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segmentation2/README.md

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----
-title: Segmentation
-emoji: 👀
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

segmentation2/app.py

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-import gradio as gr
-
-from matplotlib import gridspec
-import matplotlib.pyplot as plt
-import numpy as np
-from PIL import Image
-import tensorflow as tf
-from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
-
-feature_extractor = SegformerFeatureExtractor.from_pretrained(
-    "nvidia/segformer-b5-finetuned-ade-640-640"
-)
-model = TFSegformerForSemanticSegmentation.from_pretrained(
-    "nvidia/segformer-b5-finetuned-ade-640-640"
-)
-
-def ade_palette():
-    """ADE20K palette that maps each class to RGB values."""
-    return [
-        [204, 87, 92],
-        [112, 185, 212],
-        [45, 189, 106],
-        [234, 123, 67],
-        [78, 56, 123],
-        [210, 32, 89],
-        [90, 180, 56],
-        [155, 102, 200],
-        [33, 147, 176],
-        [255, 183, 76],
-        [67, 123, 89],
-        [190, 60, 45],
-        [134, 112, 200],
-        [56, 45, 189],
-        [200, 56, 123],
-        [87, 92, 204],
-        [120, 56, 123],
-        [45, 78, 123],
-        [156, 200, 56],
-        [32, 90, 210],
-        [56, 123, 67],
-        [180, 56, 123],
-        [123, 67, 45],
-        [45, 134, 200],
-        [67, 56, 123],
-        [78, 123, 67],
-        [32, 210, 90],
-        [45, 56, 189],
-        [123, 56, 123],
-        [56, 156, 200],
-        [189, 56, 45],
-        [112, 200, 56],
-        [56, 123, 45],
-        [200, 32, 90],
-        [123, 45, 78],
-        [200, 156, 56],
-        [45, 67, 123],
-        [56, 45, 78],
-        [45, 56, 123],
-        [123, 67, 56],
-        [56, 78, 123],
-        [210, 90, 32],
-        [123, 56, 189],
-        [45, 200, 134],
-        [67, 123, 56],
-        [123, 45, 67],
-        [90, 32, 210],
-        [200, 45, 78],
-        [32, 210, 90],
-        [45, 123, 67],
-        [165, 42, 87],
-        [72, 145, 167],
-        [15, 158, 75],
-        [209, 89, 40],
-        [32, 21, 121],
-        [184, 20, 100],
-        [56, 135, 15],
-        [128, 92, 176],
-        [1, 119, 140],
-        [220, 151, 43],
-        [41, 97, 72],
-        [148, 38, 27],
-        [107, 86, 176],
-        [21, 26, 136],
-        [174, 27, 90],
-        [91, 96, 204],
-        [108, 50, 107],
-        [27, 45, 136],
-        [168, 200, 52],
-        [7, 102, 27],
-        [42, 93, 56],
-        [140, 52, 112],
-        [92, 107, 168],
-        [17, 118, 176],
-        [59, 50, 174],
-        [206, 40, 143],
-        [44, 19, 142],
-        [23, 168, 75],
-        [54, 57, 189],
-        [144, 21, 15],
-        [15, 176, 35],
-        [107, 19, 79],
-        [204, 52, 114],
-        [48, 173, 83],
-        [11, 120, 53],
-        [206, 104, 28],
-        [20, 31, 153],
-        [27, 21, 93],
-        [11, 206, 138],
-        [112, 30, 83],
-        [68, 91, 152],
-        [153, 13, 43],
-        [25, 114, 54],
-        [92, 27, 150],
-        [108, 42, 59],
-        [194, 77, 5],
-        [145, 48, 83],
-        [7, 113, 19],
-        [25, 92, 113],
-        [60, 168, 79],
-        [78, 33, 120],
-        [89, 176, 205],
-        [27, 200, 94],
-        [210, 67, 23],
-        [123, 89, 189],
-        [225, 56, 112],
-        [75, 156, 45],
-        [172, 104, 200],
-        [15, 170, 197],
-        [240, 133, 65],
-        [89, 156, 112],
-        [214, 88, 57],
-        [156, 134, 200],
-        [78, 57, 189],
-        [200, 78, 123],
-        [106, 120, 210],
-        [145, 56, 112],
-        [89, 120, 189],
-        [185, 206, 56],
-        [47, 99, 28],
-        [112, 189, 78],
-        [200, 112, 89],
-        [89, 145, 112],
-        [78, 106, 189],
-        [112, 78, 189],
-        [156, 112, 78],
-        [28, 210, 99],
-        [78, 89, 189],
-        [189, 78, 57],
-        [112, 200, 78],
-        [189, 47, 78],
-        [205, 112, 57],
-        [78, 145, 57],
-        [200, 78, 112],
-        [99, 89, 145],
-        [200, 156, 78],
-        [57, 78, 145],
-        [78, 57, 99],
-        [57, 78, 145],
-        [145, 112, 78],
-        [78, 89, 145],
-        [210, 99, 28],
-        [145, 78, 189],
-        [57, 200, 136],
-        [89, 156, 78],
-        [145, 78, 99],
-        [99, 28, 210],
-        [189, 78, 47],
-        [28, 210, 99],
-        [78, 145, 57],
-    ]
-
-labels_list = []
-
-with open(r'labels.txt', 'r') as fp:
-    for line in fp:
-        labels_list.append(line[:-1])
-
-colormap = np.asarray(ade_palette())
-
-def label_to_color_image(label):
-    if label.ndim != 2:
-        raise ValueError("Expect 2-D input label")
-
-    if np.max(label) >= len(colormap):
-        raise ValueError("label value too large.")
-    return colormap[label]
-
-def draw_plot(pred_img, seg):
-    fig = plt.figure(figsize=(20, 15))
-
-    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
-
-    plt.subplot(grid_spec[0])
-    plt.imshow(pred_img)
-    plt.axis('off')
-    LABEL_NAMES = np.asarray(labels_list)
-    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
-    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
-
-    unique_labels = np.unique(seg.numpy().astype("uint8"))
-    ax = plt.subplot(grid_spec[1])
-    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
-    ax.yaxis.tick_right()
-    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
-    plt.xticks([], [])
-    ax.tick_params(width=0.0, labelsize=25)
-    return fig
-
-def sepia(input_img):
-    input_img = Image.fromarray(input_img)
-
-    inputs = feature_extractor(images=input_img, return_tensors="tf")
-    outputs = model(**inputs)
-    logits = outputs.logits
-
-    logits = tf.transpose(logits, [0, 2, 3, 1])
-    logits = tf.image.resize(
-        logits, input_img.size[::-1]
-    )  # We reverse the shape of `image` because `image.size` returns width and height.
-    seg = tf.math.argmax(logits, axis=-1)[0]
-
-    color_seg = np.zeros(
-        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
-    )  # height, width, 3
-    for label, color in enumerate(colormap):
-        color_seg[seg.numpy() == label, :] = color
-
-    # Show image + mask
-    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
-    pred_img = pred_img.astype(np.uint8)
-
-    fig = draw_plot(pred_img, seg)
-    return fig
-
-demo = gr.Interface(fn=sepia,
-                    inputs=gr.Image(shape=(400, 600)),
-                    outputs=['plot'],
-                    examples=["ADE_val_00000001.jpeg", "ADE_val_00001159.jpg", "ADE_val_00001248.jpg", "ADE_val_00001472.jpg"],
-                    allow_flagging='never')
-
-
-demo.launch()

