303 lines
12 KiB
Python
303 lines
12 KiB
Python
#!/usr/bin/env python
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
@project:
|
|
@File : xaiotu
|
|
@Author : qiqq
|
|
@create_time : 2023/6/29 8:57
|
|
"""
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import copy
|
|
import cv2 as cv
|
|
from tqdm import tqdm
|
|
import os
|
|
from torch.utils.data import DataLoader
|
|
|
|
import argparse
|
|
from torch.utils.data import Dataset
|
|
|
|
from torchvision import transforms
|
|
from taihuyuan_roof.dataloaders import custom_transforms as tr
|
|
import torch
|
|
import torch.nn.functional as F
|
|
|
|
from PIL import Image
|
|
|
|
'''voc数据集格式的'''
|
|
|
|
|
|
class predictandeval():
|
|
def __init__(self, model_name="xx"):
|
|
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
|
|
|
self.predict_dir = model_name + "_" + "outputs/"
|
|
self.fusion_path = model_name + "_" + "fusion/"
|
|
self.model_name = model_name
|
|
self.palette = [0, 0, 0, 0, 255, 0, 0, 255, 0]
|
|
|
|
self.fusin = True
|
|
|
|
def get_confusion_matrix(self, gt_label, pred_label, class_num):
|
|
"""
|
|
Calcute the confusion matrix by given label and pred
|
|
:param gt_label: the ground truth label
|
|
:param pred_label: the pred label
|
|
:param class_num: the nunber of class
|
|
:return: the confusion matrix
|
|
"""
|
|
index = (gt_label * class_num + pred_label).astype('int32')
|
|
label_count = np.bincount(index)
|
|
confusion_matrix = np.zeros((class_num, class_num))
|
|
|
|
for i_label in range(class_num):
|
|
for i_pred_label in range(class_num):
|
|
cur_index = i_label * class_num + i_pred_label
|
|
if cur_index < len(label_count):
|
|
confusion_matrix[i_label, i_pred_label] = label_count[cur_index]
|
|
|
|
return confusion_matrix
|
|
|
|
# 延用训练数据集格式
|
|
# def get_datasets(self, ):
|
|
# parser = argparse.ArgumentParser()
|
|
# args = parser.parse_args()
|
|
# args.resize = (512, 512)
|
|
# args.crop_size = 480 # 在验证的时候没有用 crop和filp 只是为了不报错
|
|
# args.flip_prob = 0.5
|
|
# datasets = datasets_pvtaihuyuan(args, split='val', isAug=False)
|
|
# print(len(datasets))
|
|
# return datasets
|
|
|
|
# 重写 voc格式的val或者test
|
|
def get_images(self, ):
|
|
basedir = "/home/qiqq/q3dl/datalinan/taihuyuan_roof/"
|
|
split = 'val'
|
|
_splits_dir = "/home/qiqq/q3dl/datalinan/taihuyuan_roof/"
|
|
imglist = []
|
|
with open(os.path.join(os.path.join(_splits_dir, split + '.txt')), "r") as f:
|
|
lines = f.read().splitlines()
|
|
for ii, line in enumerate(lines):
|
|
name = line
|
|
_imagepath = os.path.join(basedir, 'images1', line + ".png")
|
|
|
|
assert os.path.isfile(_imagepath)
|
|
|
|
image = Image.open(_imagepath)
|
|
|
|
orininal_h = image.size[1]
|
|
orininal_w = image.size[0]
|
|
item = {"name": name, "orininal_h": orininal_h, "orininal_w": orininal_w, "image": image}
|
|
imglist.append(item)
|
|
print("共监测到{}张原始图像和标签".format(len(imglist)))
|
|
return imglist
|
|
|
|
def get_labels(self, ):
|
|
basedir = "/home/qiqq/q3dl/datalinan/taihuyuan_roof/"
|
|
split = 'val'
|
|
_splits_dir = "/home/qiqq/q3dl/datalinan/taihuyuan_roof/"
|
|
labellist = []
|
|
with open(os.path.join(os.path.join(_splits_dir, split + '.txt')), "r") as f:
|
|
lines = f.read().splitlines()
|
|
for ii, line in enumerate(lines):
|
|
name = line
|
|
_labelpath = os.path.join(basedir, 'labels1', line + ".png")
|
|
|
|
assert os.path.isfile(_labelpath)
|
|
|
|
label = Image.open(_labelpath)
|
|
item = {"name": name, "label": label}
|
|
labellist.append(item)
|
|
print("共监测到{}张标签".format(len(labellist)))
