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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 torch
from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation, pipeline
MODEL_ID = "nvidia/segformer-b0-finetuned-cityscapes-768-768"
processor = AutoImageProcessor.from_pretrained(MODEL_ID)
model = AutoModelForSemanticSegmentation.from_pretrained(MODEL_ID)
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],
[123, 123, 123]
]
labels_list = []
with open("labels.txt", "r", encoding="utf-8") as fp:
for line in fp:
labels_list.append(line.rstrip("\n"))
colormap = np.asarray(ade_palette(), dtype=np.uint8)
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_np):
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_np.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 run_inference(input_img):
# input: numpy array from gradio -> PIL
img = Image.fromarray(input_img.astype(np.uint8)) if isinstance(input_img, np.ndarray) else input_img
if img.mode != "RGB":
img = img.convert("RGB")
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits # (1, C, h/4, w/4)
# resize to original
upsampled = torch.nn.functional.interpolate(
logits, size=img.size[::-1], mode="bilinear", align_corners=False
)
seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8) # (H,W)
# colorize & overlay
color_seg = colormap[seg] # (H,W,3)
pred_img = (np.array(img) * 0.5 + color_seg * 0.5).astype(np.uint8)
# 추가 부분
total_pixels = seg.size
unique_labels, counts = np.unique(seg, return_counts=True)
# gr.Label
label_results = {}
for label_id, count in zip(unique_labels, counts):
class_name = labels_list[label_id]
percentage = count / total_pixels
label_results[class_name] = percentage
fig = draw_plot(pred_img, seg)
return fig, label_results
# 인터페이스
demo = gr.Interface(
fn=run_inference,
inputs=gr.Image(type="numpy", label="Input Image"),
outputs=[
gr.Plot(label="Overlay + Legend"),
gr.Label(label="Class Proportions", num_top_classes=5)
],
examples=[
"ADE_val_00000001.jpeg",
"ADE_val_00001248.jpg",
"cityscapes_1.jpg",
"cityscapes_2.jpg",
"cityscapes_3.jpg",
"cityscapes_4.jpg",
"cityscapes_5.jpg"
],
flagging_mode="never",
cache_examples=False,
)
if __name__ == "__main__":
demo.launch()
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