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import numpy as np
PATCH_SIZE = 256 # Size of the patches
OVERLAP = 32 # Amount of overlap between patches
def split_image_into_patches(image):
height, width, _ = image.shape
patches = []
for y in range(0, height-PATCH_SIZE+1, PATCH_SIZE-OVERLAP):
for x in range(0, width-PATCH_SIZE+1, PATCH_SIZE-OVERLAP):
patch = (y,x,image[y:y+PATCH_SIZE, x:x+PATCH_SIZE])
patches.append(patch)
return patches
def stitch_patches_to_image(patches, image_shape):
stitched_image = np.zeros(image_shape)
overlap_mask = np.zeros(image_shape[:2])+1e-10
for patch in patches:
y, x, p = patch
try:
# Add the patch to the stitched image
stitched_image[y:y+PATCH_SIZE, x:x+PATCH_SIZE] += p
overlap_mask[y:y+PATCH_SIZE, x:x+PATCH_SIZE] += 1
except:
print(p.shape)
print(y,x)
print(image_shape)
1/0
# Normalize the stitched image by dividing with the overlap count
stitched_image = ((stitched_image/overlap_mask)>0.5)+0
return stitched_image.astype(np.uint8)
import torch
import yaml
import sys
import copy
import os
sys.path.append("/home/ubuntu/Desktop/Domain_Adaptation_Project/repos/biastuning/")
from data_utils import *
from model import *
from utils import *
label_names = ['Left Prograsp Forceps', 'Maryland Bipolar Forceps', 'Right Prograsp Forceps', 'Left Large Needle Driver', 'Right Large Needle Driver']
visualize_li = [[1,0,0],[0,1,0],[1,0,0], [0,0,1], [0,0,1]]
label_dict = {}
visualize_dict = {}
for i,ln in enumerate(label_names):
label_dict[ln] = i
visualize_dict[ln] = visualize_li[i]
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--data_folder', default='config_tmp.yml',
help='data folder file path')
parser.add_argument('--data_config', default='config_tmp.yml',
help='data config file path')
parser.add_argument('--model_config', default='model_baseline.yml',
help='model config file path')
parser.add_argument('--pretrained_path', default=None,
help='pretrained model path')
parser.add_argument('--save_path', default='checkpoints/temp.pth',
help='pretrained model path')
parser.add_argument('--device', default='cuda:0', help='device to train on')
parser.add_argument('--labels_of_interest', default='Left Prograsp Forceps,Maryland Bipolar Forceps,Right Prograsp Forceps,Left Large Needle Driver,Right Large Needle Driver', help='labels of interest')
parser.add_argument('--codes', default='1,2,1,3,3', help='numeric label to save per instrument')
args = parser.parse_args()
return args
def main():
args = parse_args()
with open(args.data_config, 'r') as f:
data_config = yaml.load(f, Loader=yaml.FullLoader)
with open(args.model_config, 'r') as f:
model_config = yaml.load(f, Loader=yaml.FullLoader)
labels_of_interest = args.labels_of_interest.split(',')
codes = args.codes.split(',')
codes = [int(c) for c in codes]
#make folder to save visualizations
os.makedirs(os.path.join(args.save_path,"preds"),exist_ok=True)
os.makedirs(os.path.join(args.save_path,"rescaled_preds"),exist_ok=True)
#load model
model = Prompt_Adapted_SAM(config=model_config, label_text_dict=label_dict, device=args.device)
model.load_state_dict(torch.load(args.pretrained_path, map_location=args.device))
model = model.eval()
model = model.to(args.device)
#load data transform
data_transform = ENDOVIS_Transform(config=data_config)
#load data
for img_name in sorted(os.listdir(args.data_folder)):
img_path = (os.path.join(args.data_folder,img_name))
# print(img_path)
original_img = torch.as_tensor(np.array(Image.open(img_path).convert("RGB")))
patches = split_image_into_patches(original_img)
patch_masks = []
for y,x,p in patches:
img = p.permute(2,0,1)
#make a dummy mask of shape 1XHXW
label = torch.zeros(img.shape)[0].unsqueeze(0)
img, _ = data_transform(img, label, is_train=False, apply_norm=True, crop=False, resize=False)
#get image embeddings
img = img.unsqueeze(0).to(args.device) #1XCXHXW
img_embeds = model.get_image_embeddings(img)
# generate masks for all labels of interest
img_embeds_repeated = img_embeds.repeat(len(labels_of_interest),1,1,1)
x_text = [t for t in labels_of_interest]
masks = model.get_masks_for_multiple_labels(img_embeds_repeated, x_text).cpu()
#for now, only handle one class at a time
masks, max_idxs = torch.max(masks,dim=0)
patch_masks.append((y,x,masks.numpy()))
# argmax_masks = torch.argmax(masks, dim=0)
# final_mask = torch.zeros(masks[0].shape)
# final_mask_rescaled = torch.zeros(masks[0].shape).unsqueeze(-1).repeat(1,1,3)
#save masks
# for i in range(final_mask.shape[0]):
# for j in range(final_mask.shape[1]):
# final_mask[i,j] = codes[argmax_masks[i,j]] if masks[argmax_masks[i,j],i,j]>=0.5 else 0
# final_mask_rescaled[i,j] = torch.Tensor(visualize_dict[(labels_of_interest[argmax_masks[i,j]])] if masks[argmax_masks[i,j],i,j]>=0.5 else [0,0,0])
#stitch masks
print("original shape: ", original_img.shape)
final_mask = stitch_patches_to_image(patch_masks, original_img.shape[:2])
print("final mask shape: ",final_mask.shape)
save_im = Image.fromarray(final_mask)
save_im.save(os.path.join(args.save_path,'preds', img_name))
# plt.imshow(final_mask_rescaled,cmap='gray')
# plt.savefig(os.path.join(args.save_path,'rescaled_preds', img_name))
# plt.close()
break
if __name__ == '__main__':
main()
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