def index():
form = LoginForm(request.form)
if request.method == 'POST' and form.validate():
result = compute(form.Sepal_Length.data, form.Sepal_Width.data,
form.Petal_Length.data, form.Petal_Width.data)
else:
result = None
return render_template('login.html',form=form, result=result)
def home(filename):
if request.method == 'GET':
if filename is not None:
labels, distance = compute(filename)
print(labels)
print(distance)
return render_template('index.html',
labels=labels,
distance=distance)
else:
return render_template('index.html')
def index():
examId = request.forms.get("exam_id")
pdfFile = request.files.get("file")
if pdfFile and examId:
Path("./saves/").mkdir(parents=True, exist_ok=True)
now = datetime.now().strftime("%Y_%m_%d__%H_%M_%S")
fileLocation = f"./saves/{now}_{pdfFile.filename}"
pdfFile.save(fileLocation)
print("Got file in:", fileLocation)
computation = main.compute(fileLocation, examId)
print(computation)
return computation
else:
return {"error": "Expected fields : file, exam_id"}
def handle_sub_view():
global PathForUploads
global tmpimage
global EmotionalDist
with app.test_request_context():
calculate_features_for_target_image(tmpimage)
des_images, accumu_dist, works = compute(tmpimage, EmotionalDist,
target_hlfeat)
image = cv2.imread(UPLOAD_FOLDER + "/" + tmpimage)
if des_images != None:
GetTransformedImage(image, des_images, accumu_dist,
OUTPUT_FOLDER + "/" + tmpimage)
else:
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imwrite(OUTPUT_FOLDER + "/" + tmpimage, gray_image)
def learn():
global films, directors, actors, genres, writers, rateds
if not films:
films = load_films()
liked = json.loads(request.args.get('liked'))
liked2 = json.loads(request.args.get('liked1'))
films_list = get_film_list()
films_to_add = []
print films_list
for k in films_list:
films_to_add.append(get_film_data(get_film_id(k)))
films_to_add = sorted(films_to_add,
key=lambda f: f["imdbRating"],
reverse=True)
for k in films_to_add:
liked.append(2)
liked2.append(2)
films.append(k)
films, directors, actors, genres, writers, rateds = extract_vector_features(
films)
print "len(films)", len(films)
print "len(liked)", len(liked)
print "len(liked2)", len(liked2)
filtered_films = [f for (i, f) in enumerate(films) if liked[i] != 0]
filtered_liked = filter(lambda l: l != 0, liked)
results1 = compute(filtered_films, directors, actors, genres, writers,
rateds, filtered_liked, len(films_list))
#if liked2:
# filtered_films = [f for (i,f) in enumerate(films) if liked2[i] != 0]
# filtered_liked = filter(lambda l: l != 0, liked2)
# results2 = compute(filtered_films, directors, actors, genres, writers, rateds, filtered_liked, len(films_list))
#else:
results2 = []
return jsonify(results1=results1,
results2=results2,
films=films[-len(films_list):])
def nlp(topic):
result = ''
if topic:
result = compute(topic)
return result
tamanos = np.arange(41, 801, 20)
distancias = np.arange(2, 16, 1)
posibles = []
for x in tamanos:
posiblesRow = []
for y in distancias:
posiblesRow.append((x, y))
posibles.append(posiblesRow)
resultados = []
for x in posibles:
resultadosRow = []
for y in x:
resultado = round(compute(y[0], y[1]), 2)
resultadosRow.append(resultado)
# print(resultado)
resultados.append(resultadosRow)
resultados_ = np.array(resultados)
def pprintMtrx(matrix):
s = [[str(e) for e in row] for row in matrix]
lens = [max(map(len, col)) for col in zip(*s)]
fmt = '\t'.join('{{:{}}}'.format(x) for x in lens)
table = [fmt.format(*row) for row in s]
return ('\n'.join(table))
def test1(self):
self.assertEqual(
main.compute(['0C73', '80C1', 'A2A9', '92F5', '9B57', 0]),
'8CB2BCEE')
def test4(self):
self.assertEqual(
main.compute(['75F5', 'B1AC', '67C1', 'A398', '00BC', 0]),
'C4590000')
def test3(self):
self.assertEqual(
main.compute(['D75C', 'EE87', 'C568', 'FCB3', '4674', 1]), '7FAF')
def test2(self):
self.assertEqual(
main.compute(['27C2', '0879', '35F6', '1A4D', '27BC', 1]), '0807')
import main
import matplotlib.pyplot as plt
import data_creation
acis = []
scores = []
for i in range(20) :
data_creation.create_data(10,80)
aci, score = main.compute()
print(aci, score)
acis.append(aci)
scores.append(score)
# plt.plot(scores, acis)
plt.scatter(scores, acis)
plt.show()
