def slot_slic(self, pyr):
'''
perform SLIC superpixel segmentation
Input:
QPixmap self.query
int32 number of superpixel
Output:
ndarray self.npimg - input image
ndarray self.nplabel - seg label
ndarray self.npdraw - draw map
QPixmap self.qdraw - a draw map with segmentation boundary
'''
#w = self.image.width()
#h = self.image.height()
# QPixmap to QImage
qimage = self.query.toImage()
# QImage to numpy
imgarr = rgb_view(qimage)
#imgarr = qimage2numpy(qimage)
self.npimg = imresize(imgarr, (self.label[pyr].size().height(), self.label[pyr].size().width()), interp='bicubic')
if pyr == 0:
self.label0 = slic.slic_n(self.npimg, 300, 10)
#contours = slic.contours(self.npimg, self.label0, 10)
else:
self.auxlabel = slic.slic_n(self.npimg, 50, 10)
def __init__(self, image, input_clf, number_regions=500, consistency=5): """def __init__(self, image, number_regions=500, input_clf):""" self.original_image = cv2.imread(image) self.hsvimage = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2HSV) self.number_regions = number_regions self.clf = joblib.load(input_clf) self.label_mask = np.array(slic.slic_n(self.original_image, number_regions, consistency),dtype=np.uint8) pass
def slot_load(self):
newDialog = QDialog()
fpath = QFileDialog.getExistingDirectory(newDialog, "Select data directory", '../')
if len(fpath) == 0:
QMessageBox.warning(None, 'Warning!', 'Nothing loaded.')
return
self.data_path = str(fpath) + '/' # loaded path
self.qfiles = sorted(glob.glob(self.data_path+'query/*'))
self.npyr = len(self.label)
self.mergeSegs = [[] for i in range(self.npyr)]
# load label data
self.save_path = self.data_path + 'parts.pkl'
print self.qfiles
if os.path.isfile(self.save_path):
# labeled data exists
f = open(self.save_path, 'rb')
self.data = cPickle.load(f)
f.close()
self.index = self.data['index']
# initialize labeled data {label0, auxlabel}
self.label0 = self.data['labels'][self.index]
self.query = QPixmap(self.qfiles[self.index])
qimage = self.query.toImage()
imgarr = rgb_view(qimage)
self.npimg = imresize(imgarr, (self.h, self.w), interp='bicubic')
self.auxlabel = slic.slic_n(self.npimg, 50, 10)
else:
# no previous annotation available
self.data = {}
self.data['index'] = self.index
self.data['labels'] = [np.zeros((self.h, self.w), dtype=np.int32) for i in range(len(self.qfiles))]
self.data['scores'] = [[] for i in range(len(self.qfiles))]
self.data['identity'] = map(lambda x: os.path.basename(x)[0:os.path.basename(x).find('_')], self.qfiles)
self.data['flags'] = np.zeros(len(self.qfiles))
self.data['sflags'] = [[] for i in range(len(self.qfiles))]
self.index = 0
self.query = QPixmap(self.qfiles[self.index])
# compute the initial label map given self.query
for i in range(self.npyr):
self.slot_slic(i)
for i in range(self.npyr):
self.show_label(i)
def load_etrims(radius, size, is08, shuffle, name, n_superpixels, compactness, t_index=None):
# ----- make up -----
isInit = t_index is None
# ----- path initialize -----
root_path = '../Dataset/etrims-db_v1/'
an_name = 'annotations/'
im_name = 'images/'
et_name = '08_etrims-ds/' if is08 else '04_etrims-ds/'
an_path = root_path + an_name + et_name
im_path = root_path + im_name + et_name
dir_list = os.listdir(an_path)
# ----- train index -----
train_index = []
DATA_SIZE = size # max=60
TRAIN_SIZE = 2 * size / 3 # max=40
if isInit:
train_index = random.sample(range(DATA_SIZE), TRAIN_SIZE) if shuffle else range(TRAIN_SIZE)
else:
train_index = t_index
# ----- test set and train set -----
train_set = []
test_set = []
test_or_train = [test_set, train_set]
for i in xrange(DATA_SIZE):
# open annotation.png and image.jpg
dis = dir_list[i]
file_name = dis.split(".")[0]
annot_path = an_path + file_name + ".png"
annotation = Image.open(annot_path)
image_path = im_path + file_name + ".jpg"
image = Image.open(image_path)
spixel = sc.slic_n(np.array(image), n_superpixels, compactness)
