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detect2.py
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detect2.py
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import cv2
import torch
from dialmodel import IRModel
from torchvision import transforms
import os
import numpy as np
import sklearn.preprocessing as sp
from time import *
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = IRModel().to(device)
weight = r'D:\杨震\unet1\unet\epoch_109_.3f.pt'
if os.path.exists(weight):
net.load_state_dict(torch.load(weight))
img_path = 'ROAD3/images/153163_sat.jpg'
mask_path = 'ROAD3/label/153163_mask.png'
mask_path1 = 'ROAD3/label/153163_mask.png'
transforms_test = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4372, 0.4372, 0.4373],
std=[0.2479, 0.2475, 0.2485])
])
def normalization(data):
_range = np.max(data) - np.min(data)
return (data - np.min(data)) / _range
if __name__ == '__main__':
img_tensor_list = []
origin = cv2.imread(img_path, 1)
origin1 = cv2.imread(img_path, 1)
cv2.imshow('origin', origin)
tr = transforms.Compose([transforms.ToTensor()])
img = transforms_test(origin)
img_tensor_list.append(img)
img1 = transforms_test(origin1)
img_tensor_list.append(img1)
img_tensor_list = torch.stack(img_tensor_list, 0)
T=cv2.imread(mask_path, 0)
T = cv2.resize(T, (1024,1024))
mask = tr(T)
mask1 = tr(cv2.imread(mask_path1, 0))
mask=mask.to(device)
# mask1 = mask1.to(device)
# pred=mask1
net.eval()
with torch.no_grad():
begin_time = time()
pred = net(img_tensor_list[0:1].cuda())
end_time = time()
time = end_time - begin_time
print('一共运行时间:', time)
heatmap = pred .squeeze().cpu()
single_map = heatmap
hm = single_map.detach().numpy()
hm = normalization(hm)
#
bin = sp.Binarizer(threshold=0.65)
hm = bin.transform(hm)
hm = np.uint8(255 * hm)
hm = cv2.applyColorMap(hm, cv2.COLORMAP_JET)
hm = cv2.resize(hm, (1024,1024))
superimposed_img = hm
cv2.imwrite("output/%d.tiff" % 1, superimposed_img)
pred[pred >= 0.4] = 1
pred[pred < 0.4] = 0
TP = ((pred == 1) & (mask == 1)).sum()
TN = ((pred == 0) & (mask == 0)).sum()
FN = ((pred == 0) & (mask == 1)).sum()
FP = ((pred == 1) & (mask == 0)).sum()
P=TP/(TP+FP)
pa = (TP + TN) / (TP + TN + FP + FN)
iou = TP / (TP + FP + FN)
R=TP/(TP+FN)
F1=(2*P*R)/(P+R)
print('pa: ', pa)
print('R: ', R)
print('P: ', P)
print('F1: ', F1)
print('iou', iou)
# cv2.imshow('origin_out', np.hstack([img, pred]))
# cv2.waitKey(0)
# cv2.destroyAllWindows()