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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(
"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]
) # 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=["person-1", "person-2", "person-3", "person-4", "person-5"],
# allow_flagging='never')
demo = gr.Interface(fn=sepia,
inputs=gr.Image(), # Remove the 'shape' argument here
outputs=['plot'],
examples=[
"person-1.jpg",
"person-2.jpg",
"person-3.jpg",
"person-4.jpg",
"person-5.jpg"
],
allow_flagging='never')
demo.launch()
#
# 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 SegformerImageProcessor, TFSegformerForSemanticSegmentation
#
# # SegformerImageProcessor 및 모델을 로드합니다.
# processor = SegformerImageProcessor.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 = processor(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.inputs.Image(shape=(400, 600)),
# outputs='plot',
# examples=["person-1", "person-2", "person-3", "person-4", "person-5"],
# allow_flagging='never')
#
# demo.launch()
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