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)))