def _predict(self, img_msg, label_msg):
# convert image
bridge = cv_bridge.CvBridge()
input_image = bridge.imgmsg_to_cv2(img_msg, 'rgb8')
input_label = bridge.imgmsg_to_cv2(label_msg)
# predict
region_imgs = []
for l in np.unique(input_label):
if l == 0: # bg_label
continue
mask = (input_label == l)
region = jsk_recognition_utils.bounding_rect_of_mask(
input_image, mask)
region_imgs.append(region)
y_proba = self.estimator.predict(region_imgs)
target_names = np.array(jsk_apc2015_common.get_object_list())
y_pred = np.argmax(y_proba, axis=-1)
label_proba = [p[i] for p, i in zip(y_proba, y_pred)]
# prepare message
res = ClassificationResult()
res.header = img_msg.header
res.labels = y_pred
res.label_names = target_names[y_pred]
res.label_proba = label_proba
res.probabilities = y_proba.reshape(-1)
res.classifier = '<jsk_2015_05_baxter_apc.ColorHistogramFeatures>'
res.target_names = target_names
self._pub.publish(res)
def _apply_tile(self, img_msg, label_msg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(img_msg)
label_img = bridge.imgmsg_to_cv2(label_msg)
imgs = []
labels = np.unique(label_img)
for label in labels:
if label == 0:
# should be skipped 0, because
# 0 is to label image as black region to mask image
continue
img_tmp = img.copy()
mask = label_img == label
img_tmp[~mask] = 0
img_tmp = bounding_rect_of_mask(img_tmp, mask)
imgs.append(img_tmp)
tile_img = get_tile_image(imgs)
tile_msg = bridge.cv2_to_imgmsg(tile_img, encoding='bgr8')
tile_msg.header = img_msg.header
self.pub_tile.publish(tile_msg)
def _apply_tile(self, img_msg, label_msg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(img_msg)
label_img = bridge.imgmsg_to_cv2(label_msg)
imgs = []
labels = np.unique(label_img)
for label in labels:
if label == 0:
# should be skipped 0, because
# 0 is to label image as black region to mask image
continue
img_tmp = img.copy()
mask = label_img == label
img_tmp[~mask] = 0
img_tmp = bounding_rect_of_mask(img_tmp, mask)
imgs.append(img_tmp)
tile_img = get_tile_image(imgs)
tile_msg = bridge.cv2_to_imgmsg(tile_img, encoding='bgr8')
tile_msg.header = img_msg.header
self.pub_tile.publish(tile_msg)
def main():
model = CAEOnes(n_param=2)
optimizer = O.Adam()
optimizer.setup(model)
S.load_hdf5('bof_data/cae_ones_model.h5', model)
S.load_hdf5('bof_data/cae_ones_optimizer.h5', optimizer)
with gzip.open('bof_data/bof_berkeley.pkl.gz', 'rb') as f:
bof = pickle.load(f)
with gzip.open('bof_data/lgr_merged.pkl.gz', 'rb') as f:
lgr = pickle.load(f)
this_dir = os.path.dirname(os.path.abspath(__file__))
data_home = os.path.abspath(os.path.join(this_dir, '../data'))
train_data_dir = os.path.join(data_home, 'in_hand_recog_{}'.format('train'))
test_data_dir = os.path.join(data_home, 'in_hand_recog_{}'.format('test'))
train_data = load_files(train_data_dir, load_content=False)
test_data = load_files(test_data_dir, load_content=False)
N_train = len(train_data.filenames)
N_test = len(test_data.filenames)
train_imgs = []
for f in train_data.filenames:
if f.endswith('_0.jpg'):
# Skip mask file
continue
img_file = f
mask_file = img_file.split('_1.jpg')[0] + '_0.jpg'
img = cv2.imread(img_file, 0)
mask = cv2.imread(mask_file)
train_imgs.append((img, mask))
test_imgs = []
for f in test_data.filenames:
if f.endswith('_0.jpg'):
# Skip mask file
continue
img_file = f
mask_file = img_file.split('_1.jpg')[0] + '_0.jpg'
img = cv2.imread(img_file, 0)
mask = cv2.imread(mask_file)
test_imgs.append((img, mask))
y_true_0 = 12
# y_proba_true_0 = np.zeros(25, dtype=np.float32)
# y_proba_true_0[12] = 1
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# test
n_batch = 10
