【PyTorch】基于YOLO的多目标检测项目（一）

【PyTorch】基于YOLO的多目标检测项目（二）

目标检测是对图像中的现有目标进行定位和分类的过程。识别的对象在图像中显示有边界框。一般的目标检测方法有两种：基于区域提议的和基于回归/分类的。这里使用一种基于回归/分类的方法，称为YOLO。

目录

准备COCO数据集

创建自定义数据集

转换数据

定义数据加载器

准备COCO数据集

COCO是一个大规模的对象检测，分割和字幕数据集。它包含80个对象类别用于对象检测。

下载以下GitHub存储库

https://github.com/pjreddie/darknethttps://github.com/pjreddie/darknet

创建一个名为config的文件夹，将darknet/cfg/coco.data、darknet/cfg/yolov3.cfg文件复制到config文件夹中。

创建一个名为data的文件夹，从以下链接获取coco.names文件，并将其放入data文件夹，coco.names文件包含COCO数据集中80个对象类别的列表。

darknet/data/coco.names at master · pjreddie/darknet · GitHubConvolutional Neural Networks. Contribute to pjreddie/darknet development by creating an account on GitHub.https://github.com/pjreddie/darknet/blob/master/data/coco.names将darknet/scripts/get_coco_dataset.sh文件复制到data文件夹中，并复制get_coco_cocoet.sh到data文件夹。接下来，打开一个终端并执行get_coco_cocoet.sh，该脚本将把完整的COCO数据集下载到名为coco的子文件夹中。也可通过以下链接下载coco数据集。

COCO2014_数据集-飞桨AI Studio星河社区 (baidu.com)https://aistudio.baidu.com/datasetdetail/165195

在images文件夹中，有两个名为train 2014和val 2014的文件夹，分别包含82783和40504个图像。在labels文件夹中，有两个名为train 2014和val 2014的标签，分别包含82081和40137文本文件。这些文本文件包含图像中对象的边界框坐标。此外，trainvalno5k.txt文件是一个包含117264张图像的列表，这些图像将用于训练模型。此列表是train2014和val2014中图像的组合，5000个图像除外。5k.txt文件包含将用于验证的5000个图像的列表。

创建自定义数据集

完成数据集下载后，使用PyTorch的Dataset和Dataloader类创建训练和验证数据集和数据加载器。

from torch.utils.data import Dataset from PIL import Image import torchvision.transforms.functional as TF import os import numpy as np  import torch device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(torch.__version__)

#定义CocoDataset类，并展示来自训练和验证数据集的一些示例图像 class CocoDataset(Dataset):     def __init__(self, path2listFile, transform=None, trans_params=None):         with open(path2listFile, "r") as file:             self.path2imgs = file.readlines()                  self.path2labels = [             path.replace("images", "labels").replace(".png", ".txt").replace(".jpg", ".txt")             for path in self.path2imgs]          self.trans_params = trans_params         self.transform = transform      def __len__(self):         return len(self.path2imgs)          def __getitem__(self, index):         path2img = self.path2imgs[index % len(self.path2imgs)].rstrip()          img = Image.open(path2img).convert('RGB')          path2label = self.path2labels[index % len(self.path2imgs)].rstrip()          labels= None         if os.path.exists(path2label):             labels = np.loadtxt(path2label).reshape(-1, 5)                      if self.transform:             img, labels = self.transform(img, labels, self.trans_params)          return img, labels, path2img    

root_data="./data/coco" path2trainList=os.path.join(root_data, "trainvalno5k.txt")  coco_train = CocoDataset(path2trainList) print(len(coco_train))

# 从coco_train中获取图像、标签和图像路径 img, labels, path2img = coco_train[1]  print("image size:", img.size, type(img)) print("labels shape:", labels.shape, type(labels)) print("labels \n", labels)

path2valList=os.path.join(root_data, "5k.txt") coco_val = CocoDataset(path2valList, transform=None, trans_params=None) print(len(coco_val))

img, labels, path2img = coco_val[7]  print("image size:", img.size, type(img)) print("labels shape:", labels.shape, type(labels)) print("labels \n", labels)

import matplotlib.pylab as plt import numpy as np from PIL import Image, ImageDraw, ImageFont from torchvision.transforms.functional import to_pil_image import random %matplotlib inline path2cocoNames="./data/coco.names" fp = open(path2cocoNames, "r") coco_names = fp.read().split("\n")[:-1] print("number of classese:", len(coco_names)) print(coco_names)

def rescale_bbox(bb,W,H):     x,y,w,h=bb     return [x*W, y*H, w*W, h*H] COLORS = np.random.randint(0, 255, size=(80, 3),dtype="uint8") # fnt = ImageFont.truetype('Pillow/Tests/fonts/FreeMono.ttf', 16) fnt = ImageFont.truetype('arial.ttf', 16) def show_img_bbox(img,targets):     if torch.is_tensor(img):         img=to_pil_image(img)     if torch.is_tensor(targets):         targets=targets.numpy()[:,1:]              W, H=img.size     draw = ImageDraw.Draw(img)          for tg in targets:         id_=int(tg[0])         bbox=tg[1:]         bbox=rescale_bbox(bbox,W,H)         xc,yc,w,h=bbox                  color = [int(c) for c in COLORS[id_]]         name=coco_names[id_]                  draw.rectangle(((xc-w/2, yc-h/2), (xc+w/2, yc+h/2)),outline=tuple(color),width=3)         draw.text((xc-w/2,yc-h/2),name, font=fnt, fill=(255,255,255,0))     plt.imshow(np.array(img))         np.random.seed(1) rnd_ind=np.random.randint(len(coco_train)) img, labels, path2img = coco_train[rnd_ind]  print(img.size, labels.shape)  plt.rcParams['figure.figsize'] = (20, 10) show_img_bbox(img,labels)

