# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import numpy as np
try:
import cPickle as pickle
except ImportError:
import pickle
import os
import math
from utils.timer import Timer
from model.nms_wrapper import nms
from utils.blob import im_list_to_blob
from model.config import cfg, get_output_dir
from model.bbox_transform import clip_boxes, bbox_transform_inv
import torch
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_scale_factors.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors)
def _get_rois_blob(im_rois, im_scale_factors):
"""Converts RoIs into network inputs.
Arguments:
im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
im_scale_factors (list): scale factors as returned by _get_image_blob
Returns:
blob (ndarray): R x 5 matrix of RoIs in the image pyramid
"""
rois_blob_real = []
for i in range(len(im_scale_factors)):
rois, levels = _project_im_rois(im_rois, np.array([im_scale_factors[i]]))
rois_blob = np.hstack((levels, rois))
rois_blob_real.append(rois_blob.astype(np.float32, copy=False))
return rois_blob_real
def _project_im_rois(im_rois, scales):
"""Project image RoIs into the image pyramid built by _get_image_blob.
Arguments:
im_rois (ndarray): R x 4 matrix of RoIs in original image coordinates
scales (list): scale factors as returned by _get_image_blob
Returns:
rois (ndarray): R x 4 matrix of projected RoI coordinates
levels (list): image pyramid levels used by each projected RoI
"""
im_rois = im_rois.astype(np.float, copy=False)
if len(scales) > 1:
widths = im_rois[:, 2] - im_rois[:, 0] + 1
heights = im_rois[:, 3] - im_rois[:, 1] + 1
areas = widths * heights
scaled_areas = areas[:, np.newaxis] * (scales[np.newaxis, :] ** 2)
diff_areas = np.abs(scaled_areas - 224 * 224)
levels = diff_areas.argmin(axis=1)[:, np.newaxis]
else:
levels = np.zeros((im_rois.shape[0], 1), dtype=np.int)
rois = im_rois * scales[levels]
return rois, levels
def _get_blobs(im, rois):
"""Convert an image and RoIs within that image into network inputs."""
blobs = {}
blobs['data'], im_scale_factors = _get_image_blob(im)
blobs['boxes'] = _get_rois_blob(rois, im_scale_factors)
return blobs, im_scale_factors
def _clip_boxes(boxes, im_shape):
"""Clip boxes to image boundaries."""
# x1 >= 0
boxes[:, 0::4] = np.maximum(boxes[:, 0::4], 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(boxes[:, 1::4], 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.minimum(boxes[:, 2::4], im_shape[1] - 1)
# y2 < im_shape[0]
boxes[:, 3::4] = np.minimum(boxes[:, 3::4], im_shape[0] - 1)
return boxes
def _rescale_boxes(boxes, inds, scales):
"""Rescale boxes according to image rescaling."""
for i in range(boxes.shape[0]):
boxes[i,:] = boxes[i,:] / scales[int(inds[i])]
return boxes
def im_detect(net, im, boxes):
blobs, im_scales = _get_blobs(im,boxes)
assert len(im_scales) == 1, "Only single-image batch implemented"
im_blob = blobs['data']
blobs['im_info'] = np.array([im_blob.shape[1], im_blob.shape[2], im_scales[0]], dtype=np.float32)
cfg.DEDUP_BOXES=1.0/16.0
for i in range(len(blobs['data'])):
if cfg.DEDUP_BOXES > 0:
v = np.array([1, 1e3, 1e6, 1e9, 1e12])
hashes = np.round(blobs['boxes'][i] * cfg.DEDUP_BOXES).dot(v)
_, index, inv_index = np.unique(hashes, return_index=True,
return_inverse=True)
blobs['boxes'][i] = blobs['boxes'][i][index, :]
boxes_tmp = boxes[index, :].copy()
else:
boxes_tmp = boxes.copy()
# TODO
# change the blobs['im_info'], now is an array
cls_prob, bbox_prob, fuse_prob, image_prob, scores_fast, bbox_pred_fast, rois = net.test_image(blobs['data'][i:i+1,:], blobs['im_info'], blobs['boxes'][i])
'''
WSDDN
'''
scores_tmp = fuse_prob
pred_boxes = np.tile(boxes_tmp, (1, fuse_prob.shape[1]))
'''
Faster rcnn
'''
boxes_fast = rois[:, 1:5] / im_scales[0]
scores_fast = np.reshape(scores_fast, [scores_fast.shape[0], -1])
