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from PIL import Image
from matplotlib.pyplot import imshow, show
import matplotlib.pyplot as plt
from torchvision import models, transforms
from torch.autograd import Variable
from torch.nn import functional as F
import torch
import torch.nn as nn
from torch import topk
import numpy as np
import os
import skimage.transform
import cv2
import math
import openslide
import argparse
import pickle
def show_cam_on_image(img, mask):
heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)
heatmap = np.float32(heatmap) / 255
cam = heatmap + np.float32(img)
cam = cam / np.max(cam)
return cam
def cam_to_mask(gray, patches, cam_matrix, w, h, w_s, h_s):
mask = np.full_like(gray, 0.).astype(np.float32)
for ind1, patch in enumerate(patches):
x, y = patch.split('.')[0].split('_')
x, y = int(x), int(y)
#if y <5 or x>w-5 or y>h-5:
# continue
mask[int(y*h_s):int((y+1)*h_s), int(x*w_s):int((x+1)*w_s)].fill(cam_matrix[ind1][0])
return mask
def main(args):
label_map = pickle.load(open(os.path.join(args.dataset_metadata_path, 'label_map.pkl'), 'rb'))
label_name_from_id = dict()
for label_name, label_id in label_map.items():
label_name_from_id[label_id] = label_name
n_class = len(label_map)#args.n_class
file_name, label = open(args.path_file, 'r').readlines()[-1].split('\t')
label = label.rstrip().strip()
#site, file_name = file_name.split('/')
file_path = os.path.join(args.path_patches, '{}_files/20.0/'.format(file_name))
print(file_name)
print(label)
p = torch.load('graphcam/prob.pt').cpu().detach().numpy()[0]
file_path = os.path.join(args.path_patches, '{}_files/20.0/'.format(file_name))
#ori = openslide.OpenSlide(os.path.join(args.path_WSI, '{}.svs').format(file_name))
ORIGINAL_FILEPATH = os.path.join(args.path_WSI,'TCGA',label, '{}.svs'.format(file_name))
print('L', ORIGINAL_FILEPATH)
ori = openslide.OpenSlide(ORIGINAL_FILEPATH)
patch_info = open(os.path.join(args.path_graph, file_name, 'c_idx.txt'), 'r')
width, height = ori.dimensions
REDUCTION_FACTOR = 10
w, h = int(width/512), int(height/512)
w_r, h_r = int(width/20), int(height/20)
resized_img = ori.get_thumbnail((width,height))#ori.get_thumbnail((w_r,h_r))
resized_img = resized_img.resize((w_r,h_r))
ratio_w, ratio_h = width/resized_img.width, height/resized_img.height
print('ratios ', ratio_w, ratio_h)
w_s, h_s = float(512/REDUCTION_FACTOR), float(512/REDUCTION_FACTOR)
print(w_s, h_s)
patch_info = patch_info.readlines()
patches = []
xmax, ymax = 0, 0
for patch in patch_info:
x, y = patch.strip('\n').split('\t')
if xmax < int(x): xmax = int(x)
if ymax < int(y): ymax = int(y)
patches.append('{}_{}.jpeg'.format(x,y))
output_img = np.asarray(resized_img)[:,:,::-1].copy()
#-----------------------------------------------------------------------------------------------------#
# GraphCAM
print('visulize GraphCAM')
assign_matrix = torch.load('graphcam/s_matrix_ori.pt')
m = nn.Softmax(dim=1)
assign_matrix = m(assign_matrix)
# Thresholding for better visualization
p = np.clip(p, 0.4, 1)
output_img_copy =np.copy(output_img)
gray = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
image_transformer_attribution = (output_img_copy - output_img_copy.min()) / (output_img_copy.max() - output_img_copy.min())
cam_matrices = []
masks = []
visualizations = []
print(len(patches))
os.makedirs('graphcam_vis', exist_ok=True)
for class_i in range(n_class):
# Load graphcam for each class
cam_matrix = torch.load(f'graphcam/cam_{class_i}.pt')
print(cam_matrix.shape)
cam_matrix = torch.mm(assign_matrix, cam_matrix.transpose(1,0))
cam_matrix = cam_matrix.cpu()
print(assign_matrix.shape)
print(cam_matrix.shape)
# Normalize the graphcam
cam_matrix = (cam_matrix - cam_matrix.min()) / (cam_matrix.max() - cam_matrix.min())
cam_matrix = cam_matrix.detach().numpy()
cam_matrix = p[class_i] * cam_matrix
cam_matrix = np.clip(cam_matrix, 0, 1)
print(cam_matrix.shape)
#print()
mask = cam_to_mask(gray, patches, cam_matrix, w, h, w_s, h_s)
