|
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( |
|
"mattmdjaga/segformer_b2_clothes" |
|
) |
|
model = TFSegformerForSemanticSegmentation.from_pretrained( |
|
"mattmdjaga/segformer_b2_clothes" |
|
) |
|
|
|
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], |
|
] |
|
|
|
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] |
|
) |
|
seg = tf.math.argmax(logits, axis=-1)[0] |
|
|
|
color_seg = np.zeros( |
|
(seg.shape[0], seg.shape[1], 3), dtype=np.uint8 |
|
) |
|
for label, color in enumerate(colormap): |
|
color_seg[seg.numpy() == label, :] = color |
|
|
|
|
|
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=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"], |
|
allow_flagging='never') |
|
|
|
|
|
demo.launch() |
|
|
