#!/usr/bin/env python # -------------------------------------------------------- # Tensorflow Faster R-CNN # Licensed under The MIT License [see LICENSE for details] # Written by Xinlei Chen, based on code from Ross Girshick # -------------------------------------------------------- """ Demo script showing detections in sample images. See README.md for installation instructions before running. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import _init_paths from model.config import cfg from model.test import im_detect from model.nms_wrapper import nms from utils.timer import Timer import matplotlib.pyplot as plt import numpy as np import os, cv2 import argparse from nets.vgg16 import vgg16 from nets.resnet_v1 import resnetv1 import torch 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_faster_rcnn_iter_%d.pth',),'res101': ('res101_faster_rcnn_iter_%d.pth',)} DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)} def vis_detections(im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return 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=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') ax.set_title(('{} detections with ' 'p({} | box) >= {:.1f}').format(class_name, class_name, thresh), fontsize=14) 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 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(torch.from_numpy(dets), NMS_THRESH) dets = dets[keep.numpy(), :] vis_detections(im, cls, dets, thresh=CONF_THRESH) def parse_args(): """Parse input arguments.""" parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo') parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]', choices=NETS.keys(), default='res101') parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]', choices=DATASETS.keys(), default='pascal_voc_0712') args = parser.parse_args() return args if __name__ == '__main__': cfg.TEST.HAS_RPN = True # Use RPN for proposals args = parse_args() # model path demonet = args.demo_net dataset = args.dataset saved_model = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0] %(70000 if dataset == 'pascal_voc' else 110000)) if not os.path.isfile(saved_model): raise IOError(('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(saved_model)) # load network if demonet == 'vgg16': net = vgg16() elif demonet == 'res101': net = resnetv1(num_layers=101) else: raise NotImplementedError net.create_architecture(21, tag='default', anchor_scales=[8, 16, 32]) net.load_state_dict(torch.load(saved_model)) net.eval() net.cuda() print('Loaded network {:s}'.format(saved_model)) 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()