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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-cityscapes-1024-1024" | |
) | |
model = TFSegformerForSemanticSegmentation.from_pretrained( | |
"nvidia/segformer-b5-finetuned-cityscapes-1024-1024" | |
) | |
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] | |
] | |
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 | |
# Obtain probabilities | |
probabilities = tf.nn.softmax(logits, axis=-1) | |
# Visualize probabilities as bar plot | |
plt.figure(figsize=(12,6)) | |
class_names = labels_list | |
y_pos = np.arange(len(class_names)) | |
plt.bart(y_pos, probabilities.numpy().mean(axis=(0, 1)), align='center') | |
plt.ytickes(y_pos, class_names) | |
plt.xlabel('Probability') | |
plt.title('Class Probabilities') | |
return plt.gcf() | |
demo = gr.Interface(fn=sepia, | |
inputs=gr.Image(shape=(400, 600)), | |
outputs=['plot'], | |
examples=["cityscapes-1.jpg", "cityscapes-2.jpg", "cityscapes-3.jpg"], | |
allow_flagging='never') | |
demo.launch() | |