def preprocessing(self, imgs, msks, sz=(48, 48), augx=0):
print len(imgs)
if augx > 0:
print "augmenting train data ..."
# augx = 2xnSample+1
n_sample = np.int(augx / 2.0) - 1
imH, imW = imgs[0].shape[0:2]
borderH = np.int(imH * 0.2)
borderW = np.int(imW * 0.2)
w = imW - borderW
h = imH - borderH
x1s = np.random.randint(0, borderW, n_sample)
y1s = np.random.randint(0, borderH, n_sample)
imgs_crop = imgs
msks_crop = msks
for img, msk in zip(imgs, msks):
imgs_crop += [imcrop(img, [x1, y1, w, h]) for x1, y1 in zip(x1s, y1s)]
msks_crop += [imcrop(msk, [x1, y1, w, h]) for x1, y1 in zip(x1s, y1s)]
print len(imgs_crop)
imgs_flip = [pl.fliplr(im) for im in imgs_crop]
msks_flip = [pl.fliplr(im) for im in msks_crop]
imgs = imgs_crop + imgs_flip
msks = msks_crop + msks_flip
print len(imgs)
imgs_rs = [imresize(im, sz, interp="bicubic") for im in imgs]
imgs_norm = [imnormalize(im) for im in imgs_rs]
msks_norm = [imresize(im, sz, interp="bicubic") for im in msks]
imgs_final, msks_final = self.convert_data(imgs_norm, msks_norm)
print len(imgs_final)
return imgs_final, msks_final
def _load_images_salmaps(self, datapath=None, imgext="bmp"):
""" load preliminary data (images, segmentations, and salience maps) """
if datapath is None:
if sys.platform == "darwin":
homedir = "/Users/rzhao/"
else:
homedir = "/home/rzhao/"
datapath = homedir + "Dropbox/ongoing/reid_jrnl/salgt/data_viper/"
filepath = datapath + "query/"
imgfiles = sorted(glob(filepath + "*." + imgext))
self.nPerson = len(imgfiles)
imgs = [imread(im) for im in imgfiles]
salfilepath = datapath + "labels.pkl"
data = loadfile(salfilepath)
segmsks, salmsks = data
imgs = [imresize(im, size=(self.imH, self.imW)) for im in imgs]
segmsks = [imresize(im, size=(self.imH, self.imW)) for im in segmsks]
salmsks = [imresize(im, size=(self.imH, self.imW)) / 255.0 for im in salmsks]
self.imgs = np.asarray(imgs)
self.segmsks = np.asarray(segmsks)
self.salmsks = np.asarray(salmsks)
# load dense colorsift features
labeled_imidx_path = "../data_viper/labeled_imidx.mat"
tmp = loadfile(labeled_imidx_path)
labeled_imidx = tmp["labeled_imidx"].flatten()
feat_path = homedir + "Dropbox/ongoing/reid_jrnl/salgt/data_viper/features.mat"
tmp = loadfile(feat_path)
self.feats = tmp["features"].astype(np.float)[labeled_imidx]
def _load_images_salmaps(self, datapath=None, imgext="bmp"):
""" load preliminary data (images, segmentations, and salience maps) """
if datapath is None:
if sys.platform == "darwin":
homedir = "/Users/rzhao/"
else:
homedir = "/home/rzhao/"
datapath = homedir + "Dropbox/ongoing/reid_jrnl/salgt/data_viper/"
filepath = datapath + "query/"
imgfiles = sorted(glob(filepath + "*." + imgext))
imgs = [imread(im) for im in imgfiles]
salfilepath = datapath + "labels.pkl"
data = loadfile(salfilepath)
segmsks, salmsks = data
imgs = [imresize(im, size=(segmsks[0].shape), interp="bicubic") for im in imgs]
# imgs_norm = [imnormalize(im) for im in imgs]
# return imgs, segmsks, salmsks
self.imgs = imgs
self.segmsks = segmsks
self.salmsks = salmsks
def main():
''' pipeline for testing and evaluation '''
# preset parameters
save_path = '../data_viper/model_feat/'
# load data
# imL = 48
bs = 100
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
# load model
modelfile_viper = '../data_viper/model_feat/model.pkl'
model = loadfile(modelfile_viper)
# evaluation and testing
# test_x = viper.test_x.get_value(borrow=True)
test_x = np.asarray(viper.test_feat)
test_y = viper.test_y
n_test = test_x.shape[0]
test_ypred = model.predict(viper.test_feat)
test_ypred = np.asarray(test_ypred).flatten()
# test_ims = test_x.reshape((n_test, imL, imL, -1))
# assign predicted scores to images
h, w = viper.imgs[0].shape[:2]
mh, mw = len(np.unique(viper.yy)), len(np.unique(viper.xx))
msk0 = np.zeros(mh*mw).astype(np.uint8)
msks = [msk0.copy() for im in viper.imgs]
showlist = []
for i in range(n_test):
imgid = viper.test_imgids[i]
patid = viper.test_ctrids[i]
score = test_ypred[i]
msks[imgid][patid] = score*255
# resize predicted salience map to match image size
msks_rs = [imresize(msk.reshape((mw, mh)).T, size=(h, w))/255. for msk in msks]
# save salience map for comparison
test_imids = np.asarray(np.unique(viper.test_imgids))
salmap_gt = np.asarray(viper.salmsks) #np.asarray([viper.salmsks[imid] for imid in test_imids])
salmap_pred = np.asarray(msks_rs) #np.asarray([msks_rs[imid]/255. for imid in test_imids])
savefile(save_path+'salmaps_comparison.pkl', [test_imids, salmap_gt, salmap_pred])
# quantize to show different test patches
# kmeans = KMeans(init='k-means++', n_clusters=10, n_init=10)
# kmeans.fit(test_ypred.reshape(n_test, 1))
# # save to result folder
# for i in range(10):
# idx = kmeans.labels_== i
# if any(idx):
# im = immontage(list(test_ims[idx]))
# imsave(save_path+'{}.jpg'.format(kmeans.cluster_centers_[i]), im)
print 'testing finished'
def test_knn():
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
viper = downsample_data(viper)
# from sklearn.neighbors import KNeighborsRegressor
# model = KNeighborsRegressor(n_neighbors=5, weights='uniform', metric='euclidean')
# model.fit(viper.train_feat, viper.train_y)
from sklearn.neighbors import KDTree
# divide into stripes
nStripe = 10
y_max = viper.yy.max()
y_min = viper.yy.min()
y_len = np.int((y_max - y_min)/10.)
