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import os
os.system("pip uninstall -y mmcv-full")
os.system("pip uninstall -y mmsegmentation")
os.system("pip install ./mmcv_full-1.5.0-cp310-cp310-linux_x86_64.whl")
os.system("pip install -r requirements-extras.txt")
# os.system("cp /home/user/data/dinov2_vitg14_ade20k_m2f.pth /home/user/.cache/torch/hub/checkpoints/dinov2_vitg14_ade20k_m2f.pth")
import gradio as gr
import base64
import cv2
import math
import itertools
from functools import partial
from PIL import Image
import numpy as np
import pandas as pd
import dinov2.eval.segmentation.utils.colormaps as colormaps
import torch
import torch.nn.functional as F
from mmseg.apis import init_segmentor, inference_segmentor
import dinov2.eval.segmentation.models
import dinov2.eval.segmentation_m2f.models.segmentors
import urllib
import mmcv
from mmcv.runner import load_checkpoint
model = None
model_loaded = False
DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2"
CONFIG_URL = f"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f_config.py"
CHECKPOINT_URL = f"{DINOV2_BASE_URL}/dinov2_vitg14/dinov2_vitg14_ade20k_m2f.pth"
def load_config_from_url(url: str) -> str:
with urllib.request.urlopen(url) as f:
return f.read().decode()
cfg_str = load_config_from_url(CONFIG_URL)
cfg = mmcv.Config.fromstring(cfg_str, file_format=".py")
DATASET_COLORMAPS = {
"ade20k": colormaps.ADE20K_COLORMAP,
"voc2012": colormaps.VOC2012_COLORMAP,
}
model = init_segmentor(cfg)
load_checkpoint(model, CHECKPOINT_URL, map_location="cpu")
model.cuda()
model.eval()
class CenterPadding(torch.nn.Module):
def __init__(self, multiple):
super().__init__()
self.multiple = multiple
def _get_pad(self, size):
new_size = math.ceil(size / self.multiple) * self.multiple
pad_size = new_size - size
pad_size_left = pad_size // 2
pad_size_right = pad_size - pad_size_left
return pad_size_left, pad_size_right
@torch.inference_mode()
def forward(self, x):
pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))
output = F.pad(x, pads)
return output
def create_segmenter(cfg, backbone_model):
model = init_segmentor(cfg)
model.backbone.forward = partial(
backbone_model.get_intermediate_layers,
n=cfg.model.backbone.out_indices,
reshape=True,
)
if hasattr(backbone_model, "patch_size"):
model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone_model.patch_size)(x[0]))
model.init_weights()
return model
def render_segmentation(segmentation_logits, dataset):
colormap = DATASET_COLORMAPS[dataset]
colormap_array = np.array(colormap, dtype=np.uint8)
segmentation_logits += 1
segmentation_values = colormap_array[segmentation_logits]
segmentation_values = segmentation_values[:, :, ::-1]
unique_labels = np.unique(segmentation_logits)
colormap_array = colormap_array[unique_labels]
df = pd.read_csv("labelmap.txt", sep="\t")
html_output = '<div style="display: flex; flex-wrap: wrap;">'
import matplotlib.pyplot as plt
for idx, color in enumerate(colormap_array):
color_box = np.zeros((20, 20, 3), dtype=np.uint8)
color_box[:, :] = color
# color_box = cv2.cvtColor(color_box, cv2.COLOR_RGB2BGR)
_, img_data = cv2.imencode(".jpg", color_box)
img_base64 = base64.b64encode(img_data).decode("utf-8")
img_data_uri = f"data:image/jpg;base64,{img_base64}"
html_output += f'<div style="margin: 10px;"><img src="{img_data_uri}" /><p>{df.iloc[unique_labels[idx]-1]["Name"]}</p></div>'
html_output += "</div>"
return Image.fromarray(segmentation_values), html_output
def predict(image_file):
array = np.array(image_file)[:, :, ::-1] # BGR
segmentation_logits = inference_segmentor(model, array)[0]
segmented_image, html_output = render_segmentation(segmentation_logits, "ade20k")
return np.array(segmented_image), html_output
description = "Gradio demo for Semantic segmentation. To use it, simply upload your image"
demo = gr.Interface(
title="Semantic Segmentation - DinoV2",
fn=predict,
inputs=gr.inputs.Image(),
outputs=[gr.outputs.Image(type="numpy"), gr.outputs.HTML()],
examples=["example_1.jpg", "example_2.jpg"],
cache_examples=False,
description=description,
)
demo.launch()
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