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图像目标检测是什么

Object Detection
判断图像中目标的位置

目标检测两要素

分类：分类向量 [p0, …, pn]
回归：回归边界框 [x1, y1, x2, y2]

模型如何完成目标检测

将3D张量映射到两个张量

分类张量：shape为 [N, c+1]
边界框张量：shape为 [N, 4]

Recent Advances in Deep Learning for Object Detection
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边界框数量N如何确定？

传统方法——滑动窗策略

缺点：

重复计算量大
窗口大小难确定

利用卷积减少重复计算
重要概念：特征图一个像素对应原图一块区域

目标检测模型简介

按流程分为：one-stage和two-stage
在这里插入图片描述

Faster RCNN

经典two stage检测网络

Faster RCNN 数据流

Feature map：[256, h_f, w_f]
2 Softmax：[num_anchors, h_f, w_f]
Regressors：[num_anchors*4, h_f, w_f]
NMS OUT：[n_proposals=2000, 4]
ROI Layer：[512, 256, 7, 7]
FC1 FC2：[512, 1024]
c+1 Softmax： [512, c+1]
Regressors：[512, (c+1)*4]

Faster RCNN 主要组件

backbone
rpn
filter_proposals(NMS)
roi_heads

Faster RCNN 行人检测

数据： PennFudanPed数据集， 70张行人照片共345行人标签
官方地址： http://www.cis.upenn.edu/~jshi/ped_html/
模型： fasterrcnn_resnet50_fpn 进行finetune
目标检测推荐github：https://github.com/amusi/awesome-object-detection

代码如下：

import os
import time
import torch.nn as nn
import torch
import random
import numpy as np
import torchvision.transforms as transforms
import torchvision
from PIL import Image
import torch.nn.functional as F
from my_dataset import PennFudanDataset
from common_tools import set_seed
from torch.utils.data import DataLoader
from matplotlib import pyplot as plt
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.transforms import functional as F
import enviromentsset_seed(1)  # 设置随机种子BASE_DIR = os.path.dirname(os.path.abspath(__file__))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")# classes_coco
COCO_INSTANCE_CATEGORY_NAMES = ['__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus','train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign','parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow','elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A','handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball','kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket','bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl','banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza','donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table','N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone','microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book','clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]def vis_bbox(img, output, classes, max_vis=40, prob_thres=0.4):fig, ax = plt.subplots(figsize=(12, 12))ax.imshow(img, aspect='equal')out_boxes = output_dict["boxes"].cpu()out_scores = output_dict["scores"].cpu()out_labels = output_dict["labels"].cpu()num_boxes = out_boxes.shape[0]for idx in range(0, min(num_boxes, max_vis)):score = out_scores[idx].numpy()bbox = out_boxes[idx].numpy()class_name = classes[out_labels[idx]]if score < prob_thres:continueax.add_patch(plt.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], fill=False,edgecolor='red', linewidth=3.5))ax.text(bbox[0], bbox[1] - 2, '{:s} {:.3f}'.format(class_name, score), bbox=dict(facecolor='blue', alpha=0.5),fontsize=14, color='white')plt.show()plt.close()class Compose(object):def __init__(self, transforms):self.transforms = transformsdef __call__(self, image, target):for t in self.transforms:image, target = t(image, target)return image, targetclass RandomHorizontalFlip(object):def __init__(self, prob):self.prob = probdef __call__(self, image, target):if random.random() < self.prob:height, width = image.shape[-2:]image = image.flip(-1)bbox = target["boxes"]bbox[:, [0, 2]] = width - bbox[:, [2, 0]]target["boxes"] = bboxreturn image, targetclass ToTensor(object):def __call__(self, image, target):image = F.to_tensor(image)return image, targetif __name__ == "__main__":# configLR = 0.001num_classes = 2batch_size = 1start_epoch, max_epoch = 0, 5train_dir = enviroments.pennFudanPed_data_dirtrain_transform = Compose([ToTensor(), RandomHorizontalFlip(0.5)])# step 1: datatrain_set = PennFudanDataset(data_dir=train_dir, transforms=train_transform)# 收集batch data的函数def collate_fn(batch):return tuple(zip(*batch))train_loader = DataLoader(train_set, batch_size=batch_size, collate_fn=collate_fn)# step 2: modelmodel = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)in_features = model.roi_heads.box_predictor.cls_score.in_featuresmodel.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) # replace the pre-trained head with a new onemodel.to(device)# step 3: loss# in lib/python3.6/site-packages/torchvision/models/detection/roi_heads.py# def fastrcnn_loss(class_logits, box_regression, labels, regression_targets)# step 4: optimizer schedulerparams = [p for p in model.parameters() if p.requires_grad]optimizer = torch.optim.SGD(params, lr=LR, momentum=0.9, weight_decay=0.0005)lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)# step 5: Iterationfor epoch in range(start_epoch, max_epoch):model.train()for iter, (images, targets) in enumerate(train_loader):images = list(image.to(device) for image in images)targets = [{k: v.to(device) for k, v in t.items()} for t in targets]# if torch.cuda.is_available():#     images, targets = images.to(device), targets.to(device)loss_dict = model(images, targets)  # images is list; targets is [ dict["boxes":**, "labels":**], dict[] ]losses = sum(loss for loss in loss_dict.values())print("Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} ".format(epoch, max_epoch, iter + 1, len(train_loader), losses.item()))optimizer.zero_grad()losses.backward()optimizer.step()lr_scheduler.step()# testmodel.eval()# configvis_num = 5vis_dir = os.path.join(BASE_DIR, "..", "..", "data", "PennFudanPed", "PNGImages")img_names = list(filter(lambda x: x.endswith(".png"), os.listdir(vis_dir)))random.shuffle(img_names)preprocess = transforms.Compose([transforms.ToTensor(), ])for i in range(0, vis_num):path_img = os.path.join(vis_dir, img_names[i])# preprocessinput_image = Image.open(path_img).convert("RGB")img_chw = preprocess(input_image)# to deviceif torch.cuda.is_available():img_chw = img_chw.to('cuda')model.to('cuda')# forwardinput_list = [img_chw]with torch.no_grad():tic = time.time()print("input img tensor shape:{}".format(input_list[0].shape))output_list = model(input_list)output_dict = output_list[0]print("pass: {:.3f}s".format(time.time() - tic))# visualizationvis_bbox(input_image, output_dict, COCO_INSTANCE_CATEGORY_NAMES, max_vis=20, prob_thres=0.5)  # for 2 epoch for nms