一般性流程 

''' IPL转换为tensor _img = Image.open(os.path.join(self.img_dir, path)).convert('RGB') img = np.array(img).astype(np.float32).transpose((2, 0, 1)) img = torch.from_numpy(img).float() img = img.cuda() tensor转换为IPL image1 = image.data.cpu().numpy() IPLimage = numpyimg.transpose((1, 2, 0)) save_img = Image.fromarray(IPLimage.astype('uint8')) '''

例子：

for i, sample in enumerate(self.test_loader): image, target = sample['image'], sample['label'] torch.cuda.synchronize() start = time.time() with torch.no_grad(): output = self.model(image) end = time.time() times = (end - start) * 1000 print(times, "ms") torch.cuda.synchronize() pred = output.data.cpu().numpy() target = target.cpu().numpy() pred = np.argmax(pred, axis=1) self.evaluator.add_batch(target, pred)

我想看一下target是否对，通过opencv保存，首先看下opencv的格式：

cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) -> dst 

fx - 水平轴上的比例因子。fy - 垂直轴上的比例因子。

numpy实现图像部分ROI截取：

for index in inds: xmin_depth = int((xmin1[index] * expected + crop_start) * scale) ymin_depth = int((ymin1[index] * expected) * scale) xmax_depth = int((xmax1[index] * expected + crop_start) * scale) ymax_depth = int((ymax1[index] * expected) * scale) depth_temp = depth[ymin_depth:ymax_depth, xmin_depth:xmax_depth].astype(float)

 首先numpy是[高度h:宽度w]

如果是x1，y1，x2，y2(左上，右下)的任务，应该是img=ori_img[y1:y2, x1:x2]

import cv2 cvimg = cv2.imread("./dog.jpg") graycvimg = cv2.cvtColor(cvimg, cv2.COLOR_BGR2GRAY) cv2.imwrite("./dog_gray.jpg", graycvimg) graycvimg_bgr = cv2.cvtColor(graycvimg, cv2.COLOR_GRAY2BGR) cv2.imwrite("./dog_gray_bgr.jpg", graycvimg_bgr)

from PIL import Image import numpy as np img = Image.open(imgsname).convert('RGB') imglabel = Image.open(imgsname).convert('P') arrayimg = np.array(img).astype(np.float32) transposeimg = arrayimg.transpose((2, 0, 1))

关于PIL和opencv还有一个区别：size的先后，PIL是W，H opencv是H，W，C

imgsname = newpath + namename + '_ccvt_' + str(j) + '.jpg' img = Image.open(imgsname).convert('RGB') W, H = img.size img = np.array(img) dst, scale_factor = mmcv.imrescale(img, (1333, 800), return_scale=True) newH, newW, newC = dst.shape # tensor 转换为 numpy numpyimg = imgarray.numpy() # numpy 转换为 IPL格式 IPLimage = numpyimg.transpose((1, 2, 0)) ''' IPL转换为tensor _img = Image.open(os.path.join(self.img_dir, path)).convert('RGB') img = np.array(img).astype(np.float32).transpose((2, 0, 1)) img = torch.from_numpy(img).float() img = img.cuda() tensor转换为IPL image1 = image.data.cpu().numpy() IPLimage = numpyimg.transpose((1, 2, 0)) save_img = Image.fromarray(IPLimage.astype('uint8')) '''

参考:

https://blog.csdn.net/m0_37382341/article/details/83548601

numpy.reshape Numpy将不管是什么形状的数组，先扁平化处理成一个一维的列表，然后按照你重新定义的形状，再把这个列表截断拼成新的形状。 在这个过程中，如果你要处理的是图片矩阵的话，就会完全改变图片信息。numpy.transpose numpy.transpose采取轴作为输入，所以你可以改变轴，这对于张量来说很有用，也很方便。比如data.transpose(1,0,2),就表示把1位置的数换到0位置，0位置的换到1位置，2没有变。

由于测试时候使用：

def transform_val(self, sample): composed_transforms = transforms.Compose([ tr.FixScaleCrop(crop_size=self.args.crop_size), tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), tr.ToTensor() ]) return composed_transforms(sample)

