CamVid

The CamVid (Cambridge-Driving Labeled Video Database) contains 701 images of urban driving scenes extracted from video sequences, with RGB color-coded segmentation masks across 32 semantic classes.

After downloading, the dataset has the following structure:

train/            369 .png images
train_labels/     369 .png masks  (suffix _L, e.g. 0001TP_009210_L.png)
val/              100 .png images
val_labels/       100 .png masks
test/             232 .png images
test_labels/      232 .png masks
class_dict.csv    class names and RGB color codes

Images are 960×720 pixels. Each mask is an RGB PNG file with the same name as its image plus the _L suffix. The class-to-color mapping is defined in class_dict.csv:

Class	R	G	B
Animal	64	128	64
Archway	192	0	128
Bicyclist	0	128	192
Bridge	0	128	64
Building	128	0	0
Car	64	0	128
CartLuggagePram	64	0	192
Child	192	128	64
Column_Pole	192	192	128
Fence	64	64	128
LaneMkgsDriv	128	0	192
LaneMkgsNonDriv	192	0	64
Misc_Text	128	128	64
MotorcycleScooter	192	0	192
OtherMoving	128	64	64
ParkingBlock	64	192	128
Pedestrian	64	64	0
Road	128	64	128
RoadShoulder	128	128	192
Sidewalk	0	0	192
SignSymbol	192	128	128
Sky	128	128	128
SUVPickupTruck	64	128	192
TrafficCone	0	0	64
TrafficLight	0	64	64
Train	192	64	128
Tree	128	128	0
Truck_Bus	192	128	192
Tunnel	64	0	64
VegetationMisc	192	192	0
Void	0	0	0
Wall	64	192	0

Image Import

Click the open folder button ( ) to open the folder selection dialog, then navigate to the train/ folder of the dataset and click Choose.

Open images folder

PyImageLabeling loads all images from the folder and displays them in the panel.

Images loaded

Annotation Import

Click the add label button ( ) to open the Label Setting form. Fill in the label name (e.g. Bicyclist), then click Import Existing Label to select the annotations folder.

Label Setting form

Navigate to the train_labels/ folder of the dataset and click Choose.

Select annotations folder

The Annotation Import Form then appears to configure how the masks should be interpreted.

Annotation Import Form

Format — select RGB colour mask since each class is encoded as a distinct RGB color in the mask.
Colour of this class in the mask — set to 0, 128, 192, which corresponds to the Bicyclist class in CamVid.

Click OK to confirm.

A final dialog asks for the destination folder where PyImageLabeling will save its annotation files. Select the desired folder and click Choose.

Select destination folder

Finally, click OK in the Label Setting form to complete the import. Repeat the operation for each class you want to label.

The result is shown below — bicyclists are fully labelled with their annotation mask applied.

Final result — labelled bicyclist