def test_read_img_PIL_pixels(self):
img = r.read_img_PIL(self.path_img1)
for ch in range(3):
for row in range(4):
for col in range(2):
assert_equal(img[ch][row][col], self.img1[row][col][ch])
def test_read_img_PIL_pixels(self):
img = r.read_img_PIL(self.path_img1)
for ch in range(3):
for row in range(4):
for col in range(2):
assert_equal(img[ch][row][col], self.img1[row][col][ch])
def test_read_img_PIL_subtract_mean(self):
m = np.array((1., 2. , 3.))
img = r.read_img_PIL(self.path_img1, mean=m)
for ch in range(3):
for row in range(4):
for col in range(2):
assert_equal(img[ch][row][col], self.img1[row][col][ch] - m[ch])
def test_read_img_PIL_subtract_mean(self):
m = np.array((1., 2., 3.))
img = r.read_img_PIL(self.path_img1, mean=m)
for ch in range(3):
for row in range(4):
for col in range(2):
assert_equal(img[ch][row][col],
self.img1[row][col][ch] - m[ch])
def main(args):
caffe.set_mode_cpu()
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
path_img = '/home/kashefy/data/VOCdevkit/VOC2012/JPEGImagesX/2008_000015.jpg'
bgr_mean = np.array((104.00698793,116.66876762,122.67891434))
im = Image.open(path_img)
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
print in_.shape
print in_
in_ -= bgr_mean
print in_
in_ = in_.transpose((2,0,1))
in_ = read_img_PIL(path_img, mean=bgr_mean)
print 'in_'
print in_[0, 0, 0:6]
print in_[1, 0, 0:6]
print in_[2, 0, 0:6]
in2 = read_img_cv2(path_img, mean=bgr_mean)
print in2.shape
#in2[0, :, :] -= 104.00698793
#in2[1, :, :] -= 116.66876762
#in2[2, :, :] -= 122.67891434
print in2[0, 0, 0:6]
print in2[1, 0, 0:6]
print in2[2, 0, 0:6]
print np.all(in_ == in2)
print in_[in_ != in2]
print in2[in_ != in2]
return 0
# load net
path_model = '/home/kashefy/data/models/fcn_segm/fcn-32s-Pascal-context/deploy.prototxt'
path_weights = '/home/kashefy/data/models/fcn_segm/fcn-32s-Pascal-context/fcn-32s-pascalcontext.caffemodel'
net = caffe.Net(path_model, path_weights, caffe.TEST)
# shape for input (data blob is N x C x H x W), set data
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
caffe.set_mode_cpu() # load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe path_img = '/home/kashefy/data/VOCdevkit/VOC2012/JPEGImagesX/2008_000015.jpg' bgr_mean = np.array((104.00698793,116.66876762,122.67891434)) im = Image.open(path_img) in_ = np.array(im, dtype=np.float32) in_ = in_[:,:,::-1] print in_.shape print in_ in_ -= bgr_mean print in_ in_ = in_.transpose((2,0,1)) in_ = read_img_PIL(path_img, mean=bgr_mean) print 'in_' print in_[0, 0, 0:6] print in_[1, 0, 0:6] print in_[2, 0, 0:6] in2 = read_img_cv2(path_img, mean=bgr_mean) print in2.shape #in2[0, :, :] -= 104.00698793 #in2[1, :, :] -= 116.66876762 #in2[2, :, :] -= 122.67891434 print in2[0, 0, 0:6] print in2[1, 0, 0:6] print in2[2, 0, 0:6]
def test_read_img_PIL_shape(self):
assert_equal(r.read_img_PIL(self.path_img1).shape, (3, 4, 2))
def test_read_img_PIL_shape(self):
assert_equal(r.read_img_PIL(self.path_img1).shape, (3, 4, 2))
def main(args):
caffe.set_mode_cpu()
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
path_img = "/home/tairuichen/Documents/PASCAL-Context/VOC2010/JPEGImages/2007_000027.jpg"
bgr_mean = np.array((104.00698793, 116.66876762, 122.67891434))
im = Image.open(path_img)
in_ = np.array(im, dtype=np.float32)
in_ = in_[:, :, ::-1]
print in_.shape
print in_
in_ -= bgr_mean
print in_
in_ = in_.transpose((2, 0, 1))
in_ = read_img_PIL(path_img, mean=bgr_mean)
print "in_"
print in_[0, 0, 0:6]
print in_[1, 0, 0:6]
print in_[2, 0, 0:6]
in2 = read_img_cv2(path_img, mean=bgr_mean)
print in2.shape
# in2[0, :, :] -= 104.00698793
# in2[1, :, :] -= 116.66876762
# in2[2, :, :] -= 122.67891434
print in2[0, 0, 0:6]
print in2[1, 0, 0:6]
print in2[2, 0, 0:6]
print np.all(in_ == in2)
print in_[in_ != in2]
print in2[in_ != in2]
# return 0
# load net
path_model = "fcn-32s-Pascal-context/fcn-32s-pascal-deploy.prototxt"
path_weights = "fcn-32s-Pascal-context/fcn-32s-pascal.caffemodel"
net = caffe.Net(path_model, path_weights, caffe.TEST)
# shape for input (data blob is N x C x H x W), set data
net.blobs["data"].reshape(1, *in_.shape)
net.blobs["data"].data[...] = in_
# run net and take argmax for prediction
net.forward()
out = net.blobs["score"].data[0].argmax(axis=0)
print "data after fwd"
print net.blobs["data"].data[
net.blobs["data"].data.shape[0] / 2 - 3 : net.blobs["data"].data.shape[0] / 2 + 3,
net.blobs["data"].data.shape[1] / 2 - 3 : net.blobs["data"].data.shape[1] / 2 + 3,
]
print "out"
print out[out.shape[0] / 2 - 3 : out.shape[0] / 2 + 3, out.shape[1] / 2 - 3 : out.shape[1] / 2 + 3]
plt.imshow(out)
plt.show()