File size: 3,949 Bytes
15d4ec3 a01cc0f 15d4ec3 19cb3cd 15d4ec3 2a305f6 19cb3cd 15d4ec3 2a305f6 15d4ec3 2a305f6 15d4ec3 2a305f6 b3a567a 15d4ec3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
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(
"nickmuchi/segformer-b4-finetuned-segments-sidewalk"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
"nickmuchi/segformer-b4-finetuned-segments-sidewalk", from_pt=True
)
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],
[156, 200, 56],
[32, 90, 210],
[56, 123, 67],
[180, 56, 123],
[123, 67, 45],
[45, 134, 200],
[67, 56, 123],
[78, 123, 67],
[32, 210, 90],
[45, 56, 189],
[123, 56, 123],
[56, 156, 200],
[189, 56, 45],
[112, 200, 56],
[56, 123, 45],
[200, 32, 90],
[123, 45, 78],
]
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=(30, 15)) # figsize를 조절하여 전체 그림 크기 변경
# 그리드의 구성을 변경하여 입력 상자와 출력 상자의 위치를 바꿈
grid_spec = gridspec.GridSpec(1, 2, width_ratios=[2, 8]) # width_ratios 값을 조절하여 비율 변경
# 출력 상자를 먼저 플로팅
ax = plt.subplot(grid_spec[1])
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.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)
# 입력 상자를 다음으로 플로팅
ax = plt.subplot(grid_spec[0])
ax.imshow(pred_img)
ax.axis('off')
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.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
allow_flagging='never')
demo.launch()
|