应该把注释改掉：

def transform_val(self, sample): composed_transforms = transforms.Compose([ tr.FixScaleCrop(crop_size=self.args.crop_size), #tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), tr.ToTensor() ]) return composed_transforms(sample)

这样方便我们保存Image对比 

import cv2 target = target.cpu().numpy() image = image.data.cpu().numpy() image1 = image[0, :] target1 = target[0, :] #image1.reshape([image1.size[1],image1.size[2],image1.size[3]]) #target1.reshape([image1.size[1],image1.size[2],image1.size[3]]) image1 = image1.transpose(2,1,0) #target1 = target1.transpose(2,1,0) image1 = cv2.cvtColor(image1, cv2.COLOR_RGB2BGR) cv2.imwrite("./image1.jpg",image1) cv2.imwrite("./target1.jpg", target1)

我这里出现一些问题，target方向错误了，debug一下，看看载入时候有没有问题:

def _make_img_gt_point_pair(self, index): coco = self.coco img_id = self.ids[index] img_metadata = coco.loadImgs(img_id)[0] path = img_metadata['file_name'] _img = Image.open(os.path.join(self.img_dir, path)).convert('RGB') cocotarget = coco.loadAnns(coco.getAnnIds(imgIds=img_id)) _target = Image.fromarray(self._gen_seg_mask( cocotarget, img_metadata['height'], img_metadata['width'])) image1 = cv2.cvtColor(np.asarray(_img), cv2.COLOR_RGB2BGR) target1 = cv2.cvtColor(np.asarray(_target), cv2.COLOR_GRAY2BGR) cv2.imwrite("./image1.jpg", image1) cv2.imwrite("./target1.jpg", target1) return _img, _target def __getitem__(self, index): _img, _target = self._make_img_gt_point_pair(index) sample = {'image': _img, 'label': _target} if self.split == "train": return self.transform_tr(sample) elif self.split == 'val': return self.transform_val(sample) elif self.split == 'test': X = self.transform_val(sample) aa = X['image'] bb = X['label'] aa = aa.cpu().numpy() bb = bb.cpu().numpy() aa = aa.transpose(2, 1, 0) image1 = cv2.cvtColor(aa, cv2.COLOR_RGB2BGR) target1 = cv2.cvtColor(bb, cv2.COLOR_GRAY2BGR) cv2.imwrite("./image2.jpg", image1) cv2.imwrite("./target2.jpg", target1) return X

 原图resize后方向变了，果然。。。。。。。

原图：

因为项目中使用了一个torch函数进行预处理：

pytorch的transforms.py

 

class Compose(object): """Composes several transforms together. Args: transforms (list of ``Transform`` objects): list of transforms to compose. Example: >>> transforms.Compose([ >>> transforms.CenterCrop(10), >>> transforms.ToTensor(), >>> ]) """ def __init__(self, transforms): self.transforms = transforms def __call__(self, img): for t in self.transforms: img = t(img) return img

首先

class FixScaleCrop(object): def __init__(self, crop_size): self.crop_size = crop_size def __call__(self, sample): img = sample['image'] mask = sample['label'] w, h = img.size if w > h: oh = self.crop_size ow = int(1.0 * w * oh / h) else: ow = self.crop_size oh = int(1.0 * h * ow / w) img = img.resize((ow, oh), Image.BILINEAR) mask = mask.resize((ow, oh), Image.NEAREST) # center crop w, h = img.size x1 = int(round((w - self.crop_size) / 2.)) y1 = int(round((h - self.crop_size) / 2.)) img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) return {'image': img, 'label': mask} class FixScaleCrop(object): def __init__(self, crop_size): self.crop_size = crop_size def __call__(self, sample): img = sample['image'] mask = sample['label'] w, h = img.size if w > h: oh = self.crop_size ow = int(1.0 * w * oh / h) else: ow = self.crop_size oh = int(1.0 * h * ow / w) img = img.resize((ow, oh), Image.BILINEAR) mask = mask.resize((ow, oh), Image.NEAREST) # center crop w, h = img.size x1 = int(round((w - self.crop_size) / 2.)) y1 = int(round((h - self.crop_size) / 2.)) img = img.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) mask = mask.crop((x1, y1, x1 + self.crop_size, y1 + self.crop_size)) import cv2 image1 = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) target1 = cv2.cvtColor(np.asarray(mask), cv2.COLOR_GRAY2BGR) cv2.imwrite("./image3.jpg", image1) cv2.imwrite("./target3.jpg", target1) return {'image': img, 'label': mask}

