407 lines
16 KiB
Python
407 lines
16 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@project:
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@File : xaiotu
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@Author : qiqq
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@create_time : 2023/6/29 8:57
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import copy
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import cv2 as cv
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from tqdm import tqdm
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import os
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from torch.utils.data import DataLoader
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import argparse
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from torch.utils.data import Dataset
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from torchvision import transforms
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from taihuyuan_pv.dataloaders import custom_transforms as tr
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import torch
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import torch.nn.functional as F
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from PIL import Image
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'''voc数据集格式的'''
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class datasets_pvtaihuyuan(Dataset):
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def __init__(self,
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args,
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split='val',
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isAug=False
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):
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super(datasets_pvtaihuyuan, self).__init__()
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self.args = args
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self.resize = args.resize
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self.crop_size = args.crop_size # 是单数比如256，
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self.flip_prob = args.flip_prob # 是0-1
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self.isAug = isAug
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self._base_dir = "/home/qiqq/q3dl/datalinan/taihuyuan_pv/"
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self._image_dir = os.path.join(self._base_dir, 'images')
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self._cat_dir = os.path.join(self._base_dir, 'labels')
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if isinstance(split, str):
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self.split = [split]
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else:
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split.sort()
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self.split = split
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_splits_dir = "/home/qiqq/q3dl/datalinan/taihuyuan_pv/"
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self.im_ids = []
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self.images = []
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self.categories = []
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for splt in self.split:
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with open(os.path.join(os.path.join(_splits_dir, splt + '.txt')), "r") as f:
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lines = f.read().splitlines()
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for ii, line in enumerate(lines):
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_image = os.path.join(self._image_dir, line + ".png") # 注意这个地方有的图片可能是jpg有的可能是png自己看着改
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_cat = os.path.join(self._cat_dir, line + ".png") # 注意格式
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assert os.path.isfile(_image)
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assert os.path.isfile(_cat)
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self.im_ids.append(line)
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self.images.append(_image)
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self.categories.append(_cat)
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assert (len(self.images) == len(self.categories))
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# Display stats
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print('Number of images in {}: {:d}'.format(split, len(self.images)))
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def __len__(self):
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return len(self.images)
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def __getitem__(self, index):
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_img, _target, _name = self._make_img_gt_point_pair(index)
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sample = {'image': _img, 'label': _target, "name": _name}
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for split in self.split:
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if split == 'val':
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return self.transform_val(sample)
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def _make_img_gt_point_pair(self, index):
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_img = Image.open(self.images[index]).convert('RGB')
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_target = Image.open(self.categories[index])
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name = self.images[index].split(".")[0]
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return _img, _target, name
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def transform_val(self, sample):
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composed_transforms = transforms.Compose([
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tr.Resize(self.args.resize),
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tr.Normalize_simple(),
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tr.ToTensor()])
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return composed_transforms(sample)
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def __str__(self):
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return 'linan(split=' + str(self.split) + ')'
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#
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class predictandeval():
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def __init__(self, model_name="xx"):
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.predict_dir = model_name + "_" + "outputs/"
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self.fusion_path = model_name + "_" + "fusion/"
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self.model_name = model_name
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self.palette = [0, 0, 0, 0, 255, 0, 0, 255, 0]
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self.fusin = True
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def get_confusion_matrix(self, gt_label, pred_label, class_num):
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"""
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Calcute the confusion matrix by given label and pred
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:param gt_label: the ground truth label
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:param pred_label: the pred label
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:param class_num: the nunber of class
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:return: the confusion matrix
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"""
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index = (gt_label * class_num + pred_label).astype('int32')
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label_count = np.bincount(index)
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confusion_matrix = np.zeros((class_num, class_num))
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for i_label in range(class_num):
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for i_pred_label in range(class_num):
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cur_index = i_label * class_num + i_pred_label
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if cur_index < len(label_count):
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confusion_matrix[i_label, i_pred_label] = label_count[cur_index]
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return confusion_matrix
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# 延用训练数据集格式
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# def get_datasets(self, ):
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# parser = argparse.ArgumentParser()
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# args = parser.parse_args()
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# args.resize = (512, 512)
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# args.crop_size = 480 # 在验证的时候没有用 crop和filp 只是为了不报错
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# args.flip_prob = 0.5
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# datasets = datasets_pvtaihuyuan(args, split='val', isAug=False)
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# print(len(datasets))
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# return datasets
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# 重写 voc格式的val或者test
