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import gradio as gr | |
import numpy as np | |
import tensorflow as tf | |
from PIL import Image | |
from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation | |
import matplotlib.pyplot as plt | |
from matplotlib import gridspec | |
feature_extractor = SegformerFeatureExtractor.from_pretrained( | |
"nvidia/segformer-b0-finetuned-cityscapes-1024-1024" | |
) | |
model = TFSegformerForSemanticSegmentation.from_pretrained( | |
"nvidia/segformer-b0-finetuned-cityscapes-1024-1024" | |
) | |
def ade_palette(): | |
"""ADE20K palette that maps each class to RGB values.""" | |
return [ | |
[255, 0, 0], | |
[255, 187, 0], | |
[255, 228, 0], | |
[29, 219, 22], | |
[178, 204, 255], | |
[1, 0, 255], | |
[165, 102, 255], | |
[217, 65, 197], | |
[116, 116, 116], | |
[204, 114, 61], | |
[206, 242, 121], | |
[61, 183, 204], | |
[94, 94, 94], | |
[196, 183, 59], | |
[246, 246, 246], | |
[209, 178, 255], | |
[0, 87, 102], | |
[153, 0, 76], | |
[47, 157, 39] | |
] | |
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]) | |
seg = tf.math.argmax(logits, axis=-1)[0] | |
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) | |
for label, color in enumerate(colormap): | |
color_seg[seg.numpy() == label, :] = color | |
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) | |
# ๊ฐ ๋ฌผ์ฒด์ ๋ํ ์์ธก ํด๋์ค์ ํ๋ฅ ์ป๊ธฐ | |
unique_labels = np.unique(seg.numpy().astype("uint8")) | |
class_probabilities = {} | |
for label in unique_labels: | |
mask = (seg.numpy() == label) | |
class_name = labels_list[label] | |
class_prob = tf.nn.softmax(logits.numpy()[0][:, :, label]) # softmax ์ ์ฉ | |
class_prob = np.mean(class_prob[mask]) | |
class_probabilities[class_name] = class_prob * 100 # ๋ฐฑ๋ถ์จ๋ก ๋ณํ | |
# Gradio Interface์ ์ถ๋ ฅํ ๋ฌธ์์ด ์์ฑ | |
output_text = "Predicted class probabilities:\n" | |
for class_name, prob in class_probabilities.items(): | |
output_text += f"{class_name}: {prob:.2f}%\n" | |
# ์ ํ์ฑ์ด ๊ฐ์ฅ ๋์ ๋ฌผ์ฒด ์ ๋ณด ์ถ๋ ฅ | |
max_prob_class = max(class_probabilities, key=class_probabilities.get) | |
max_prob_value = class_probabilities[max_prob_class] | |
output_text += f"\nPredicted class with highest probability: {max_prob_class}, Probability: {max_prob_value:.4f}" | |
return fig, output_text | |
demo = gr.Interface(fn=sepia, | |
inputs=gr.Image(shape=(400, 600)), | |
outputs=['plot', 'text'], | |
examples=["citiscapes-1.jpeg", "citiscapes-2.jpeg"], | |
allow_flagging='never') | |
demo.launch() | |