233 lines
9.4 KiB
Python
233 lines
9.4 KiB
Python
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#!/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 : predict
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@Author : qiqq
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@create_time : 2023/4/24 20:08
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单纯的生成预测结果图
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单纯的的单通道图和合成的fusion
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"""
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#!/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 : predict
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@Author : qiqq
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@create_time : 2023/3/27 16:29
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所有测试集都在一个文件夹中
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"""
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import argparse
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import scipy
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from scipy import ndimage
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import cv2
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import numpy as np
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import sys
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import copy
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import random
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import json
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from tqdm import tqdm
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import torch
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from torch.autograd import Variable
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import torchvision.models as models
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import torch.nn.functional as F
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from torch.utils import data
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from PIL import Image
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Image.MAX_IMAGE_PIXELS = None
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from collections import OrderedDict
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import os
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import scipy.ndimage as nd
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from math import ceil
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from PIL import Image as PILImage
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import torch.nn as nn
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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'''专门为预测整个临安建立的
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/home/qiqq/q3dl/test_temple/wholelinan/
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分了t1 t2 t3
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'''
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class mypv_bigmap:
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def __init__(self,model_name="tongzhou",):
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self.basedir = "/home/qiqq/q3dl/test_temple/wholelinan/"
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self.modelnamedir=model_name
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if not os.path.exists(self.modelnamedir):
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os.makedirs(self.modelnamedir)
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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.imglist=[]
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self.labellist=[]
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self.fusin=True
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def pad_image(self,img, target_size):
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"""Pad an image up to the target size."""
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rows_missing = target_size[0] - img.shape[2]
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cols_missing = target_size[1] - img.shape[3]
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padded_img = np.pad(img, ((0, 0), (0, 0), (0, rows_missing), (0, cols_missing)), 'constant')
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return padded_img
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def predict_sliding(self,net, image, tile_size, classes):
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interp = nn.Upsample(size=tile_size, mode='bilinear', align_corners=True)
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image_size = image.shape
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overlap = 1 / 3
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stride = ceil(tile_size[0] * (1 - overlap)) # ceil() 函数返回数字的上入整数 #512重叠1/3 的话这里就是 步长是32 也就是一个512的窗口滑动342个像素
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tile_rows = int(ceil((image_size[2] - tile_size[0]) / stride) + 1) # 6 strided convolution formula
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tile_cols = int(
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ceil((image_size[3] - tile_size[1]) / stride) + 1) # 11 也就是image_size[3]在image_size[3]这个方向上会滑动11次？
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# print("Need %i x %i prediction tiles @ stride %i px" % (tile_cols, tile_rows, stride))
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full_probs = np.zeros((image_size[2], image_size[3], classes)) # 一个全零的 大图 #初始化全概率矩阵
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count_predictions = np.zeros((image_size[2], image_size[3], classes)) # 初始化计数矩阵 shape(2160,3840,3)
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tile_counter = 0
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for row in range(tile_rows):
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for col in range(tile_cols):
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x1 = int(col * stride) # 起始位置
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y1 = int(row * stride)
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x2 = min(x1 + tile_size[1], image_size[3]) # 末位置 莫位置如果超出边界就以边界
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y2 = min(y1 + tile_size[0], image_size[2])
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x1 = max(int(x2 - tile_size[1]), 0) # for portrait images the x1 underflows sometimes #重新校准起始位置x1 =
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y1 = max(int(y2 - tile_size[0]), 0) # for very few rows y1 underflows
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img = image[:, :, y1:y2, x1:x2] # 要输入网络的小图
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padded_img = self.pad_image(img, tile_size) # padding 确保扣下来的图像为tile_size如果不够的话就padding
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# plt.imshow(padded_img)
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# plt.show()
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tile_counter += 1 ##计数加1
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# print("Predicting tile %i" % tile_counter)
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with torch.no_grad():
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# padded_prediction = net(Variable(torch.from_numpy(padded_img), volatile=True).cuda()) # 预测
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padded_prediction =net(torch.from_numpy(padded_img).cuda()) # 预测
