segmentation

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-b0-finetuned-ade-512-512"
+)
+model = TFSegformerForSemanticSegmentation.from_pretrained(
+    "nvidia/segformer-b0-finetuned-ade-512-512"
+)
+
+def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
+    return [
+        [22, 122, 213],
+        [240, 3, 156],
+        [87, 176, 33],
+        [154, 88, 111],
+        [63, 54, 244],
+        [201, 235, 59],
+        [102, 66, 183],
+        [94, 147, 5],
+        [39, 198, 247],
+        [17, 149, 92],
+        [130, 78, 184],
+        [246, 119, 107],
+        [225, 23, 68],
+        [52, 189, 140],
+        [142, 10, 22],
+        [114, 161, 251],
+        [168, 55, 34],
+        [75, 203, 89],
+        [32, 45, 235],
+        [74, 1, 129],
+        [31, 166, 96],
+        [223, 51, 202],
+        [57, 72, 27],
+        [143, 191, 176],
+        [111, 33, 244],
+        [20, 155, 62],
+        [128, 99, 209],
+        [254, 120, 14],
+        [229, 67, 175],
+        [53, 206, 40],
+        [198, 77, 10],
+        [8, 166, 142],
+        [133, 45, 111],
+        [222, 199, 239],
+        [56, 18, 90],
+        [164, 98, 206],
+        [239, 135, 60],
+        [106, 28, 139],
+        [49, 172, 224],
+        [179, 109, 34],
+        [12, 191, 157],
+        [121, 64, 88],
+        [243, 214, 127],
+        [82, 11, 165],
+        [158, 37, 192],
+        [31, 144, 55],
+        [176, 220, 252],
+        [68, 5, 123],
+        [220, 157, 73],
+        [41, 183, 210],
+        [173, 85, 14],
+        [16, 131, 99],
+        [135, 50, 177],
+        [227, 202, 244],
+        [47, 175, 217],
+        [181, 112, 28],
+        [15, 190, 160],
+        [124, 66, 91],
+        [241, 217, 130],
+        [80, 13, 168],
+        [157, 40, 195],
+        [30, 147, 52],
+        [175, 223, 249],
+        [67, 7, 126],
+        [218, 160, 76],
+        [235, 141, 45],
+        [101, 33, 149],
+        [46, 178, 220],
+        [182, 114, 31],
+        [14, 193, 163],
+        [122, 69, 94],
+        [240, 219, 133],
+        [79, 16, 171],
+        [156, 43, 198],
+        [29, 150, 58],
+        [225, 207, 243],
+        [51, 27, 121],
+        [159, 107, 229],
+        [234, 143, 48],
+        [100, 35, 152],
+        [239, 221, 136],
+        [78, 19, 174],
+        [155, 46, 201],
+        [28, 152, 61],
+        [173, 227, 243],
+        [20, 127, 108],
+        [138, 59, 179],
+        [224, 209, 245],
+        [50, 29, 124],
+        [161, 109, 232],
+        [233, 145, 51],
+        [99, 37, 155],
+        [44, 174, 226],
+        [184, 118, 20],
+        [12, 195, 169],
+        [125, 73, 100],
+        [238, 223, 139],
+        [77, 22, 177],
+        [154, 49, 204],
+        [27, 154, 64],
+        [51, 86, 205]
+    ]
+
+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=(800, 600)),
+                    outputs=['plot'],
+                    examples=["indoor.jpg", "indoor1.jpg", "indoor2.jpg", "indoor3.jpg"],
+                    allow_flagging='never')
+
+
+demo.launch()

labels.txt

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+wall
+building
+floor
+ceiling
+bed
+windowpane
+cabinet
+sidewalk
+person
+door
+table
+plant
+curtain
+chair
+water
+painting
+sofa
+shelf
+mirror
+rug
+field
+armchair
+seat
+fence
+desk
+wardrobe
+lamp
+bathtub
+cushion
+base
+box
+column
+chest of drawers
+counter
+sand
+sink
+fireplace
+refrigerator
+grandstand
+path
+stairs
+case
+pool table
+pillow
+screen door
+stairway
+bridge
+bookcase
+blind
+coffee table
+toilet
+book
+bench
+stove
+palm
+kitchen island
+computer
+swivel chair
+bar
+towel
+light
+chandelier
+booth
+television receiver
+apparel
+bannister
+bottle
+buffet
+poster
+stage
+van
+conveyer belt
+canopy
+washer
+plaything
+stool
+basket
+bag
+cradle
+oven
+ball
+food
+step
+trade name
+microwave
+pot
+dishwasher
+screen
+blanket
+hood
+vase
+ashcan
+fan
+crt screen
+plate
+monitor
+shower
+radiator
+glass
+clock

requirements.txt

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+torch
+transformers
+tensorflow
+numpy
+Image
+matplotlib