print('mask shape ', mask.shape)
print('imgtf attr ', image_transformer_attribution.shape)
vis = show_cam_on_image(image_transformer_attribution, mask)
vis = np.uint8(255 * vis)
cam_matrices.append(cam_matrix)
masks.append(mask)
visualizations.append(vis)
print()
cv2.imwrite('graphcam_vis/{}_all_types_cam_{}.png'.format(file_name, label_name_from_id[class_i] ), vis)
h, w, _ = output_img.shape
if h > w:
vis_merge = cv2.hconcat([output_img] + visualizations)
else:
vis_merge = cv2.vconcat([output_img] + visualizations)
cv2.imwrite('graphcam_vis/{}_all_types_cam_all.png'.format(file_name), vis_merge)
cv2.imwrite('graphcam_vis/{}_all_types_ori.png'.format(file_name ), output_img)
'''
# Load graphcam for differnet class
cam_matrix_0 = torch.load('graphcam/cam_0.pt')
cam_matrix_0 = torch.mm(assign_matrix, cam_matrix_0.transpose(1,0))
cam_matrix_0 = cam_matrix_0.cpu()
cam_matrix_1 = torch.load('graphcam/cam_1.pt')
cam_matrix_1 = torch.mm(assign_matrix, cam_matrix_1.transpose(1,0))
cam_matrix_1 = cam_matrix_1.cpu()
cam_matrix_2 = torch.load('graphcam/cam_2.pt')
cam_matrix_2 = torch.mm(assign_matrix, cam_matrix_2.transpose(1,0))
cam_matrix_2 = cam_matrix_2.cpu()
# Normalize the graphcam
cam_matrix_0 = (cam_matrix_0 - cam_matrix_0.min()) / (cam_matrix_0.max() - cam_matrix_0.min())
cam_matrix_0 = cam_matrix_0.detach().numpy()
cam_matrix_0 = p[0] * cam_matrix_0
cam_matrix_0 = np.clip(cam_matrix_0, 0, 1)
cam_matrix_1 = (cam_matrix_1 - cam_matrix_1.min()) / (cam_matrix_1.max() - cam_matrix_1.min())
cam_matrix_1 = cam_matrix_1.detach().numpy()
cam_matrix_1 = p[1] * cam_matrix_1
cam_matrix_1 = np.clip(cam_matrix_1, 0, 1)
cam_matrix_2 = (cam_matrix_2 - cam_matrix_2.min()) / (cam_matrix_2.max() - cam_matrix_2.min())
cam_matrix_2 = cam_matrix_2.detach().numpy()
cam_matrix_2 = p[2] * cam_matrix_2
cam_matrix_2 = np.clip(cam_matrix_2, 0, 1)
output_img_copy =np.copy(output_img)
gray = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
image_transformer_attribution = (output_img_copy - output_img_copy.min()) / (output_img_copy.max() - output_img_copy.min())
mask0 = cam_to_mask(gray, patches, cam_matrix_0, w, h, w_s, h_s)
vis0 = show_cam_on_image(image_transformer_attribution, mask0)
vis0 = np.uint8(255 * vis0)
mask1 = cam_to_mask(gray, patches, cam_matrix_1, w, h, w_s, h_s)
vis1 = show_cam_on_image(image_transformer_attribution, mask1)
vis1 = np.uint8(255 * vis1)
mask2 = cam_to_mask(gray, patches, cam_matrix_2, w, h, w_s, h_s)
vis2 = show_cam_on_image(image_transformer_attribution, mask2)
vis2 = np.uint8(255 * vis2)
##########################################
h, w, _ = output_img.shape
if h > w:
vis_merge = cv2.hconcat([output_img, vis0, vis1, vis2])
else:
vis_merge = cv2.vconcat([output_img, vis0, vis1, vis2])
#cv2.imwrite('graphcam_vis/{}_{}_all_types_cam_all.png'.format(file_name, site), vis_merge)
#cv2.imwrite('graphcam_vis/{}_{}_all_types_ori.png'.format(file_name, site), output_img)
#cv2.imwrite('graphcam_vis/{}_{}_all_types_cam_luad.png'.format(file_name, site), vis1)
#cv2.imwrite('graphcam_vis/{}_{}_all_types_cam_lscc.png'.format(file_name, site), vis2)
cv2.imwrite('graphcam_vis/{}_all_types_cam_all.png'.format(file_name, ), vis_merge)
cv2.imwrite('graphcam_vis/{}_all_types_ori.png'.format(file_name ), output_img)
cv2.imwrite('graphcam_vis/{}_all_types_cam_luad.png'.format(file_name ), vis1)
cv2.imwrite('graphcam_vis/{}_all_types_cam_lscc.png'.format(file_name ), vis2)
'''
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='GraphCAM')
parser.add_argument('--path_file', type=str, default='test.txt', help='txt file contains test sample')
parser.add_argument('--path_patches', type=str, default='', help='')
parser.add_argument('--path_WSI', type=str, default='', help='')
parser.add_argument('--path_graph', type=str, default='', help='')
parser.add_argument('--dataset_metadata_path', type=str, help='Location of the metadata associated with the created dataset: label mapping, splits and so on')
args = parser.parse_args()
main(args) |