np.random.seed(1) rnd_ind=np.random.randint(len(coco_val)) img, labels, path2img = coco_val[rnd_ind]  print(img.size, labels.shape)  plt.rcParams['figure.figsize'] = (20, 10) show_img_bbox(img,labels)

转换数据

定义一个转换函数和传递给CocoDataset类的参数

def pad_to_square(img, boxes, pad_value=0, normalized_labels=True):     w, h = img.size     w_factor, h_factor = (w,h) if normalized_labels else (1, 1)          dim_diff = np.abs(h - w)     pad1= dim_diff // 2     pad2= dim_diff - pad1          if h<=w:         left, top, right, bottom= 0, pad1, 0, pad2     else:         left, top, right, bottom= pad1, 0, pad2, 0     padding= (left, top, right, bottom)      img_padded = TF.pad(img, padding=padding, fill=pad_value)     w_padded, h_padded = img_padded.size                  x1 = w_factor * (boxes[:, 1] - boxes[:, 3] / 2)     y1 = h_factor * (boxes[:, 2] - boxes[:, 4] / 2)     x2 = w_factor * (boxes[:, 1] + boxes[:, 3] / 2)     y2 = h_factor * (boxes[:, 2] + boxes[:, 4] / 2)              x1 += padding[0] # 左     y1 += padding[1] # 上     x2 += padding[2] # 右     y2 += padding[3] # 下                  boxes[:, 1] = ((x1 + x2) / 2) / w_padded     boxes[:, 2] = ((y1 + y2) / 2) / h_padded     boxes[:, 3] *= w_factor / w_padded     boxes[:, 4] *= h_factor / h_padded      return img_padded, boxes    

def hflip(image, labels):     image = TF.hflip(image)     labels[:, 1] = 1.0 - labels[:, 1]     return image, labels  def transformer(image, labels, params):     if params["pad2square"] is True:         image,labels= pad_to_square(image, labels)          image = TF.resize(image,params["target_size"])      if random.random() < params["p_hflip"]:         image,labels=hflip(image,labels)      image=TF.to_tensor(image)     targets = torch.zeros((len(labels), 6))     targets[:, 1:] = torch.from_numpy(labels)          return image, targets

trans_params_train={     "target_size" : (416, 416),     "pad2square": True,     "p_hflip" : 1.0,     "normalized_labels": True, } coco_train=CocoDataset(path2trainList,transform=transformer,trans_params=trans_params_train)  np.random.seed(100) rnd_ind=np.random.randint(len(coco_train)) img, targets, path2img = coco_train[rnd_ind]  print("image shape:", img.shape) print("labels shape:", targets.shape)   plt.rcParams['figure.figsize'] = (20, 10) COLORS = np.random.randint(0, 255, size=(80, 3),dtype="uint8") show_img_bbox(img,targets)

通过传递 transformer 函数来定义 CocoDataset 的一个对象来验证数据 

trans_params_val={     "target_size" : (416, 416),     "pad2square": True,     "p_hflip" : 0.0,     "normalized_labels": True, } coco_val= CocoDataset(path2valList,                       transform=transformer,                       trans_params=trans_params_val)  np.random.seed(55) rnd_ind=np.random.randint(len(coco_val)) img, targets, path2img = coco_val[rnd_ind]  print("image shape:", img.shape) print("labels shape:", targets.shape)   plt.rcParams['figure.figsize'] = (20, 10) COLORS = np.random.randint(0, 255, size=(80, 3),dtype="uint8") show_img_bbox(img,targets)

定义数据加载器

定义两个用于训练和验证数据集的数据加载器，从coco_train和coco_val中获取小批量数据。

from torch.utils.data import DataLoader  batch_size=8 def collate_fn(batch):     imgs, targets, paths = list(zip(*batch))          targets = [boxes for boxes in targets if boxes is not None]          for b_i, boxes in enumerate(targets):         boxes[:, 0] = b_i     targets = torch.cat(targets, 0)     imgs = torch.stack([img for img in imgs])     return imgs, targets, paths  train_dl = DataLoader(         coco_train,         batch_size=batch_size,         shuffle=True,         num_workers=0,         pin_memory=True,         collate_fn=collate_fn,         )  torch.manual_seed(0) for imgs_batch,tg_batch,path_batch in train_dl:     break print(imgs_batch.shape) print(tg_batch.shape,tg_batch.dtype)

val_dl = DataLoader(         coco_val,         batch_size=batch_size,         shuffle=False,         num_workers=0,         pin_memory=True,         collate_fn=collate_fn,         )  torch.manual_seed(0) for imgs_batch,tg_batch,path_batch in val_dl:     break print(imgs_batch.shape) print(tg_batch.shape,tg_batch.dtype)