinta (d) Figura {}: Mozaic hexagonal {} compus din imagini din setul CIFAR cu eticheta {}, cu proprietatea ca nu exista doua piese adiacente identice.'
num = 0
for imgpath in imagelist:
print(imgpath)
cat = catchoice[num]
num += 1
img_idx[0] += 1
label = comment.format(img_idx[0], imgpath.name.split('.')[0], cat)
img = imgresize(genfunc(imgpath, 30, './data/{}/'.format(cat)))
add_picture_to_canvas(img, label, c)
# Cerinta e
comment = 'Cerinta (e) Figura {}: Mozaic {} din flori hexagonale dispuse Grid, numarPieseMozaicOrizontala={}.'
genfunc = lambda imgpath, size : generate(imgpath, COLLECTION_DIR, K_HORIZONTAL=size, K_RANDOM_PLACE=False, K_GRID_ALLOW_DUPLICATES=True, K_HEXAGONAL=True)
cerinta(imagelist, img_idx, comment, genfunc, c)
# Cerinta f
comment = 'Cerinta (f) Figura {}: Mozaic {} din flori hexagonale dispuse Grid, numarPieseMozaicOrizontala={}, cu proprietatea ca nu exista doua piese adiacente identice.'
genfunc = lambda imgpath, size : generate(imgpath, COLLECTION_DIR, K_HORIZONTAL=size, K_RANDOM_PLACE=False, K_GRID_ALLOW_DUPLICATES=False, K_HEXAGONAL=True)
cerinta(imagelist, img_idx, comment, genfunc, c)
c.save()
def cer_f5(c):
(a, r, o) = generate('./data/cat.jpg', 'amplificaContinut', 'greedy', None)
add_picture_to_canvas(c, a, 'Cerinta (f) Figura 10.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (f) Figura 10.2: Amplificare continut cu factor 20%. (algoritm greedy)')
add_picture_to_canvas(c, o, 'Cerinta (f) Figura 10.3 (ESEC): Amplificare continut cu factor 50%. (algoritm greedy)')
def cer_f3(c):
(a, r, o) = generate('./data/stalin.jpg', 'eliminaObiect', 'programareDinamica', [75, 195, 245, 280])
add_picture_to_canvas(c, a, 'Cerinta (f) Figura 8.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (f) Figura 8.2: Delimitarea dusmanului.')
add_picture_to_canvas(c, o, 'Cerinta (f) Figura 8.3: Eliminarea.')
def cer_f1(c):
(a, r, o) = generate('./data/capucino.jpeg', 'miscoreazaInaltime', 'greedy', 100)
add_picture_to_canvas(c, a, 'Cerinta (f) Figura 6.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (f) Figura 6.2: Micsorat 100 pixeli in inaltime cu imresize.')
add_picture_to_canvas(c, o, 'Cerinta (f) Figura 6.3 (ESEC): Micsorat 100 pixeli in inaltime cu content-aware seam carving. (algoritm greedy)')
add_picture_to_canvas(c, generate('./data/capucino.jpeg', 'energie', None, None), 'Cerinta (e) Figura 6.4: Partea de sus are noise mai mic decat cea de jos fiind blurata.')
def test_generate_nynorsk_male_name():
element = generate(version=Version.NYNORSK, gender=Gender.MALE)
assert element != None
assert len(element) == 2
def test_generate_norwegian_male_name():
element = generate(version=Version.NORWEGIAN, gender=Gender.MALE)
assert element != None
assert len(element) == 2
def getValues():
#vDivsText.set(name.get())
main.generate(instrument.get().lower(), image_path, noteDuration.get(),
hDivs.get(), vDivs.get(), name.get(), output_dir_path)
def test_generate_animal_works():
result = generate(Version.ANIMAL)
assert result != None
async def display(ctx):
await ctx.send(main.generate(main.reset(), "l"))
async def female(ctx):
await ctx.send(main.generate(main.reset(), "f"))
def cer_d(c):
(a, r, o) = generate('./data/arcTriumf.jpg', 'amplificaContinut', 'greedy', None)
add_picture_to_canvas(c, a, 'Cerinta (d) Figura 4.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (d) Figura 4.2: Amplificare continut cu factor 20%. (algoritm greedy)')
add_picture_to_canvas(c, o, 'Cerinta (d) Figura 4.3: Amplificare continut cu factor 50%. (algoritm greedy)')
def cer_e(c):
(a, r, o) = generate('./data/lac.jpg', 'eliminaObiect', 'programareDinamica', [168, 397, 205, 430])
add_picture_to_canvas(c, a, 'Cerinta (e) Figura 5.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (e) Figura 5.2: Delimitarea zonei ce trebuie eliminata.')