# get index and set picture
index = i in train_index
picture = Pic(image, annotation, spixel)
test_or_train[index].append(picture)
# print filename
if isInit:
print_time("eTRIMS: %s" % file_name, name)
# ----- finish -----
if isInit:
print_parameter(train_index, name)
print_time("eTRIMS: train=%d test=%d" % (len(train_set), len(test_set)), name)
return train_set, test_set
def __call__(self, im):
if self.check_cache(im) is False:
arrIn = np.zeros(( im.getSize()[0], im.getSize()[1], 3 ))
if im.getTypeAsString()=="RGB":
image_slices = sp.Image()
sp.splitChannels(im, image_slices)
for i in range(3):
arrtmp = image_slices.getSlice(i)
arrIn[:, :, i] = arrtmp.getNumArray()
else:
for i in range(3):
arrIn[:, :, i] = im.getNumArray()
region_labels = slic.slic_n(np.uint8(arrIn), self._params["nb_regions"], self._params["m"])
imout = sp.Image(region_labels.shape[0], region_labels.shape[1])
arrOut = imout.getNumArray()
for i in range(region_labels.shape[0]):
for j in range(region_labels.shape[1]):
arrOut[i, j] = region_labels[i, j]
##
copie16 = sp.Image(imout, "UINT16")
sp.copy(imout, copie16)
self.update_cache(im, copie16)
return self._cache_seg
import numpy as np
import PIL.Image
import matplotlib.pyplot as plt
import slic
im = np.array(PIL.Image.open("grass.jpg"))
region_labels = slic.slic_n(im, 1000, 10)
print type(region_labels)
contours = slic.contours(im, region_labels, 10)
plt.imshow(contours[:, :, :-1].copy())
plt.show()
# im8bit = (d[objects[ind]]*mask_erode)
# im8bit = im8bit / np.ceil(im8bit.max()/256.0)
im8bit = deepcopy(posMat)
for i in range(3):
im8bit[:, :,
i][im8bit[:, :, i] != 0] -= im8bit[:, :,
i][im8bit[:, :,
i] != 0].min()
im8bit[:, :, i] /= im8bit[:, :, i].max() / 256
im8bit = np.array(im8bit, dtype=uint8)
# im8bit = im8bit[:,:,2]
im4d = np.dstack([mask_erode, im8bit])
# im4d = np.dstack([mask_erode, im8bit, im8bit, im8bit])
# im4d = np.dstack([mask_erode, dists2Tot, dists2Tot, dists2Tot])
# im4d = np.dstack([mask_erode, im8bit, dists2Tot, mask_erode])
regions = slic.slic_n(np.array(im4d, dtype=uint8), 50, 10) #2
# regions = slic.slic_s(np.array(im4d, dtype=uint8), 550,3)
regions *= mask_erode
imshow(regions)
avgColor = np.zeros([regions.shape[0], regions.shape[1], 3])
# avgColor = np.zeros([regions.shape[0],regions.shape[1],4])
regionCount = regions.max()
regionLabels = [[0]]
goodRegions = 0
bodyMean = np.array([
posMat[mask_erode, 0].mean(), posMat[mask_erode, 1].mean(),
posMat[mask_erode, 2].mean()
])
for i in range(1, regionCount + 2):
# dists2Tot[dists2Tot>1000] = 1000 # im8bit = (d[objects[ind]]*mask_erode) # im8bit = im8bit / np.ceil(im8bit.max()/256.0) im8bit = deepcopy(posMat) for i in range(3): im8bit[:,:,i][im8bit[:,:,i]!=0] -= im8bit[:,:,i][im8bit[:,:,i]!=0].min() im8bit[:,:,i] /= im8bit[:,:,i].max()/256 im8bit = np.array(im8bit, dtype=uint8) # im8bit = im8bit[:,:,2] im4d = np.dstack([mask_erode, im8bit]) # im4d = np.dstack([mask_erode, im8bit, im8bit, im8bit]) # im4d = np.dstack([mask_erode, dists2Tot, dists2Tot, dists2Tot]) # im4d = np.dstack([mask_erode, im8bit, dists2Tot, mask_erode]) regions = slic.slic_n(np.array(im4d, dtype=uint8), 50,10)#2 # regions = slic.slic_s(np.array(im4d, dtype=uint8), 550,3) regions *= mask_erode imshow(regions) avgColor = np.zeros([regions.shape[0],regions.shape[1],3]) # avgColor = np.zeros([regions.shape[0],regions.shape[1],4]) regionCount = regions.max() regionLabels = [[0]] goodRegions = 0 bodyMean = np.array([posMat[mask_erode,0].mean(),posMat[mask_erode,1].mean(),posMat[mask_erode,2].mean()]) for i in range(1, regionCount+2): if np.sum(np.equal(regions,i)) < 50: regions[regions==i] = 0 else:
import numpy as np
import Image
import matplotlib.pyplot as plt
import slic
im = np.array(Image.open("grass.jpg"))
image_argb = np.dstack([im[:, :, :1], im]).copy("C")
region_labels = slic.slic_n(image_argb, 1000, 10)
slic.contours(image_argb, region_labels, 10)