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def get_images(self, ):
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basedir = "/home/qiqq/q3dl/datalinan/taihuyuan_pv/"
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split = 'val'
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_splits_dir = "/home/qiqq/q3dl/datalinan/taihuyuan_pv/"
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imglist = []
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with open(os.path.join(os.path.join(_splits_dir, split + '.txt')), "r") as f:
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lines = f.read().splitlines()
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for ii, line in enumerate(lines):
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name = line
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_imagepath = os.path.join(basedir, 'images', line + ".png")
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assert os.path.isfile(_imagepath)
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image = Image.open(_imagepath)
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orininal_h = image.size[1]
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orininal_w = image.size[0]
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item = {"name": name, "orininal_h": orininal_h, "orininal_w": orininal_w, "image": image}
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imglist.append(item)
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print("共监测到{}张原始图像和标签".format(len(imglist)))
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return imglist
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def get_labels(self, ):
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basedir = "/home/qiqq/q3dl/datalinan/taihuyuan_pv/"
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split = 'val'
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_splits_dir = "/home/qiqq/q3dl/datalinan/taihuyuan_pv/"
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labellist = []
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with open(os.path.join(os.path.join(_splits_dir, split + '.txt')), "r") as f:
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lines = f.read().splitlines()
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for ii, line in enumerate(lines):
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name = line
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_labelpath = os.path.join(basedir, 'labels', line + ".png")
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assert os.path.isfile(_labelpath)
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label = Image.open(_labelpath)
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item = {"name": name, "label": label}
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labellist.append(item)
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print("共监测到{}张标签".format(len(labellist)))
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return labellist
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# def get_result1(self,net):
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#
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# datase = self.get_datasets1()
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# dataloader = DataLoader(datase, batch_size=1, shuffle=False, num_workers=0)
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# for ii, sample in enumerate(dataloader):
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# image, mask_true = sample['image'], sample['label']
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# name=sample["name"]
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# image = image.to(device=self.device, dtype=torch.float32)
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# orininal_h, orininal_w =image.shape[2:]
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# with torch.no_grad():
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# out = net(image) #
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# 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）
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# out = out[0] # 就取第一个
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# pr = F.softmax(out.permute(1, 2, 0), dim=-1).cpu().numpy()
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# result = pr.argmax(axis=-1)
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# if not os.path.exists(self.predict_dir):
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# os.makedirs(self.predict_dir)
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# output_im = Image.fromarray(np.uint8(result)).convert('P')
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#
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# output_im.putpalette(self.palette)
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# output_im.save(self.predict_dir + name + '.png')
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#
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# # hunhe #没有fusion
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def get_result(self, net):
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imglist = self.get_images()
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assert len(imglist) != 0
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for i in tqdm(imglist):
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image = i["image"]
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name = i["name"]
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orininal_w = i["orininal_w"]
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orininal_h = i["orininal_h"]
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old_img = copy.deepcopy(image)
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imaged = cv.resize(np.array(image), dsize=(512, 512), interpolation=cv.INTER_LINEAR)
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image_data = np.expand_dims(
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np.transpose(self.preprocess_input(np.array(imaged, np.float32), md=False), (2, 0, 1)), 0)
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with torch.no_grad():
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images = torch.from_numpy(image_data)
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image = images.to(device="cuda", dtype=torch.float32)
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model = net.to(device="cuda")
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out = model(image)
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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）
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out = out[0] # 就取第一个
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out = out[0] # 去掉batch
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pr = F.softmax(out.permute(1, 2, 0), dim=-1).cpu().numpy()
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result = pr.argmax(axis=-1)
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# 结果图
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if not os.path.exists(self.predict_dir):
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os.makedirs(self.predict_dir)
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output_im = Image.fromarray(np.uint8(result)).convert('P')
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output_im.putpalette(self.palette)
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output_im.save(self.predict_dir + name + '.png')
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# hunhe
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if self.fusin:
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if not os.path.exists(self.fusion_path):
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os.makedirs(self.fusion_path)
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PALETTE = [(0, 0, 0), (255, 0, 0), (0, 255, 0)]
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seg_img0 = np.reshape(np.array(PALETTE, np.uint8)[np.reshape(result, [-1])],
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[orininal_h, orininal_w, -1])
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image0 = Image.fromarray(np.uint8(seg_img0))
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fusion = Image.blend(old_img, image0, 0.4)
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fusion.save(self.fusion_path + self.model_name + "_" + name + '.png')
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def preprocess_input(self, image, md=False):
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mean = (0.231, 0.217, 0.22) # 针对北京的数据集
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std = (0.104, 0.086, 0.085)
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if md:
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image /= 255.0
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image -= mean
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image /= std
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return image
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else:
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image /= 255.0
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return image
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def compute_evalue(self, predict_dir, ):
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labellist = self.get_labels() # 存的额是没转换成nmpy的png