add_picture_to_canvas(c, o, 'Cerinta (e) Figura 5.3: Eliminarea unui obiect din imagine. (algoritm programareDinamica)')
def test_generate_norse_female_name():
element = generate(version=Version.NORSE, gender=Gender.FEMALE)
assert element != None
assert len(element) == 2
def cer_f2(c):
(a, r, o) = generate('./data/ship.jpg', 'maresteLatime', 'programareDinamica', 100)
add_picture_to_canvas(c, a, 'Cerinta (f) Figura 7.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (f) Figura 7.2: Marit 100 pixeli pe latime cu imresize.')
add_picture_to_canvas(c, o, 'Cerinta (f) Figura 7.3: Marit 100 pixeli pe latime cu content-aware seam carving. (algoritm programareDinamica)')
from main import generate
from matplotlib import cbook
from matplotlib import cm
from matplotlib.colors import LightSource
import matplotlib.pyplot as plt
import numpy as np
z = generate(6, 3)
x = np.arange(len(z))
y = np.arange(len(z))
X, Y = np.meshgrid(x, y)
# Set up plot
# fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
fig = plt.figure()
ax = fig.gca(projection='3d')
ls = LightSource(270, 45)
# To use a custom hillshading mode, override the built-in shading and pass
# in the rgb colors of the shaded surface calculated from "shade".
rgb = ls.shade(z, cmap=cm.jet, vert_exag=0.1, blend_mode='soft')
# surf = ax.plot_surface(X, Y, z)
ax.set_zlim(-10, 10)
surf = ax.plot_surface(X,
Y,
z,
rstride=1,
cstride=1,
facecolors=rgb,
def cer_f4(c):
(a, r, o) = generate('./data/lib.jpg', 'micsoreazaLatime', 'greedy', 100)
add_picture_to_canvas(c, a, 'Cerinta (f) Figura 9.1: Imaginea initiala.')
add_picture_to_canvas(c, r, 'Cerinta (f) Figura 9.2: Micsorat 100 pixeli in latime cu imresize.')
add_picture_to_canvas(c, o, 'Cerinta (f) Figura 9.3: Micsorat 100 pixeli in latime cu content-aware seam carving. (algoritm greedy)')
def test_generate(self):
load_dotenv(find_dotenv(), override=True)
path_length = int(os.getenv('PATH_LENGTH'))
ans = generate()
assert (re.compile(r'[a-zA-Z0-9]').match(ans).endpos == path_length)
model.load_state_dict(
torch.load('./outputs/current_params_' + gen_cfg['model'] + '.pt',
map_location='cpu'))
model.to(computing_device)
test_loader = create_generation_loader(gen_cfg['batch_size'],
test_data_fname,
extras=extras)
texts = []
with torch.no_grad():
for minibatch_count, (beers, ratings) in enumerate(test_loader, 0):
model.reset_hidden()
batch = process_test_data(beers, ratings, computing_device)
text = generate(model, batch, gen_cfg, computing_device)
print(text[0])
texts.extend(text)
# val_samples = 0
# bleu_score_avg = 0.
# with torch.no_grad():
# for val_minibatch_count, (val_text, val_beer, val_rating) in enumerate(test_loader, 0):
# val_samples += len(val_beer)
#
# # validation
# validation_loss = 0
# model.reset_hidden()
#
#
# # generate reviews and check bleu scores.
async def on_message(message):
if (message.author.bot):
return
if (message.channel.name.startswith('quote')):
submitTask(message.channel.send(main.generate(message.conntent)))
