def load_exercice(exo_setting_path, group):
exercice_name = choice("Choose exercice", os.listdir(exo_setting_path))
exercice_path = os.path.join(exo_setting_path, exercice_name)
exercice = Exercice(exercice_name, exercice_path)
exercice.save_path = os.path.join(exercice_path, "save",
group.name.split(".")[0])
if os.path.exists(exercice.save_path):
paths = os.listdir(exercice.save_path)
for path in paths:
with open(os.path.join(exercice.save_path, path), "r") as file:
try:
reader = csv.reader(file, delimiter=",")
line = next(reader)
students_codes = line[:-2]
grade = float(line[-2])
comment = line[-1]
students = []
for student_code in students_codes:
if (student := find_student(group, student_code)):
students.append(student)
else:
print("ERROR cannot find code : ", student_code)
exit(1)
exercice.entries.append(
ExerciceEntry(students, grade, comment))
except Exception as e:
print("Reading file ",
os.path.join(exercice.save_path, path), "got error",
e)
def open_exercice(exercice, path):
possible_path = os.path.join(path, exercice.name + ".py")
if not os.path.exists(possible_path):
possible_path = os.path.join(
path, choice("File not found, choose plz", os.listdir(path)))
print(possible_path)
os.system("code \"" + possible_path + "\"")
def load_students(group_path):
group_name = choice("Choose group", os.listdir(group_path))
group_path = os.path.join(group_path, group_name)
group = Group(group_name, group_path)
with open(group_path, encoding='utf-8') as file:
reader = csv.reader(file, delimiter=",")
# skip first column
next(reader)
for row in reader:
group.students.append(Student(*(row[:5])))
return group
def augment_background(im, fgmask, rng):
path = util.choice(get_inria_holiday_background_paths(), rng)
background_im = improc.imread_jpeg(path)
cam = cameralib.Camera.create2D(background_im.shape)
cam_new = cam.copy()
zoom_aug_factor = rng.uniform(1.2, 1.5)
cam_new.zoom(zoom_aug_factor *
np.max(im.shape[:2] / np.asarray(background_im.shape[:2])))
cam_new.center_principal_point(im.shape)
cam_new.shift_image(util.random_uniform_disc(rng) * im.shape[0] * 0.1)
warped_background_im = cameralib.reproject_image(background_im, cam,
cam_new, im.shape)
return improc.blend_image(warped_background_im, im, fgmask)
def object_occlude(im, rng, inplace=True):
# Following [Sárándi et al., arxiv:1808.09316, arxiv:1809.04987]
factor = im.shape[0] / 256
count = rng.randint(1, 8)
occluders = augmentation.voc_loader.load_occluders()
for i in range(count):
occluder, occ_mask = util.choice(occluders, rng)
rescale_factor = rng.uniform(0.2, 1.0) * factor * FLAGS.occlude_aug_scale
occ_mask = improc.resize_by_factor(occ_mask, rescale_factor)
occluder = improc.resize_by_factor(occluder, rescale_factor)
center = rng.uniform(0, im.shape[0], size=2)
im = improc.paste_over(occluder, im, alpha=occ_mask, center=center, inplace=inplace)
return im
def augment_background(im, fgmask, rng):
path = util.choice(get_inria_holiday_background_paths(), rng)
background_im = improc.imread_jpeg(path)
cam = cameralib.Camera.create2D(background_im.shape)
cam_new = cam.copy()
zoom_aug_factor = rng.uniform(1.2, 1.5)
cam_new.zoom(zoom_aug_factor *
np.max(im.shape[:2] / np.asarray(background_im.shape[:2])))
cam_new.center_principal_point(im.shape)
cam_new.shift_image(util.random_uniform_disc(rng) * im.shape[0] * 0.1)
interp_str = FLAGS.image_interpolation_train
antialias = FLAGS.antialias_train
interp = getattr(cv2, 'INTER_' + interp_str.upper())
warped_background_im = cameralib.reproject_image(
background_im,
cam,
cam_new,
im.shape,
interp=interp,
antialias_factor=antialias)
return improc.blend_image(warped_background_im, im, fgmask)