|
|
return labellist
|
|
|
|
def get_result(self, net):
|
|
|
|
imglist = self.get_images()
|
|
assert len(imglist) != 0
|
|
for i in tqdm(imglist):
|
|
image = i["image"]
|
|
name = i["name"]
|
|
orininal_w = i["orininal_w"]
|
|
orininal_h = i["orininal_h"]
|
|
old_img = copy.deepcopy(image)
|
|
imaged = cv.resize(np.array(image), dsize=(512, 512), interpolation=cv.INTER_LINEAR)
|
|
image_data = np.expand_dims(
|
|
np.transpose(self.preprocess_input(np.array(imaged, np.float32), md=False), (2, 0, 1)), 0)
|
|
|
|
with torch.no_grad():
|
|
images = torch.from_numpy(image_data)
|
|
image = images.to(device="cuda", dtype=torch.float32)
|
|
model = net.to(device="cuda")
|
|
out = model(image)
|
|
if isinstance(out, list) or isinstance(out, tuple): # 可能有多个输出（这里把辅助解码头的也输出的所以是多个）
|
|
out = out[0] # 就取第一个
|
|
out = out[0] # 去掉batch
|
|
|
|
pr = F.softmax(out.permute(1, 2, 0), dim=-1).cpu().numpy()
|
|
result = pr.argmax(axis=-1)
|
|
# 结果图
|
|
if not os.path.exists(self.predict_dir):
|
|
os.makedirs(self.predict_dir)
|
|
output_im = Image.fromarray(np.uint8(result)).convert('P')
|
|
|
|
output_im.putpalette(self.palette)
|
|
output_im.save(self.predict_dir + name + '.png')
|
|
|
|
# hunhe
|
|
if self.fusin:
|
|
if not os.path.exists(self.fusion_path):
|
|
os.makedirs(self.fusion_path)
|
|
PALETTE = [(0, 0, 0), (255, 0, 0), (0, 255, 0)]
|
|
|
|
seg_img0 = np.reshape(np.array(PALETTE, np.uint8)[np.reshape(result, [-1])],
|
|
[orininal_h, orininal_w, -1])
|
|
|
|
image0 = Image.fromarray(np.uint8(seg_img0))
|
|
|
|
fusion = Image.blend(old_img, image0, 0.4)
|
|
|
|
fusion.save(self.fusion_path + self.model_name + "_" + name + '.png')
|
|
|
|
def preprocess_input(self, image, md=False):
|
|
mean = (0.231, 0.217, 0.22) # 针对北京的数据集
|
|
std = (0.104, 0.086, 0.085)
|
|
if md:
|
|
image /= 255.0
|
|
image -= mean
|
|
image /= std
|
|
return image
|
|
else:
|
|
image /= 255.0
|
|
return image
|
|
|
|
def compute_evalue(self, predict_dir, ):
|
|
labellist = self.get_labels() # 存的额是没转换成nmpy的png
|
|
predictresultlist = os.listdir(predict_dir)
|
|
assert len(labellist) == len(predictresultlist)
|
|
num_classes = 2
|
|
|
|
confusion_matrix = np.zeros((num_classes, num_classes))
|
|
|
|
for i in tqdm(labellist):
|
|
name = i["name"]
|
|
seg_gt = np.array(i["label"])
|
|
seg_pred = np.array(Image.open(os.path.join(predict_dir, name + ".png")))
|
|
#
|
|
ignore_index = seg_gt != 255 #
|
|
seg_gt = seg_gt[ignore_index]
|
|
seg_pred = seg_pred[ignore_index]
|
|
|
|
confusion_matrix += self.get_confusion_matrix(seg_gt, seg_pred, num_classes)
|
|
|
|
pos = confusion_matrix.sum(1) # 得到的每个数都是每个类别真实的像素点数量 (相当于tp+fn)
|
|
res = confusion_matrix.sum(0) # 得到的每个数都是被预测为这个类别的像素点数量 (tp+fp)
|
|
tp = np.diag(confusion_matrix)
|
|
|
|
IU_array = (tp / np.maximum(1.0, pos + res - tp))
|
|
|
|
mean_IU = IU_array.mean()
|
|
roof_iou = IU_array[1:].mean()
|
|
precion = tp / res
|
|
|
|
recall = tp / pos
|
|
|
|
f1 = (2 * precion * recall) / (precion + recall)
|
|
mf1 = f1.mean()
|
|
acc_global = tp.sum() / pos.sum()
|
|
|
|
roof_f1 = f1[1:].mean()
|
|
roof_precison = precion[1:].mean()
|
|
roof_recall = recall[1:].mean()
|
|
|
|
print("测试结果")
|
|
print("acc_global:", round(acc_global, 4))
|
|
print("IU_array:", np.round(IU_array, 4))
|
|
print("precion:", np.round(precion, 4))
|
|
print("recall:", np.round(recall, 4))
|
|
print("f1:", np.round(f1, 4))
|
|
print("miou:", round(mean_IU, 4))
|
|
print("roof_iou:", round(roof_iou, 4))
|
|
print("roof_precison:", round(roof_precison, 4))
