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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()
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