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predictresultlist = os.listdir(predict_dir)
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assert len(labellist) == len(predictresultlist)
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num_classes = 2
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confusion_matrix = np.zeros((num_classes, num_classes))
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for i in tqdm(labellist):
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name = i["name"]
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seg_gt = np.array(i["label"])
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seg_pred = np.array(Image.open(os.path.join(predict_dir, name + ".png")))
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#
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ignore_index = seg_gt != 255 #
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seg_gt = seg_gt[ignore_index]
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seg_pred = seg_pred[ignore_index]
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confusion_matrix += self.get_confusion_matrix(seg_gt, seg_pred, num_classes)
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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)
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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)
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tp = np.diag(confusion_matrix)
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IU_array = (tp / np.maximum(1.0, pos + res - tp))
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mean_IU = IU_array.mean()
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pv_iou = IU_array[1:].mean()
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precion = tp / res
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recall = tp / pos
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f1 = (2 * precion * recall) / (precion + recall)
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mf1 = f1.mean()
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acc_global = tp.sum() / pos.sum()
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pv_f1 = f1[1:].mean()
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pv_precion = precion[1:].mean()
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pv_recall = recall[1:].mean()
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print("测试结果")
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print("acc_global:", round(acc_global, 4))
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print("IU_array:", np.round(IU_array, 4))
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print("precion:", np.round(precion, 4))
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print("recall:", np.round(recall, 4))
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print("f1:", np.round(f1, 4))
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print("miou:", round(mean_IU, 4))
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print("pv_iou:", round(pv_iou, 4))
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print("pv_precion:", round(pv_precion, 4))
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print("pv_recall:", round(pv_recall, 4))
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print("pv_f1:", round(pv_f1, 4))
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return pv_iou, pv_f1, pv_precion, pv_recall, acc_global
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def get_args():
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parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')
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parser.add_argument('--epochs', '-e', metavar='E', type=int, default=200, help='Number of epochs')
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parser.add_argument('--train_batch-size', '-tb', dest='train_batch_size', metavar='TB', type=int, default=8,
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help='Train_Batch size')
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parser.add_argument('--val_batch-size', '-vb', dest='val_batch_size', metavar='VB', type=int, default=1,
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help='Val_Batch size')
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parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=1e-3,
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help='Learning rate', dest='lr')
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parser.add_argument('--load', '-f', type=str,
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default="/home/qiqq/q3dl/code/pretrain_weight/pretrained/resnet/resnet50-0676ba61.pth",
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help='Load model from a .pth file') # 有没有预训练。。
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parser.add_argument('--ignore_index', '-i', type=int, dest='ignore_index', default=255,
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help='ignore index defult 100')
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parser.add_argument('--origin_shape', action='store_true', default=(512,512), help='原始输入尺寸')
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parser.add_argument('--resume', '-r', type=str, default="", help='is use Resume')
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parser.add_argument('--useDice', '-ud', type=str, default=False, help='训练的时候是否使用dice')
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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')
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parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes')
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parser.add_argument('--num_classes', type=int,
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default=2, help='output channel of network')
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parser.add_argument('--img_size', type=int,
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default=512, help='input patch size of network input')
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parser.add_argument('--cfg', type=str,
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default="/home/qiqq/q3dl/code/rooftoprecognition/pv_recognition/compared_experiment/mySwinUnet/configs/swin_tiny_patch4_window7_224_lite.yaml",
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metavar="FILE", help='path to config file', )
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parser.add_argument(
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"--opts",
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help="Modify config options by adding 'KEY VALUE' pairs. ",
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default=None,
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nargs='+',
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)
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return parser.parse_args()
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if __name__ == '__main__':
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from taihuyuan_pv.compared_experiment.mySwinUnet.model.vision_transformer import SwinUnet
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from taihuyuan_pv.compared_experiment.mySwinUnet.configs.config import get_config
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args = get_args()
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config = get_config(args)
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model = SwinUnet(config, img_size=args.img_size, num_classes=args.num_classes)
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model_path = "/home/qiqq/q3dl/code/applicationproject/taihuyuan_pv/compared_experiment/mySwinUnet/train_val_test/checkpoints/2023-08-09-00.08/best.pth"
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model_dict = torch.load(model_path)
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model.load_state_dict(model_dict['net'])
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print("权重加载")
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model.eval()
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model.cuda()
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testss = predictandeval(model_name="swinunethypv")
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testss.get_result(net=model)
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predictoutpath = "/home/qiqq/q3dl/code/applicationproject/taihuyuan_pv/compared_experiment/mySwinUnet/test/swinunethypv_outputs/"
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pv_iou, pv_f1, pv_precion, pv_recall, acc_global = testss.compute_evalue(predictoutpath)
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resultsavepath = "./swinunethypv.txt"
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with open(resultsavepath, "a") as f:
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f.write("测试结果\n")
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f.write(f"acc_global:{round(acc_global, 4)}\n")
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f.write(f"pv_iou:{round(pv_iou, 4)}\n")
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f.write(f"pv_f1:{round(pv_f1, 4)}\n")
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f.write(f"pv_precion:{round(pv_precion, 4)}\n")
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f.write(f"pv_recall:{round(pv_recall, 4)}\n")
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print("写入完成")
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