过程记录 yolov3目标检测输出目标坐标和原图物体像素坐标比较

悠悠 2022-10-19 08:36 281阅读 0赞

设备：jetsonnano B01 一个usb广角摄像头  
语言为python 库为opencv 框架pytorch  
大家都知道yolov3的坐标变换过程比较复杂，大概要经过图片缩放，对数空间变换等等，然后在pytorch中是用一个tensor类型来表示张量的，所以一直认为目标检测的坐标是一个奇奇怪怪的值，需要经过变换才能得到原图目标的像素坐标  
之前一直纠结怎么坐标转化，但今天实验之后怀疑其实检测结果的坐标转化为int数值以后跟实际原图像素坐标是一样的？  
先上结果：  
这是原始图片上的坐标点  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70]  
左下角为像素坐标  
（鼠标放在中间油渍的位置时显示的坐标

检测结果：  
![在这里插入图片描述][20210710141455752.png]  
结果输出的坐标（转化为整数之后输出的）  
![在这里插入图片描述][2021071014123663.png]  
两个坐标几乎一样，感觉其实它们两个是一个数值，而tensor类型后面多了一串小数点  
完整代码  
用opencv打开摄像头查看图片某点像素坐标：

import cv2
    import os
    import sys
    import time
    import datetime
    import argparse
    cap = cv2.VideoCapture(0)
    i = 1
    
    
    while(cap.isOpened()):
      ret, frame = cap.read()
      cv2.imshow('frame',frame)
      if cv2.waitKey(30) == ord('q'):
          #ret, frame = cap.read()
          cv2.imwrite('data/custom/dd/'+str(i)+".jpg",frame) 
          frame=cv2.imread('data/custom/dd/'+str(i)+".jpg")
          break

检测部分显示结果坐标：  
pytorch python

if detections is not None:
                # Rescale boxes to original image
                detections = rescale_boxes(detections, opt.img_size, img.shape[:2])
                unique_labels = detections[:, -1].cpu().unique()
                n_cls_preds = len(unique_labels)
                bbox_colors = random.sample(colors, n_cls_preds)
                for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
    
                    print("\t+ Label: %s, Conf: %.5f" % (classes[int(cls_pred)], cls_conf.item()))                              
     
                    box_w = x2 - x1
                    box_h = y2 - y1
                    centx = int((x1+x2)*0.1/2)
                    centy = int((y1+y2)*0.1/2)
                    thex.append(centx)
                    they.append(centy)
                    str1+="x:"
                    str1+=str(centx)
                    str1+=",y:"
                    str1+=str(centy)
                    area = int(box_w*0.1)*int(box_h*0.1)
                    if area > 600: 
                     time = 30
                    print("x和y"+str((x2+x1)/2)+","+str((y1+y2)/2))
    
                    print("x:")
                    print(int((x1+x2)/2))
                    print("y:")
                    print(int((y1+y2)/2))
                    print(int(box_w*0.1))
                    print(int(box_h*0.1))
                  
                    color = bbox_colors[int(np.where(unique_labels == int(cls_pred))[0])]
                    # Create a Rectangle patch
                    bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=color, facecolor="none")
                    print(int(box_w)*int(box_h)*0.01)
                    print(str1)
                    if(box_w*box_h>50):
                        se.write("1".encode())
                        time.sleep(3)
                        se.write("0".encode())
                        se.write(str1.encode())
                        data_read = se.readline()
                        print(data_read.decode())
                    # Add the bbox to the plot
                    ax.add_patch(bbox)
                    # Add label
                    plt.text(
                        x1,
                        y1,
                        s=classes[int(cls_pred)],
                        color="white",
                        verticalalignment="top",
                        bbox={ "color": color, "pad": 0},
                    )
    
