def RestTest():
l_2w = ReadFile(data_path + 'train_2w.txt')
l_all = ReadFile(data_path + 'list.txt')
te_out = open(data_path + 'test_rest.txt', 'w')
tr_imgs_path = []
te_imgs_path = []
for ind, line in enumerate(l_2w):
tr_imgs_path.append(line.split(' ')[0].split('/')[-1].split('.')[0])
print str(len(tr_imgs_path))
for l in l_all:
if l + '_128' not in tr_imgs_path:
im_path = img_path + l + '_128.jpg'
p3_path = para_path + l + '_pose.mat'
Euler = sio.loadmat(p3_path)['Euler_Para'][0]
pitch = degrees(Euler[0])
yaw = degrees(Euler[1])
roll = degrees(Euler[2])
roll = roll - align_degree[prefix.index(l)] - disturb_degree[
prefix.index(l)]
te_imgs_path.append(l)
te_out.writelines(im_path + ' ' + str(pitch) + ' ' + str(yaw) +
' ' + str(roll))
te_out.write('\n')
print str(len(te_imgs_path))
te_out.close()
def invFile(filenameIn, filenameOut):
print " -- Checking if we need to invert?",
# Find the amount of translation we need to do!
data = ReadFile(filenameIn)
dataLines = [l for l in data.split("\n") if l.strip() and not l.strip().startswith("#") ]
CommentLines = [l for l in data.split("\n") if l.strip() and l.strip().startswith("#") ]
xmin,xmax = None,None
outData = CommentLines
for l in dataLines:
i,t,x,y,z,X,Y,Z,p = extractXYZLine(l)
xmin = min( [xmin,x,X] ) if xmin != None else min( [x,X] )
xmax = max( [xmax,x,X] ) if xmax != None else max( [x,X] )
if not( xmin<=0 and xmax <=0):
print " No, xmin,max:", xmin,xmax
WriteFile(filenameOut, data)
else:
print " Yes, xmin,max:", xmin,xmax
# Ok, we have no positive x's:
outData = CommentLines
for l in dataLines:
i,t,x,y,z,X,Y,Z,p = extractXYZLine(l)
x = x * -1
X = X * -1
outData.append( "\t".join( [str(g) for g in [i,t,x,y,z,X,Y,Z,p] ] ) )
WriteFile( filenameOut, "\n".join( outData) )
def nrnXYZToSWC(srcFilename, sinkFilename):
datestr = datetime.datetime.now().strftime("%d-%B-%Y")
print " -- Converting to SWC"
# Find the amount of translation we need to do!
data = ReadFile(srcFilename)
dataLines = [l for l in data.split("\n") if l.strip() and not l.strip().startswith("#") ]
CommentLines = [l for l in data.split("\n") if l.strip and l.strip().startswith("#") ]
outData = CommentLines + ["# Converted from xyz to swc using nrnXYZtoSWC.py" + " on: %s" %datestr] #["# Translated by (%d,%d,%d) using nrnTranslate.py"%translation + " On: %s"%datestr] + ["# Corrected for shrinkage (x1.28) using nrnScaled.py" + " On: %s"%datestr] + ["# Files Concatenated by nrnConcatenate.py" + " on: %s"%datestr] + ["# Converted from xyz to swc using nrnXYZtoSWC.py" + " on: %s" %datestr]
for l in dataLines:
i,t,x,y,z,X,Y,Z,p = extractXYZLine(l)
sX = (float(x)+float(X)) / 2.0
sY = (float(y)+float(Y)) / 2.0
sZ = (float(z)+float(Z)) / 2.0
dX = float(x) - float(X)
dY = float(y) - float(Y)
dZ = float(z) - float(Z)
d = math.sqrt( dX**2 + dY**2 + dZ**2 )
r = d / 2.0
r = r if r > 0.15 else 0.15
outData.append( "\t".join( [str(g) for g in [i,t,sX,sY,sZ,r,p] ] ) )
WriteFile( sinkFilename, "\n".join( outData) )
def nrnScale(srcFilename, sinkFilename, scaleFactor):
datestr = datetime.datetime.now().strftime("%d-%B-%Y")
