一.开始

本文记录了实验mmdet训练完以后，使用训练得到的权值文件进行图片批量检测，并且将结果图片保存到文件夹的流程

二.代码

1.创建图片名字的txt文件
创建一个.py文件，然后将下列代码放到和图片同一级文件夹内，然后运行即可得到对应的txt文件，内容是图片的名字

# -*- coding:utf-8 -*-
import glob
imageList = glob.glob("*.jpg")       # 图片所在文件夹的路径
f = open('val.txt', 'a')            # 创建标签文件存放图片文件名
for item in imageList:
    # print(item)                                # images_test/m1_a_018_1.jpg
    img_name = item.split('/')[-1]               # 图片文件名018.jpg
    f.write(img_name + '\n')                     # 将图片文件名逐行写入txt      
   
print('OK')

2.批量检测代码
使用下列代码，修改对应的参数，运行即可进行批量检测并且保存结果

from argparse import ArgumentParser
import os
from mmdet.apis import inference_detector, init_detector  #, show_result_pyplot
import cv2
 
def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)):
    """Visualize the detection results on the image.
    Args:
        model (nn.Module): The loaded detector.
        img (str or np.ndarray): Image filename or loaded image.
        result (tuple[list] or list): The detection result, can be either
            (bbox, segm) or just bbox.
        score_thr (float): The threshold to visualize the bboxes and masks.
        fig_size (tuple): Figure size of the pyplot figure.
    """
    if hasattr(model, 'module'):
        model = model.module
    img = model.show_result(img, result, score_thr=score_thr, show=False)
    return img
    # plt.figure(figsize=fig_size)
    # plt.imshow(mmcv.bgr2rgb(img))
    # plt.show()
 
 
def main():
    # config文件
    config_file = './configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
    # 训练好的模型
    checkpoint_file = './work_dirs/faster_rcnn_r50_fpn_1x_coco/epoch_200.pth'
 
    # model = init_detector(config_file, checkpoint_file)
    model = init_detector(config_file, checkpoint_file, device='cuda:0')
 
    # 图片路径
    img_dir = './data/val/'
    # 检测后存放图片路径
    out_dir = './faster_rcnn_result/'
 
    if not os.path.exists(out_dir):
        os.mkdir(out_dir)
    
    # 测试集的图片名称txt
    test_path = './val.txt'
    fp = open(test_path, 'r')
    test_list = fp.readlines()
 
    count = 0
    imgs = []
    for test in test_list:
        test = test.replace('\n', '')
        test = test.split('.')[0]     # 如果test里面内容的名字是xxx.jpg，需要这行语句，是因为生成的图片会出现.jpg.jpg，否则不需要。
        name = img_dir + test + '.jpg'
        
        count += 1
        print('model is processing the {}/{} images.'.format(count, len(test_list)))
        # result = inference_detector(model, name)
        # model = init_detector(config_file, checkpoint_file, device='cuda:0')
        result = inference_detector(model, name)
        img = show_result_pyplot(model, name, result, score_thr=0.8)
        cv2.imwrite("{}/{}.jpg".format(out_dir, test), img)
 
 
if __name__ == '__main__':
    main()