plt.imshow(image_argb[:, :, 1:].copy())
plt.show()
def getSuperPixels_SLIC(image, nbSegments, compactness):
return slic.slic_n(image, nbSegments, compactness)
import numpy as np
import slic
from sklearn.svm import SVC
from sklearn.externals import joblib
original = cv2.imread('./banana.jpg')
o_im = cv2.cvtColor(original, cv2.COLOR_BGR2HSV)
# testing
number_regions = 500
region_labels = slic.slic_n(original, 500, 5)
label_mask = np.array(region_labels.copy(),dtype=np.uint8)
clf = joblib.load("banana_clf.pkl")
def label_color(label, number_regions, shape):
out = np.ones(shape,dtype=np.uint8)
for i in range(number_regions):
newval = np.random.randint(256, size=3)
out[label==i] = newval
return out
segment_result = label_color(region_labels,number_regions,original.shape )
# region_labels = slic.slic_n(image_argb, 100, 0) # slic.contours(image_argb, region_labels, 1) # plt.imshow(image_argb[:, :, 0]) # regions = slic.slic_n(np.array(np.dstack([im,im[:,:,2]]), dtype=uint8), 50,10) dists2Tot[dists2Tot>1000] = 1000 im8bit = (d[objects[ind]]*mask_erode) im8bit = im8bit / np.ceil(im8bit.max()/256.0) im4d = np.dstack([mask_erode, im8bit, im8bit, im8bit]) # im4d = np.dstack([mask_erode, dists2Tot, dists2Tot, dists2Tot]) # im4d = np.dstack([mask_erode, im8bit, dists2Tot, mask_erode]) regions = slic.slic_n(np.array(im4d, dtype=uint8), 100,5) # regions = slic.slic_s(np.array(im4d, dtype=uint8), 300,2) regions *= mask_erode imshow(regions) avgColor = np.zeros([regions.shape[0],regions.shape[1],3]) # avgColor = np.zeros([regions.shape[0],regions.shape[1],4]) regionCount = regions.max() regionLabels = [] for i in range(regionCount): if 1: #if using x/y/z meanPos = posMat[regions==i,:].mean(0) if 0: # If using distance map meanPos = np.array([posMat[regions==i,:].mean(0)[0], posMat[regions==i,:].mean(0)[1],
def getSuperPixels_SLIC(image, nbSegments, compactness):
return slic.slic_n(image, nbSegments, compactness)
def setupUi(self, MainWindow):
self.w = 270 #self.label[0].size().width()
self.h = 720 #self.label[0].size().height()
MainWindow.setObjectName(_fromUtf8("MainWindow"))
MainWindow.resize(3*self.w +180, self.h+90)
self.centralwidget = QtGui.QWidget(MainWindow)
self.centralwidget.setObjectName(_fromUtf8("centralwidget"))
#self.label = CLabel(self.centralwidget)
#self.label.setGeometry(QtCore.QRect(180, 30, 270, 720))
#self.label.setObjectName(_fromUtf8("label"))
self.widget = QtGui.QWidget(self.centralwidget)
self.widget.setGeometry(QtCore.QRect(20, 70, 110, 611))
self.widget.setObjectName(_fromUtf8("widget"))
self.verticalLayout = QtGui.QVBoxLayout(self.widget)
# self.verticalLayout.setMargin(0)
self.verticalLayout.setObjectName(_fromUtf8("verticalLayout"))
self.pushButton = []
for i in range(7):
button = QPushButton(self.widget)
button.setObjectName('pushButton_{}'.format(i))
self.pushButton.append(button)
self.verticalLayout.addWidget(self.pushButton[-1])
self.widget = QtGui.QWidget(self.centralwidget)
self.widget.move(150, 20)
self.widget.setObjectName(_fromUtf8("widget"))
self.horizontalLayout = QtGui.QHBoxLayout(self.widget)
# self.horizontalLayout.setMargin(0)
self.horizontalLayout.setObjectName(_fromUtf8("horizontalLayout"))
self.label = []
for i in range(2):
label = CLabel(self.widget)
label.setObjectName(_fromUtf8("label_{}".format(i)))
self.label.append(label)
self.label[-1].setGeometry(QRect(150, 20, self.w, self.h))
self.horizontalLayout.addWidget(self.label[-1])
self.reflabel = QLabel(self.widget)
self.reflabel.setObjectName('label_{}'.format(i+1))
self.reflabel.setGeometry(QRect(150, 20, self.w, self.h))
self.horizontalLayout.addWidget(self.reflabel)
self.readme = QPlainTextEdit(self.centralwidget)
self.readme.setReadOnly(True)
self.readme.setGeometry(QRect(150, self.h+40, 3*self.w, 25))
self.readme.setPlainText("Important: before clicking 'Save Parts' button, please select all body part!")