# Find the amount of translation we need to do!
data = ReadFile(srcFilename)
dataLines = [l for l in data.split("\n") if l.strip() and not l.strip().startswith("#") ]
CommentLines = [l for l in data.split("\n") if l.strip() and l.strip().startswith("#") ]
outData = CommentLines + ["# Corrected for shrinkage (x1.28) using nrnScaled.py" + " On: %s"%datestr]
for l in dataLines:
i,t,x,y,z,X,Y,Z,p = extractXYZLine(l)
x *= scaleFactor
y *= scaleFactor
z *= scaleFactor
X *= scaleFactor
Y *= scaleFactor
Z *= scaleFactor
outData.append( "\t".join( [str(g) for g in [i,t,x,y,z,X,Y,Z,p] ] ) )
WriteFile( sinkFilename, "\n".join( outData) )
def nrnTranslate( nsFilenameSrc, nrnFilenameSrc,nsFilenameSink, nrnFilenameSink):
print " -- Finding Translation"
nsData = ReadFile(nsFilenameSrc)
# Find the amount of translation we need to do!
dataLines = [l for l in nsData.split("\n") if l.strip() and not l.strip().startswith("#") ]
tPoint = None
for l in dataLines:
i,t,x,y,z,X,Y,Z,p = extractXYZLine(l)
if p==-1 and t==9:
assert not tPoint
tPoint = (-1.0*x,-1.0*y,-1.0*z)
assert tPoint
print " -- Translating by", tPoint
TranslateFile(nsFilenameSrc, nsFilenameSink, tPoint)
TranslateFile(nrnFilenameSrc, nrnFilenameSink, tPoint)
def CheckInFile(file_name, Checker):
lines = ReadFile(file_name)
comment_lines = Checker.GetCommentLines(lines)
single_line_comments = Checker.GetSingleLineComments(lines)
block_comment_lines = Checker.GetBlockCommentLines(lines)
total_block_comments = Checker.CountBlockComments(lines)
total_TODOs = Checker.CountTODOs(lines)
print("Total # of lines: ", len(lines))
print("Total # of comment lines: ", len(comment_lines))
print("Total # of single line comments: ", len(single_line_comments))
print("Total # of comment lines within block comments: ",
len(block_comment_lines))
print("Total # of block line comments: ", total_block_comments)
print("Total # of TODO's: ", total_TODOs)
def TranslateFile(filenameIn, filenameOut, translation):
datestr = datetime.datetime.now().strftime("%d-%B-%Y")
# Find the amount of translation we need to do!
data = ReadFile(filenameIn)
dataLines = [l for l in data.split("\n") if l.strip() and not l.strip().startswith("#") ]
CommentLines = [l for l in data.split("\n") if l.strip() and l.strip().startswith("#") ]
# copies comment lines and adds comment line showing the processing that's been done plus date.
#outData = CommentLines + ["# Translated by (%d,%d,%d) using nrnTranslate.py"%translation] + ["# On: %s"%datestr]
outData = CommentLines + ["# Translated by (%d,%d,%d) using nrnTranslate.py"%translation + " On: %s"%datestr]
#outData = outData + ["# On: %s"%datestr]
for l in dataLines:
i,t,x,y,z,X,Y,Z,p = extractXYZLine(l)
x = x + translation[0]
y = y + translation[0]
z = z + translation[0]
X = X + translation[0]
Y = Y + translation[0]
Z = Z + translation[0]
outData.append( "\t".join( [str(g) for g in [i,t,x,y,z,X,Y,Z,p] ] ) )
WriteFile( filenameOut, "\n".join( outData) )
def SplitTxt():
lines = ReadFile(data_path + 'test_3w6.txt')
new_ind = np.random.permutation(len(lines)).tolist()
new_lines = []
for ind in new_ind:
new_lines.append(lines[ind])
tr_num = 20000
tr_lines = new_lines[:tr_num]
te_lines = new_lines[tr_num:]
out_tr = open(data_path + 'train_rest_2w.txt', 'w')
out_te = open(data_path + 'test_rest_1w6.txt', 'w')
for tr in tr_lines:
out_tr.writelines(tr)
out_tr.write('\n')
out_tr.close()
for te in te_lines:
out_te.writelines(te)
out_te.write('\n')
out_te.close()
def nrnConcatenateXYZS( srcFilename1, srcFilename2, sinkFilename):
datestr = datetime.datetime.now().strftime("%d-%B-%Y")
print " -- Concatentating Files"
D1 = ReadFile(srcFilename1)
D2 = ReadFile(srcFilename2)
dataLines1 = [l for l in D1.split("\n") if l.strip() and not l.strip().startswith("#") ]
dataLines2 = [l for l in D2.split("\n") if l.strip() and not l.strip().startswith("#") ]
CommentLines1 = [l for l in D1.split("\n") if l.strip() and l.strip().startswith("#") ]
CommentLines2 = [l for l in D2.split("\n") if l.strip() and l.strip().startswith("#") ]
