forked from GriffinLiang/vrd-dsr
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demo.py
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demo.py
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#from __future__ import absolute_import
import os
import os.path as osp
import sys
import time
import cPickle
import argparse
import torch
import torch.nn as nn
import torch.nn.init
import cv2
import numpy as np
import sys
import os.path as osp
from lib.nets.Vrd_Model import Vrd_Model
import lib.network as network
from lib.data_layers.vrd_data_layer import VrdDataLayer
from lib.model import test_pre_net, test_rel_net
from lib.blob import prep_im_for_blob
from easydict import EasyDict
class vrd_module():
def __init__(self):
self.args = EasyDict()
self.args.dataset = 'vrd'
self.args.use_so = True
self.args.use_obj = True
self.args.no_obj_prior = True
self.args.loc_type = 0
self.args.num_relations = 70
self.args.num_classes = 100 # add background
with open('data/vrd/so_prior.pkl', 'rb') as fid:
self.so_prior = cPickle.load(fid)
with open('data/vrd/obj.txt') as f:
self.vrd_classes = [ x.strip() for x in f.readlines() ]
with open('data/vrd/rel.txt') as f:
self.vrd_rels = [ x.strip() for x in f.readlines() ]
# Model
self.net = Vrd_Model(self.args)
self.net.cuda()
self.net.eval()
model_path = 'models/epoch_4_checkpoint.pth.tar'
if osp.isfile(model_path):
print("=> loading model '{}'".format(model_path))
checkpoint = torch.load(model_path)
self.net.load_state_dict(checkpoint['state_dict'])
else:
print "=> no model found at '{}'".format(model_path)
def relation_im(self, im_path, res):
boxes_img = res['box']
pred_cls_img = np.array(res['cls'])
pred_confs = np.array(res['confs'])
time1 = time.time()
im = cv2.imread(im_path)
ih = im.shape[0]
iw = im.shape[1]
PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])
image_blob, im_scale = prep_im_for_blob(im, PIXEL_MEANS)
blob = np.zeros((1,)+image_blob.shape, dtype=np.float32)
blob[0] = image_blob
# Reshape net's input blobs
boxes = np.zeros((boxes_img.shape[0], 5))
boxes[:, 1:5] = boxes_img * im_scale
classes = pred_cls_img
ix1 = []
ix2 = []
n_rel_inst = len(pred_cls_img)*(len(pred_cls_img)-1)
rel_boxes = np.zeros((n_rel_inst, 5))
SpatialFea = np.zeros((n_rel_inst, 8))
rel_so_prior = np.zeros((n_rel_inst, 70))
i_rel_inst = 0
for s_idx in range(len(pred_cls_img)):
for o_idx in range(len(pred_cls_img)):
if(s_idx == o_idx):
continue
ix1.append(s_idx)
ix2.append(o_idx)
sBBox = boxes_img[s_idx]
oBBox = boxes_img[o_idx]
rBBox = self.getUnionBBox(sBBox, oBBox, ih, iw)
rel_boxes[i_rel_inst, 1:5] = np.array(rBBox) * im_scale
SpatialFea[i_rel_inst] = self.getRelativeLoc(sBBox, oBBox)
rel_so_prior[i_rel_inst] = self.so_prior[classes[s_idx], classes[o_idx]]
i_rel_inst += 1
boxes = boxes.astype(np.float32, copy=False)
classes = classes.astype(np.float32, copy=False)
ix1 = np.array(ix1)
ix2 = np.array(ix2)
obj_score, rel_score = self.net(blob, boxes, rel_boxes, SpatialFea, classes, ix1, ix2, self.args)
rel_prob = rel_score.data.cpu().numpy()
rel_prob += np.log(0.5*(rel_so_prior+1.0/self.args.num_relations))
rlp_labels_im = np.zeros((rel_prob.shape[0]*rel_prob.shape[1], 5), dtype = np.int)
tuple_confs_im = []
n_idx = 0
for tuple_idx in range(rel_prob.shape[0]):
sub = ix1[tuple_idx]
obj = ix2[tuple_idx]
for rel in range(rel_prob.shape[1]):
conf = rel_prob[tuple_idx, rel]
rlp_labels_im[n_idx] = [classes[sub], sub, rel, classes[obj], obj]
tuple_confs_im.append(conf)
n_idx += 1
tuple_confs_im = np.array(tuple_confs_im)
idx_order = tuple_confs_im.argsort()[::-1][:20]
rlp_labels_im = rlp_labels_im[idx_order,:]
tuple_confs_im = tuple_confs_im[idx_order]
vrd_res = []
for tuple_idx in range(rlp_labels_im.shape[0]):
label_tuple = rlp_labels_im[tuple_idx]
sub_cls = self.vrd_classes[label_tuple[0]]
obj_cls = self.vrd_classes[label_tuple[3]]
rel_cls = self.vrd_rels[label_tuple[2]]
vrd_res.append(('%s%d-%s-%s%d'%(sub_cls, label_tuple[1], rel_cls, obj_cls, label_tuple[4]), tuple_confs_im[tuple_idx]))
print vrd_res
time2 = time.time()
print "TEST Time:%s" % (time.strftime('%H:%M:%S', time.gmtime(int(time2 - time1))))
return vrd_res
def getUnionBBox(self, aBB, bBB, ih, iw, margin = 10):
return [max(0, min(aBB[0], bBB[0]) - margin), \
max(0, min(aBB[1], bBB[1]) - margin), \
min(iw, max(aBB[2], bBB[2]) + margin), \
min(ih, max(aBB[3], bBB[3]) + margin)]
def getRelativeLoc(self, aBB, bBB):
sx1, sy1, sx2, sy2 = aBB.astype(np.float32)
ox1, oy1, ox2, oy2 = bBB.astype(np.float32)
sw, sh, ow, oh = sx2-sx1, sy2-sy1, ox2-ox1, oy2-oy1
xy = np.array([(sx1-ox1)/ow, (sy1-oy1)/oh, (ox1-sx1)/sw, (oy1-sy1)/sh])
wh = np.log(np.array([sw/ow, sh/oh, ow/sw, oh/sh]))
return np.hstack((xy, wh))
def pre_demo():
vrdet = vrd_module()
with open('data/vrd/test.pkl', 'rb') as fid:
anno = cPickle.load(fid)
with open('data/vrd/proposal.pkl', 'rb') as fid:
proposals = cPickle.load(fid)
anno_img = anno[0]
im_path = 'img/3845770407_1a8cd41230_b.jpg'
print im_path
res = {}
res['box'] = proposals['boxes'][0]
res['cls'] = proposals['cls'][0]
res['confs'] = proposals['confs'][0]
print vrdet.relation_im(im_path, res)
def vrd_demo():
from detector import detector
im_path = 'img/3845770407_1a8cd41230_b.jpg'
det = detector()
vrdet = vrd_module()
det_res = det.det_im(im_path)
vrd_res = vrdet.relation_im(im_path, det_res)
print vrd_res
if __name__ == '__main__':
vrd_demo()
#from IPython import embed; embed()