 

程序在这里还是没问题的，结果接下来会进入：

class ToTensor(object): """Convert ndarrays in sample to Tensors.""" def __call__(self, sample): # swap color axis because # numpy image: H x W x C # torch image: C X H X W img = sample['image'] mask = sample['label'] img = np.array(img).astype(np.float32).transpose((2, 0, 1)) mask = np.array(mask).astype(np.float32) img = torch.from_numpy(img).float() mask = torch.from_numpy(mask).float() return {'image': img, 'label': mask} class ToTensor(object): """Convert ndarrays in sample to Tensors.""" def __call__(self, sample): # swap color axis because # numpy image: H x W x C # torch image: C X H X W img = sample['image'] mask = sample['label'] img = np.array(img).astype(np.float32).transpose((2, 0, 1)) mask = np.array(mask).astype(np.float32) img = torch.from_numpy(img).float() mask = torch.from_numpy(mask).float() import cv2 image1=img.cpu().numpy() target1=mask.cpu().numpy() image1 = image1.transpose(2, 1, 0) image1 = cv2.cvtColor(image1, cv2.COLOR_RGB2BGR) target1 = cv2.cvtColor(target1, cv2.COLOR_GRAY2BGR) cv2.imwrite("./image4.jpg", image1) cv2.imwrite("./target4.jpg", target1) return {'image': img, 'label': mask}

 这里出错了，方向不对了

如果将代码改为；

img = np.array(img).astype(np.float32).transpose((2, 1, 0))

 方向就都对了，那么作者原本为什么那样写？？？？？？

        img = np.array(img).astype(np.float32).transpose((2, 0, 1))

到底有什么用，

class ToTensor(object): """Convert ndarrays in sample to Tensors.""" def __call__(self, sample): # swap color axis because # numpy image: H x W x C # torch image: C X H X W img = sample['image'] mask = sample['label'] import cv2 image1 = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) target1 = cv2.cvtColor(np.asarray(mask), cv2.COLOR_GRAY2BGR) cv2.imwrite("./image5.jpg", image1) cv2.imwrite("./target5.jpg", target1) xxx = np.array(img).astype(np.float32) import copy xxx1 = copy.deepcopy(xxx) xxx2 = copy.deepcopy(xxx) img1 = np.array(xxx1).astype(np.float32).transpose((2, 1, 0)) img2 = np.array(xxx2).astype(np.float32).transpose((2, 0, 1)) img = np.array(img).astype(np.float32).transpose((2, 1, 0)) mask = np.array(mask).astype(np.float32) img = torch.from_numpy(img).float() mask = torch.from_numpy(mask).float()

513*513*3---3* 513*513   

.transpose((2, 1, 0)) 

513*513*3---3* 513*513

.transpose((2, 0, 1))

 其实实验做到这里我已经明白是我错了，

原本是

513*513*3

我们通过.transpose((2, 0, 1))，正常变换，我错在test显示的时候：

import cv2 target = target.cpu().numpy() image = image.data.cpu().numpy() image1 = image[0, :] target1 = target[0, :] #image1.reshape([image1.size[1],image1.size[2],image1.size[3]]) #target1.reshape([image1.size[1],image1.size[2],image1.size[3]]) image1 = image1.transpose(1,2,0) image1 = cv2.cvtColor(image1, cv2.COLOR_RGB2BGR) cv2.imwrite("./image1.jpg",image1) cv2.imwrite("./target1.jpg", target1)

这里应该是

image1 = image1.transpose(1,2,0)

因为原本

for i, sample in enumerate(self.test_loader): image, target = sample['image'], sample['label']

image为：torch.Size([1, 3, 513, 513])

target为：<class 'tuple'>: (1, 513, 513)

