In [1]:
RCNN_DIR = '/opt/py-faster-rcnn/'
import os
os.environ["PYTHONPATH"] = RCNN_DIR + 'caffe-fast-rcnn/python'
!sudo unlink /usr/local/cuda
!sudo ln -s /usr/local/cuda-7.5 /usr/local/cuda
In [2]:
import os.path as osp
import sys
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
caffe_path = osp.join(RCNN_DIR, 'caffe-fast-rcnn', 'python')
add_path(caffe_path)
lib_path = osp.join(RCNN_DIR, 'lib')
add_path(lib_path)
In [3]:
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
%matplotlib inline
In [4]:
NET = 'vgg16' # Options: vgg16, zf
GPU = 0 # Set to GPU ID
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')}
In [5]:
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()
In [6]:
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(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections(im, cls, dets, thresh=CONF_THRESH)
In [7]:
if __name__ == '__main__':
cfg.TEST.HAS_RPN = True # Use RPN for proposals
prototxt = os.path.join(cfg.MODELS_DIR, NETS[NET][0],
'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')
caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
NETS[NET][1])
if not os.path.isfile(caffemodel):
raise IOError(('{:s} not found.\nDid you run ./data/scripts/'
'fetch_faster_rcnn_models.sh?').format(caffemodel))
caffe.set_mode_gpu()
caffe.set_device(GPU)
cfg.GPU_ID = GPU
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)
# List of image names to run
im_names = ['x5.jpg', 'multiple.jpg', 'multiple2.jpg', 'rear1.jpg', 'angles.jpg']
# Run demo script for each image
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for {}'.format(im_name)
demo(net, im_name)
plt.show()
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