Spaces:
Runtime error
Runtime error
import gradio as gr | |
import numpy as np | |
from PIL import Image | |
from transformers import MaskFormerImageProcessor, MaskFormerForInstanceSegmentation | |
# Load the model using TensorFlow | |
model = MaskFormerForInstanceSegmentation.from_pretrained( | |
"facebook/maskformer-swin-large-ade" | |
) | |
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], | |
[200, 156, 56], | |
[45, 67, 123], | |
[56, 45, 78], | |
[45, 56, 123], | |
[123, 67, 56], | |
[56, 78, 123], | |
[210, 90, 32], | |
[123, 56, 189], | |
[45, 200, 134], | |
[67, 123, 56], | |
[123, 45, 67], | |
[90, 32, 210], | |
[200, 45, 78], | |
[32, 210, 90], | |
[45, 123, 67], | |
[165, 42, 87], | |
[72, 145, 167], | |
[15, 158, 75], | |
[209, 89, 40], | |
[32, 21, 121], | |
[184, 20, 100], | |
[56, 135, 15], | |
[128, 92, 176], | |
[1, 119, 140], | |
[220, 151, 43], | |
[41, 97, 72], | |
[148, 38, 27], | |
[107, 86, 176], | |
[21, 26, 136], | |
[174, 27, 90], | |
[91, 96, 204], | |
[108, 50, 107], | |
[27, 45, 136], | |
[168, 200, 52], | |
[7, 102, 27], | |
[42, 93, 56], | |
[140, 52, 112], | |
[92, 107, 168], | |
[17, 118, 176], | |
[59, 50, 174], | |
[206, 40, 143], | |
[44, 19, 142], | |
[23, 168, 75], | |
[54, 57, 189], | |
[144, 21, 15], | |
[15, 176, 35], | |
[107, 19, 79], | |
[204, 52, 114], | |
[48, 173, 83], | |
[11, 120, 53], | |
[206, 104, 28], | |
[20, 31, 153], | |
[27, 21, 93], | |
[11, 206, 138], | |
[112, 30, 83], | |
[68, 91, 152], | |
[153, 13, 43], | |
[25, 114, 54], | |
[92, 27, 150], | |
[108, 42, 59], | |
[194, 77, 5], | |
[145, 48, 83], | |
[7, 113, 19], | |
[25, 92, 113], | |
[60, 168, 79], | |
[78, 33, 120], | |
[89, 176, 205], | |
[27, 200, 94], | |
[210, 67, 23], | |
[123, 89, 189], | |
[225, 56, 112], | |
[75, 156, 45], | |
[172, 104, 200], | |
[15, 170, 197], | |
[240, 133, 65], | |
[89, 156, 112], | |
[214, 88, 57], | |
[156, 134, 200], | |
[78, 57, 189], | |
[200, 78, 123], | |
[106, 120, 210], | |
[145, 56, 112], | |
[89, 120, 189], | |
[185, 206, 56], | |
[47, 99, 28], | |
[112, 189, 78], | |
[200, 112, 89], | |
[89, 145, 112], | |
[78, 106, 189], | |
[112, 78, 189], | |
[156, 112, 78], | |
[28, 210, 99], | |
[78, 89, 189], | |
[189, 78, 57], | |
[112, 200, 78], | |
[189, 47, 78], | |
[205, 112, 57], | |
[78, 145, 57], | |
[200, 78, 112], | |
[99, 89, 145], | |
[200, 156, 78], | |
[57, 78, 145], | |
[78, 57, 99], | |
[57, 78, 145], | |
[145, 112, 78], | |
[78, 89, 145], | |
[210, 99, 28], | |
[145, 78, 189], | |
[57, 200, 136], | |
[89, 156, 78], | |
[145, 78, 99], | |
[99, 28, 210], | |
[189, 78, 47], | |
[28, 210, 99], | |
[26, 165, 222], | |
] | |
labels_list = [] | |
with open('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 sepia(input_img): | |
input_img = Image.fromarray(input_img) | |
feature_extractor = MaskFormerImageProcessor.from_pretrained( | |
"nvidia/segformer-b0-finetuned-cityscapes-1024-1024" | |
) | |
inputs = feature_extractor(images=input_img) | |
outputs = model(**inputs) | |
logits = outputs.logits | |
seg = np.argmax(logits, axis=-1) | |
color_seg = np.zeros( | |
(seg.shape[0], seg.shape[1], 3), dtype=np.uint8 | |
) | |
for label, color in enumerate(colormap): | |
color_seg[seg == label, :] = color | |
pred_img = np.array(input_img) * 0.5 + color_seg * 0.5 | |
pred_img = pred_img.astype(np.uint8) | |
return pred_img | |
demo = gr.Interface(fn=sepia, | |
inputs=gr.Image(shape=(400, 600)), | |
outputs=gr.Image(), | |
examples=["image-1.jpg", "image-2.jpg", "image-3.jpg", "image-4.jpeg", "image-5.jpg"], | |
allow_flagging='never') | |
demo.launch() | |