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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 SegformerFeatureExtractor, AutoModelForSemanticSegmentation
feature_extractor = SegformerFeatureExtractor.from_pretrained(
"mattmdjaga/segformer_b2_clothes"
)
model = AutoModelForSemanticSegmentation.from_pretrained(
"mattmdjaga/segformer_b2_clothes"
)
def ade_palette():
"""ADE20K palette that maps each class to RGB values."""
return [
[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80],
[140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
[150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 5, 153], [6, 51, 255], [255, 153, 5]
]
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 = feature_extractor(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)
fig = draw_plot(pred_img, seg)
return fig
demo = gr.Interface(
fn=run_inference,
inputs=gr.Image(type="numpy", label="Input Image"),
outputs=gr.Plot(label="Overlay + Legend"),
examples=[
"person-1.jpg",
"person-2.jpg",
"person-3.jpg",
"person-4.jpg",
"person-5.jpg"
],
flagging_mode="never",
cache_examples=False,
)
if __name__ == "__main__":
demo.launch()
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