app.py

import gradio as gr

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", from_pt=True
)

def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [
        [120, 120, 120],
        [180, 120, 120],
        [6, 230, 230],
        [80, 50, 50],
        [4, 200, 3],
        [120, 120, 80],
        [140, 140, 140],
        [204, 5, 255],
        [230, 230, 230],
        [4, 250, 7],
        [224, 5, 255],
        [235, 255, 7],
        [150, 5, 61],
        [120, 120, 70],
        [8, 255, 51],
        [255, 6, 82],
        [143, 255, 140],
        [204, 255, 4],
        [255, 51, 7],
        [204, 70, 3],
        [0, 102, 200],
        [61, 230, 250],
        [255, 6, 51],
        [11, 102, 255],
        [255, 7, 71],
        [255, 9, 224],
        [9, 7, 230],
        [220, 220, 220],
        [255, 9, 92],
        [112, 9, 255],
        [8, 255, 214],
        [7, 255, 224],
        [255, 184, 6],
        [10, 255, 71],
        [255, 41, 10],
        [7, 255, 255],
        [224, 255, 8],
        [102, 8, 255],
        [255, 61, 6],
        [255, 194, 7],
        [255, 122, 8],
        [0, 255, 20],
        [255, 8, 41],
        [255, 5, 153],
        [6, 51, 255],
        [235, 12, 255],
        [160, 150, 20],
        [0, 163, 255],
        [140, 140, 140],
        [250, 10, 15],
        [20, 255, 0],
        [31, 255, 0],
        [255, 31, 0],
        [255, 224, 0],
        [153, 255, 0],
        [0, 0, 255],
        [255, 71, 0],
        [0, 235, 255],
        [0, 173, 255],
        [31, 0, 255],
        [11, 200, 200],
        [255, 82, 0],
        [0, 255, 245],
        [0, 61, 255],
        [0, 255, 112],
        [0, 255, 133],
        [255, 0, 0],
        [255, 163, 0],
        [255, 102, 0],
        [194, 255, 0],
        [0, 143, 255],
        [51, 255, 0],
        [0, 82, 255],
        [0, 255, 41],
        [0, 255, 173],
        [10, 0, 255],
        [173, 255, 0],
        [0, 255, 153],
        [255, 92, 0],
        [255, 0, 255],
        [255, 0, 245],
        [255, 0, 102],
        [255, 173, 0],
        [255, 0, 20],
        [255, 184, 184],
        [0, 31, 255],
        [0, 255, 61],
        [0, 71, 255],
        [255, 0, 204],
        [0, 255, 194],
        [0, 255, 82],
        [0, 10, 255],
        [0, 112, 255],
        [51, 0, 255],
        [0, 194, 255],
        [0, 122, 255],
        [0, 255, 163],
        [255, 153, 0],
        [0, 255, 10],
        [255, 112, 0],
        [143, 255, 0],
        [82, 0, 255],
        [163, 255, 0],
        [255, 235, 0],
        [8, 184, 170],
        [133, 0, 255],
        [0, 255, 92],
        [184, 0, 255],
        [255, 0, 31],
        [0, 184, 255],
        [0, 214, 255],
        [255, 0, 112],
        [92, 255, 0],
        [0, 224, 255],
        [112, 224, 255],
        [70, 184, 160],
        [163, 0, 255],
        [153, 0, 255],
        [71, 255, 0],
        [255, 0, 163],
        [255, 204, 0],
        [255, 0, 143],
        [0, 255, 235],
        [133, 255, 0],
        [255, 0, 235],
        [245, 0, 255],
        [255, 0, 122],
        [255, 245, 0],
        [10, 190, 212],
        [214, 255, 0],
        [0, 204, 255],
        [20, 0, 255],
        [255, 255, 0],
        [0, 153, 255],
        [0, 41, 255],
        [0, 255, 204],
        [41, 0, 255],
        [41, 255, 0],
        [173, 0, 255],
        [0, 245, 255],
        [71, 0, 255],
        [122, 0, 255],
        [0, 255, 184],
        [0, 92, 255],
        [184, 255, 0],
        [0, 133, 255],
        [255, 214, 0],
        [25, 194, 194],
        [102, 255, 0],
        [92, 0, 255],
    ]

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
    palette = np.array(ade_palette())

    for label, color in enumerate(palette):
        color_seg[seg == label, :] = color

    # Convert to BGR
    color_seg = color_seg[..., ::-1]

    # Show image + mask
    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
    pred_img = pred_img.astype(np.uint8)    
    return pred_img

demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")

demo.launch()