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title: sribdmed | |
emoji: ⚡ | |
colorFrom: pink | |
colorTo: gray | |
sdk: gradio | |
sdk_version: 3.24.1 | |
app_file: app.py | |
pinned: false | |
license: apache-2.0 | |
# Model Card for cell-seg-sribd | |
<!-- Provide a quick summary of what the model is/does. --> | |
This repository provides the solution of team Sribd-med for NeurIPS-CellSeg Challenge. The details of our method are described in our paper [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images]. Some parts of the codes are from the baseline codes of the NeurIPS-CellSeg-Baseline repository, | |
You can reproduce our method as follows step by step: | |
### How to Get Started with the Model | |
Install requirements by python -m pip install -r requirements.txt | |
## Training Details | |
### Training Data | |
The competition training and tuning data can be downloaded from https://neurips22-cellseg.grand-challenge.org/dataset/ Besides, you can download three publiced data from the following link: Cellpose: https://www.cellpose.org/dataset Omnipose: http://www.cellpose.org/dataset_omnipose Sartorius: https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation/overview | |
## Environments and Requirements: | |
Install requirements by | |
```shell | |
python -m pip install -r requirements.txt | |
``` | |
### How to use | |
Here is how to use this model: | |
```python | |
from skimage import io, segmentation, morphology, measure, exposure | |
from sribd_cellseg_models import MultiStreamCellSegModel,ModelConfig | |
import numpy as np | |
import tifffile as tif | |
import requests | |
import torch | |
from PIL import Image | |
from overlay import visualize_instances_map | |
import cv2 | |
img_name = 'test_images/cell_00551.tiff' | |
def normalize_channel(img, lower=1, upper=99): | |
non_zero_vals = img[np.nonzero(img)] | |
percentiles = np.percentile(non_zero_vals, [lower, upper]) | |
if percentiles[1] - percentiles[0] > 0.001: | |
img_norm = exposure.rescale_intensity(img, in_range=(percentiles[0], percentiles[1]), out_range='uint8') | |
else: | |
img_norm = img | |
return img_norm.astype(np.uint8) | |
if img_name.endswith('.tif') or img_name.endswith('.tiff'): | |
img_data = tif.imread(img_name) | |
else: | |
img_data = io.imread(img_name) | |
# normalize image data | |
if len(img_data.shape) == 2: | |
img_data = np.repeat(np.expand_dims(img_data, axis=-1), 3, axis=-1) | |
elif len(img_data.shape) == 3 and img_data.shape[-1] > 3: | |
img_data = img_data[:,:, :3] | |
else: | |
pass | |
pre_img_data = np.zeros(img_data.shape, dtype=np.uint8) | |
for i in range(3): | |
img_channel_i = img_data[:,:,i] | |
if len(img_channel_i[np.nonzero(img_channel_i)])>0: | |
pre_img_data[:,:,i] = normalize_channel(img_channel_i, lower=1, upper=99) | |
#dummy_input = np.zeros((512,512,3)).astype(np.uint8) | |
my_model = MultiStreamCellSegModel.from_pretrained("Lewislou/cellseg_sribd") | |
checkpoints = torch.load('model.pt') | |
my_model.__init__(ModelConfig()) | |
my_model.load_checkpoints(checkpoints) | |
with torch.no_grad(): | |
output = my_model(pre_img_data) | |
overlay = visualize_instances_map(pre_img_data,star_label) | |
cv2.imwrite('prediction.png', cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR)) | |
``` | |
## Citation | |
If any part of this code is used, please acknowledge it appropriately and cite the paper: | |
```bibtex | |
@misc{ | |
lou2022multistream, | |
title={Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images}, | |
author={WEI LOU and Xinyi Yu and Chenyu Liu and Xiang Wan and Guanbin Li and Siqi Liu and Haofeng Li}, | |
year={2022}, | |
url={https://openreview.net/forum?id=G24BybwKe9} | |
} | |
``` |