【github】https://github.com/DocF/multispectral-object-detection
一.环境
1.1 环境
基本依赖和yolov5基本相同，当然也可以配置在虚拟环境中
git clone https://github.com/DocF/multispectral-object-detectioncd multispectral-object-detectionpip install -r requirements.txt1.2 报错解决
1.2.1 找不到sppf
AttributeError: Can't get attribute 'SPPF' on <module 'models.common' from '/hy-tmp/multispectral-object-detection/models/common.py'>【参考文章】找不到SPPF错误
在models/common.py下找到ssp，将下面这段添加到ssp之前
class SPPF(nn.Module): def __init__(self, c1, c2, k=5): super().__init__() c_ = c1 // 2 self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * 4, c2, 1, 1) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): x = self.cv1(x) with warnings.catch_warnings(): warnings.simplefilter('ignore') y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))1.2.2
RuntimeError: result type Float can't be cast to the desired output type __int64【参考】报错解决方法
将下面这段替换utils/loss.py中build_targets函数，注意保留返回值
for i in range(self.nl): anchors, shape = self.anchors[i], p[i].shape gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain # Match targets to anchors t = targets * gain # shape(3,n,7) if nt: # Matches r = t[..., 4:6] / anchors[:, None] # wh ratio j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) t = t[j] # filter # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse j, k = ((gxy % 1 < g) & (gxy > 1)).T l, m = ((gxi % 1 < g) & (gxi > 1)).T j = torch.stack((torch.ones_like(j), j, k, l, m)) t = t.repeat((5, 1, 1))[j] offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] else: t = targets[0] offsets = 0 # Define bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class gij = (gxy - offsets).long() gi, gj = gij.T # grid indices # Append indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid tbox.append(torch.cat((gxy - gij, gwh), 1)) # box anch.append(anchors[a]) # anchors tcls.append(c) # class二. 数据集处理
2.1 数据集下载
【github】https://github.com/DocF/multispectral-object-detection包含了对应的链接
链接：https://pan.baidu.com/s/1zO_1Olognq2atY6m4StZUA” />
2.3 数据集预处理成txt
以FLIR（就是那个align）为例
2.3.1 训练集验证集
split_train_val.py
import osimport randomimport argparseparser = argparse.ArgumentParser()parser.add_argument('--xml_path', type=str, help='input xml label path')parser.add_argument('--txt_path', type=str, help='output txt label path')opt = parser.parse_args()trainval_percent = 1.0train_percent = 0.9xmlfilepath = opt.xml_pathtxtsavepath = opt.txt_pathtotal_xml = os.listdir(xmlfilepath)if not os.path.exists(txtsavepath): os.makedirs(txtsavepath)num=len(total_xml)list=range(num)ftrainval = open(txtsavepath + '/trainval.txt', 'w')ftest = open(txtsavepath + '/test.txt', 'w')ftrain = open(txtsavepath + '/train.txt', 'w')fval = open(txtsavepath + '/val.txt', 'w')for i in list: name=total_xml[i][:-4]+'\n' ftrainval.write(name) if i%7 == 0: fval.write(name) else: ftrain.write(name)ftrainval.close()ftrain.close()fval.close()ftest.close()输入命令：
python split_train_val.py --xml_path xml文件路径 --txt_path 输出txt文件路径（1）xml文件路径：我是先将xml为文件全部放到一个文件夹里面
以我的为例就是：cp D:\computervision\cross\detection\align\Annotations\*.xml D:\computervision\cross\detection\align\annotation(2)输出txt文件路径：直接输出到前面提到的datasets下
得到下面这四个
2.3.2 格式转换
voc_label.py文件，应该改一下路径就可以用了，就不多说了
import xml.etree.ElementTree as ETimport pickleimport osfrom os import listdir, getcwdfrom os.path import joinsets=['train', 'val', 'test']classes = ['person','car','bicycle']abs_path = os.getcwd()def convert(size, box): dw = 1./(size[0]) dh = 1./(size[1]) x = (box[0] + box[1])/2.0 - 1 y = (box[2] + box[3])/2.0 - 1 w = box[1] - box[0] h = box[3] - box[2] x = x*dw w = w*dw y = y*dh h = h*dh return (x,y,w,h)def convert_annotation(image_id ,RGBid ): in_file = open(r'D:\computervision\cross\detection\align\annotation\%s.xml'%( image_id)) irout_file = open('D:\computervision\cross\detection\multispectral-object-detection-main\datasets\IR\labels\%s.txt'%(image_id), 'w') rgbout_file= open('D:\computervision\cross\detection\multispectral-object-detection-main\datasets\RGB\labels\%s.txt'%(RGBid), 'w') tree=ET.parse(in_file) root = tree.getroot() size = root.find('size') w = int(size.find('width').text) h = int(size.find('height').text) for obj in root.iter('object'): #difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes : continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)) bb = convert((w,h), b) irout_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') rgbout_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')for image_set in sets: # if not os.path.exists('D:\computervision\cross\detection\multispectral-object-detection-main\datasets'): # os.makedirs('D:\computervision\cross\detection\multispectral-object-detection-main\datasets') #创建两个txt文件 #（1）先创建rgb文件 # image_ids = open('D:\computervision\cross\detection\multispectral-object-detection-main\datasets\%s.txt'%(image_set)).read().strip().split() ir_file = open('D:\computervision\cross\detection\multispectral-object-detection-main\datasets\IR\%s.txt'%(image_set), 'w') rgb_file= open('D:\computervision\cross\detection\multispectral-object-detection-main\datasets\RGB\%s.txt'%(image_set), 'w') for image_id in image_ids: ir_file.write('D:\computervision\cross\detection\multispectral-object-detection-main\datasets\IR\images\%s.jpeg\n'%(image_id)) id=image_id.split("_")[1] RGBid='FLIR_'+id+"_RGB" rgb_file.write( 'D:\computervision\cross\detection\multispectral-object-detection-main\datasets\RGB\images\%s.jpg\n' % (RGBid)) convert_annotation(image_id,RGBid) ir_file.close() rgb_file.close()三 .训练
修改data/multispectral/FLIR_aligned.yaml文件夹
直接python train.py