            # Save generated image with detections
            plt.axis("off")
            plt.gca().xaxis.set_major_locator(NullLocator())
            plt.gca().yaxis.set_major_locator(NullLocator())
            filename = os.path.basename(path).split(".")[0]
            #output_path = os.path.join("output", f"{filename}.png")
            #output_path = os.path.join("/data/custom/dd", f"{filename}.png")
            plt.savefig(filename, bbox_inches="tight", pad_inches=0.0)
            plt.close()

if detections is not None:
        后面的代码是输出坐标的

关于目标检测的完整检测代码：（很长，熟悉的人不必看）

from __future__ import division
    import serial as ser
    import time
    se=ser.Serial("/dev/ttyUSB0",115200,timeout=1)
    from models import *
    from utils.utils import *
    from utils.datasets import *
    from utils.augmentations import *
    from utils.transforms import *
    import cv2
    import os
    import sys
    import time
    import datetime
    import argparse
    
    from PIL import Image
    
    import torch
    import torchvision.transforms as transforms
    from torch.utils.data import DataLoader
    from torchvision import datasets
    from torch.autograd import Variable
    
    import matplotlib.pyplot as plt
    import matplotlib.patches as patches
    from matplotlib.ticker import NullLocator
    i = 1
    cap = cv2.VideoCapture(0)
    array_of_img = []
    start = time.time()
    directory_name=r'output'
    
    
    
    while(cap.isOpened()):
      ret, frame = cap.read()
      cv2.imshow('frame',frame)
      if cv2.waitKey(30) == ord('q'):
          #ret, frame = cap.read()
          cv2.imwrite('data/custom/dd/'+str(i)+".jpg",frame) 
          frame=cv2.imread('data/custom/dd/'+str(i)+".jpg")
          break
    # When everything done, release the capture
    
    cap.release()
    
    cv2.destroyAllWindows()
    
    
    washtime = []
    thex = []
    they = []
    area = 0
    str1=""
    if __name__ == "__main__":
        parser = argparse.ArgumentParser()
        parser.add_argument("--image_folder", type=str, default="data/samples", help="path to dataset")
        parser.add_argument("--model_def", type=str, default="config/yolov3.cfg", help="path to model definition file")
        parser.add_argument("--weights_path", type=str, default="weights/yolov3.weights", help="path to weights file")
        parser.add_argument("--class_path", type=str, default="data/coco.names", help="path to class label file")
        parser.add_argument("--conf_thres", type=float, default=0.8, help="object confidence threshold")
        parser.add_argument("--nms_thres", type=float, default=0.4, help="iou thresshold for non-maximum suppression")
        parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
        parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation")
        parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
        parser.add_argument("--checkpoint_model", type=str, help="path to checkpoint model")
        opt = parser.parse_args()
        print(opt)
    
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
        os.makedirs("output", exist_ok=True)
    
        # Set up modella
        model = Darknet(opt.model_def, img_size=opt.img_size).to(device)
    
        if opt.weights_path.endswith(".weights"):
            # Load darknet weights
            model.load_darknet_weights(opt.weights_path)
        else:
            # Load checkpoint weights
            model.load_state_dict(torch.load(opt.weights_path))
    
        model.eval()  # Set in evaluation mode
    
        dataloader = DataLoader(
            ImageFolder(opt.image_folder, transform= \
                transforms.Compose([DEFAULT_TRANSFORMS, Resize(opt.img_size)])),
            batch_size=opt.batch_size,
            shuffle=False,
            num_workers=opt.n_cpu,
        )
    
        classes = load_classes(opt.class_path)  # Extracts class labels from file
    
        Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
    
        imgs = []  # Stores image paths
        img_detections = []  # Stores detections for each image index
    
        print("\nPerforming object detection:")
        prev_time = time.time()
        for batch_i, (img_paths, input_imgs) in enumerate(dataloader):
            # Configure input
            input_imgs = Variable(input_imgs.type(Tensor))
    
            # Get detections
            with torch.no_grad():
                detections = model(input_imgs)
                detections = non_max_suppression(detections, opt.conf_thres, opt.nms_thres)
    
            # Log progress
            current_time = time.time()
            inference_time = datetime.timedelta(seconds=current_time - prev_time)
            prev_time = current_time
            print("\t+ Batch %d, Inference Time: %s" % (batch_i, inference_time))
    
            # Save image and detections
            imgs.extend(img_paths)
            img_detections.extend(detections)
    
        # Bounding-box colors
        cmap = plt.get_cmap("tab20b")
        colors = [cmap(i) for i in np.linspace(0, 1, 20)]
    
        print("\nSaving images:")
        # Iterate through images and save plot of detections
        for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):
    
            print("(%d) Image: '%s'" % (img_i, path))
    
            # Create plot
            img = np.array(Image.open(path))
            plt.figure()
            fig, ax = plt.subplots(1)
            ax.imshow(img)
    
