TaskOfCityscape / app.py
DDingcheol's picture
Update app.py
963ac87
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— ๋Œ€ํ•œ ์˜ˆ์ธก ํด๋ž˜์Šค์™€ ํ™•๋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 ์ ์šฉ
class_prob = np.mean(class_prob[mask])
class_probabilities[class_name] = class_prob * 100 # ๋ฐฑ๋ถ„์œจ๋กœ ๋ณ€ํ™˜
# Gradio Interface์— ์ถœ๋ ฅํ•  ๋ฌธ์ž์—ด ์ƒ์„ฑ
output_text = "Predicted class probabilities:\n"
for class_name, prob in class_probabilities.items():
output_text += f"{class_name}: {prob:.2f}%\n"
# ์ •ํ™•์„ฑ์ด ๊ฐ€์žฅ ๋†’์€ ๋ฌผ์ฒด ์ •๋ณด ์ถœ๋ 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} \n 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", "citiscapes-3.jpeg", "citiscapes-4.jpeg"],
allow_flagging='never')
demo.launch()