266 lines
9.4 KiB
Python
266 lines
9.4 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_roof.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 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_roof/"
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split = 'val'
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_splits_dir = "/home/qiqq/q3dl/datalinan/taihuyuan_roof/"
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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, 'images1', 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_roof/"
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split = 'val'
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_splits_dir = "/home/qiqq/q3dl/datalinan/taihuyuan_roof/"
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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, 'labels1', 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_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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roof_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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roof_f1 = f1[1:].mean()
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roof_precison = precion[1:].mean()
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roof_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("roof_iou:", round(roof_iou, 4))
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print("roof_precison:", round(roof_precison, 4))
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print("roof_recall:", round(roof_recall, 4))
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print("roof_f1:", round(roof_f1, 4))
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return roof_iou, roof_f1, roof_precison, roof_recall, acc_global
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if __name__ == '__main__':
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from taihuyuan_roof.compared_experiment.unet.model.unet_model import UNet
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model = UNet(n_channels=3,n_classes=2)
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model_path = "/home/qiqq/q3dl/codeR/applicationprojectR/taihuyuan_roof/compared_experiment/unet/train_val_test/checkpoints/2024-01-27-20.48/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="unetthyroof")
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testss.get_result(net=model)
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predictoutpath = "./unetthyroof_outputs"
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roof_iou, roof_f1, roof_precison, roof_recall, acc_global = testss.compute_evalue(predictoutpath)
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resultsavepath="./unetthyroof.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"roof_iou:{round(roof_iou, 4)}\n")
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f.write(f"roof_f1:{round(roof_f1, 4)}\n")
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f.write(f"roof_precison:{round(roof_precison, 4)}\n")
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f.write(f"roof_recall:{round(roof_recall, 4)}\n")
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print("写入完成")
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