Commit
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Parent(s):
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Upload 7 files
Browse files- ADE_val_00000001.jpeg +0 -0
- ADE_val_00001159.jpg +0 -0
- ADE_val_00001248.jpg +0 -0
- ADE_val_00001472.jpg +0 -0
- README.md +12 -0
- app (1).py +242 -0
- requirements.txt +6 -0
ADE_val_00000001.jpeg
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ADE_val_00001159.jpg
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ADE_val_00001248.jpg
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ADE_val_00001472.jpg
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README.md
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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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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app (1).py
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import gradio as gr
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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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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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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],
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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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labels_list = []
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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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colormap = np.asarray(ade_palette())
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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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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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def draw_plot(pred_img, seg):
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fig = plt.figure(figsize=(20, 15))
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grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
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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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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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def sepia(input_img):
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input_img = Image.fromarray(input_img)
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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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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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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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# 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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fig = draw_plot(pred_img, seg)
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return fig
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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=["ADE_val_00000001.jpeg", "ADE_val_00001159.jpg", "ADE_val_00001248.jpg", "ADE_val_00001472.jpg"],
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allow_flagging='never')
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demo.launch()
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requirements.txt
ADDED
@@ -0,0 +1,6 @@
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1 |
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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
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