MainWindow.setCentralWidget(self.centralwidget)
self.statusbar = QtGui.QStatusBar(MainWindow)
self.statusbar.setObjectName(_fromUtf8("statusbar"))
MainWindow.setStatusBar(self.statusbar)
self.retranslateUi(MainWindow)
QObject.connect(self.pushButton[2], QtCore.SIGNAL(_fromUtf8("clicked()")), self.slot_previous)
QObject.connect(self.pushButton[3], QtCore.SIGNAL(_fromUtf8("clicked()")), self.slot_next)
QObject.connect(self.pushButton[1], QtCore.SIGNAL(_fromUtf8("clicked()")), self.slot_merge)
QObject.connect(self.pushButton[5], QtCore.SIGNAL(_fromUtf8("clicked()")), self.slot_save)
QObject.connect(self.pushButton[4], QtCore.SIGNAL(_fromUtf8("clicked()")), self.slot_reset)
QObject.connect(self.pushButton[6], QtCore.SIGNAL(_fromUtf8("clicked()")), self.slot_exit)
QObject.connect(self.pushButton[0], QtCore.SIGNAL(_fromUtf8("clicked()")), self.slot_load)
for i in range(len(self.label)):
QObject.connect(self.label[i], SIGNAL('clicked()'), lambda pyr = i: self.slot_click(pyr))
QtCore.QMetaObject.connectSlotsByName(MainWindow)
# load default files
self.data_path = '../data_cuhk/' # default path
self.qfiles = sorted(glob.glob(self.data_path+'query/*'))
self.npyr = len(self.label)
self.mergeSegs = [[] for i in range(self.npyr)]
# load label data
self.save_path = self.data_path + 'parts.pkl'
if os.path.isfile(self.save_path):
# labeled data exists
f = open(self.save_path, 'rb')
self.data = cPickle.load(f)
f.close()
self.index = self.data['index']
# initialize labeled data {label0, auxlabel}
self.label0 = self.data['labels'][self.index]
self.query = QPixmap(self.qfiles[self.index])
qimage = self.query.toImage()
imgarr = rgb_view(qimage)
self.npimg = imresize(imgarr, (self.h, self.w), interp='bicubic')
self.auxlabel = slic.slic_n(self.npimg, 50, 10)
else:
# no previous annotation available
self.index = 0
self.query = QPixmap(self.qfiles[self.index])
self.data = {}
self.data['index'] = self.index
self.data['labels'] = [np.zeros((self.h, self.w), dtype=np.int32) for i in range(len(self.qfiles))]
self.data['scores'] = [[] for i in range(len(self.qfiles))]
self.data['identity'] = map(lambda x: os.path.basename(x)[0:os.path.basename(x).find('_')], self.qfiles)
self.data['flags'] = np.zeros(len(self.qfiles))
self.data['sflags'] = [[] for i in range(len(self.qfiles))]
# compute the initial label map given self.query
for i in range(self.npyr):
self.slot_slic(i)
for i in range(self.npyr):
self.show_label(i)
import numpy as np
import Image
import matplotlib.pyplot as plt
import slic
im = np.array(Image.open("tennis.png"))
region_labels = slic.slic_n(im, 100, 10)
contours = slic.contours(im, region_labels, 10)
plt.imshow(contours[:, :, :-1].copy())
plt.show()