initial_params = [8, 4] # size, iterations
epoch = 0
model.train = False
accuracies = []
print('testing')
N = len(test_imgs)
perm = np.random.permutation(len(test_imgs))
for i in xrange(0, N, n_batch):
print('test_batch: ', i)
test_batch = [test_imgs[p_index] for p_index in perm[i:i+n_batch]]
y_true = np.repeat([y_true_0], len(test_batch), axis=0)
# y_proba_true = np.repeat(y_proba_true_0, len(test_batch), axis=0)
x_data = []
for img, mask in test_batch:
mask = resize(mask, (267, 178), preserve_range=True)
x_data.append(apc_od.im_to_blob(mask))
x_data = np.array(x_data, dtype=np.float32)
x = Variable(x_data, volatile='on')
z = model.encode(x)
param_scale = z.data
params = param_scale * initial_params
X = []
for k, param in enumerate(params):
size, iterations = map(int, param)
if size <= 0 or size > 50 or iterations <= 0 or iterations > 50:
rand = 1. * np.ones(2) / param_scale
params = rand * param_scale * initial_params
size, iterations = map(int, params[0])
print('test:', size, iterations)
if size <= 0 or size > 50 or iterations <= 0 or iterations > 50:
size, iterations = initial_params
kernel = np.ones((size, size), dtype=np.uint8)
img, mask = test_batch[k]
closed = cv2.morphologyEx(mask[:,:,0], cv2.MORPH_CLOSE, kernel, iterations=iterations)
cropped = bounding_rect_of_mask(img, closed)
frames, desc = get_sift_keypoints(cropped)
X.append(desc)
X = np.array(X)
if X.size == 0:
print('test: skipping')
N -= n_batch
continue
X_trans = bof.transform(X)
X_trans = normalize(X_trans)
y_pred = lgr.predict(X_trans)
accuracy = accuracy_score(y_true, y_pred)
accuracies.append(accuracy)
# y_proba = lgr.predict_proba(X_trans)
# square_error = np.sum(np.power(y_proba - y_true, 2))
# sum_error += square_error
mean_accuracy = np.array(accuracy).mean()
msg = 'epoch:{:02d}; test mean accuracy={};'.format(epoch, mean_accuracy)
write_log(msg)
print(msg)
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
n_batch = 10
learning_n_sample = 100
learning_rate = 0.1
initial_params = [8, 4] # size, iterations
for epoch in xrange(1, 11):
print('epoch:', epoch)
# train
model.train = True
sum_loss = 0
accuracies = []
N = len(train_imgs)
N_train = len(train_imgs)
perm = np.random.permutation(N_train)
for i in range(0, N_train, n_batch):
print('train_batch: ', i)
train_batch = [train_imgs[p_index] for p_index in perm[i:i+n_batch]]
y_true = np.repeat([y_true_0], len(train_batch), axis=0)
# y_proba_true = np.repeat(y_proba_true_0, len(train_batch), axis=0)
x_data = []
for img, mask in train_batch:
mask = resize(mask, (267, 178), preserve_range=True)
x_data.append(apc_od.im_to_blob(mask))
x_data = np.array(x_data, dtype=np.float32)
x = Variable(x_data, volatile='off')
z = model.encode(x)
param_scale = z.data
rands_shape = [learning_n_sample] + list(param_scale.shape)
rands = 1. * learning_rate * (11 - epoch) / 11 * (2 * np.random.random(rands_shape) - 1) + 1
rands[0] = np.ones(param_scale.shape) # ones
min_rand = None
max_accuracy = -np.inf
optimizer.zero_grads()
for j, rand in enumerate(rands):
params = rand * param_scale * initial_params
X = []
for k, param in enumerate(params):
size, iterations = map(int, param)
if size <= 0 or size > 50 or iterations <= 0 or iterations > 50:
size, iterations = initial_params
kernel = np.ones((size, size), dtype=np.uint8)
img, mask = train_batch[k]
closed = cv2.morphologyEx(mask[:,:,0], cv2.MORPH_CLOSE, kernel, iterations=iterations)
cropped = bounding_rect_of_mask(img, closed)
frames, desc = get_sift_keypoints(cropped)
X.append(desc)
X = np.array(X)
if X.size == 0:
continue
X_trans = bof.transform(X)
X_trans = normalize(X_trans)