y_centers = np.round(np.linspace(y_min+y_len, y_max-y_len, nStripe))
k = 5
y_ctr = y_centers[k]
stripe_idx = np.where((viper.yy[viper.train_ctrids] >= y_ctr-y_len) & (viper.yy[viper.train_ctrids] < y_ctr+y_len))[0]
model = KDTree(viper.train_feat[stripe_idx, :288], metric='euclidean')
train_patset = viper.get_patchset('train')
test_patset = viper.get_patchset('test')
test_ids = np.where((viper.yy[viper.test_ctrids] >= y_ctr-y_len) & (viper.yy[viper.test_ctrids] < y_ctr+y_len))[0]
np.random.shuffle(test_ids)
for i in test_ids:
get_testrect = lambda i: [viper.xx[viper.test_ctrids[i]] - viper.patL/2,
viper.yy[viper.test_ctrids[i]] - viper.patL/2,
viper.patL, viper.patL]
get_trainrect = lambda i: [viper.xx[viper.train_ctrids[i]] - viper.patL/2,
viper.yy[viper.train_ctrids[i]] - viper.patL/2,
viper.patL, viper.patL]
gray2color = lambda grayim: np.dstack((grayim, grayim, grayim))
imlist = []
patlist = []
maplist = []
patlist.append(imresize(test_patset[i], size=(100, 100)))
imlist.append(drawrect(viper.imgs[viper.test_imgids[i]], get_testrect(i)))
maplist.append(viper.salmsks[viper.test_imgids[i]])
dist, ind = model.query(viper.test_feat[i, :288], k=30, return_distance=True)
print viper.test_y[i]
hist = np.histogram(viper.train_y[stripe_idx[ind[0]]])
print hist[0]
print hist[1]
print dist
for id in stripe_idx[ind[0]]:
patlist.append(imresize(train_patset[id], size=(100, 100)))
imlist.append(drawrect(viper.imgs[viper.train_imgids[id]], get_trainrect(id)))
maplist.append(viper.salmsks[viper.train_imgids[id]])
pats = immontage(patlist)
imgs = immontage(imlist)
maps = immontage(maplist)
imsave('tmp1.jpg', pats)
imsave('tmp2.jpg', imgs)
imsave('tmp3.jpg', maps)
raw_input()
os.system('xdg-open tmp1.jpg')
def print_labeling(data_path = None):
# if data_path is None:
# newDialog = QDialog()
# fpath = QFileDialog.getExistingDirectory(newDialog, "Select data directory", '../')
# if len(fpath) == 0:
# QMessageBox.warning(None, 'Warning!', 'Nothing loaded.')
# return
# data_path = str(fpath) + '/' # loaded path
src_file = data_path + 'parts.pkl'
usr_file = sorted(glob(data_path + '#*.pkl'))
src = DataMan(src_file)
srcdata = src.load()
usrhits = []
for f in usr_file:
tmp = DataMan(f)
tmpdata = tmp.load()
usrhits.append(tmpdata['scores'])
save_path = data_path + 'result/'
qfiles = sorted(glob(data_path + 'query/*'))
im = imread(qfiles[0])
imsz = im.shape[0:2]
msk0 = np.zeros(srcdata['labels'][0].shape)
segmsks = []
salmsks = []
for i in range(len(qfiles)):
im = imread(qfiles[i])
seg = msk0.copy()
sal = msk0.copy()
for k in usrhits[0][i].keys():
idx = srcdata['labels'][i] == k
nhits = np.asarray([nhit[i][k] for nhit in usrhits])
sal[idx] = hit2score(nhits)
seg[idx] = k
salmsks.append(sal)
segmsks.append(seg)
# normalize all msk
# scaler = MinMaxScaler()
# salscores = scaler.fit_transform(np.asarray(salmsks))
# save label and salience score map
savefile([segmsks, salmsks], data_path + 'labels.pkl')
for i in range(len(qfiles)):
im = imread(qfiles[i])
msk = salmsks[i]*255.
im_rs = imresize(im, msk0.shape, interp='bicubic')
pl.figure(1)
pl.clf()
pl.subplot(1, 2, 1)
pl.imshow(im_rs)
pl.subplot(1, 2, 2)
pl.imshow(color.rgb2grey(im_rs), cmap='gray', alpha=0.6)
pl.imshow(msk, cmap='hot', vmin=0, vmax=255, alpha=0.6)
pl.savefig(save_path+'{0:03d}.jpg'.format(i))
print save_path +'{0:03d}.jpg'.format(i) + ' saved!'
visualize_imfolder(save_path)