所以应该使用image1 = image1.transpose(1,2,0)

这下就对了

现在还有一个问题摆在面前，

我做测试时候，COCO数据集格式，自己的数据集，

图片有153张，但是最后输出只有25张pred，

找原因：

pytorch-deeplab-xception/dataloaders/datasets/coco.py

在处理coco数据之前，会生成一个test_ids_2017.pth

id对应文件，新ID与旧ID相对应，

用于知道哪些ID被保留下来，用于接下来的测试

if os.path.exists(ids_file): self.ids = torch.load(ids_file) else: ids = list(self.coco.imgs.keys()) self.ids = self._preprocess(ids, ids_file, self.split) self.args = args

判断条件在函数self._preprocess(ids, ids_file, self.split)

def _preprocess(self, ids, ids_file, split): print("Preprocessing mask, this will take a while. " + \ "But don't worry, it only run once for each split.") tbar = trange(len(ids)) new_ids = [] for i in tbar: img_id = ids[i] cocotarget = self.coco.loadAnns(self.coco.getAnnIds(imgIds=img_id)) img_metadata = self.coco.loadImgs(img_id)[0] savemaskname=img_metadata['file_name'] image = ids_file.split("annotations")[0]+'images/'+split+str(self.year) + '/' +savemaskname oriimg = cv2.imread(image) h,w,c = oriimg.shape mask = self._gen_seg_mask(cocotarget, h, w) cv2.imwrite('/home/spple/paddle/DeepGlint/deepglint-adv/pytorch-deeplab-xception/mask/'+split+'/'+savemaskname, mask) # more than 1k pixels if (mask > 0).sum() > 1000: new_ids.append(img_id) tbar.set_description('Doing: {}/{}, got {} qualified images'. \ format(i, len(ids), len(new_ids))) print('Found number of qualified images: ', len(new_ids)) torch.save(new_ids, ids_file) return new_ids

通过函数def _gen_seg_mask(self, target, h, w): 获取mask

def _gen_seg_mask(self, target, h, w): mask = np.zeros((h, w), dtype=np.uint8) coco_mask = self.coco_mask for instance in target: rle = coco_mask.frPyObjects(instance['segmentation'], h, w) m = coco_mask.decode(rle) cat = instance['category_id'] if cat in self.CAT_LIST: c = self.CAT_LIST.index(cat) else: continue if len(m.shape) < 3: mask[:, :] += (mask == 0) * (m * c) else: mask[:, :] += (mask == 0) * (((np.sum(m, axis=2)) > 0) * c).astype(np.uint8) return mask

但是这里有个问题，判断依据是mask分割像素点必须是1000以上，但是对于小图像，可能达不到，这里，我们要修改

if (mask > 0).sum() > 1000: new_ids.append(img_id)

修改为：

if (mask > 0).sum() > 50: new_ids.append(img_id)

