0039-pytorch入门-利用pytorch内嵌的faster-rcnn进行目标检测

墨蓝 2023-01-12 10:11 127阅读 0赞

# -*- coding: utf-8 -*-
    # @Time    : 2021/1/28 10:09
    # @Author  : Johnson
    import cv2 as cv
    import torchvision
    import torch
    from torchvision import transforms
    import numpy as np
    
    with open("coco.names") as f: #获取类别名称
        coco_names = [line.strip() for line in f.readlines()]
        
    #在torchvision框架可以直接加载预训练模型
    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
    model.eval()
    
    '''
    简单介绍一下transforms，transforms是pytorch的图像预处理包一般用compose把多个步骤整合在一起
        Resize：把给定的图片resize到given size；
    	Normalize：Normalized an tensor image with mean and standard deviation；
    	ToTensor：convert a PIL image to tensor (H*W*C) in range [0,255] to a torch.Tensor(C*H*W) in the range [0.0,1.0]；
    	ToPILImage: convert a tensor to PIL imageScale：目前已经不用了，推荐用ResizeCenterCrop；
    	ResizeCenterCrop：在图片的中间区域进行裁剪；
    	RandomCrop：在一个随机的位置进行裁剪；
    	RandomHorizontalFlip：以0.5的概率水平翻转给定的PIL图像；
    	RandomVerticalFlip：以0.5的概率竖直翻转给定的PIL图像；
    	RandomResizedCrop：将PIL图像裁剪成任意大小和纵横比；
    	Grayscale：将图像转换为灰度图像；
    	RandomGrayscale：将图像以一定的概率转换为灰度图像；
    	FiceCrop：把图像裁剪为四个角和一个中心T；
    	enCropPad：填充ColorJitter：随机改变图像的亮度对比度和饱和度
    '''
    # tf = transforms.Compose([
    #             transforms.Resize(256),
    #             transforms.CenterCrop(224),
    #             transforms.ToTensor(),
    #             transforms.Normalize(
    #             mean=[0.485, 0.456, 0.406],
    #             std=[0.229, 0.224, 0.225]
    #         )])
    transform = transforms.Compose([transforms.ToTensor()])
    
    def tf(image): #将图片转Tensor
        image = cv.resize(image,(224,224))
        image = np.float32(image)/255.0
        image[:, :, ] -= (np.float32(0.485), np.float32(0.456), np.float32(0.406))
        image[:, :, ] /= (np.float32(0.229), np.float32(0.224), np.float32(0.225))
        image = image.transpose((2,0,1))
        input_x = torch.from_numpy(image).unsqueeze(0)
        return input_x
    
    #如果有GPU可以添加GPU支持
    if torch.cuda.is_available():
        model.cuda()
    
    
    # 主函数
    def faster_rcnn_detection(path):
        '''
        利用faster-rcnn进行目标检测
    
        Parameters
        ----------
        path : TYPE
            输入的图片路径
    
        Returns：输出有三个信息
        boxes:表示对象框
        scores:表示每个对象得分
        labels:表示对象的分类标签
        -------
        None.
    
        '''
        image = cv.imread(path)
        blob = transform(image)
        c, h, w = blob.shape
        input_x = blob.view(1, c, h, w)
        output = model(input_x)[0]  # 这里如果是GPU.cuda()
        boxes = output['boxes'].cpu().detach().numpy()
        scores = output['scores'].cpu().detach().numpy()
        labels = output['labels'].cpu().detach().numpy()
        index = 0
        for x1, y1, x2, y2 in boxes:
            if scores[index] > 0.5:
                print("boxes info", x1, y1, x2, y2)
                cv.rectangle(image, (np.int32(x1), np.int32(y1)),
                             (np.int32(x2), np.int32(y2)), (0, 255, 255), 1, 8, 0)
                label_id = labels[index]
                label_txt = coco_names[label_id]
                cv.putText(image, label_txt, (np.int32(x1), np.int32(y1)), cv.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 255), 1)
            index += 1
        cv.imshow("Faster-RCNN Detection Demo", image)
        cv.waitKey(0)
        cv.destroyAllWindows()
    
    
    def video_detection(path):
        '''
        对视频或者摄像头进行读取
    
        Parameters
        ----------
        path : 0或者视频路径
    
        Returns
        -------
        None.
    
        '''
        capture = cv.VideoCapture(path)
        while True:
            ret, frame = capture.read()
            if ret is not True:
                break
            blob = transform(frame)
            c, h, w = blob.shape
            input_x = blob.view(1, c, h, w)
            output = model(input_x)[0]  # 这里如果是GPU.cuda()
            boxes = output['boxes'].cpu().detach().numpy()
            scores = output['scores'].cpu().detach().numpy()
            labels = output['labels'].cpu().detach().numpy()
            index = 0
            for x1, y1, x2, y2 in boxes:
                if scores[index] > 0.5:
                    print("boxes info", x1, y1, x2, y2)
                    cv.rectangle(frame, (np.int32(x1), np.int32(y1)),
                                 (np.int32(x2), np.int32(y2)), (0, 255, 255), 1, 8, 0)
                    label_id = labels[index]
                    label_txt = coco_names[label_id]
                    cv.putText(frame, label_txt, (np.int32(x1), np.int32(y1)), cv.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 255), 1)
                index += 1
            wk = cv.waitKey(1)
            if wk == 27:
                break
            cv.imshow("video detection", frame)
    
    
    # faster_rcnn_detection("D:/images_GRPC/faces_proto/05.jpg")
    video_detection(0)