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Incheon_city.jpeg ADDED
Incheon_stadium.jpeg ADDED
app.py ADDED
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+ import gradio as gr
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+
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+ from matplotlib import gridspec
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+ import matplotlib.pyplot as plt
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+ import numpy as np
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+ from PIL import Image
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+ import tensorflow as tf
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+ from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
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+
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+ feature_extractor = SegformerFeatureExtractor.from_pretrained(
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+ "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
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+ )
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+ model = TFSegformerForSemanticSegmentation.from_pretrained(
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+ "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
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+ )
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+
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+ def ade_palette():
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+ """ADE20K palette that maps each class to RGB values."""
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+ return [
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+ [0, 0, 0],
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+ [255, 0, 0],
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+ [255, 255, 0],
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+ [255, 255, 255],
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+ [255, 0, 255],
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+ [0, 255, 0],
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+ [0, 255, 255],
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+ [0, 0, 255],
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+ [33, 147, 176],
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+ [255, 183, 76],
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+ [67, 123, 89],
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+ [190, 60, 45],
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+ [134, 112, 200],
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+ [56, 45, 189],
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+ [200, 56, 123],
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+ [87, 92, 204],
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+ [120, 56, 123],
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+ [45, 78, 123],
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+ [156, 200, 56],
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+ [32, 90, 210],
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+ [56, 123, 67],
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+ [180, 56, 123],
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+ [123, 67, 45],
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+ [45, 134, 200],
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+ [67, 56, 123],
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+ [78, 123, 67],
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+ [32, 210, 90],
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+ [45, 56, 189],
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+ [123, 56, 123],
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+ [56, 156, 200],
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+ [189, 56, 45],
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+ [112, 200, 56],
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+ [56, 123, 45],
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+ [200, 32, 90],
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+ [123, 45, 78],
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+ [200, 156, 56],
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+ [45, 67, 123],
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+ [56, 45, 78],
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+ [45, 56, 123],
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+ [123, 67, 56],
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+ [56, 78, 123],
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+ [210, 90, 32],
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+ [123, 56, 189],
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+ [45, 200, 134],
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+ [67, 123, 56],
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+ [123, 45, 67],
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+ [90, 32, 210],
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+ [200, 45, 78],
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+ [32, 210, 90],
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+ [45, 123, 67],
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+ [165, 42, 87],
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+ [72, 145, 167],
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+ [15, 158, 75],
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+ [209, 89, 40],
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+ [32, 21, 121],
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+ [184, 20, 100],
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+ [56, 135, 15],
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+ [128, 92, 176],
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+ [1, 119, 140],
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+ [220, 151, 43],
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+ [41, 97, 72],
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+ [148, 38, 27],
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+ [107, 86, 176],
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+ [21, 26, 136],
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+ [174, 27, 90],
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+ [91, 96, 204],
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+ [108, 50, 107],
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+ [27, 45, 136],
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+ [168, 200, 52],
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+ [7, 102, 27],
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+ [42, 93, 56],
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+ [140, 52, 112],
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+ [92, 107, 168],
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+ [17, 118, 176],
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+ [59, 50, 174],
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+ [206, 40, 143],
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+ [44, 19, 142],
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+ [23, 168, 75],
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+ [54, 57, 189],
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+ [144, 21, 15],
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+ [15, 176, 35],
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+ [107, 19, 79],
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+ [204, 52, 114],
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+ [48, 173, 83],
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+ [11, 120, 53],
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+ [206, 104, 28],
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+ [20, 31, 153],
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+ [27, 21, 93],
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+ [11, 206, 138],
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+ [112, 30, 83],
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+ [68, 91, 152],
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+ [153, 13, 43],
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+ [25, 114, 54],
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+ [92, 27, 150],
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+ [108, 42, 59],
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+ [194, 77, 5],
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+ [145, 48, 83],
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+ [7, 113, 19],
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+ [25, 92, 113],
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+ [60, 168, 79],
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+ [78, 33, 120],
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+ [89, 176, 205],
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+ [27, 200, 94],
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+ [210, 67, 23],
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+ [123, 89, 189],
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+ [225, 56, 112],
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+ [75, 156, 45],
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+ [172, 104, 200],
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+ [15, 170, 197],
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+ [240, 133, 65],
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+ [89, 156, 112],
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+ [214, 88, 57],
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+ [156, 134, 200],
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+ [78, 57, 189],
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+ [200, 78, 123],
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+ [106, 120, 210],
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+ [145, 56, 112],
