def show_local_descr(model_dir, im_fls, train_dirs, cls):
vh = calc2.vh
vw = calc2.vw
im_fls = im_fls.split(',')
assert len(im_fls) == 3
train_fls = []
for i in range(len(train_dirs)):
for f in listdir(train_dirs[i]):
train_fls.append(path.join(train_dirs[i], f))
from sklearn.decomposition import KernelPCA as PCA
import matplotlib.patches as mpatches
N = 2
train_ims = np.empty((len(train_fls), vh, vw, 3), dtype=np.float32)
for i in range(len(train_fls)):
train_ims[i] = cv2.cvtColor(
cv2.resize(cv2.imread(train_fls[i]),
(vw, vh)), cv2.COLOR_BGR2RGB) / 255.
ims = np.empty((3, vh, vw, 3), dtype=np.float32)
for i in range(len(im_fls)):
ims[i] = cv2.cvtColor(cv2.resize(cv2.imread(im_fls[i]),
(vw, vh)), cv2.COLOR_BGR2RGB) / 255.
with tf.compat.v1.Session() as sess:
calc = utils.CALC2(model_dir, sess)
d_train = calc.run(train_ims).reshape(
(len(train_fls), vh // 16 * vw // 16,
4 * (1 + len(calc_classes.keys()))))
# Each class has 4 local descriptors
didx = 4 * (1 + calc_classes[cls[0]])
d_cls_train = d_train[:, :,
didx:didx + 4].reshape(4 * len(train_fls), -1)
didx2 = 4 * (1 + calc_classes[cls[1]])
d_cls2_train = d_train[:, :, didx2:didx2 + 4].reshape(
4 * len(train_fls), -1)
d_app_train = d_train[:, :, :4].reshape(4 * len(train_fls), -1)
d = calc.run(ims).reshape(
(3, vh // 16 * vw // 16, 4 * (1 + len(calc_classes.keys()))))
# Now just take the first local descriptor
d_cls = d[:, :, didx:didx + 1].reshape(3, -1)
d_cls2 = d[:, :, didx2:didx2 + 1].reshape(3, -1)
d_app = d[:, :, :1].reshape(3, -1)
pca = PCA(N)
pca.fit(d_cls_train)
dcc1 = pca.transform(d_cls) # calculate the principal components
dcc1 = dcc1 / np.linalg.norm(dcc1, axis=-1)[..., np.newaxis]
pca.fit(d_cls2_train)
dcc2 = pca.transform(d_cls2) # calculate the principal components
dcc2 = dcc2 / np.linalg.norm(dcc2, axis=-1)[..., np.newaxis]
pca.fit(d_app_train)
dac = pca.transform(d_app) # calculate the principal components
dac = dac / np.linalg.norm(dac, axis=-1)[..., np.newaxis]
minx = -1.1 #min(np.min(dac[:,0]), np.min(dcc1[:,0]))-.1
maxx = 1.1 # max(np.max(dac[:,0]), np.max(dcc1[:,0]))+.1
miny = -1.1 #min(np.min(dac[:,1]), np.min(dcc1[:,1]))-.1
maxy = 1.1 #max(np.max(dac[:,1]), np.max(dcc1[:,1]))+.1
x = np.zeros_like(dac[:, 0])
rcParams['font.sans-serif'] = 'DejaVu Sans'
rcParams['font.size'] = 10
rcParams['patch.linewidth'] = .5
rcParams['figure.figsize'] = [8.0, 3.0]
rcParams['figure.subplot.bottom'] = 0.2
rcParams['savefig.dpi'] = 200.0
rcParams['figure.dpi'] = 200.0
fig = plt.figure()
ax = fig.add_subplot(131, aspect='equal')
ax.quiver(x,
x,
dcc1[:, 0],
dcc1[:, 1],
color=['b', 'g', 'r'],
scale=1,
units='xy',
width=.02)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim([minx, maxx])
ax.set_ylim([miny, maxy])
plt.title(cls[0])
ax = fig.add_subplot(132, aspect='equal')
ax.quiver(x,
x,
dcc2[:, 0],
dcc2[:, 1],
color=['b', 'g', 'r'],
scale=1,
units='xy',
width=.02)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim([minx, maxx])
ax.set_ylim([miny, maxy])
plt.title(cls[1])
ax = fig.add_subplot(133, aspect='equal')
ax.quiver(x,
x,
dac[:, 0],
dac[:, 1],
color=['b', 'g', 'r'],
scale=1,
units='xy',
width=.02)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim([minx, maxx])
ax.set_ylim([miny, maxy])
plt.title('appearance')
l1 = mpatches.Patch(color='b', label='database')
l2 = mpatches.Patch(color='g', label='positive')
l3 = mpatches.Patch(color='r', label='negative')
h = plt.legend(handles=[l1, l2, l3])
h.get_frame().set_alpha(0.0) # transluscent legend :D
h.set_draggable(True)
plt.show()
from matplotlib.patches import Patch
from calc2 import vh, vw, vss
import time
import traceback
import layers
from dataset.coco_classes import calc_classes, calc_class_names
gtdata = None
preddata = None
imdata = None
recdata = None
N_CLASSES = len(calc_classes.keys())
class CALC2(object):
def __init__(self, model_dir, sess,
use_mu=False, ret_c_centers=False, ret_c5=False,
checkpoint=None):
assert not (ret_c_centers and ret_c5)
self.sess = sess
self.ret_c5 = ret_c5
self.ret_c_centers = ret_c_centers
self.images = tf.placeholder(tf.float32, [None, vh, vw, 3])
ret = vss(self.images, False, True,
ret_mu=use_mu, ret_c_centers=ret_c_centers,
ret_c5=ret_c5)
if ret_c5:
self.descriptor = ret[0]
def show_local_descr(model_dir, im_fls, cls):
vh = calc2.vh
vw = calc2.vw
im_fls = im_fls.split(',')
assert len(im_fls) == 3
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import skcuda.linalg as linalg
from skcuda.linalg import PCA as cuPCA
import skcuda.misc as cumisc
import matplotlib.patches as mpatches
N = 2
ims = np.empty((3, vh, vw, 3), dtype=np.float32)
for i in range(len(im_fls)):
ims[i] = cv2.cvtColor(cv2.resize(cv2.imread(im_fls[i]),
(vw, vh)), cv2.COLOR_BGR2RGB) / 255.
