def add_teacher():
data = request.json
t_obj = teacher(name=data['name'], contact=data['contact'])
session.add(t_obj)
session.commit()
session.close()
return json.dumps({"data": "{} - Added Succesfully ".format(data['name'])})
def add_teacher(request):
if request.method == 'POST':
form = teacherForm(request.POST)
if form.is_valid():
a = teacher(first_name=form.cleaned_data["first_name"],
last_name=form.cleaned_data["last_name"],
email=form.cleaned_data["email"],
office_details=form.cleaned_data['office_details'],
phone=form.cleaned_data['phone'])
a.save()
return HttpResponseRedirect('/all-teacher/')
else:
form = teacherForm()
return render_to_response('add-teacher.html', {'form': form}, RequestContext(request))
with tf.Graph().as_default(), tf.Session() as sess:
# placeholders for training data
phone_ = tf.placeholder(tf.float32, [None, PATCH_SIZE])
phone_image = tf.reshape(phone_, [-1, PATCH_HEIGHT, PATCH_WIDTH, 3])
dslr_ = tf.placeholder(tf.float32, [None, PATCH_SIZE])
dslr_image = tf.reshape(dslr_, [-1, PATCH_HEIGHT, PATCH_WIDTH, 3])
adv_ = tf.placeholder(tf.float32, [None, 1])
# get processed enhanced image
enhanced, _, _ = models.teacher(phone_image)
# transform both dslr and enhanced images to grayscale
enhanced_gray = tf.reshape(tf.image.rgb_to_grayscale(enhanced),
[-1, PATCH_WIDTH * PATCH_HEIGHT])
dslr_gray = tf.reshape(tf.image.rgb_to_grayscale(dslr_image),
[-1, PATCH_WIDTH * PATCH_HEIGHT])
# push randomly the enhanced or dslr image to an adversarial CNN-discriminator
adversarial_ = tf.multiply(enhanced_gray, 1 - adv_) + tf.multiply(
dslr_gray, adv_) # if adv_ = 0, enhanced_gray
adversarial_image = tf.reshape(adversarial_,
[-1, PATCH_HEIGHT, PATCH_WIDTH, 1])
with tf.Graph().as_default(), tf.Session() as sess:
# placeholders for training data
phone_ = tf.placeholder(tf.float32, [None, PATCH_SIZE])
phone_image = tf.reshape(phone_, [-1, PATCH_HEIGHT, PATCH_WIDTH, 3])
dslr_ = tf.placeholder(tf.float32, [None, PATCH_SIZE])
dslr_image = tf.reshape(dslr_, [-1, PATCH_HEIGHT, PATCH_WIDTH, 3])
adv_ = tf.placeholder(tf.float32, [None, 1])
# get processed enhanced image
enhanced, at1, at2 = models.student(phone_image)
enhanced_teacher, at1_, at2_ = models.teacher(phone_image)
# transform both dslr and enhanced images to grayscale
enhanced_gray = tf.reshape(tf.image.rgb_to_grayscale(enhanced),
[-1, PATCH_WIDTH * PATCH_HEIGHT])
dslr_gray = tf.reshape(tf.image.rgb_to_grayscale(dslr_image),
[-1, PATCH_WIDTH * PATCH_HEIGHT])
# push randomly the enhanced or dslr image to an adversarial CNN-discriminator
adversarial_ = tf.multiply(enhanced_gray, 1 - adv_) + tf.multiply(
dslr_gray, adv_) # if adv_ = 0, enhanced_gray
adversarial_image = tf.reshape(adversarial_,
[-1, PATCH_HEIGHT, PATCH_WIDTH, 1])
from models import Session, teacher, student session0 = Session() teacher0 = teacher(teacher_id = 1, name = "Ya", email = "*****@*****.**", password = "******") student0 = student(student_id = 1, name = "Tlumok",s_bal = 10, marks = [9,11) session0.add(teacher0) session0.add(student0) session0.commit() session0.close() # psql -h localhost -d students_rating -U postgres -p 5432 -a -q -f create_table.sql # python add_models.py # чекнути пгадмін # alembic revision --autogenerate # alembic upgrade head # env LDFLAGS="-I/usr/local/opt/openssl/include -L/usr/local/opt/openssl/lib" pip install psycopg2
# get all available image resolutions
res_sizes = utils.get_resolutions()
# get the specified image resolution
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE = utils.get_specified_res(res_sizes, phone, resolution)
# disable gpu if specified
config = tf.ConfigProto(device_count={'GPU': 0}) if use_gpu == "false" else None
# create placeholders for input images
x_ = tf.placeholder(tf.float32, [None, IMAGE_SIZE])
x_image = tf.reshape(x_, [-1, IMAGE_HEIGHT, IMAGE_WIDTH, 3])
# generate enhanced image
# enhanced,_,_ = student(x_image)
enhanced,_,_ = teacher(x_image)
with tf.Session(config=config) as sess:
test_dir = dped_dir + phone.replace("_orig", "") + "/test_data/full_size_test_images/"
test_photos = [f for f in os.listdir(test_dir) if os.path.isfile(test_dir + f)]
if test_subset == "small":
# use five first images only
test_photos = test_photos[0:5]
if phone.endswith("_orig"):
# load pre-trained model
saver = tf.train.Saver()
saver.restore(sess, "pretrained_teacher_model/teacher.ckpt")