def get_tusimple(params):
# augmentation
flip = Flip()
translate = Translate()
rotate = Rotate()
add_noise = AddGaussianNoise()
change_intensity = ChangeIntensity()
resize = Resize(rows=256, cols=512)
norm_to_1 = NormalizeInstensity()
whc_to_cwh = TransposeNumpyArray((2, 0, 1))
train_dataset = DatasetTusimple(
root_path=params.train_root_url,
json_files=params.train_json_file,
transform=transforms.Compose([
flip, translate, rotate, add_noise, change_intensity, resize,
norm_to_1, whc_to_cwh
]),
)
val_dataset = DatasetTusimple(
params.val_root_url,
params.val_json_file,
transform=transforms.Compose([resize, norm_to_1, whc_to_cwh]),
)
return train_dataset, val_dataset
def sample(self):
render = DrawLanemarks(draw_line=True)
chw_to_hwc = TransposeNumpyArray((1, 2, 0))
for i in range(len(self.train_dataset)):
each_sample = self.train_dataset[i]
print(i, each_sample["image_path"])
img_src = cv2.imread(each_sample["image_path"])
each_sample = chw_to_hwc(each_sample)
img = render(**each_sample)
cv2.imshow("culane sample", img[:, :, ::-1])
cv2.imshow("original image", img_src)
if cv2.waitKey(0) == 27:
break
def __init__(self, ):
print("usage examples:")
print("python -m dataset.culane.test sample")
print("python -m dataset.culane.test batch")
print("python -m dataset.culane.test batch shuffle=False")
flip = Flip(1.0)
translate = Translate(1.0)
rotate = Rotate(1.0)
add_noise = AddGaussianNoise(1.0)
change_intensity = ChangeIntensity(1.0)
resize = Resize(rows=256, cols=512)
hwc_to_chw = TransposeNumpyArray((2, 0, 1))
norm_to_1 = NormalizeInstensity()
self.train_dataset = DatasetCollections(transform=transforms.Compose([
flip, translate, rotate, add_noise, change_intensity, resize,
norm_to_1, hwc_to_chw
]), )
def __init__(self,):
flip = Flip(1.0)
translate = Translate(1.0)
rotate = Rotate(1.0)
add_noise = AddGaussianNoise(1.0)
change_intensity = ChangeIntensity(1.0)
resize = Resize(rows=256, cols=512)
hwc_to_chw = TransposeNumpyArray((2, 0, 1))
norm_to_1 = NormalizeInstensity()
json_file = ['label_data_0313.json', 'label_data_0531.json', 'label_data_0601.json']
self.train_dataset = DatasetTusimple(root_path="/media/zzhou/data-tusimple/lane_detection/train_set/",
json_files=json_file,
transform=transforms.Compose([flip,
translate,
rotate,
add_noise,
change_intensity,
resize,
norm_to_1,
hwc_to_chw]),)
def on_tusimple(self):
from dataset.tusimple import DatasetTusimple
from configs.PINet import ParamsTuSimple
dataset_param = ParamsTuSimple()
resize = Resize(rows=256, cols=512)
hwc_to_chw = TransposeNumpyArray((2, 0, 1))
norm_to_1 = NormalizeInstensity()
dataset = DatasetTusimple(
root_path=dataset_param.train_root_url,
json_files=dataset_param.train_json_file,
transform=transforms.Compose([resize, norm_to_1, hwc_to_chw]),
)
for i in range(len(dataset)):
sample = dataset[i]
img_src = cv2.imread(sample["image_path"])
out_x, out_y = self.detector.test_on_image(
np.array([sample["image"]]))
vis_image = draw_points(out_x[0], out_y[0], img_src)
cv2.imshow("sample", vis_image)
cv2.imshow("original image", img_src)
if cv2.waitKey(0) == 27:
break
def on_culane(self, save=False):
from dataset.culane import DatasetCULane
from configs.PINet import ParamsCuLane
dataset_param = ParamsCuLane()
resize = Resize(rows=256, cols=512)
hwc_to_chw = TransposeNumpyArray((2, 0, 1))
norm_to_1 = NormalizeInstensity()
dataset = DatasetCULane(
root_path=dataset_param.train_root_url,
index_file=dataset_param.train_json_file,
transform=transforms.Compose([resize, norm_to_1, hwc_to_chw]),
)
if save:
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
out = cv2.VideoWriter("./output.avi", fourcc, 20.0, (1640, 590))
else:
out = None
for i in range(len(dataset)):
sample = dataset[i]
img_src = cv2.imread(sample["image_path"])
out_x, out_y = self.detector.test_on_image(
np.array([sample["image"]]))
vis_image = draw_points(out_x[0],
out_y[0],
img_src,
scale_x=1640 / 512,
scale_y=590 / 256)
if save:
out.write(vis_image)
cv2.imshow("sample", vis_image)
cv2.imshow("original image", img_src)
if cv2.waitKey(1) == 27:
break
if save:
out.release()
def __init__(self,):
print("usage examples:")
print("python -m dataset.bdd100k.test sample")
print("python -m dataset.bdd100k.test batch")
print("python -m dataset.bdd100k.test batch shuffle=False")
flip = Flip(1.0)
translate = Translate(1.0)
rotate = Rotate(1.0)
add_noise = AddGaussianNoise(1.0)
change_intensity = ChangeIntensity(1.0)
resize = Resize(rows=256, cols=512)
hwc_to_chw = TransposeNumpyArray((2, 0, 1))
norm_to_1 = NormalizeInstensity()
self.train_dataset = DatasetBDD100K(root_path="/media/zzhou/data-BDD100K/bdd100k/",
json_files="labels/bdd100k_labels_images_train.json",
transform=transforms.Compose([flip,
translate,
rotate,
add_noise,
change_intensity,
resize,
norm_to_1,
hwc_to_chw]), )