还有之前的函数只是简单的保存是参考：

https://github.com/jfzhang95/pytorch-deeplab-xception/issues/122

import argparse import os import numpy as np import tqdm import torch from PIL import Image from dataloaders import make_data_loader from modeling.deeplab import * from dataloaders.utils import get_pascal_labels from utils.metrics import Evaluator class Tester(object): def __init__(self, args): if not os.path.isfile(args.model): raise RuntimeError("no checkpoint found at '{}'".fromat(args.model)) self.args = args self.color_map = get_pascal_labels() self.test_loader, self.ids, self.nclass = make_data_loader(args) #Define model model = DeepLab(num_classes=self.nclass, backbone=args.backbone, output_stride=args.out_stride, sync_bn=False, freeze_bn=False) self.model = model device = torch.device('cpu') checkpoint = torch.load(args.model, map_location=device) self.model.load_state_dict(checkpoint['state_dict']) self.evaluator = Evaluator(self.nclass) def save_image(self, array, id, op): text = 'gt' if op == 0: text = 'pred' file_name = str(id)+'_'+text+'.png' r = array.copy() g = array.copy() b = array.copy() for i in range(self.nclass): r[array == i] = self.color_map[i][0] g[array == i] = self.color_map[i][1] b[array == i] = self.color_map[i][2] rgb = np.dstack((r, g, b)) save_img = Image.fromarray(rgb.astype('uint8')) save_img.save(self.args.save_path+os.sep+file_name) def test(self): self.model.eval() self.evaluator.reset() # tbar = tqdm(self.test_loader, desc='\r') for i, sample in enumerate(self.test_loader): image, target = sample['image'], sample['label'] with torch.no_grad(): output = self.model(image) pred = output.data.cpu().numpy() target = target.cpu().numpy() pred = np.argmax(pred, axis=1) self.save_image(pred[0], self.ids[i], 0) self.save_image(target[0], self.ids[i], 1) self.evaluator.add_batch(target, pred) Acc = self.evaluator.Pixel_Accuracy() Acc_class = self.evaluator.Pixel_Accuracy_Class() print('Acc:{}, Acc_class:{}'.format(Acc, Acc_class)) def main(): parser = argparse.ArgumentParser(description='Pytorch DeeplabV3Plus Test your data') parser.add_argument('--test', action='store_true', default=True, help='test your data') parser.add_argument('--dataset', default='pascal', help='datset format') parser.add_argument('--backbone', default='xception', help='what is your network backbone') parser.add_argument('--out_stride', type=int, default=16, help='output stride') parser.add_argument('--crop_size', type=int, default=513, help='image size') parser.add_argument('--model', type=str, default='', help='load your model') parser.add_argument('--save_path', type=str, default='', help='save your prediction data') args = parser.parse_args() if args.test: tester = Tester(args) tester.test() if __name__ == "__main__": main()

这里保存完后是：

def save_image(self, array, id, op, oriimg=None, image111=None): import cv2 text = 'gt' if op == 0: text = 'pred' file_name = str(id)+'_'+text+'.png' drow_ori_name = str(id)+'_'+'vis'+'.png' #513*513 r = array.copy() g = array.copy() b = array.copy() if oriimg is True: image111 = image111.data.cpu().numpy() image111 = image111[0, :] image111 = image111.transpose(1,2,0) oneimg = image111 for i in range(self.nclass): r[array == i] = self.color_map[i][2] g[array == i] = self.color_map[i][1] b[array == i] = self.color_map[i][0] rgb = np.dstack((r, g, b)) hh,ww,_ = rgb.shape if oriimg is True: for i in range(self.nclass): if i != 0: index = np.argwhere(array == i) for key in index: oneimg[key[0]][key[1]][0] = self.color_map[i][0] oneimg[key[0]][key[1]][1] = self.color_map[i][1] oneimg[key[0]][key[1]][2] = self.color_map[i][2] oneimg = cv2.cvtColor(oneimg, cv2.COLOR_RGB2BGR) cv2.imwrite(self.args.save_path + os.sep + drow_ori_name, oneimg)

这样完全覆盖了，我们并不能看到真实样貌，应该参考mask_rcnn,透明效果：

其实就是将原始图像和预测类的颜色，不同比例结合，生成可视化图像：

oneimg[key[0]][key[1]][0] = oneimg[key[0]][key[1]][0] * 0.5 + self.color_map[i][0] * 0.5 oneimg[key[0]][key[1]][1] = oneimg[key[0]][key[1]][1] * 0.5 + self.color_map[i][1] * 0.5 oneimg[key[0]][key[1]][2] = oneimg[key[0]][key[1]][2] * 0.5 + self.color_map[i][2] * 0.5

这里还有一个问题

我们进行测试时候显示：

Acc:0.9829744103317358, Acc_class:0.7640047637800897, mIoU:0.7015250613321066 /home/spple/pytorch-deeplab-xception/utils/metrics.py:14: RuntimeWarning: invalid value encountered in true_divide Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) /home/spple/pytorch-deeplab-xception/utils/metrics.py:24: RuntimeWarning: invalid value encountered in true_divide np.diag(self.confusion_matrix))

