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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
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/darknet
https://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, targetstrans_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)