|
|
print("roof_recall:", round(roof_recall, 4))
|
|
print("roof_f1:", round(roof_f1, 4))
|
|
|
|
return roof_iou, roof_f1, roof_precison, roof_recall, acc_global
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')
|
|
parser.add_argument('--epochs', '-e', metavar='E', type=int, default=200, help='Number of epochs')
|
|
parser.add_argument('--train_batch-size', '-tb', dest='train_batch_size', metavar='TB', type=int, default=8,
|
|
help='Train_Batch size')
|
|
parser.add_argument('--val_batch-size', '-vb', dest='val_batch_size', metavar='VB', type=int, default=1,
|
|
help='Val_Batch size')
|
|
parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=1e-3,
|
|
help='Learning rate', dest='lr')
|
|
parser.add_argument('--load', '-f', type=str,
|
|
default="/home/qiqq/q3dl/code/pretrain_weight/pretrained/resnet/resnet50-0676ba61.pth",
|
|
help='Load model from a .pth file') # 有没有预训练。。
|
|
parser.add_argument('--ignore_index', '-i', type=int, dest='ignore_index', default=255,
|
|
help='ignore index defult 100')
|
|
parser.add_argument('--origin_shape', action='store_true', default=(512,512), help='原始输入尺寸')
|
|
parser.add_argument('--resume', '-r', type=str, default="", help='is use Resume')
|
|
parser.add_argument('--useDice', '-ud', type=str, default=False, help='训练的时候是否使用dice')
|
|
parser.add_argument('--valIndex', '-vI', type=str, default=["Valloss","IouMiouP"], help='评价指标要使用哪些,注意IouMiouP= acc_global, acc, iu,precion,recall,f1,miou,并且建议旨在2分类的时候用dice否则会出错')
|
|
parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes')
|
|
parser.add_argument('--num_classes', type=int,
|
|
default=2, help='output channel of network')
|
|
|
|
parser.add_argument('--img_size', type=int,
|
|
default=512, help='input patch size of network input')
|
|
|
|
parser.add_argument('--cfg', type=str,
|
|
default="/home/qiqq/q3dl/code/rooftoprecognition/pv_recognition/compared_experiment/mySwinUnet/configs/swin_tiny_patch4_window7_224_lite.yaml",
|
|
metavar="FILE", help='path to config file', )
|
|
|
|
parser.add_argument(
|
|
"--opts",
|
|
help="Modify config options by adding 'KEY VALUE' pairs. ",
|
|
default=None,
|
|
nargs='+',
|
|
)
|
|
|
|
return parser.parse_args()
|
|
|
|
if __name__ == '__main__':
|
|
from taihuyuan_roof.compared_experiment.mySwinUnet.model.vision_transformer import SwinUnet
|
|
from taihuyuan_roof.compared_experiment.mySwinUnet.configs.config import get_config
|
|
|
|
|
|
args = get_args()
|
|
config = get_config(args)
|
|
model = SwinUnet(config, img_size=args.img_size, num_classes=args.num_classes)
|
|
|
|
model_path = "/home/qiqq/q3dl/codeR/applicationprojectR/taihuyuan_roof/compared_experiment/mySwinUnet/train_val_test/checkpoints/2024-01-29-09.09/best.pth"
|
|
|
|
model_dict = torch.load(model_path)
|
|
model.load_state_dict(model_dict['net'])
|
|
print("权重加载")
|
|
model.eval()
|
|
model.cuda()
|
|
|
|
testss = predictandeval(model_name="swinunethyroof")
|
|
testss.get_result(net=model)
|
|
predictoutpath = "./swinunethyroof_outputs"
|
|
roof_iou, roof_f1, roof_precison, roof_recall, acc_global = testss.compute_evalue(predictoutpath)
|
|
|
|
resultsavepath = "./swinunethyroof.txt"
|
|
with open(resultsavepath, "a") as f:
|
|
f.write("测试结果\n")
|
|
f.write(f"acc_global:{round(acc_global, 4)}\n")
|
|
f.write(f"roof_iou:{round(roof_iou, 4)}\n")
|
|
f.write(f"roof_f1:{round(roof_f1, 4)}\n")
|
|
f.write(f"roof_precison:{round(roof_precison, 4)}\n")
|
|
f.write(f"roof_recall:{round(roof_recall, 4)}\n")
|
|
print("写入完成")
|
|
|
|
|
|
|
|
|
|
|