原来是因为数组分母有为0的

比如：

def Pixel_Accuracy_Class(self): a = np.diag(self.confusion_matrix) b = self.confusion_matrix.sum(axis=1) #Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1) Acc = a/b Acc = np.nanmean(Acc) return Acc

a： 

b：

Acc：

Acc = np.nanmean(Acc)：

0.7640047637800897=(0.993579+0.534430)/2

 

顺便做了一个实验：

import numpy as np a = np.array([[12],[6]]) b = np.array([3,3]) Acc_1= a/b c = np.array([[12,1],[1,6]]) x2 = np.diag(c) Acc_2= x2/b x1 = np.zeros((2,)*1) x1[0]=3 x1[1]=3

向量相除，如果最后只想得到向量，分子分母shape应该 是<class 'tuple'>: (2,)

a

b

Acc_1

c

x2

Acc_2

x1

test.py

import argparse import os import numpy as np import tqdm import torch import time #https://github.com/jfzhang95/pytorch-deeplab-xception/issues/122 from PIL import Image from dataloaders import make_data_loader from modeling.deeplab import * from dataloaders.utils import get_pascal_labels from utils.metrics import Evaluator import cv2 class Tester(object): def __init__(self, args): if not os.path.isfile(args.model): raise RuntimeError("no checkpoint found at '{}'".fromat(args.model)) self.args = args self.color_map = get_pascal_labels() self.test_loader, self.nclass= make_data_loader(args) #Define model model = DeepLab(num_classes=self.nclass, backbone=args.backbone, output_stride=args.out_stride, sync_bn=False, freeze_bn=False) self.model = model device = torch.device('cpu') checkpoint = torch.load(args.model, map_location=device) self.model.load_state_dict(checkpoint['state_dict']) self.evaluator = Evaluator(self.nclass) #--dataset pascal --backbone resnet --out_stride 16 --crop_size 513 --model /home/spple/paddle/DeepGlint/deepglint-adv/pytorch-deeplab-xception/checkpoint-gray/model_best.pth.tar --save_path /home/spple/paddle/DeepGlint/deepglint-adv/pytorch-deeplab-xception/prediction_gray # --dataset pascal --backbone resnet --out_stride 16 --crop_size 513 --model /home/spple/paddle/DeepGlint/deepglint-adv/pytorch-deeplab-xception/checkpoint/checkpoint.pth.tar --save_path /home/spple/paddle/DeepGlint/deepglint-adv/pytorch-deeplab-xception/prediction def save_image(self, array, id, op, oriimg=None, image111=None): import cv2 text = 'gt' if op == 0: text = 'pred' file_name = str(id)+'_'+text+'.png' drow_ori_name = str(id)+'_'+'vis'+'.png' #513*513 r = array.copy() g = array.copy() b = array.copy() if oriimg is True: oneimgpath = str(id) + '.jpg' from mypath import Path #JPEGImages_gray image111 = image111.data.cpu().numpy() image111 = image111[0, :] image111 = image111.transpose(1,2,0) oneimg = image111 for i in range(self.nclass): r[array == i] = self.color_map[i][2] g[array == i] = self.color_map[i][1] b[array == i] = self.color_map[i][0] #513*513*3 rgb = np.dstack((r, g, b)) hh,ww,_ = rgb.shape #if oriimg is True: #oneimg = oneimg.resize((hh, ww), Image.ANTIALIAS) # 原图 #image1 = cv2.cvtColor(oneimg, cv2.COLOR_RGB2BGR) #oneimg.save(self.args.save_path + os.sep + ori_name, quality=100) #cv2.imwrite(self.args.save_path + os.sep + ori_name, image1) #----gt ---- pred cv2.imwrite(self.args.save_path+os.sep+file_name, rgb) #save_img = Image.fromarray(rgb.astype('uint8')) # pred #save_img.save(self.args.save_path+os.sep+file_name, quality=100) #oneimg = oneimg.transpose(2, 0, 1) if oriimg is True: #oneimg = np.array(oneimg) for