# copies comment lines and adds comment line showing the processing that's been done plus date.
#outData = [ "#Comment From File1: %s"%srcFilename1 ] + CommentLines1 + [ "#Comment From File2: %s"%srcFilename2 ] + CommentLines2 + ["# Files Concatenated by nrnConcatenate.py" + " on: %s"%datestr]
outData = CommentLines1 + CommentLines2 + ["# Files Concatenated by nrnConcatenate.py" + " on: %s"%datestr]
# Remap the id's
# Write Datafile 1
idMap = {}
for l in dataLines1:
i,t,x,y,z,X,Y,Z,p = extractXYZLine(l)
new_id = len(idMap) + 1
new_p = p if p ==-1 else idMap[ (0,p) ]
outData.append( "\t".join( [str(g) for g in [new_id,t,x,y,z,X,Y,Z,new_p] ] ) )
idMap[ (0,i) ] = new_id
#print srcFilename2
# Write Datafile 2
for l in dataLines2:
#print l
#print
i,t,x,y,z,X,Y,Z,p = extractXYZLine(l)
new_id = len(idMap) + 1
#print p
new_p = p if p ==-1 else idMap[(1,p)]
outData.append( "\t".join( [str(g) for g in [new_id,t,x,y,z,X,Y,Z,new_p] ] ) )
idMap[(1,i)] = new_id
WriteFile( sinkFilename, "\n".join( outData) )
def WriteHdf5(setname):
IMAGE_SIZE = (128, 128)
MEAN_VALUE = 0
filename = data_path + setname + '.txt'
lines = ReadFile(filename)
np.random.shuffle(lines)
sample_size = len(lines)
imgs = np.zeros((
sample_size,
3,
) + IMAGE_SIZE, dtype=np.float32)
poses = np.zeros((sample_size, 3), dtype=np.float32)
h5_filename = '{}.h5'.format(setname)
with h5py.File(h5_filename, 'w') as h:
for i, line in enumerate(lines):
image_name, pitch, yaw, roll = line.split()
img = cv2.imread(image_name).astype(np.float32)
img = img.transpose(2, 0, 1)
img = img.reshape((1, ) + img.shape)
img -= MEAN_VALUE
imgs[i] = img
poses[i] = [float(pitch), float(yaw), float(roll)]
if (i + 1) % 1000 == 0:
print('processed {} images!'.format(i + 1))
h.create_dataset('data', data=imgs)
h.create_dataset('pose', data=poses)
print "save h5 file"
with open('{}_h5.txt'.format(setname), 'w') as f:
f.write(h5_filename)
net.blobs['data'].reshape(*(image.shape))
forward_kwargs = {'data': image.astype(np.float32)}
blobs_out = net.forward(**forward_kwargs)
euler = net.blobs['poselayer'].data[0].tolist()
return euler
model_dir = '/Users/momo/Desktop/Euler/Model/'
prototxt = model_dir + 'resSmallV2_noBN_Ridge_bigyawpitch_731.prototxt'
caffemodel = prototxt.replace('prototxt', 'caffemodel')
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
imgSize = 128
train_names = ReadFile('/Users/momo/Desktop/MoGaze/data/train/head/img.txt')
train_names = np.random.permutation(train_names)
'''
#test_names = ReadFile('/Users/momo/Desktop/MoGaze/data/train/head/img.txt')
f = open('./test_head_lo.txt','w')
for name in train_names:
ori_img = cv2.imread(name)