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+ [89, 120, 189],
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+ [185, 206, 56],
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+ [47, 99, 28],
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+ [112, 189, 78],
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+ [200, 112, 89],
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+ [89, 145, 112],
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+ [78, 106, 189],
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+ [112, 78, 189],
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+ [156, 112, 78],
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+ [28, 210, 99],
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+ [78, 89, 189],
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+ [189, 78, 57],
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+ [112, 200, 78],
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+ [189, 47, 78],
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+ [205, 112, 57],
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+ [78, 145, 57],
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+ [200, 78, 112],
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+ [99, 89, 145],
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+ [200, 156, 78],
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+ [57, 78, 145],
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+ [78, 57, 99],
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+ [57, 78, 145],
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+ [145, 112, 78],
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+ [78, 89, 145],
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+ [210, 99, 28],
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+ [145, 78, 189],
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+ [57, 200, 136],
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+ [89, 156, 78],
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+ [145, 78, 99],
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+ [99, 28, 210],
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+ [189, 78, 47],
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+ [28, 210, 99],
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+ [78, 145, 57],
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+ ]
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+
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+ labels_list = []
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+
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+ with open(r'labels.txt', 'r') as fp:
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+ for line in fp:
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+ labels_list.append(line[:-1])
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+
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+ colormap = np.asarray(ade_palette())
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+
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+ def label_to_color_image(label):
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+ if label.ndim != 2:
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+ raise ValueError("Expect 2-D input label")
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+
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+ if np.max(label) >= len(colormap):
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+ raise ValueError("label value too large.")
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+ return colormap[label]
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+
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+ def draw_plot(pred_img, seg):
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+ fig = plt.figure(figsize=(20, 15))
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+
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+ grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
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+
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+ plt.subplot(grid_spec[0])
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+ plt.imshow(pred_img)
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+ plt.axis('off')
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+ LABEL_NAMES = np.asarray(labels_list)
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+ FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
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+ FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
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+
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+ unique_labels = np.unique(seg.numpy().astype("uint8"))
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+ ax = plt.subplot(grid_spec[1])
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+ plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
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+ ax.yaxis.tick_right()
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+ plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
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+ plt.xticks([], [])
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+ ax.tick_params(width=0.0, labelsize=25)
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+ return fig
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+
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+ def sepia(input_img):
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+ input_img = Image.fromarray(input_img)
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+
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+ inputs = feature_extractor(images=input_img, return_tensors="tf")
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+ outputs = model(**inputs)
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+ logits = outputs.logits
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+
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+ logits = tf.transpose(logits, [0, 2, 3, 1])
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+ logits = tf.image.resize(
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+ logits, input_img.size[::-1]
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+ ) # We reverse the shape of `image` because `image.size` returns width and height.
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+ seg = tf.math.argmax(logits, axis=-1)[0]
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+
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+ color_seg = np.zeros(
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+ (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
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+ ) # height, width, 3
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+ for label, color in enumerate(colormap):
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+ color_seg[seg.numpy() == label, :] = color
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+
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+ # Show image + mask
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+ pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
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+ pred_img = pred_img.astype(np.uint8)
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+
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+ fig = draw_plot(pred_img, seg)
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+ return fig
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+
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+ demo = gr.Interface(fn=sepia,
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+ inputs=gr.Image(shape=(400, 600)),
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+ outputs=['plot'],
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+ examples=["cheonggyecheon_stream_in_seoul_city.jpg", "Incheon_stadium.jpeg", "Incheon_city.jpeg"],
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+ allow_flagging='never')
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+
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+
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+ demo.launch()
cheonggyecheon_stream_in_seoul_city.jpg ADDED
labels.txt ADDED
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+ road
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+ sidewalk
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+ building
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+ wall
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+ fence
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+ pole
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+ traffic light
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+ traffic sign
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+ vegetation
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+ terrain
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+ sky
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+ person
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+ rider
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+ car
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+ truck
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+ bus
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+ train
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+ motorcycle
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+ bicycle
requirements.txt ADDED
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+ torch
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+ transformers
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+ tensorflow
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+ numpy
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+ Image
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+ matplotlib