i in range(self.nclass): if i != 0: index = np.argwhere(array == i) for key in index: oneimg[key[0]][key[1]][0] = oneimg[key[0]][key[1]][0] * 0.5 + self.color_map[i][0] * 0.5 oneimg[key[0]][key[1]][1] = oneimg[key[0]][key[1]][1] * 0.5 + self.color_map[i][1] * 0.5 oneimg[key[0]][key[1]][2] = oneimg[key[0]][key[1]][2] * 0.5 + self.color_map[i][2] * 0.5 #img_show[mask] = img_show[mask] * 0.5 + color_mask * 0.5 #oneimg = Image.fromarray(oneimg.astype('uint8')) #可视化 oneimg = cv2.cvtColor(oneimg, cv2.COLOR_RGB2BGR) #oneimg.save(self.args.save_path + os.sep + ori_name, quality=100) cv2.imwrite(self.args.save_path + os.sep + drow_ori_name, oneimg) #oneimg.save(self.args.save_path+os.sep+drow_ori_name, quality=100) def test(self): self.model.eval() self.evaluator.reset() # tbar = tqdm(self.test_loader, desc='\r') num = len(self.test_loader) for i, sample in enumerate(self.test_loader): image, target = sample['image'], sample['label'] print(i,"/",num) torch.cuda.synchronize() start = time.time() with torch.no_grad(): output = self.model(image) end = time.time() times = (end - start) * 1000 print(times, "ms") torch.cuda.synchronize() pred = output.data.cpu().numpy() target = target.cpu().numpy() image1 = image.data.cpu().numpy() # #target1 = target.cpu().numpy() image1 = image1[0, :] target1 = target[0, :] # #image1.reshape([image1.size[1],image1.size[2],image1.size[3]]) # #target1.reshape([image1.size[1],image1.size[2],image1.size[3]]) image1 = image1.transpose(1,2,0) # #target1 = target1.transpose(2,1,0) # import cv2 # image1 = cv2.cvtColor(image1, cv2.COLOR_RGB2BGR) # import cv2 # cv2.imwrite("./image1.jpg",image1) cv2.imwrite("./target111.jpg", target1) pred = np.argmax(pred, axis=1) self.save_image(pred[0], i, 0, True, sample['ori_image']) self.save_image(target[0], i, 1, None, sample['ori_image']) self.evaluator.add_batch(target, pred) Acc = self.evaluator.Pixel_Accuracy() Acc_class = self.evaluator.Pixel_Accuracy_Class() mIoU = self.evaluator.Mean_Intersection_over_Union() print('Acc:{}, Acc_class:{}, mIoU:{}'.format(Acc, Acc_class, mIoU)) def main(): # import cv2 # cvimg = cv2.imread("./dog.jpg") # graycvimg = cv2.cvtColor(cvimg, cv2.COLOR_BGR2GRAY) # cv2.imwrite("./dog_gray.jpg", graycvimg) # graycvimg_bgr = cv2.cvtColor(graycvimg, cv2.COLOR_GRAY2BGR) # cv2.imwrite("./dog_gray_bgr.jpg", graycvimg_bgr) parser = argparse.ArgumentParser(description='Pytorch DeeplabV3Plus Test your data') parser.add_argument('--test', action='store_true', default=True, help='test your data') parser.add_argument('--dataset', default='pascal', help='datset format') parser.add_argument('--backbone', default='xception', help='what is your network backbone') parser.add_argument('--out_stride', type=int, default=16, help='output stride') parser.add_argument('--crop_size', type=int, default=513, help='image size') parser.add_argument('--model', type=str, default='/Users/jaeminjung/develop/aidentify/MoE_ws/result/cheonan_24/model_best.pth.tar', help='load your model') parser.add_argument('--save_path', type=str, default='/Users/jaeminjung/develop/aidentify/MoE_ws/result/20191001_img', help='save your prediction data') args = parser.parse_args() if args.test: tester = Tester(args) tester.test() if __name__ == "__main__": main()