segmentation2/labels.txt

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-wall
-building
-sky
-floor
-tree
-ceiling
-road
-bed
-windowpane
-grass
-cabinet
-sidewalk
-person
-earth
-door
-table
-mountain
-plant
-curtain
-chair
-car
-water
-painting
-sofa
-shelf
-house
-sea
-mirror
-rug
-field
-armchair
-seat
-fence
-desk
-rock
-wardrobe
-lamp
-bathtub
-railing
-cushion
-base
-box
-column
-signboard
-chest of drawers
-counter
-sand
-sink
-skyscraper
-fireplace
-refrigerator
-grandstand
-path
-stairs
-runway
-case
-pool table
-pillow
-screen door
-stairway
-river
-bridge
-bookcase
-blind
-coffee table
-toilet
-flower
-book
-hill
-bench
-countertop
-stove
-palm
-kitchen island
-computer
-swivel chair
-boat
-bar
-arcade machine
-hovel
-bus
-towel
-light
-truck
-tower
-chandelier
-awning
-streetlight
-booth
-television receiver
-airplane
-dirt track
-apparel
-pole
-land
-bannister
-escalator
-ottoman
-bottle
-buffet
-poster
-stage
-van
-ship
-fountain
-conveyer belt
-canopy
-washer
-plaything
-swimming pool
-stool
-barrel
-basket
-waterfall
-tent
-bag
-minibike
-cradle
-oven
-ball
-food
-step
-tank
-trade name
-microwave
-pot
-animal
-bicycle
-lake
-dishwasher
-screen
-blanket
-sculpture
-hood
-sconce
-vase
-traffic light
-tray
-ashcan
-fan
-pier
-crt screen
-plate
-monitor
-bulletin board
-shower
-radiator
-glass
-clock
-flag

segmentation2/requirements.txt

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-torch
-transformers
-tensorflow
-numpy
-Image
-matplotlib