def main():
"""
change the parameter in sp_label20 , sp_label19 , a and b . You can show the seg tracking from
previous frame you want.
"""
mat = scipy.io.loadmat(
"/Users/zhufucai/Documents/GitHub/TSP_matlab/results/sp_labels_banana.mat"
)
# image no. 100 ; mat no. 99 : because mat start at 0 n image start at 1 !!!!
sp_labels2 = mat['sp_labels'][:, :, 94]
sp_labels2 = np.array(sp_labels2, dtype=np.int32)
# sp_labels1 = mat['sp_labels'][:,:,18]
sp_labels1 = mat['sp_labels'][:, :, 81]
a = Joe_segmentation('./frame/0008s2.jpg', './svmfile.pkl')
sp20 = np.array(tranpose(sp_labels2), dtype=np.int32)
sp19 = np.array(tranpose(sp_labels1), dtype=np.int32)
[result, list_seg19, list_seg19_proba] = a.segmentation_tsp(sp19)
result19 = np.array(result, dtype=np.uint8)
seg = tracking(list_seg19, sp_labels1, sp_labels2)
contours_seg = slic.contours(seg, sp20)
b = Joe_segmentation('./frame/00095.jpg', './svmfile.pkl')
[result, list_seg20, list_seg20_proba] = b.segmentation_tsp(sp20)
result20 = np.array(result, dtype=np.uint8)
# sp20 = np.array(tranpose(sp_labels2),dtype = np.int32)
contours20 = slic.contours(result20, sp20)
# print np.unique(sp19)
# print "\n"
# print list_seg19_proba
# print "\n"
# print list_seg20_proba
# combine labels
combination_result = label_combination(np.unique(sp19), list_seg19_proba,
list_seg20_proba, sp20)
contours_combination = slic.contours(combination_result, sp20)
cv2.imshow("current_image", b.original_image)
cv2.imshow("result_track", seg)
cv2.imshow("result_seg", result20)
cv2.imshow("combination_result", combination_result)
cv2.waitKey(0)
def color_point(img, index):
temp = np.ones([img.shape[0], img.shape[1]], dtype=np.uint16)
for i in range(500):
temp[index[i][0], index[i][1]] = 255
out = cv2.merge((temp, temp, temp))
out = np.array(out, dtype=np.uint8)
result = slic.contours(out, seg.slic_result)
return result
def show_label(self, pyr):
'''
Update map when click
Given QPixmap self.query
'''
try:
self.npimg
except: # self.npimg not exist
qimage = self.query.toImage()
imgarr = rgb_view(qimage)
self.npimg = imresize(imgarr, (self.h, self.w), interp='bicubic')
contours = slic.contours(self.npimg, self.label0, 10)
draw0 = contours[:, :, :-1]
self.drawMask(draw0, self.label0, self.mergeSegs[0], 0)
if pyr is not 0:
contours = slic.contours(self.npimg, self.auxlabel, 10)
draw = contours[:, :, :-1]
self.drawMask(draw, self.auxlabel, self.mergeSegs[pyr], pyr)
# show reference label
draw = self.npimg.copy()
xx = np.tile(np.asarray(range(self.w)), (self.h, 1))
yy = np.tile(np.asarray(range(self.h)), (self.w, 1)).transpose()
idx_x = abs(xx - self.label[0].cx) < 5
idx_y = abs(yy -self.label[0].cy) < 5
for i in range(3):
draw[:, :, i][idx_x & idx_y] = 255
qimage = array2qimage(draw)
qpixmap = QPixmap.fromImage(qimage)
self.reflabel.setPixmap(qpixmap.scaled(self.reflabel.size(), Qt.KeepAspectRatio))
def test_tsp(sp_labels):
output_loc = "./segmentation_result/"
a = Joe_segmentation('./frame/00020.jpg', './svmfile.pkl')
result = np.array(a.segmentation_tsp(sp_labels), dtype=np.uint8)
# contours = slic.contours(result, a.slic_result)
sp = np.array(tranpose(sp_labels), dtype=np.int32)
contours = slic.contours(result, sp)
cv2.imshow("original", a.original_image)
cv2.imshow("result", contours)
cv2.waitKey(0)
def generate_semantic_segmantation_result():
output_loc = "./segmentation_result/"
try:
os.mkdir(output_loc)
except OSError:
pass
range(100)
for i in range(100):
print i
a = Joe_segmentation('./frame/%#05d.jpg' % (i + 1), './svmfile.pkl')
result = np.array(a.segmentation(), dtype=np.uint8)
region_labels = a.slic_result
contours = slic.contours(result, region_labels)
cv2.imwrite(output_loc + "/%#05d.jpg" % (i + 1), contours)
def color_point(self, img, index):
'''
color_point(img,index):
img: the canvas you want to draw the points on
index: index (x,y) of centroids of superpixels
'''
temp = np.ones([img.shape[0], img.shape[1]], dtype=np.uint16)
for i in range(self.number_regions):
temp[int(index[i][0]), int(index[i][1])] = 255
out = cv2.merge((temp, temp, temp))
out = np.array(out, dtype=np.uint8)
result = slic.contours(out, self.slic_result)
return result
def test_something_wrong():
# c = svm_training('./feature/outfile_sk.npy', './feature/outfile_fl.npy', './feature/outfile_ot.npy')
a = Joe_segmentation('./frame/00020.jpg', './svmfile.pkl')
result = np.array(a.segmentation(), dtype=np.uint8)
print result
# slic labels dtype will cuase buffer mismatch if it's changed into other type i.e. uint8 rather than original uint8_t(according to .dtype = int32)
region_labels = a.slic_result
im = np.array(a.original_image, dtype=np.uint16)
sum_temp = np.add(result, im)
im[np.where(sum_temp > 255)] = 255
im = np.array(im, dtype=np.uint8)
contours = slic.contours(result, region_labels, 1)
cv2.imshow("result", contours)
cv2.waitKey(0)
return True
def generate_tsp_seg_result():
output_loc = "./segmentation_result/"
try:
os.mkdir(output_loc)
except OSError:
pass
range(100)
for i in range(100):
print i
a = Joe_segmentation('./frame/%#05d.jpg'%(i+1), './svmfile.pkl')
mat = scipy.io.loadmat("/Users/zhufucai/Documents/GitHub/TSP_matlab/results/sp_labels_banana.mat")
sp_labels = np.array(mat['sp_labels'][:,:,i], dtype = np.int32)
print sp_labels.shape
result = np.array(a.segmentation_tsp(sp_labels),dtype=np.uint8)
# fix the problem of mat & C++ dont share the same indexing way
sp = np.array(tranpose(sp_labels,sp_labels.shape[0],sp_labels.shape[1]),dtype = np.int32)
contours = slic.contours(result, sp)
cv2.imwrite(output_loc + "/%#05d.jpg"%(i+1), contours)
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()
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()
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()