crop_img = ori_img[ori_img.shape[0] // 2 - 200:ori_img.shape[0] // 2 + 200,
ori_img.shape[1] // 2 - 200:ori_img.shape[1] // 2 + 200]
euler = TestAImage(crop_img, net)
euler = np.asarray([euler[1], euler[0]]).astype(np.float32)
f.writelines(name.split('/')[-1].strip('.png') + '\n')
f.writelines("{:.3f}".format(euler[0]))
def Gen_txt():
'''
生成Euler回归txt的list
'''
pre_list = ReadFile(data_path + 'list_96.txt')
tr_num = 36000
te_num = 1000
tr_out = open(data_path + '96_train_%d.txt' % (tr_num), 'w')
te_out = open(data_path + '96_test_%d.txt' % (te_num), 'w')
re_out = open(data_path + '96_test_rest.txt', 'w')
tr_start_ind = np.random.randint(0, len(pre_list) - tr_num - te_num)
new_ind = np.random.permutation(len(pre_list))
tr_ind = new_ind[tr_start_ind:tr_start_ind + tr_num]
te_ind = new_ind[tr_start_ind + tr_num:tr_start_ind + tr_num + te_num]
for ti in tr_ind:
p = pre_list[ti]
im_path = img_path + p + '_128.jpg'
p3_path = para_path + p + '_pose.mat'
Euler = sio.loadmat(p3_path)['Euler_Para'][0]
pitch = degrees(Euler[0])
yaw = degrees(Euler[1])
roll = degrees(Euler[2])
# ***** validation roll *********
# img = cv2.imread(im_path.replace('/input_96/', '/img/').replace('_128.jpg', '.jpg'))
# cv2.imshow("ori_img", img)
#
# small_img_ro = cv2.imread(im_path)
#
# M = cv2.getRotationMatrix2D((img.shape[1] / 2, img.shape[0] / 2), roll, 1)
# big_img_ro = cv2.warpAffine(img, M, (img.shape[0], img.shape[1]))
#
# cv2.putText(small_img_ro, str(roll), (40, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1,
# (200, 20, 200), 1)
#
# cv2.imshow("ori_img_ro", big_img_ro)
# cv2.imshow("small_img_ro", small_img_ro)
# cv2.waitKey()
# *********************************
tr_out.writelines(im_path + ' ' + str(pitch) + ' ' + str(yaw) + ' ' +
str(roll))
tr_out.write('\n')
tr_out.close()
for te in te_ind:
p = pre_list[te]
im_path = img_path + p + '_128.jpg'
p3_path = para_path + p + '_pose.mat'
Euler = sio.loadmat(p3_path)['Euler_Para'][0]
pitch = degrees(Euler[0])
yaw = degrees(Euler[1])
roll = degrees(Euler[2])
te_out.writelines(im_path + ' ' + str(pitch) + ' ' + str(yaw) + ' ' +
str(roll))
te_out.write('\n')
te_out.close()
cou = 0
for s in new_ind:
if s not in tr_ind and s not in te_ind:
p = pre_list[s]
im_path = img_path + p + '_128.jpg'
p3_path = para_path + p + '_pose.mat'
Euler = sio.loadmat(p3_path)['Euler_Para'][0]
pitch = degrees(Euler[0])
yaw = degrees(Euler[1])
roll = degrees(Euler[2])
re_out.writelines(im_path + ' ' + str(pitch) + ' ' + str(yaw) +
' ' + str(roll))
re_out.write('\n')
else:
cou += 1
re_out.close()
print " the rest number of samples : " + str(len(pre_list) - cou)
print " = " + str(len(pre_list) - len(tr_ind) - len(te_ind))
