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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 | |
import requests | |
feature_extractor = SegformerFeatureExtractor.from_pretrained( | |
"nvidia/segformer-b5-finetuned-ade-640-640" | |
) | |
model = TFSegformerForSemanticSegmentation.from_pretrained( | |
"nvidia/segformer-b5-finetuned-ade-640-640" | |
) | |
def ade_palette(): | |
"""ADE20K palette that maps each class to RGB values.""" | |
return [ | |
[215, 252, 54], | |
[219, 99, 20], | |
[30, 125, 246], | |
[21, 211, 22], | |
[117, 165, 201], | |
[122, 2, 6], | |
[52, 144, 140], | |
[136, 36, 114], | |
[208, 249, 44], | |
[210, 245, 157], | |
[48, 222, 84], | |
[175, 182, 112], | |
[117, 9, 240], | |
[153, 38, 30], | |
[75, 225, 231], | |
[232, 170, 70], | |
[154, 35, 115], | |
[45, 61, 35], | |
[73, 144, 2], | |
[54, 80, 136], | |
[143, 200, 212], | |
[75, 104, 98], | |
[17, 211, 27], | |
[205, 195, 241], | |
[234, 251, 104], | |
[33, 174, 95], | |
[160, 174, 99], | |
[141, 26, 157], | |
[84, 247, 88], | |
[19, 248, 198], | |
[4, 217, 155], | |
[204, 163, 16], | |
[148, 209, 143], | |
[211, 97, 65], | |
[19, 4, 131], | |
[40, 196, 45], | |
[39, 64, 20], | |
[166, 107, 50], | |
[108, 103, 78], | |
[188, 11, 213], | |
[24, 156, 152], | |
[230, 162, 223], | |
[30, 126, 220], | |
[74, 10, 238], | |
[186, 128, 227], | |
[83, 188, 220], | |
[9, 132, 231], | |
[96, 99, 79], | |
[196, 139, 187], | |
[117, 122, 171], | |
[0, 156, 220], | |
[243, 249, 189], | |
[243, 245, 211], | |
[103, 146, 83], | |
[237, 144, 197], | |
[35, 151, 20], | |
[15, 61, 139], | |
[78, 223, 132], | |
[120, 49, 9], | |
[67, 160, 234], | |
[183, 244, 210], | |
[245, 161, 139], | |
[57, 70, 189], | |
[105, 150, 31], | |
[219, 85, 49], | |
[206, 81, 97], | |
[30, 171, 92], | |
[251, 42, 67], | |
[121, 183, 220], | |
[221, 33, 43], | |
[8, 96, 100], | |
[76, 149, 53], | |
[29, 201, 129], | |
[7, 213, 227], | |
[143, 93, 153], | |
[205, 35, 110], | |
[37, 94, 142], | |
[131, 157, 110], | |
[215, 166, 147], | |
[164, 94, 252], | |
[179, 108, 233], | |
[35, 157, 209], | |
[145, 252, 241], | |
[155, 60, 40], | |
[70, 25, 44], | |
[53, 83, 133], | |
[150, 42, 191], | |
[142, 245, 58], | |
[150, 198, 69], | |
[0, 139, 86], | |
[123, 212, 143], | |
[210, 166, 191], | |
[148, 194, 130], | |
[35, 213, 154], | |
[203, 139, 93], | |
[59, 86, 45], | |
[9, 50, 169], | |
[207, 118, 246], | |
[200, 82, 65], | |
[37, 75, 120], | |
[237, 99, 63], | |
[168, 145, 190], | |
[225, 48, 16], | |
[17, 184, 115], | |
[224, 124, 15], | |
[148, 167, 47], | |
[162, 25, 116], | |
[154, 90, 36], | |
[185, 247, 43], | |
[183, 138, 202], | |
[64, 96, 117], | |
[187, 140, 140], | |
[121, 116, 188], | |
[252, 251, 162], | |
[85, 50, 40], | |
[209, 241, 228], | |
[30, 41, 95], | |
[246, 217, 64], | |
[151, 149, 197], | |
[117, 42, 205], | |
[26, 248, 30], | |
[28, 224, 232], | |
[228, 89, 96], | |
[198, 44, 113], | |
[220, 68, 218], | |
[59, 85, 210], | |
[24, 230, 191], | |
[145, 192, 181], | |
[132, 189, 92], | |
[47, 29, 128], | |
[11, 245, 204], | |
[182, 79, 207], | |
[42, 64, 187], | |
[72, 4, 37], | |
[105, 67, 133], | |
[86, 27, 200], | |
[243, 211, 40], | |
[150, 136, 40], | |
[3, 192, 172], | |
[34, 96, 149], | |
[32, 108, 56], | |
[128, 10, 137], | |
[94, 211, 108], | |
[78, 250, 243], | |
[6, 74, 205], | |
[6, 7, 38], | |
[161, 26, 40], | |
[145, 254, 27], | |
[119, 145, 127], | |
[13, 82, 153], | |
] | |
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(), | |
outputs=['plot'], | |
examples=["image-1.jpg", "image-2.jpg", "image-3.jpg", "image-4.jpeg", "image-5.jpg"], | |
allow_flagging='never') | |
demo.launch() | |