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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(
"prem-timsina/segformer-b0-finetuned-food"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
"prem-timsina/segformer-b0-finetuned-food"
)
def ade_palette():
"""ADE20K palette that maps each class to RGB values."""
return [
[93, 93, 93],
[43, 240, 132],
[139, 136, 240],
[158, 83, 109],
[6, 76, 151],
[95, 170, 87],
[273, 236, 139],
[21, 155, 160],
[188, 220, 166],
[238, 96, 247],
[223, 180, 221],
[29, 97, 24],
[3, 233, 248],
[105, 118, 44],
[203, 237, 63],
[234, 100, 240],
[19, 179, 164],
[65, 22, 115],
[111, 128, 194],
[232, 41, 17],
[11, 250, 159],
[137, 163, 129],
[212, 223, 210],
[51, 37, 4],
[37, 63, 239],
[257, 180, 163],
[172, 53, 105],
[104, 150, 99],
[80, 157, 133],
[195, 104, 202],
[42, 187, 110],
[133, 225, 66],
[132, 99, 213],
[178, 248, 209],
[93, 147, 60],
[105, 109, 115],
[26, 65, 115],
[239, 52, 182],
[242, 19, 204],
[157, 101, 214],
[248, 85, 198],
[103, 198, 171],
[44, 129, 75],
[159, 32, 120],
[155, 77, 71],
[233, 231, 155],
[135, 196, 206],
[81, 53, 51],
[134, 221, 213],
[192, 27, 152],
[127, 127, 194],
[82, 161, 1],
[71, 80, 161],
[148, 9, 159],
[91, 110, 124],
[127, 157, 223],
[25, 210, 232],
[129, 0, 114],
[231, 187, 138],
[23, 17, 224],
[25, 255, 29],
[158, 19, 53],
[157, 190, 176],
[114, 140, 221],
[46, 104, 87],
[17, 114, 122],
[221, 12, 229],
[54, 20, 92],
[215, 191, 252],
[144, 127, 146],
[141, 116, 77],
[100, 89, 89],
[104, 115, 249],
[179, 212, 38],
[140, 248, 179],
[177, 230, 240],
[219, 98, 8],
[74, 219, 53],
[161, 28, 243],
[64, 57, 184],
[147, 193, 113],
[182, 15, 30],
[151, 204, 109],
[187, 76, 21],
[118, 163, 155],
[158, 30, 220],
[227, 170, 63],
[199, 186, 72],
[0, 241, 168],
[80, 150, 225],
[237, 250, 4],
[29, 210, 181],
[176, 120, 81],
[134, 47, 123],
[240, 141, 130],
[250, 41, 115],
[29, 88, 143],
[66, 151, 87],
[241, 231, 144],
[238, 107, 153],
[181, 96, 220],
[239, 122, 133],
[205, 120, 21],
[168, 12, 77],
]
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=["food-1.jpg","food-2.jpg", "food-3.jpg", "food-4.jpg", "food-5.jpg", "food-6.jpg"],
allow_flagging='never')
demo.launch()
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