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import gradio as gr | |
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 | |
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) | |
def predict(filename, model=None, device=None, reduce_labels=True): | |
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) | |
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)) | |
return overlay | |
gr.Interface( | |
predict, | |
inputs=[gr.components.Image(label="Upload Input Image", type="filepath"), | |
gr.components.Textbox(label='Model Name', value='sribd_med', max_lines=1)], | |
outputs=[gr.Image(label="Processed Image"), | |
gr.Image(label="Label Image"), | |
], | |
title="Cell Segmentation Results", | |
).launch() |