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import gradio as gr
from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
from torch import nn
from PIL import Image
from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
feature_extractor = SegformerFeatureExtractor.from_pretrained("zoheb/mit-b5-finetuned-sidewalk-semantic")
model = SegformerForSemanticSegmentation.from_pretrained("zoheb/mit-b5-finetuned-sidewalk-semantic")
def sidewalk_palette():
"""Sidewalk palette that maps each class to RGB values."""
return [
[0, 0, 0],
[216, 82, 24],
[255, 255, 0],
[125, 46, 141],
[118, 171, 47],
[161, 19, 46],
[255, 0, 0],
[0, 128, 128],
[190, 190, 0],
[0, 255, 0],
[0, 0, 255],
[170, 0, 255],
[84, 84, 0],
[84, 170, 0],
[84, 255, 0],
[170, 84, 0],
[170, 170, 0],
[170, 255, 0],
[255, 84, 0],
[255, 170, 0],
[255, 255, 0],
[33, 138, 200],
[0, 170, 127],
[0, 255, 127],
[84, 0, 127],
[84, 84, 127],
[84, 170, 127],
[84, 255, 127],
[170, 0, 127],
[170, 84, 127],
[170, 170, 127],
[170, 255, 127],
[255, 0, 127],
[255, 84, 127],
[255, 170, 127],
]
labels_list = []
with open(r'labels.txt', 'r') as fp:
labels_list.extend(line[:-1] for line in fp)
colormap = np.asarray(sidewalk_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 main(input_img):
input_img = Image.fromarray(input_img)
inputs = feature_extractor(images=input_img, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4)
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(
logits,
size=input_img.size[::-1], # (height, width)
mode='bilinear',
align_corners=False
)
# Second, apply argmax on the class dimension
pred_seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((pred_seg.shape[0], pred_seg.shape[1], 3), dtype=np.uint8) # height, width, 3
palette = np.array(sidewalk_palette())
for label, color in enumerate(palette):
color_seg[pred_seg == label, :] = color
# Show image + mask
img = np.array(input_img) * 0.5 + color_seg * 0.5
pred_img = img.astype(np.uint8)
return draw_plot(pred_img, pred_seg)
demo = gr.Interface(main,
gr.Image(shape=(200, 200)),
outputs=['plot'],
examples=["test.jpg"],
allow_flagging='never')
demo.launch()
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