            # Draw bounding boxes and labels of detections
            if detections is not None:
                # Rescale boxes to original image
                detections = rescale_boxes(detections, opt.img_size, img.shape[:2])
                unique_labels = detections[:, -1].cpu().unique()
                n_cls_preds = len(unique_labels)
                bbox_colors = random.sample(colors, n_cls_preds)
                for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
    
                    print("\t+ Label: %s, Conf: %.5f" % (classes[int(cls_pred)], cls_conf.item()))                              
     
                    box_w = x2 - x1
                    box_h = y2 - y1
                    centx = int((x1+x2)*0.1/2)
                    centy = int((y1+y2)*0.1/2)
                    thex.append(centx)
                    they.append(centy)
                    str1+="x:"
                    str1+=str(centx)
                    str1+=",y:"
                    str1+=str(centy)
                    area = int(box_w*0.1)*int(box_h*0.1)
                    if area > 600: 
                     time = 30
                    print("x和y"+str((x2+x1)/2)+","+str((y1+y2)/2))
    
                    print("x:")
                    print(int((x1+x2)/2))
                    print("y:")
                    print(int((y1+y2)/2))
                    print(int(box_w*0.1))
                    print(int(box_h*0.1))
                  
                    color = bbox_colors[int(np.where(unique_labels == int(cls_pred))[0])]
                    # Create a Rectangle patch
                    bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=color, facecolor="none")
                    print(int(box_w)*int(box_h)*0.01)
                    print(str1)
                    if(box_w*box_h>50):
                        se.write("1".encode())
                        time.sleep(3)
                        se.write("0".encode())
                        se.write(str1.encode())
                        data_read = se.readline()
                        print(data_read.decode())
                    # Add the bbox to the plot
                    ax.add_patch(bbox)
                    # Add label
                    plt.text(
                        x1,
                        y1,
                        s=classes[int(cls_pred)],
                        color="white",
                        verticalalignment="top",
                        bbox={ "color": color, "pad": 0},
                    )
    
            # Save generated image with detections
            plt.axis("off")
            plt.gca().xaxis.set_major_locator(NullLocator())
            plt.gca().yaxis.set_major_locator(NullLocator())
            filename = os.path.basename(path).split(".")[0]
            #output_path = os.path.join("output", f"{filename}.png")
            #output_path = os.path.join("/data/custom/dd", f"{filename}.png")
            plt.savefig(filename, bbox_inches="tight", pad_inches=0.0)
            plt.close()

串口：

se.write("50".encode())
    print("缓存中的字节数")
    print(se.inWaiting())
    time.sleep(2)
    se.write("100".encode())
    print("缓存中的字节数")
    print(se.inWaiting())#：返回接收缓存中的字节数
    data = se.readline()
    print(data.decode())
    data = se.readline()
    print(data.decode())
    print(se.inWaiting())
    se.write("my87u".encode())
    print("缓存中的字节数")
    print(se.inWaiting())
    se.flush() #等待所有数据写出。
    data = se.readline()
    print(data.decode())
    data = se.readline()
    print(data.decode())

参数：

parser = argparse.ArgumentParser()
        parser.add_argument("--image_folder", type=str, default="data/custom/dd2", help="path to dataset")
        parser.add_argument("--model_def", type=str, default="config/yolov3-custom.cfg", help="path to model definition file")
        parser.add_argument("--weights_path", type=str, default="checkpoints/ckpt_86.pth", help="path to weights file")
        parser.add_argument("--class_path", type=str, default="data/custom/classes.names", help="path to class label file")
        parser.add_argument("--conf_thres", type=float, default=0.8, help="object confidence threshold")
        parser.add_argument("--nms_thres", type=float, default=0.4, help="iou thresshold for non-maximum suppression")
        parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
        parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation")
        parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
        parser.add_argument("--checkpoint_model",type=str,default="checkpoints/ckpt_86.pth", help="path to checkpoint model")
        opt = parser.parse_args()
        print(opt)

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMzU4NTc0_size_16_color_FFFFFF_t_70]: /images/20221005/03aee4c01933485db0890c47fee236e9.png
[20210710141455752.png]: /images/20221005/4604966c4417485e9157a8b61f93e86d.png
[2021071014123663.png]: /images/20221005/7b658c288d5441cfb81e87a869cb4a1f.png