def test_compare_crop_and_pad(img_dtype, px, percent, pad_mode, pad_cval, keep_size):
h, w, c = 100, 100, 3
mode_mapping = {
cv2.BORDER_CONSTANT: "constant",
cv2.BORDER_REPLICATE: "edge",
cv2.BORDER_REFLECT101: "reflect",
cv2.BORDER_WRAP: "wrap",
}
pad_mode_iaa = mode_mapping[pad_mode]
bbox_params = A.BboxParams(format="pascal_voc")
keypoint_params = A.KeypointParams(format="xy", remove_invisible=False)
keypoints = np.random.randint(0, min(h, w), [10, 2])
bboxes = []
for i in range(10):
x1, y1 = np.random.randint(0, min(h, w) - 2, 2)
x2 = np.random.randint(x1 + 1, w - 1)
y2 = np.random.randint(y1 + 1, h - 1)
bboxes.append([x1, y1, x2, y2, 0])
transform_albu = A.Compose(
[
A.CropAndPad(
px=px,
percent=percent,
pad_mode=pad_mode,
pad_cval=pad_cval,
keep_size=keep_size,
p=1,
interpolation=cv2.INTER_AREA
if (px is not None and px < 0) or (percent is not None and percent < 0)
else cv2.INTER_LINEAR,
)
],
bbox_params=bbox_params,
keypoint_params=keypoint_params,
)
transform_iaa = A.Compose(
[A.IAACropAndPad(px=px, percent=percent, pad_mode=pad_mode_iaa, pad_cval=pad_cval, keep_size=keep_size, p=1)],
bbox_params=bbox_params,
keypoint_params=keypoint_params,
)
if img_dtype == np.uint8:
img = np.random.randint(0, 256, (h, w, c), dtype=np.uint8)
else:
img = np.random.random((h, w, c)).astype(img_dtype)
res_albu = transform_albu(image=img, keypoints=keypoints, bboxes=bboxes)
res_iaa = transform_iaa(image=img, keypoints=keypoints, bboxes=bboxes)
for key, item in res_albu.items():
if key == "bboxes":
bboxes = np.array(res_iaa[key])
h = bboxes[:, 3] - bboxes[:, 1]
w = bboxes[:, 2] - bboxes[:, 0]
res_iaa[key] = bboxes[(h > 0) & (w > 0)]
assert np.allclose(item, res_iaa[key]), f"{key} are not equal"
folder_cross1 = '%s/dataset2/Camera1' % (path)
folder_cross2 = '%s/dataset2/Camera2' % (path)
plt_name = "classes"
car_name = "classes_carros"
plt_folder = "*/classes"
car_folder = "*/classes_carros"
ocr_file = '%s/OCR/output.txt' % (path)
metadata_length = 35
tam_max = 3
L1_layer = Lambda(lambda tensor: K.abs(tensor[0] - tensor[1]))
train_augs = [[], [], [], [], [], []]
test_augs = [[], [], [], []]
keys = ['Set01', 'Set02', 'Set03', 'Set04', 'Set05']
seq_car = albu.Compose([
albu.IAACropAndPad(px=(0, 8)),
albu.IAAAffine(scale=(0.8, 1.2),
shear=(-8, 8),
order=[0, 1],
cval=(0),
mode='constant'),
albu.ToFloat(max_value=255)
],
p=0.7)
seq_car2 = albu.Compose([
albu.IAACropAndPad(px=(0, 8)),
albu.IAAAffine(scale=(0.8, 1.2),
shear=(-8, 8),
order=[0, 1],
cval=(0),
import albumentations as A
aug = A.Compose([
A.Resize(384, 512, interpolation=1, p=1),
#A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),
# A.HueSaturationValue(hue_shift_limit=2, sat_shift_limit=15, val_shift_limit=20,p = 0.5),
A.OneOf([
A.Blur(blur_limit=5, p=0.3),
A.GaussNoise(var_limit=(5.0, 10.0), p=0.3),
A.IAASharpen(alpha=(0.1, 0.3), lightness=(0.5, 1.0), p=0.4)
],
p=1.0),
A.Flip(p=0.5),
#A.Transpose(p = 0.5),
#A.RandomRotate90(p = 0.5),
A.OneOf([
A.IAACropAndPad(percent=(-0.1, 0.1), keep_size=True, p=0.5),
A.ShiftScaleRotate(p=0.5)
],
p=1.0)
])
for fds in fd_in:
flist = [str(fn) for fn in Path(fds).glob('*.jpg')]
im_fn = flist[0]
# for im_fn in tqdm(flist):
img = cv2.cvtColor(cv2.imread(im_fn), cv2.COLOR_BGR2RGB)
for n_test in range(9):
#img_aug = (aug(image= img)['image']*255).astype('uint8')
img_aug = aug(image=img)['image']
#img_aug = cv2.cvtColor(cv2.imread(im_fn), cv2.COLOR_RGB2BGR)