我们不测试val，直接生成test的预测图：

import argparse import os import numpy as np import tqdm import torch from PIL import Image from dataloaders import make_data_loader from modeling.deeplab import * from dataloaders.utils import get_pascal_labels from utils.metrics import Evaluator class Tester(object): def __init__(self, args): if not os.path.isfile(args.model): raise RuntimeError("no checkpoint found at '{}'".fromat(args.model)) self.args = args self.color_map = get_pascal_labels() self.nclass = 2 # Define model model = DeepLab(num_classes=self.nclass, backbone=args.backbone, output_stride=args.out_stride, sync_bn=False, freeze_bn=False) self.model = model device = torch.device('cpu') checkpoint = torch.load(args.model, map_location=device) self.model.load_state_dict(checkpoint['state_dict']) def save_image(self, imgarray, array, id, op): text = 'gt' if op == 0: text = 'pred' file_name = str(id) + '_' + text + '.png' # r = array.copy() # g = array.copy() # b = array.copy() # for i in range(self.nclass): # r[array == i] = self.color_map[i][0] # g[array == i] = self.color_map[i][1] # b[array == i] = self.color_map[i][2] # rgb = np.dstack((r, g, b)) #tensor 转换为 numpy numpyimg = imgarray.numpy() #numpy 转换为 IPL格式 IPLimage = numpyimg.transpose((1, 2, 0)) ''' IPL转换为tensor _img = Image.open(os.path.join(self.img_dir, path)).convert('RGB') img = np.array(img).astype(np.float32).transpose((2, 0, 1)) img = torch.from_numpy(img).float() img = img.cuda() tensor转换为IPL image1 = image.data.cpu().numpy() IPLimage = numpyimg.transpose((1, 2, 0)) save_img = Image.fromarray(IPLimage.astype('uint8')) ''' for i in range(self.nclass): if i != 0: index = np.argwhere(array == i) for key in index: IPLimage[key[0]][key[1]][0] = IPLimage[key[0]][key[1]][0] * 0.5 + self.color_map[i][0] * 0.5 IPLimage[key[0]][key[1]][1] = IPLimage[key[0]][key[1]][1] * 0.5 + self.color_map[i][1] * 0.5 IPLimage[key[0]][key[1]][2] = IPLimage[key[0]][key[1]][2] * 0.5 + self.color_map[i][2] * 0.5 save_img = Image.fromarray(IPLimage.astype('uint8')) save_img.save(self.args.save_path + os.sep + file_name) def transform_val(self, sample): from torchvision import transforms from dataloaders import custom_transforms as tr composed_transforms = transforms.Compose([ tr.FixScaleCrop(crop_size=self.args.crop_size), tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), tr.ToTensor() ]) return composed_transforms(sample) def test(self): self.model.eval() from PIL import Image file = open('./test_marker.txt', 'r') newpath = "/media/spple/新加卷/Dataset/data/marker_data/marker20191021/all/" text_lines = file.readlines() for i in range(len(text_lines)): namename = text_lines[i].replace("\n", "") namename = namename.replace("\t", "") imgsname = newpath + namename img = Image.open(imgsname).convert('RGB') imglabel = Image.open(imgsname).convert('P') #arrayimg = np.array(img).astype(np.float32) #transposeimg = arrayimg.transpose((2, 0, 1)) sample = {'image': img, 'label': imglabel, 'ori_image': img, 'path': None} imgdist = self.transform_val(sample) image = imgdist['image'] ori_image = imgdist['ori_image'] image = image.unsqueeze(0) with torch.no_grad(): output = self.model(image) pred = output.data.cpu().numpy() pred = np.argmax(pred, axis=1) self.save_image(ori_image, pred[0], namename.split(".jpg")[0], 0) def main(): parser = argparse.ArgumentParser(description='Pytorch DeeplabV3Plus Test your data') parser.add_argument('--test', action='store_true', default=True, help='test your data') parser.add_argument('--dataset', default='pascal', help='datset format') parser.add_argument('--backbone', default='xception', help='what is your network backbone') parser.add_argument('--out_stride', type=int, default=16, help='output stride') parser.add_argument('--crop_size', type=int, default=513, help='image size') parser.add_argument('--model', type=str, default='', help='load your model') parser.add_argument('--save_path', type=str, default='', help='save your prediction data') args = parser.parse_args() if args.test: tester = Tester(args) tester.test() if __name__ == "__main__": main()