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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(
"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 [
[120, 120, 120],
[180, 120, 120],
[6, 230, 230],
[80, 50, 50],
[4, 200, 3],
[120, 120, 80],
[140, 140, 140],
[204, 5, 255],
[230, 230, 230],
[4, 250, 7],
[224, 5, 255],
[235, 255, 7],
[150, 5, 61],
[120, 120, 70],
[8, 255, 51],
[255, 6, 82],
[143, 255, 140],
[204, 255, 4],
[255, 51, 7],
[204, 70, 3],
[0, 102, 200],
[61, 230, 250],
[255, 6, 51],
[11, 102, 255],
[255, 7, 71],
[255, 9, 224],
[9, 7, 230],
[220, 220, 220],
[255, 9, 92],
[112, 9, 255],
[8, 255, 214],
[7, 255, 224],
[255, 184, 6],
[10, 255, 71],
[255, 41, 10],
[7, 255, 255],
[224, 255, 8],
[102, 8, 255],
[255, 61, 6],
[255, 194, 7],
[255, 122, 8],
[0, 255, 20],
[255, 8, 41],
[255, 5, 153],
[6, 51, 255],
[235, 12, 255],
[160, 150, 20],
[0, 163, 255],
[140, 140, 140],
[250, 10, 15],
[20, 255, 0],
[31, 255, 0],
[255, 31, 0],
[255, 224, 0],
[153, 255, 0],
[0, 0, 255],
[255, 71, 0],
[0, 235, 255],
[0, 173, 255],
[31, 0, 255],
[11, 200, 200],
[255, 82, 0],
[0, 255, 245],
[0, 61, 255],
[0, 255, 112],
[0, 255, 133],
[255, 0, 0],
[255, 163, 0],
[255, 102, 0],
[194, 255, 0],
[0, 143, 255],
[51, 255, 0],
[0, 82, 255],
[0, 255, 41],
[0, 255, 173],
[10, 0, 255],
[173, 255, 0],
[0, 255, 153],
[255, 92, 0],
[255, 0, 255],
[255, 0, 245],
[255, 0, 102],
[255, 173, 0],
[255, 0, 20],
[255, 184, 184],
[0, 31, 255],
[0, 255, 61],
[0, 71, 255],
[255, 0, 204],
[0, 255, 194],
[0, 255, 82],
[0, 10, 255],
[0, 112, 255],
[51, 0, 255],
[0, 194, 255],
[0, 122, 255],
[0, 255, 163],
[255, 153, 0],
[0, 255, 10],
[255, 112, 0],
[143, 255, 0],
[82, 0, 255],
[163, 255, 0],
[255, 235, 0],
[8, 184, 170],
[133, 0, 255],
[0, 255, 92],
[184, 0, 255],
[255, 0, 31],
[0, 184, 255],
[0, 214, 255],
[255, 0, 112],
[92, 255, 0],
[0, 224, 255],
[112, 224, 255],
[70, 184, 160],
[163, 0, 255],
[153, 0, 255],
[71, 255, 0],
[255, 0, 163],
[255, 204, 0],
[255, 0, 143],
[0, 255, 235],
[133, 255, 0],
[255, 0, 235],
[245, 0, 255],
[255, 0, 122],
[255, 245, 0],
[10, 190, 212],
[214, 255, 0],
[0, 204, 255],
[20, 0, 255],
[255, 255, 0],
[0, 153, 255],
[0, 41, 255],
[0, 255, 204],
[41, 0, 255],
[41, 255, 0],
[173, 0, 255],
[0, 245, 255],
[71, 0, 255],
[122, 0, 255],
[0, 255, 184],
[0, 92, 255],
[184, 255, 0],
[0, 133, 255],
[255, 214, 0],
[25, 194, 194],
[102, 255, 0],
[92, 0, 255],
]
labels_list = [
'wall',
'building;edifice',
'sky',
'floor;flooring',
'tree',
'ceiling',
'road;route',
'bed',
'windowpane;window',
'grass',
'cabinet',
'sidewalk;pavement',
'person;individual;someone;somebody;mortal;soul',
'earth;ground',
'door;double;door',
'table',
'mountain;mount',
'plant;flora;plant;life',
'curtain;drape;drapery;mantle;pall',
'chair',
'car;auto;automobile;machine;motorcar',
'water',
'painting;picture',
'sofa;couch;lounge',
'shelf',
'house',
'sea',
'mirror',
'rug;carpet;carpeting',
'field',
'armchair',
'seat',
'fence;fencing',
'desk',
'rock;stone',
'wardrobe;closet;press',
'lamp',
'bathtub;bathing;tub;bath;tub',
'railing;rail',
'cushion',
'base;pedestal;stand',
'box',
'column;pillar',
'signboard;sign',
'chest;of;drawers;chest;bureau;dresser',
'counter',
'sand',
'sink',
