语义分割标签
我是Trynna通过U-NET制作语义分割的划痕代码。我将使用CityScapes数据集。我正在尝试制作由密钥(汽车,火车,人类等)和值(RGB信息)组成的字典(Python)。如何将字典与我的dract_truth数据匹配?
标记字典的示例如下
color_map = {
'0': [0, 0, 0], # unlabelled
'1': [128, 64, 128], # road
'2': [244, 35, 232], # sidewalk
'3': [70, 70, 70], # building
'4': [102, 102, 156], # wall
'5': [190, 153, 153], # fence
'6': [153, 153, 153], # pole
'7': [250,170, 30], # traffic_light
'8': [220, 220, 0], # traffic_sign
'9': [107, 142, 35], # vegetation
'10': [152, 251, 152], # terrain
'11': [0, 130, 180], # sky
'12': [220, 20, 60], # person
'13': [255, 0, 0], # rider
'14': [0, 0, 142], # car
'15': [0, 0, 70], # truck
'16': [0, 60, 100], # bus
'17': [0, 80, 100], # train
'18': [0, 0, 230], # motorcycle
'19': [119, 11, 32] # bicycle
}
I'm trynna make a scratch code of Semantic segmentation through U-Net. I'll use Cityscapes Dataset. I'm trying to make a dictionary(python) composed of the key(car, train, human, etc) and the value(rgb info). How can I match the dictionary with my ground_truth data?
example of labeling dictionary is like below
color_map = {
'0': [0, 0, 0], # unlabelled
'1': [128, 64, 128], # road
'2': [244, 35, 232], # sidewalk
'3': [70, 70, 70], # building
'4': [102, 102, 156], # wall
'5': [190, 153, 153], # fence
'6': [153, 153, 153], # pole
'7': [250,170, 30], # traffic_light
'8': [220, 220, 0], # traffic_sign
'9': [107, 142, 35], # vegetation
'10': [152, 251, 152], # terrain
'11': [0, 130, 180], # sky
'12': [220, 20, 60], # person
'13': [255, 0, 0], # rider
'14': [0, 0, 142], # car
'15': [0, 0, 70], # truck
'16': [0, 60, 100], # bus
'17': [0, 80, 100], # train
'18': [0, 0, 230], # motorcycle
'19': [119, 11, 32] # bicycle
}
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