y_pred = lgr.predict(X_trans)
accuracy = accuracy_score(y_true, y_pred)
# y_proba = lgr.predict_proba(X_trans)[0]
# square_error = 1. * np.sum(np.power(y_proba - y_true, 2)) / len(train_batch)
if accuracy > max_accuracy:
max_accuracy = accuracy
min_rand = rand
if min_rand is None:
print('train: skipping')
N -= n_batch
continue
t_data = np.array(min_rand * param_scale, dtype=np.float32)
t = Variable(t_data, volatile='off')
loss = F.mean_squared_error(t, z)
loss.backward()
optimizer.update()
sum_loss += float(loss.data) * len(train_batch)
accuracies.append(accuracy)
try:
mean_loss = 1. * sum_loss / N
except ZeroDivisionError:
mean_loss = np.inf
mean_accuracy = np.array(accuracies).mean()
msg = 'epoch:{:02d}; train mean loss={};'.format(epoch, mean_loss)
write_log(msg)
print(msg)
msg = 'epoch:{:02d}; train mean accuracy={};'.format(epoch, mean_accuracy)
write_log(msg)
print(msg)
# test
model.train = False
sum_error = 0
print('testing')
accuracies = []
N = len(test_imgs)
perm = np.random.permutation(len(test_imgs))
for i in xrange(0, N, n_batch):
print('test_batch: ', i)
test_batch = [test_imgs[p_index] for p_index in perm[i:i+n_batch]]
y_true = np.repeat([y_true_0], len(test_batch), axis=0)
x_data = []
for img, mask in test_batch:
mask = resize(mask, (267, 178), preserve_range=True)
x_data.append(apc_od.im_to_blob(mask))
x_data = np.array(x_data, dtype=np.float32)
x = Variable(x_data, volatile='on')
z = model.encode(x)
param_scale = z.data
params = param_scale * initial_params
X = []
for k, param in enumerate(params):
size, iterations = map(int, param)
if size <= 0 or size > 50 or iterations <= 0 or iterations > 50:
rand = 1. * np.ones(2) / param_scale
params = rand * param_scale * initial_params
size, iterations = map(int, params[0])
print('test:', size, iterations)
if size <= 0 or size > 50 or iterations <= 0 or iterations > 50:
size, iterations = initial_params
kernel = np.ones((size, size), dtype=np.uint8)
img, mask = test_batch[k]
closed = cv2.morphologyEx(mask[:,:,0], cv2.MORPH_CLOSE, kernel, iterations=iterations)
cropped = bounding_rect_of_mask(img, closed)
frames, desc = get_sift_keypoints(cropped)
X.append(desc)
X = np.array(X)
if X.size == 0:
print('test: skipping')
N -= n_batch
continue
X_trans = bof.transform(X)
X_trans = normalize(X_trans)
y_pred = lgr.predict(X_trans)
accuracy = accuracy_score(y_true, y_pred)
accuracies.append(accuracy)
# y_proba = lgr.predict_proba(X_trans)[0]
# square_error = np.sum(np.power(y_proba - y_true, 2))
# sum_error += square_error
mean_accuracy = np.array(accuracies).mean()
msg = 'epoch:{:02d}; test mean accuracy={};'.format(epoch, mean_accuracy)
write_log(msg)
print(msg)
S.save_hdf5('bof_data/cae_ones_model_trained_{}.h5'.format(epoch), model)
S.save_hdf5('bof_data/cae_ones_optimizer_trained_{}.h5'.format(epoch), optimizer)
print('creating output directory: {}'.format(output_dir))
os.mkdir(output_dir)
categs = os.listdir(container_path)
os.chdir(container_path)
for categ in categs:
os.chdir(categ)
print('processing category: {}'.format(categ))
files = os.listdir('.')
img_files = filter(lambda x: re.match('^N\d*?_\d*?.jpg', x), files)
print('found {} images'.format(len(img_files)))
categ_output_dir = os.path.join(output_dir, categ)
if not os.path.exists(categ_output_dir):
os.mkdir(categ_output_dir)
for img_file in img_files:
base, _ = os.path.splitext(img_file)
mask_file = os.path.join('masks', base + '_mask.pbm')
img = cv2.imread(img_file)
mask = cv2.imread(mask_file, 0)
applied = bounding_rect_of_mask(img, ~mask)
cv2.imwrite(os.path.join(output_dir, categ, img_file), applied)
os.chdir('..')
os.chdir('..')