'skyscraper',
'fireplace;hearth;open;fireplace',
'refrigerator;icebox',
'grandstand;covered;stand',
'path',
'stairs;steps',
'runway',
'case;display;case;showcase;vitrine',
'pool;table;billiard;table;snooker;table',
'pillow',
'screen;door;screen',
'stairway;staircase',
'river',
'bridge;span',
'bookcase',
'blind;screen',
'coffee;table;cocktail;table',
'toilet;can;commode;crapper;pot;potty;stool;throne',
'flower',
'book',
'hill',
'bench',
'countertop',
'stove;kitchen;stove;range;kitchen;range;cooking;stove',
'palm;palm;tree',
'kitchen;island',
'computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system',
'swivel;chair',
'boat',
'bar',
'arcade;machine',
'hovel;hut;hutch;shack;shanty',
'bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle',
'towel',
'light;light;source',
'truck;motortruck',
'tower',
'chandelier;pendant;pendent',
'awning;sunshade;sunblind',
'streetlight;street;lamp',
'booth;cubicle;stall;kiosk',
'television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box',
'airplane;aeroplane;plane',
'dirt;track',
'apparel;wearing;apparel;dress;clothes',
'pole',
'land;ground;soil',
'bannister;banister;balustrade;balusters;handrail',
'escalator;moving;staircase;moving;stairway',
'ottoman;pouf;pouffe;puff;hassock',
'bottle',
'buffet;counter;sideboard',
'poster;posting;placard;notice;bill;card',
'stage',
'van',
'ship',
'fountain',
'conveyer;belt;conveyor;belt;conveyer;conveyor;transporter',
'canopy',
'washer;automatic;washer;washing;machine',
'plaything;toy',
'swimming;pool;swimming;bath;natatorium',
'stool',
'barrel;cask',
'basket;handbasket',
'waterfall;falls',
'tent;collapsible;shelter',
'bag',
'minibike;motorbike',
'cradle',
'oven',
'ball',
'food;solid;food',
'step;stair',
'tank;storage;tank',
'trade;name;brand;name;brand;marque',
'microwave;microwave;oven',
'pot;flowerpot',
'animal;animate;being;beast;brute;creature;fauna',
'bicycle;bike;wheel;cycle',
'lake',
'dishwasher;dish;washer;dishwashing;machine',
'screen;silver;screen;projection;screen',
'blanket;cover',
'sculpture',
'hood;exhaust;hood',
'sconce',
'vase',
'traffic;light;traffic;signal;stoplight',
'tray',
'ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin',
'fan',
'pier;wharf;wharfage;dock',
'crt;screen',
'plate',
'monitor;monitoring;device',
'bulletin;board;notice;board',
'shower',
'radiator',
'glass;drinking;glass',
'clock',
'flag']
def label_to_color_image(label):
"""Adds color defined by the dataset colormap to the label.
Args:
label: A 2D array with integer type, storing the segmentation label.
Returns:
result: A 2D array with floating type. The element of the array
is the color indexed by the corresponding element in the input label
to the PASCAL color map.
Raises:
ValueError: If label is not of rank 2 or its value is larger than color
map maximum entry.
"""
if label.ndim != 2:
raise ValueError("Expect 2-D input label")
colormap = np.asarray(ade_palette())
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
palette = np.array(ade_palette())
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]
# 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(sepia, gr.Image(shape=(200, 200)), outputs=['plot'], examples=["ADE_val_00000001.jpeg"])
demo.launch()