with tf.Session() as sess:
calc = utils.CALC2(model_dir, sess)
d = calc.run(ims).reshape(
(3, vh // 16 * vw // 16, 4 * (1 + len(calc_classes.keys()))))
didx = 4 * (1 + calc_classes[cls[0]])
d_cls = d[:, :, didx:didx + 4].reshape(3, -1)
didx2 = 4 * (1 + calc_classes[cls[1]])
d_cls2 = d[:, :, didx2:didx2 + 4].reshape(3, -1)
d_app = d[:, :, :4].reshape(3, -1)
pca = cuPCA(N)
dcls_gpu = gpuarray.GPUArray(d_cls.shape, np.float32, order="F")
dcls_gpu.set(d_cls) # copy data to gpu
dcc1 = pca.fit_transform(
dcls_gpu).get() # calculate the principal components
dcc1 = dcc1 / np.linalg.norm(dcc1, axis=-1)[..., np.newaxis]
dcls2_gpu = gpuarray.GPUArray(d_cls.shape, np.float32, order="F")
dcls2_gpu.set(d_cls2) # copy data to gpu
dcc2 = pca.fit_transform(
dcls2_gpu).get() # calculate the principal components
dcc2 = dcc2 / np.linalg.norm(dcc2, axis=-1)[..., np.newaxis]
dapp_gpu = gpuarray.GPUArray(d_cls.shape, np.float32, order="F")
dapp_gpu.set(d_app) # copy data to gpu
dac = pca.fit_transform(
dapp_gpu).get() # calculate the principal components
dac = dac / np.linalg.norm(dac, axis=-1)[..., np.newaxis]
minx = -1.1 #min(np.min(dac[:,0]), np.min(dcc1[:,0]))-.1
maxx = 1.1 # max(np.max(dac[:,0]), np.max(dcc1[:,0]))+.1
miny = -1.1 #min(np.min(dac[:,1]), np.min(dcc1[:,1]))-.1
maxy = 1.1 #max(np.max(dac[:,1]), np.max(dcc1[:,1]))+.1
'''
minz = min(np.min(dac[:,2]), np.min(dcc[:,2]))
maxz = max(np.max(dac[:,2]), np.max(dcc[:,2]))
'''
x = np.zeros_like(dac[:, 0])
rcParams['font.sans-serif'] = 'DejaVu Sans'
rcParams['font.size'] = 10
rcParams['patch.linewidth'] = .5
rcParams['figure.figsize'] = [8.0, 3.0]
rcParams['figure.subplot.bottom'] = 0.2
rcParams['savefig.dpi'] = 200.0
rcParams['figure.dpi'] = 200.0
fig = plt.figure()
ax = fig.add_subplot(131, aspect='equal')
ax.quiver(x,
x,
dcc1[:, 0],
dcc1[:, 1],
color=['b', 'g', 'r'],
scale=1,
units='xy',
width=.02)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim([minx, maxx])
ax.set_ylim([miny, maxy])
plt.title(cls[0])
ax = fig.add_subplot(132, aspect='equal')
ax.quiver(x,
x,
dcc2[:, 0],
dcc2[:, 1],
color=['b', 'g', 'r'],
scale=1,
units='xy',
width=.02)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim([minx, maxx])
ax.set_ylim([miny, maxy])
plt.title(cls[1])
ax = fig.add_subplot(133, aspect='equal')
ax.quiver(x,
x,
dac[:, 0],
dac[:, 1],
color=['b', 'g', 'r'],
scale=1,
units='xy',
width=.02)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim([minx, maxx])
ax.set_ylim([miny, maxy])
plt.title('appearance')
l1 = mpatches.Patch(color='b', label='database')
l2 = mpatches.Patch(color='g', label='positive')
l3 = mpatches.Patch(color='r', label='negative')
h = plt.legend(handles=[l1, l2, l3])
h.get_frame().set_alpha(0.0) # transluscent legend :D
h.set_draggable(True)
plt.show()