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#-*- encoding: utf-8 -*-
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-b4-finetuned-cityscapes-1024-1024"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b4-finetuned-cityscapes-1024-1024"
)
def ade_palette():
"""ADE20K palette that maps each class to RGB values."""
return [
[0, 0, 0], # black
[140, 140, 140], # gray
[95, 0, 255], # purple
[221, 126, 255], # light purple
[1, 0, 255], # blue
[0, 216, 255], # light blue
[35, 164, 26], # green
[29, 219, 22], # light green
[255, 228, 0], # yellow
[255, 187, 0], # light orange
[255, 94, 0], # orange
[255, 0, 0], # red
[255, 167, 167], # pink
[153, 56, 0], # brown
[207, 166, 54],
[180, 40, 180],
[120, 56, 123],
[45, 56, 28],
[67, 56, 123],
]
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
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
with gr.Tab("Semantic Segmentation with Cityscape Image"):
with gr.Row():
with gr.Column(scale=1):
input_gallery = gr.Gallery(label="Select an Image",
value="city_1.jpg",
items=["city_1.jpg", "city_2.jpg", "city_3.jpg",
"city_4.jpg", "city_5.jpg", "city_6.jpg",
"city_7.jpg", "city_8.jpg"]).style(grid=3)
input_image = gr.Image(label="Uploaded Image", interactive=True, type="pil")
input_gallery.change(fn=lambda x: x, inputs=input_gallery, outputs=input_image)
process_button = gr.Button("Process Image")
with gr.Column(scale=2):
output_image = gr.Plot(label="Segmented Image")
process_button.click(sepia, inputs=input_image, outputs=output_image)
with gr.Accordion("Information"):
gr.Markdown("A Gradio-based page which performs Semantic Segmentation into 19 classes for an example image")
demo.launch()
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