bbox_pred_fast = np.reshape(bbox_pred_fast, [bbox_pred_fast.shape[0], -1])
if cfg.TEST.BBOX_REG:
# Apply bounding-box regression deltas
box_deltas = bbox_pred_fast
pred_boxes_fast = bbox_transform_inv(torch.from_numpy(boxes_fast), torch.from_numpy(box_deltas)).numpy()
pred_boxes_fast = _clip_boxes(pred_boxes_fast, im.shape)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes_fast, (1, scores_fast.shape[1]))
cfg.TEST.USE_FLIPPED=True
if cfg.TEST.USE_FLIPPED:
blobs['data'][i:i+1] = blobs['data'][i:i+1][:, :, ::-1, :]
width = blobs['data'][i:i+1].shape[2]
oldx1 = blobs['boxes'][i][:, 1].copy()
oldx2 = blobs['boxes'][i][:, 3].copy()
blobs['boxes'][i][:, 1] = width - oldx2 - 1
blobs['boxes'][i][:, 3] = width - oldx1 - 1
assert (blobs['boxes'][i][:, 3] >= blobs['boxes'][i][:, 1]).all()
cls_prob, bbox_prob, fuse_prob, image_prob, _ , _ , _= net.test_image(blobs['data'][i:i+1,:], blobs['im_info'], blobs['boxes'][i])
scores_tmp += fuse_prob
if cfg.DEDUP_BOXES > 0:
# Map scores and predictions back to the original set of boxes
scores_tmp = scores_tmp[inv_index, :]
pred_boxes = pred_boxes[inv_index, :]
if i == 0:
scores = np.copy(scores_tmp)
else:
scores += scores_tmp
scores /= len(blobs['data']) * (1. + cfg.TEST.USE_FLIPPED)
return scores, pred_boxes, scores_fast, pred_boxes_fast
def apply_nms(all_boxes, thresh):
"""Apply non-maximum suppression to all predicted boxes output by the
test_net method.
"""
num_classes = len(all_boxes)
num_images = len(all_boxes[0])
nms_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)]
for cls_ind in range(num_classes):
for im_ind in range(num_images):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
inds = np.where((x2 > x1) & (y2 > y1))[0]
dets = dets[inds,:]
if dets == []:
continue
keep = nms(torch.from_numpy(dets), thresh).numpy()
if len(keep) == 0:
continue
nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
return nms_boxes
def test_train_net(net, imdb, weights_filename, max_per_image=100, thresh=0.):
np.random.seed(cfg.RNG_SEED)
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
all_boxes_fast = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes+1)]
output_dir = get_output_dir(imdb, weights_filename) #voc_2007_test/default(tag)/vgg16_faster_rcnn_iter_15000
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
roidb = imdb.roidb
for i in range(num_images):
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
scores, boxes, scores_fast, boxes_fast = im_detect(net, im, roidb[i]['boxes'])
_t['im_detect'].toc()
_t['misc'].tic()
for j in range(0, imdb.num_classes):
inds = np.argmax(scores[:, j])
all_boxes[j][i] = \
np.hstack((boxes[inds, j*4:(j+1)*4].reshape(1, -1),
np.array([[scores[inds, j]]])))
'''
start of faster part
'''
# skip j = 0, because it's the background class
for j in range(1, imdb.num_classes+1):
inds = np.argmax(scores_fast[:, j])
all_boxes_fast[j][i] = \
np.hstack((boxes_fast[inds, j*4:(j+1)*4].reshape(1, -1),
np.array([[scores_fast[inds, j]]])))
'''
end of faster part
'''
_t['misc'].toc()
print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time(),
_t['misc'].average_time()))
output_dir_ws = output_dir + '/' + 'wsddn'
if not os.path.exists(output_dir_ws):
os.makedirs(output_dir_ws)
det_file = os.path.join(output_dir_ws, 'discovery.pkl')
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_discovery(all_boxes, output_dir_ws)
all_boxes_fast = all_boxes_fast[1:] # filter the background boxes
output_dir_fast = output_dir + '/' + 'faster'
if not os.path.exists(output_dir_fast):
os.makedirs(output_dir_fast)
det_file = os.path.join(output_dir_fast, 'discovery.pkl')
with open(det_file, 'wb') as f:
pickle.dump(all_boxes_fast, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_discovery(all_boxes_fast, output_dir_fast)
