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  1. README.md +5 -5
  2. app.py +242 -0
  3. labels.txt +150 -0
  4. requirements.txt +6 -0
README.md CHANGED
@@ -1,10 +1,10 @@
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  ---
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- title: Segmentation2
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- emoji: 🐢
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- colorFrom: purple
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- colorTo: green
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  sdk: gradio
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- sdk_version: 3.47.1
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  app_file: app.py
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  pinned: false
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  ---
 
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  ---
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+ title: Segmentation
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+ emoji: 👀
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+ colorFrom: red
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+ colorTo: blue
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  sdk: gradio
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+ sdk_version: 3.44.4
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  app_file: app.py
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  pinned: false
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  ---
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-ade-640-640"
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+ )
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+ model = TFSegformerForSemanticSegmentation.from_pretrained(
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+ "nvidia/segformer-b5-finetuned-ade-640-640"
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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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+ [204, 87, 92],
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+ [112, 185, 212],
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+ [45, 189, 106],
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+ [234, 123, 67],
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+ [78, 56, 123],
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+ [210, 32, 89],
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+ [90, 180, 56],
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+ [155, 102, 200],
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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],
134
+ [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=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
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+ allow_flagging='never')
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+
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+
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+ demo.launch()
labels.txt ADDED
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+ wall
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+ building
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+ sky
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+ floor
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+ tree
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+ ceiling
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+ road
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+ bed
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+ windowpane
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+ grass
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+ cabinet
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+ sidewalk
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+ person
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+ earth
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+ door
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+ table
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+ mountain
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+ plant
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+ curtain
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+ chair
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+ car
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+ water
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+ painting
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+ sofa
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+ shelf
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+ house
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+ sea
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+ mirror
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+ rug
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+ field
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+ armchair
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+ seat
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+ fence
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+ desk
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+ rock
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+ wardrobe
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+ lamp
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+ bathtub
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+ railing
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+ cushion
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+ base
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+ box
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+ column
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+ signboard
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+ chest of drawers
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+ counter
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+ sand
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+ sink
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+ skyscraper
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+ fireplace
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+ refrigerator
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+ grandstand
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+ path
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+ stairs
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+ runway
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+ case
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+ pool table
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+ pillow
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+ screen door
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+ stairway
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+ river
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+ bridge
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+ bookcase
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+ blind
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+ coffee table
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+ toilet
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+ flower
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+ book
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+ hill
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+ bench
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+ countertop
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+ stove
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+ palm
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+ kitchen island
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+ computer
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+ swivel chair
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+ boat
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+ bar
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+ arcade machine
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+ hovel
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+ bus
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+ towel
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+ light
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+ truck
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+ tower
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+ chandelier
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+ awning
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+ streetlight
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+ booth
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+ television receiver
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+ airplane
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+ dirt track
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+ apparel
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+ pole
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+ land
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+ bannister
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+ escalator
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+ ottoman
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+ bottle
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+ buffet
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+ poster
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+ stage
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+ van
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+ ship
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+ fountain
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+ conveyer belt
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+ canopy
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+ washer
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+ plaything
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+ swimming pool
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+ stool
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+ barrel
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+ basket
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+ waterfall
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+ tent
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+ bag
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+ minibike
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+ cradle
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+ oven
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+ ball
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+ food
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+ step
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+ tank
124
+ trade name
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+ microwave
126
+ pot
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+ animal
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+ bicycle
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+ lake
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+ dishwasher
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+ screen
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+ blanket
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+ sculpture
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+ hood
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+ sconce
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+ vase
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+ traffic light
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+ tray
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+ ashcan
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+ fan
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+ pier
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+ crt screen
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+ plate
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+ monitor
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+ bulletin board
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+ shower
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+ radiator
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+ glass
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+ clock
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+ flag
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