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import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as func
from captum.attr import IntegratedGradients
import __main__
class ConvNet(nn.Module):
def __init__(self):
super().__init__()
# размер исходной картинки 180x180
self.conv1 = nn.Conv2d(3, 8, 3, padding=1)
self.batchnorm1 = nn.BatchNorm2d(8)
self.pool1 = nn.MaxPool2d((2, 2))
self.conv2 = nn.Conv2d(8, 16, 8, padding=1)
self.dropout2 = nn.Dropout(0.25)
self.batchnorm2 = nn.BatchNorm2d(16)
self.pool2 = nn.MaxPool2d((2, 2))
self.conv3 = nn.Conv2d(16, 32, 2, padding=1)
self.dropout3 = nn.Dropout(0.25)
self.batchnorm3 = nn.BatchNorm2d(32)
self.pool3 = nn.MaxPool2d((2, 2))
self.conv4 = nn.Conv2d(32, 16, 16, padding=1)
self.dropout4 = nn.Dropout(0.25)
self.batchnorm4 = nn.BatchNorm2d(16)
# flatten
self.flatten = nn.Flatten()
self.fc_2_1 = nn.Linear(28224, 512)
self.fc_2_2 = nn.Linear(512, 4)
# linear 1
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 4)
def forward(self, x):
x = func.relu(self.conv1(x))
x = self.batchnorm1(x)
x = self.pool1(x)
x = func.relu(self.conv2(x))
x = self.dropout2(x)
x = self.batchnorm2(x)
x = self.pool2(x)
x_1 = func.relu(self.conv3(x))
x_1 = self.dropout3(x_1)
x_1 = self.batchnorm3(x_1)
x_1 = self.pool3(x_1)
x_1 = func.relu(self.conv4(x_1))
x_1 = self.dropout4(x_1)
x_1 = self.batchnorm4(x_1)
x_1 = self.flatten(x_1)
x_1 = func.relu(self.fc1(x_1))
x_1 = self.fc2(x_1)
x_2 = self.flatten(x)
x_2 = func.relu(self.fc_2_1(x_2))
x_2 = self.fc_2_2(x_2)
return x_1 + x_2
setattr(__main__, "ConvNet", ConvNet)
device = 'cpu'
model_ = torch.load('model_5_7_14_27_0.993125_final')
model_.eval()
def get_class_of_demension(idx):
classes = ['NonDemented', 'VeryMildDemented', 'MildDemented', 'ModerateDemented']
return classes[idx]
def get_segmented_map(image_attr: np.array,
color_map: str = 'positive',
borders: tuple = (20, 20)) -> np.array:
"""arg: color_map: [positive, all]"""
if color_map != 'all':
for i in range(len(image_attr)):
for j in range(len(image_attr[i])):
flag_zero = False
if color_map == 'positive':
if max(image_attr[i][j]) != image_attr[i][j][1]:
flag_zero = True
else:
if sum(image_attr[i][j]) - max(image_attr[i][j]) > borders[1]:
flag_zero = True
elif color_map == 'negative':
if max(image_attr[i][j]) == image_attr[i][j][1] or max(image_attr[i][j]) == image_attr[i][j][2]:
flag_zero = True
else:
if sum(image_attr[i][j]) - max(image_attr[i][j]) > borders[0]:
flag_zero = True
if flag_zero:
image_attr[i][j] = [0, 0, 0]
return image_attr
def show_pack_of_images(images, labels):
f, axes = plt.subplots(1, len(images), figsize=(30, 5))
for i, axis in enumerate(axes):
img = images[i]
axes[i].imshow(img)
axes[i].set_title(labels[i])
plt.show()
def create_color_map_igrad(net, img_path: str) -> tuple:
integrated_gradients = IntegratedGradients(net)
img = cv2.cvtColor(cv2.resize(cv2.imread(img_path, 0), (180, 180)), cv2.COLOR_GRAY2RGB)
img_tensor = torch.from_numpy(np.array(img).astype(np.float32)).to('cpu')
img_tensor = img_tensor.permute(2, 0, 1) / 255
img_tensor = img_tensor.unsqueeze(0)
output = model_(img_tensor)
prob = func.sigmoid(output)
probability = float(np.max(prob.detach().numpy()))
prediction_score, pred_label_idx = torch.topk(output, 1)
pred_label_idx.squeeze_()
predicted_label = pred_label_idx.item()
attributions_ig = integrated_gradients.attribute(img_tensor, target=pred_label_idx, n_steps=200)
imgs = [(img_tensor.squeeze().permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8),
(np.transpose(attributions_ig.squeeze().cpu().detach().numpy(), (1, 2, 0)) * 255).astype(np.uint8)]
imgs.extend([get_segmented_map(imgs[1].copy(), 'negative'), get_segmented_map(imgs[1].copy(), 'positive')])
labels = [get_class_of_demension(predicted_label), 'all', 'negative', 'positive']
return imgs, labels, probability
def get_results_model(image_path, model):
images, labels, probability = create_color_map_igrad(model, image_path)
img = images[3].copy()
original = images[0].copy()
result = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY);
result = cv2.blur(result, (5, 5));
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
ret, result = cv2.threshold(result, 0.3 * max_val, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(result, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
for element in contours:
if 150 > len(element) > 35:
color = (255, 0, 0)
x, y, w, h = cv2.boundingRect(element)
cv2.rectangle(original, (x - 2, y - 2), (x + w + 1, y + h + 1), color, 1)
return original, labels[0], probability
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