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demo.py
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demo.py
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#!/usr/bin/env python
# --------------------------------------------------------
# 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, cfg_from_file
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
from quad.sort_points import sort_points
CLASSES = ('__background__',
'text')
NETS = {'vgg16': ('VGG16',
'VGG16_faster_rcnn_final.caffemodel'),
'zf': ('ZF',
'ZF_faster_rcnn_final.caffemodel')}
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 vis_quads(im, class_name, dets):
"""Visual debugging of detections."""
import matplotlib.pyplot as plt
quads = dets[:, :8]
for pts in quads:
# im = cv2.polylines(im, pts, True, (0, 255, 0), 3)
cv2.line(im, (pts[0], pts[1]), (pts[2], pts[3]), (0, 255, 0), 3)
cv2.line(im, (pts[2], pts[3]), (pts[4], pts[5]), (0, 255, 0), 3)
cv2.line(im, (pts[4], pts[5]), (pts[6], pts[7]), (0, 255, 0), 3)
cv2.line(im, (pts[6], pts[7]), (pts[0], pts[1]), (0, 255, 0), 3)
im = im[:, :, (2, 1, 0)]
plt.cla()
plt.imshow(im)
plt.show()
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()
# Visualize detections for each class
if boxes.shape[1] == 5:
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
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)
else:
CONF_THRESH = 0.5
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
inds = np.where(scores[:, cls_ind] >= CONF_THRESH)[0]
cls_scores = scores[inds, cls_ind]
cls_boxes = boxes[inds, cls_ind * 8:(cls_ind + 1) * 8]
cls_boxes = sort_points(cls_boxes)
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
print ('Detection took {:.3f}s for '
'{:d} text regions').format(timer.total_time, len(keep))
vis_quads(im, cls, cls_dets)
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=None)
parser.add_argument('--model', dest='model', help='*.caffemodel file',
default=None)
args = parser.parse_args()
return args
if __name__ == '__main__':
cfg.TEST.HAS_RPN = True # Use RPN for proposals
args = parse_args()
#
if args.demo_net is None:
prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0], 'faster_rcnn_end2end', 'test.prototxt')
cfg_file = None
else:
prototxt = os.path.join('./models', args.demo_net, 'test.pt')
cfg_file = os.path.join('./models', args.demo_net, 'config.yml')
if cfg_file is not None:
cfg_from_file(cfg_file)
if args.model is None:
caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models', NETS[args.demo_net][1])
else:
caffemodel = args.model
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 = ['img_10.jpg', 'img_14.jpg', 'img_45.jpg']
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for data/demo/{}'.format(im_name)
demo(net, im_name)
plt.show()