import random

import numpy as np

random.seed(0)

# detection model classes
CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
           'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
           'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
           'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
           'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
           'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
           'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
           'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
           'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
           'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
           'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
           'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
           'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
           'scissors', 'teddy bear', 'hair drier', 'toothbrush')

# colors for per classes
COLORS = {
    cls: [random.randint(0, 255) for _ in range(3)]
    for i, cls in enumerate(CLASSES)
}

# colors for segment masks
MASK_COLORS = np.array([(255, 56, 56), (255, 157, 151), (255, 112, 31),
                        (255, 178, 29), (207, 210, 49), (72, 249, 10),
                        (146, 204, 23), (61, 219, 134), (26, 147, 52),
                        (0, 212, 187), (44, 153, 168), (0, 194, 255),
                        (52, 69, 147), (100, 115, 255), (0, 24, 236),
                        (132, 56, 255), (82, 0, 133), (203, 56, 255),
                        (255, 149, 200), (255, 55, 199)],
                       dtype=np.float32) / 255.

# alpha for segment masks
ALPHA = 0.5