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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(img_name, 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',map_location=torch.device('cpu'))
my_model.__init__(ModelConfig())
my_model.load_checkpoints(checkpoints)
with torch.no_grad():
output = my_model(pre_img_data)
print(output.shape)
overlay = visualize_instances_map(pre_img_data,output)
print(pre_img_data.shape,overlay.shape)
#cv2.imwrite('prediction.png', cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))
return pre_img_data,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="Pred Image"),
],
title="Cell Segmentation Results",
).launch() |