caffe fastercbnnahdemo

https://download.csdn.net/download/zefan7564/10148990

https://blog.csdn.net/qq_37124237/article/details/81087505

目标检测 Faster R-CNN运行及实时性DEMO测试

2018年07月17日 20:01:59 qq_37124237 阅读数：202

 

1. #!/usr/bin/env python
2.  
3. # --------------------------------------------------------
4. # Faster R-CNN
5. # Copyright (c) 2015 Microsoft
6. # Licensed under The MIT License [see LICENSE for details]
7. # Written by Ross Girshick
8. # --------------------------------------------------------
9.  
10. """
11. Demo script showing detections in sample images.
12.  
13. See README.md for installation instructions before running.
14. """
15.  
16. import _init_paths
17. from fast_rcnn.config import cfg
18. from fast_rcnn.test import im_detect
19. from fast_rcnn.nms_wrapper import nms
20. from utils.timer import Timer
21. import matplotlib.pyplot as plt
22. import numpy as np
23. import scipy.io as sio
24. import caffe, os, sys, cv2
25. import argparse
26.  
27. CLASSES = ('__background__',
28. 'ship')
29.  
30. NETS = {'vgg16': ('VGG16',
31. 'VGG16_faster_rcnn_final.caffemodel'),
32. 'zf': ('ZF',
33. 'ZF_faster_rcnn_final.caffemodel'),
34. 'wyx': ('wyx','vgg_cnn_m_1024_faster_rcnn_iter_1000.caffemodel')}
35.  
36.  
37. def vis_detections(im, class_name, dets, thresh=0.5):
38. """Draw detected bounding boxes."""
39. inds = np.where(dets[:, -1] >= thresh)[0]
40. if len(inds) == 0:
41. return
42.  
43. im = im[:, :, (2, 1, 0)]
44. fig, ax = plt.subplots(figsize=(12, 12))
45. ax.imshow(im, aspect='equal')
46. for i in inds:
47. bbox = dets[i, :4]
48. score = dets[i, -1]
49.  
50. ax.add_patch(
51. plt.Rectangle((bbox[0], bbox[1]),
52. bbox[2] - bbox[0],
53. bbox[3] - bbox[1], fill=False,
54. edgecolor='red', linewidth=3.5)
55. )
56. ax.text(bbox[0], bbox[1] - 2,
57. '{:s} {:.3f}'.format(class_name, score),
58. bbox=dict(facecolor='blue', alpha=0.5),
59. fontsize=14, color='white')
60.  
61. ax.set_title(('{} detections with '
62. 'p({} | box) >= {:.1f}').format(class_name, class_name,
63. thresh),
64. fontsize=14)
65. plt.axis('off')
66. plt.tight_layout()
67. plt.draw()
68.  
69.  
70. def vis_detections_video(im, class_name, dets, thresh=0.5):
71. """Draw detected bounding boxes."""
72. global lastColor,frameRate
73. inds = np.where(dets[:, -1] >= thresh)[0]
74. if len(inds) == 0:
75. return im
76.  
77. for i in inds:
78. bbox = dets[i, :4]
79. score = dets[i, -1]
80. cv2.rectangle(im,(bbox[0],bbox[1]),(bbox[2],bbox[3]),(0,0,255),2)
81. cv2.rectangle(im,(int(bbox[0]),int(bbox[1]-20)),(int(bbox[0]+200),int(bbox[1])),(10,10,10),-1)
82. cv2.putText(im,'{:s} {:.3f}'.format(class_name, score),(int(bbox[0]),int(bbox[1]-2)),cv2.FONT_HERSHEY_SIMPLEX,.75,(255,255,255))#,cv2.CV_AA)
83.  
84. return im
85.  
86.  
87.  
88. def demo(net, im):
89. """Detect object classes in an image using pre-computed object proposals."""
90. global frameRate
91. # Load the demo image
92. #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
93. #im = cv2.imread(im_file)
94.  
95. # Detect all object classes and regress object bounds
96. timer = Timer()
97. timer.tic()
98. scores, boxes = im_detect(net, im)
99. timer.toc()
100. print ('Detection took {:.3f}s for '
101. '{:d} object proposals').format(timer.total_time, boxes.shape[0])
102. frameRate = 1.0/timer.total_time
103. print "fps: " + str(frameRate)
104. # Visualize detections for each class
105. CONF_THRESH = 0.8
106. NMS_THRESH = 0.3
107. for cls_ind, cls in enumerate(CLASSES[1:]):
108. cls_ind += 1 # because we skipped background
109. cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
110. cls_scores = scores[:, cls_ind]
111. dets = np.hstack((cls_boxes,
112. cls_scores[:, np.newaxis])).astype(np.float32)
113. keep = nms(dets, NMS_THRESH)
114. dets = dets[keep, :]
115. vis_detections_video(im, cls, dets, thresh=CONF_THRESH)
116. cv2.putText(im,'{:s} {:.2f}'.format("FPS:", frameRate),(1750,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255))
117. cv2.imshow(videoFilePath.split('/')[len(videoFilePath.split('/'))-1],im)
118. cv2.waitKey(20)
119.  
120.  
121. def parse_args():
122. """Parse input arguments."""
123. parser = argparse.ArgumentParser(description='Faster R-CNN demo')
124. parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
125. default=0, type=int)
126. parser.add_argument('--cpu', dest='cpu_mode',
127. help='Use CPU mode (overrides --gpu)',
128. action='store_true')
129. parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
130. choices=NETS.keys(), default='vgg16')
131.  
132. args = parser.parse_args()
133.  
134. return args
135.  
136.  
137.  
138.  
139. if __name__ == '__main__':
140. cfg.TEST.HAS_RPN = True # Use RPN for proposals
141.  
142. args = parse_args()
143.  
144. # prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
145. # 'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')
146. prototxt = '/home/yexin/py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_end2end/test.prototxt'
147. # print 'see prototxt path{}'.format(prototxt)
148.  
149.  
150. # caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
151. # NETS[args.demo_net][1])
152. caffemodel = '/home/yexin/py-faster-rcnn/output/faster_rcnn_end2end/voc_2007_trainval/vgg_cnn_m_1024_faster_rcnn_iter_100.caffemodel'
153.  
154.  
155. # print '\n\nok'
156.  
157. if not os.path.isfile(caffemodel):
158. raise IOError(('{:s} not found.\nDid you run ./data/script/'
159. 'fetch_faster_rcnn_models.sh?').format(caffemodel))
160. print '\n\nok'
161.  
162. if args.cpu_mode:
163. caffe.set_mode_cpu()
164. else:
165. caffe.set_mode_gpu()
166. caffe.set_device(args.gpu_id)
167. cfg.GPU_ID = args.gpu_id
168. net = caffe.Net(prototxt, caffemodel, caffe.TEST)
169.  
170. print '\n\nLoaded network {:s}'.format(caffemodel)
171.  
172. # Warmup on a dummy image
173. im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
174. for i in xrange(2):
175. _, _= im_detect(net, im)
176.  
177. videoFilePath = '/home/yexin/py-faster-rcnn/data/demo/test_1-3.mp4'
178. videoCapture = cv2.VideoCapture(videoFilePath)
179. #success, im = videoCapture.read()
180. while True :
181. success, im = videoCapture.read()
182. demo(net, im)
183. if cv2.waitKey(10) & 0xFF == ord('q'):
184. break
185. videoCapture.release()
186. cv2.destroyAllWindows()
187.