Spaces:
Sleeping
Sleeping
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-b0-finetuned-ade-512-512" | |
) | |
model = TFSegformerForSemanticSegmentation.from_pretrained( | |
"nvidia/segformer-b0-finetuned-ade-512-512" | |
) | |
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], | |
[230, 127, 34], | |
[179, 51, 126], | |
[122, 122, 201], | |
[255, 221, 101], | |
[97, 48, 88], | |
[225, 49, 112], | |
[55, 120, 254], | |
[181, 43, 25], | |
[212, 59, 3], | |
[51, 0, 0], | |
[0, 51, 0], | |
[0, 0, 51], | |
[153, 153, 153], | |
[255, 127, 0], | |
[128, 255, 0], | |
[0, 128, 255], | |
[255, 0, 128], | |
[128, 255, 128], | |
[255, 0, 0], | |
[128, 255, 0], | |
[255, 0, 128], | |
[0, 128, 0], | |
[0, 0, 128], | |
[0, 128, 255], | |
[128, 0, 255], | |
[255, 0, 128], | |
[128, 255, 128], | |
[255, 0, 0], | |
[0, 128, 255], | |
[128, 0, 255], | |
[0, 0, 0], | |
[255, 128, 0], | |
[0, 255, 0], | |
[0, 0, 128], | |
[0, 0, 0], | |
[255, 0, 0], | |
[128, 0, 255], | |
[0, 128, 0], | |
[255, 255, 128], | |
[255, 0, 255], | |
[255, 255, 0], | |
[128, 0, 0], | |
[255, 128, 128], | |
[0, 128, 255], | |
[128, 0, 255], | |
[0, 0, 255], | |
[0, 255, 255], | |
[255, 255, 0], | |
[255, 0, 255], | |
[255, 128, 0], | |
[255, 255, 255], | |
[128, 0, 0], | |
[255, 0, 255], | |
[255, 255, 0], | |
[0, 0, 128], | |
[255, 255, 255], | |
[0, 255, 0], | |
[0, 0, 0], | |
[255, 128, 0], | |
[0, 255, 128], | |
[255, 0, 0], | |
[0, 0, 255], | |
[128, 255, 0], | |
[255, 255, 128], | |
[255, 255, 0], | |
[255, 128, 128], | |
[255, 0, 128], | |
[255, 128, 255], | |
[255, 0, 128], | |
[255, 255, 0], | |
[255, 128, 0], | |
[204, 87, 92], | |
[128, 255, 0], | |
[255, 0, 255], | |
[0, 255, 128], | |
[90, 180, 56], | |
[91, 1, 5], | |
[92, 64, 34], | |
[93, 128, 0], | |
[94, 255, 0], | |
[95, 34, 87], | |
[96, 86, 145], | |
[97, 123, 98], | |
[98, 0, 255], | |
[99, 255, 128], | |
[100, 45, 122], | |
[101, 134, 245], | |
[102, 32, 23], | |
[103, 56, 0], | |
[104, 76, 98], | |
[105, 176, 90], | |
[106, 102, 200], | |
[107, 56, 78], | |
[108, 23, 89], | |
[109, 45, 200], | |
[110, 87, 5], | |
[111, 200, 67], | |
[112, 34, 23], | |
[113, 98, 76], | |
[114, 122, 56], | |
[115, 56, 23], | |
[116, 78, 90], | |
[117, 200, 45], | |
[118, 23, 56], | |
[119, 56, 189], | |
[120, 0, 45], | |
[121, 0, 0], | |
[122, 89, 34], | |
[123, 200, 1], | |
[124, 32, 45], | |
[125, 89, 0], | |
[126, 0, 200], | |
[127, 90, 200], | |
[128, 45, 200], | |
[129, 0, 123], | |
[130, 200, 23], | |
[131, 32, 200], | |
[132, 56, 23], | |
[133, 87, 98], | |
[134, 0, 32], | |
[135, 90, 0], | |
[136, 45, 23], | |
[137, 0, 89], | |
[138, 200, 0], | |
[139, 45, 23], | |
[140, 123, 0], | |
[141, 45, 200], | |
[142, 98, 23], | |
[143, 0, 98], | |
[144, 200, 45], | |
[145, 0, 23], | |
[146, 23, 87], | |
[147, 45, 0], | |
[148, 0, 89], | |
[149, 200, 32] | |
] | |
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=["test1.jpg", "test2.jpg", "test3.jpg", "test4.jpg"], | |
allow_flagging='never') | |
demo.launch() | |