def main():
img = lena()
frames, desc = imagesift.get_sift_keypoints(img)
out = imagesift.draw_sift_frames(img, frames)
cv2.imshow('sift image', out)
cv2.waitKey(0)
def main():
rp = rospkg.RosPack()
imgpath = os.path.join(rp.get_path('jsk_perception'), 'sample/ros_fuerte.jpg')
img = cv2.imread(imgpath, 0) # gray-scale image
frames, desc = imagesift.get_sift_keypoints(img)
out = imagesift.draw_sift_frames(img, frames)
cv2.imshow('sift image', out)
cv2.waitKey(0)
def create_sift_dataset():
parser = argparse.ArgumentParser()
parser.add_argument('container_path', help='image data container path')
parser.add_argument('-O', '--output', default=None, help='output file')
args = parser.parse_args()
container_path = args.container_path
output = (args.output or
os.path.basename(container_path) + '_sift_feature.pkl.gz')
# See: http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_files.html
bunch_files = load_files(container_path=container_path,
description='image data',
shuffle=False,
load_content=False)
targets, pos_list, scale_list, ori_list, desc_list = [], [], [], [], []
for i, (filename, target) in enumerate(zip(bunch_files.filenames,
bunch_files.target)):
print('filename: {}, label: {}'.format(filename, target))
targets.append(target)
# extract feature
img = cv2.imread(filename, 0)
frames, desc = imagesift.get_sift_keypoints(img)
# save feature data
pos_list.append(np.hstack([frames[:, 0], frames[:, 1]]))
ori_list.append(frames[:, 2])
scale_list.append(frames[:, 3])
desc_list.append(desc)
dataset = Bunch(target=np.array(targets),
target_names=bunch_files.target_names,
positions=pos_list,
scales=scale_list,
orientations=ori_list,
descriptors=desc_list)
# save features
print('saving sift feature dataset')
with gzip.open(output, 'wb') as f:
pickle.dump(dataset, f)
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)
with gzip.open('bof_trained_data/apc2015_bof.pkl.gz', 'rb') as f:
bof = pickle.load(f)
if 'n_jobs' not in bof.nn.__dict__:
bof.nn.n_jobs = 1
with gzip.open('bof_trained_data/apc2015_lgr.pkl.gz', 'rb') as f:
lgr = pickle.load(f)
y_true = []
y_pred = []
for index in dataset.test:
gray = imread(dataset.img_files[index], mode='L')
if dataset.mask_files[index] is not None:
mask = imread(dataset.mask_files[index], mode='L')
gray = fcn.util.apply_mask(gray, mask, crop=True)
frames, desc = get_sift_keypoints(gray)
if desc.size == 0:
continue
# for inserted 'background' label at index 0
y_true.append(dataset.target[index] - 1)
X = bof.transform([desc])
normalize(X, copy=False)
y_proba = lgr.predict_proba(X)[0]
assert len(y_proba) == len(dataset.target_names[1:])
y_pred.append(np.argmax(y_proba))
acc = accuracy_score(y_true, y_pred)
print('Mean Accuracy: {0}'.format(acc))
parser.add_argument('clf_path')
args = parser.parse_args()
container_path = args.container_path
bof_path = args.bof_path
clf_path = args.clf_path
bunch_files = load_files(container_path=container_path,
description='images',
shuffle=False,
load_content=False)
with gzip.open(bof_path, 'rb') as f:
bof = pickle.load(f)
with gzip.open(clf_path, 'rb') as f:
clf = pickle.load(f)
descs = []
for fname in bunch_files.filenames:
img = cv2.imread(fname, 0)
_, desc = get_sift_keypoints(img)
descs.append(desc)
X = bof.transform(descs)
normalize(X, copy=False)
y_pred = clf.predict(X)
y = bunch_files.target
print accuracy_score(y, y_pred)
print classification_report(y, y_pred, target_names=clf.target_names_)