forked from CharlesShang/TFFRCNN
/
demo.py
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demo.py
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import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import os, sys, cv2
import argparse
import os.path as osp
import glob
this_dir = osp.dirname(__file__)
print(this_dir)
from lib.networks.factory import get_network
from lib.fast_rcnn.config import cfg
from lib.fast_rcnn.test import im_detect
from lib.fast_rcnn.nms_wrapper import nms
from lib.utils.timer import Timer
CLASSES = ('__background__',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
# CLASSES = ('__background__','person','bike','motorbike','car','bus')
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
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(sess, net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im = cv2.imread(image_name)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, 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
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
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, ax, thresh=CONF_THRESH)
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]',
default='VGGnet_test')
parser.add_argument('--model', dest='model', help='Model path',
default=' ')
args = parser.parse_args()
return args
if __name__ == '__main__':
cfg.TEST.HAS_RPN = True # Use RPN for proposals
args = parse_args()
if args.model == ' ' or not os.path.exists(args.model):
print ('current path is ' + os.path.abspath(__file__))
raise IOError(('Error: Model not found.\n'))
# init session
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# load network
net = get_network(args.demo_net)
# load model
print ('Loading network {:s}... '.format(args.demo_net)),
saver = tf.train.Saver()
saver.restore(sess, args.model)
print (' done.')
# Warmup on a dummy image
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in xrange(2):
_, _ = im_detect(sess, net, im)
im_names = glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.png')) + \
glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.jpg'))
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for {:s}'.format(im_name)
demo(sess, net, im_name)
plt.show()