faster rcnn demo.py:在一个窗口显示所有类别标注

约定不等于承诺〃 2022-06-18 07:18 177阅读 0赞

faster rcnn 的demo.py运行时，对于同一个图像，每个类别显示一个窗口，看起来不太方便，顺便小改一下，让一幅图像中检测到的所有类别物体都在一个窗口下标注，就方便多了。  
代码改动也不复杂，就是把vis\_detections函数中for循环前后三行代码移动到demo函数的for循环前后。  
完整代码如下(顺便把标注框的线宽改成了1，以前是3.5太粗了，不好看)：  
py-faster-rcnn/tools/demo.py (注意代码中本人添加的中文注释)

#!/usr/bin/env python
    #coding=utf8
    # 因为代码中我加了中文注释，所以 上面这行用于指定编码 ,否则python代码执行会报错 
    # --------------------------------------------------------
    # Faster R-CNN
    # Copyright (c) 2015 Microsoft
    # Licensed under The MIT License [see LICENSE for details]
    # Written by Ross Girshick
    # --------------------------------------------------------
    
    """
    Demo script showing detections in sample images.
    
    See README.md for installation instructions before running.
    """
    
    import _init_paths
    from fast_rcnn.config import cfg
    from fast_rcnn.test import im_detect
    from fast_rcnn.nms_wrapper import nms
    from utils.timer import Timer
    import matplotlib.pyplot as plt
    import numpy as np
    import scipy.io as sio
    import caffe, os, sys, cv2
    import argparse
    
    CLASSES = ('__background__',
               'aeroplane', 'bicycle', 'bird', 'boat',
               'bottle', 'bus', 'car', 'cat', 'chair',
               'cow', 'diningtable', 'dog', 'horse',
               'motorbike', 'person', 'pottedplant',
               'sheep', 'sofa', 'train', 'tvmonitor')
    
    NETS = {
       'vgg16': ('VGG16',
                      'VGG16_faster_rcnn_final.caffemodel'),
            'zf': ('ZF',
                      'ZF_faster_rcnn_final.caffemodel')}
    
    #增加ax参数
    def vis_detections(im, class_name, dets, ax, thresh=0.5):
        """Draw detected bounding boxes."""
        inds = np.where(dets[:, -1] >= thresh)[0]
        if len(inds) == 0:
            return
    # 删除这三行
    #     im = im[:, :, (2, 1, 0)]
    #     fig, ax = plt.subplots(figsize=(12, 12))
    #     ax.imshow(im, aspect='equal')
        for i in inds:
            bbox = dets[i, :4]
            score = dets[i, -1]
    
            ax.add_patch(
                plt.Rectangle((bbox[0], bbox[1]),
                              bbox[2] - bbox[0],
                              bbox[3] - bbox[1], fill=False,
                              edgecolor='red', linewidth=1) # 矩形线宽从3.5改为1
                )
            ax.text(bbox[0], bbox[1] - 2,
                    '{:s} {:.3f}'.format(class_name, score),
                    bbox=dict(facecolor='blue', alpha=0.5),
                    fontsize=14, color='white')
    
        ax.set_title(('{} detections with '
                      'p({} | box) >= {:.1f}').format(class_name, class_name,
                                                      thresh),
                      fontsize=14)
    # 删除这三行
    #     plt.axis('off')
    #     plt.tight_layout()
    #     plt.draw()
    
    def demo(net, image_name):
        """Detect object classes in an image using pre-computed object proposals."""
    
        # Load the demo image
        im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
        im = cv2.imread(im_file)
    
        # Detect all object classes and regress object bounds
        timer = Timer()
        timer.tic()
        scores, boxes = im_detect(net, im)
        timer.toc()
        print ('Detection took {:.3f}s for '
               '{:d} object proposals').format(timer.total_time, boxes.shape[0])
    
        # Visualize detections for each class
        CONF_THRESH = 0.8
        NMS_THRESH = 0.3
        # 将vis_detections 函数中for 循环之前的3行代码移动到这里
        im = im[:, :, (2, 1, 0)]
        fig,ax = plt.subplots(figsize=(12, 12))
        ax.imshow(im, aspect='equal')
        for cls_ind, cls in enumerate(CLASSES[1:]):
            cls_ind += 1 # because we skipped background
            cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
            cls_scores = scores[:, cls_ind]
            dets = np.hstack((cls_boxes,
                              cls_scores[:, np.newaxis])).astype(np.float32)
            keep = nms(dets, NMS_THRESH)
            dets = dets[keep, :]
            #将ax做为参数传入vis_detections
            vis_detections(im, cls, dets, ax,thresh=CONF_THRESH)
        # 将vis_detections 函数中for 循环之后的3行代码移动到这里
        plt.axis('off')
        plt.tight_layout()
        plt.draw()
    
    def parse_args():
        """Parse input arguments."""
        parser = argparse.ArgumentParser(description='Faster R-CNN demo')
        parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
                            default=0, type=int)
        parser.add_argument('--cpu', dest='cpu_mode',
                            help='Use CPU mode (overrides --gpu)',
                            action='store_true')
        parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
                            choices=NETS.keys(), default='vgg16')
    
        args = parser.parse_args()
    
        return args
    
    if __name__ == '__main__':
        cfg.TEST.HAS_RPN = True  # Use RPN for proposals
    
        args = parse_args()
    
        prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
                                'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')
        caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
                                  NETS[args.demo_net][1])
    
        if not os.path.isfile(caffemodel):
            raise IOError(('{:s} not found.\nDid you run ./data/script/'
                           'fetch_faster_rcnn_models.sh?').format(caffemodel))
    
        if args.cpu_mode:
            caffe.set_mode_cpu()
        else:
            caffe.set_mode_gpu()
            caffe.set_device(args.gpu_id)
            cfg.GPU_ID = args.gpu_id
        net = caffe.Net(prototxt, caffemodel, caffe.TEST)
    
        print '\n\nLoaded network {:s}'.format(caffemodel)
    
        # Warmup on a dummy image
        im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
        for i in xrange(2):
            _, _= im_detect(net, im)
    
        im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
                    '001763.jpg', '004545.jpg']
        for im_name in im_names:
            print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
            print 'Demo for data/demo/{}'.format(im_name)
            demo(net, im_name)
    
        plt.show()

![这里写图片描述][SouthEast]

[SouthEast]: /images/20220618/0c553df217d14861944f28a698320bad.png