cell-seg-sribd / predict.py
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import os
join = os.path.join
import argparse
import numpy as np
import torch
import torch.nn as nn
from collections import OrderedDict
from torchvision import datasets, models, transforms
from classifiers import resnet10, resnet18
from utils_modify import sliding_window_inference,sliding_window_inference_large,__proc_np_hv
from PIL import Image
import torch.nn.functional as F
from skimage import io, segmentation, morphology, measure, exposure
import tifffile as tif
from models.flexible_unet_convnext import FlexibleUNet_star,FlexibleUNet_hv
#from overlay import visualize_instances_map
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)
#torch.cuda.synchronize()
parser = argparse.ArgumentParser('Baseline for Microscopy image segmentation', add_help=False)
# Dataset parameters
parser.add_argument('-i', '--input_path', default='./inputs', type=str, help='training data path; subfolders: images, labels')
parser.add_argument("-o", '--output_path', default='./outputs', type=str, help='output path')
parser.add_argument('--model_path', default='./models', help='path where to save models and segmentation results')
parser.add_argument('--show_overlay', required=False, default=False, action="store_true", help='save segmentation overlay')
# Model parameters
parser.add_argument('--model_name', default='efficientunet', help='select mode: unet, unetr, swinunetr')
parser.add_argument('--input_size', default=512, type=int, help='segmentation classes')
args = parser.parse_args()
input_path = args.input_path
output_path = args.output_path
model_path = args.model_path
os.makedirs(output_path, exist_ok=True)
#overlay_path = 'overlays/'
#print(input_path)
img_names = sorted(os.listdir(join(input_path)))
#print(img_names)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
preprocess=transforms.Compose([
transforms.Resize(size=256),
transforms.CenterCrop(size=224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
roi_size = (512, 512)
overlap = 0.5
np_thres, ksize, overall_thres, obj_size_thres = 0.6, 15, 0.4, 100
n_rays = 32
sw_batch_size = 4
num_classes= 4
block_size = 2048
min_overlap = 128
context = 128
with torch.no_grad():
for img_name in img_names:
#print(img_name)
if img_name.endswith('.tif') or img_name.endswith('.tiff'):
img_data = tif.imread(join(input_path, img_name))
else:
img_data = io.imread(join(input_path, 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)
inputs=preprocess(Image.fromarray(pre_img_data)).unsqueeze(0).to(device)
cls_MODEL = model_path + '/cls/resnet18_4class_all_modified.tar'
model = resnet18().to(device)
model.load_state_dict(torch.load(cls_MODEL))
model.eval()
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
label=preds[0].cpu().numpy()
#print(label)
test_npy01 = pre_img_data
if label in [0,1,2] or img_data.shape[0] > 4000:
if label == 0:
model = FlexibleUNet_star(in_channels=3,out_channels=n_rays+1,backbone='convnext_small',pretrained=False,n_rays=n_rays,prob_out_channels=1,).to(device)
checkpoint = torch.load(model_path+'/0/best_model.pth', map_location=torch.device(device))
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
output_label = sliding_window_inference_large(test_npy01,block_size,min_overlap,context, roi_size,sw_batch_size,predictor=model,device=device)
tif.imwrite(join(output_path, img_name.split('.')[0]+'_label.tiff'), output_label)
elif label == 1:
model = FlexibleUNet_star(in_channels=3,out_channels=n_rays+1,backbone='convnext_small',pretrained=False,n_rays=n_rays,prob_out_channels=1,).to(device)
checkpoint = torch.load(model_path+'/1/best_model.pth', map_location=torch.device(device))
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
output_label = sliding_window_inference_large(test_npy01,block_size,min_overlap,context, roi_size,sw_batch_size,predictor=model,device=device)
tif.imwrite(join(output_path, img_name.split('.')[0]+'_label.tiff'), output_label)
elif label == 2:
model = FlexibleUNet_star(in_channels=3,out_channels=n_rays+1,backbone='convnext_small',pretrained=False,n_rays=n_rays,prob_out_channels=1,).to(device)
checkpoint = torch.load(model_path+'/2/best_model.pth', map_location=torch.device(device))
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
output_label = sliding_window_inference_large(test_npy01,block_size,min_overlap,context, roi_size,sw_batch_size,predictor=model,device=device)
tif.imwrite(join(output_path, img_name.split('.')[0]+'_label.tiff'), output_label)
else:
model = FlexibleUNet_hv(in_channels=3,out_channels=2+2,backbone='convnext_small',pretrained=False,n_rays=2,prob_out_channels=2,).to(device)
checkpoint = torch.load(model_path+'/3/best_model_converted.pth', map_location=torch.device(device))
#model.load_state_dict(checkpoint['model_state_dict'])
#od = OrderedDict()
#for k, v in checkpoint['model_state_dict'].items():
#od[k.replace('module.', '')] = v
model.load_state_dict(checkpoint)
model.to(device)
model.eval()
test_tensor = torch.from_numpy(np.expand_dims(test_npy01, 0)).permute(0, 3, 1, 2).type(torch.FloatTensor).to(device)
if isinstance(roi_size, tuple):
roi = roi_size
output_hv, output_np = sliding_window_inference(test_tensor, roi, sw_batch_size, model, overlap=overlap)
pred_dict = {'np': output_np, 'hv': output_hv}
pred_dict = OrderedDict(
[[k, v.permute(0, 2, 3, 1).contiguous()] for k, v in pred_dict.items()] # NHWC
)
pred_dict["np"] = F.softmax(pred_dict["np"], dim=-1)[..., 1:]
pred_output = torch.cat(list(pred_dict.values()), -1).cpu().numpy() # NHW3
pred_map = np.squeeze(pred_output) # HW3
pred_inst = __proc_np_hv(pred_map, np_thres, ksize, overall_thres, obj_size_thres)
raw_pred_shape = pred_inst.shape[:2]
output_label = pred_inst
tif.imwrite(join(output_path, img_name.split('.')[0]+'_label.tiff'), output_label)