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if isinstance(padded_prediction, list): # 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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padded_prediction = padded_prediction[0] # 就取第一个
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# padded_prediction = padded_prediction[0]
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padded_prediction = interp(padded_prediction).cpu().data[0].numpy().transpose(1, 2, 0) # 插值到原大小
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prediction = padded_prediction[0:img.shape[2], 0:img.shape[3], :]
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count_predictions[y1:y2, x1:x2] += 1 # 这几个位置上的像素预测次数+1 ##窗口区域内的计数矩阵加
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full_probs[y1:y2,
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x1:x2] += prediction # 把它搞到原图上# #窗口区域内的全概率矩阵叠加预测结果accumulate the predictions also in the overlapping regions
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'''
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full_probs[y1:y2, x1:x2] += prediction 这个地方的如果涉及到重叠的而区域他是这么处理的
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一般情况下不重叠的就是预测了1次，如果是重叠的一个像素 它被预测了3次比如这三次的概率（以屋顶类为例）是 0.8 0.7 0.6
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那count_predictions矩阵上这个位置的值就是3因为是用了3次
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那最后的结果取谁呢？取这三个的平均值full_probs上这个像素在三次后的值是 0.8+0.7+0.6 那么 下边的这一句
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full_probs /= count_predictions就是取位置上的平均值
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'''
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# average the predictions in the overlapping regions
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full_probs /= count_predictions
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return full_probs
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#直接调用这个就可以
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def get_resut(self,net,titlesize=(512,512),classes=2):
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d0 = os.listdir(self.basedir)
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hacc=["gaohong","jinbei"]
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for i in d0:#t1 t2 T3
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d1 = os.path.join(self.basedir, i)
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d2 = os.listdir(d1) #taiyang gaohong
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for j in d2: #
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if j in hacc:
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continue
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# fpath=os.path.join(self.basedir ,i, j)
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fpath=os.path.join(d1,j)
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print("没问题过了",j)
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self.predict_dir=os.path.join(self.model_name,j)
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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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imglist=self.get_imgs(fpath)
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print("正在预测", j," ","一共",len(self.imglist))
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assert len(self.imglist)!=0
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for i in tqdm(self.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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imagedata=np.expand_dims(np.transpose(self.preprocess_input(np.array(image, np.float32),md=False), (2, 0, 1)), 0)
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output=self.predict_sliding(net=net,image=imagedata,tile_size=titlesize,classes=classes)
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output = F.softmax(torch.from_numpy(output), dim=2).numpy()
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seg_pred = np.asarray(np.argmax(output, axis=2), dtype=np.uint8)
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output_im = PILImage.fromarray(seg_pred)
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output_im.putpalette(self.palette)
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output_im.save(os.path.join(self.predict_dir , name + '.png'))
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print("预测结果图生成")
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def cvtColor(self,image):
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if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:
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return image
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elif np.shape(image)[2] == 4:
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image = image.convert('RGB')
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return image
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else:
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image = image.convert('RGB')
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return image
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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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#m没有标签的读取，通常就是纯预测的
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def get_imgs(self,fpath):
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self.imglist=[]
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#单个模式
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imagescomplete=[]
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fpth=os.path.join(fpath,"images")
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lists1 = [i for i in os.listdir(fpth) if i.endswith("png")]
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for i in lists1:
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imagescomplepath=os.path.join(fpth,i)
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imagescomplete.append(imagescomplepath)
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for j in imagescomplete:
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# name=j.split(".")[0]
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name=j.split("/")[-1].split(".")[0]
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image=Image.open(j) #zhuyu
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orininal_h = image.size[1]
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orininal_w = image.size[0]
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image = self.cvtColor(image, )
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item={"name":name,"orininal_h":orininal_h,"orininal_w":orininal_w,"image":image}
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self.imglist.append(item)
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print("共监测到{}张测试图像".format(len(self.imglist)))
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return self.imglist
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if __name__ == '__main__':
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from taihuyuan_pv.mitunet.model.resunet import resUnetpamcarb
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model = resUnetpamcarb()
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model_path ="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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mypvtest=mypv_bigmap(model_name="manet_wholelinan_pv")
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mypvtest.get_resut(model)
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