def parse_resize_image_and_labels():
print('Resizing and packing images and labels to bin files.\n')
np.random.seed(1701) # to fix test set
if not os.path.exists(FLAGS.resized_dir):
os.mkdir(FLAGS.resized_dir)
jnt_fn = FLAGS.data_dir + 'joints.mat'
joints = loadmat(jnt_fn)
joints = joints['joints'].swapaxes(0, 2).swapaxes(1, 2)
invisible_joints = joints[:, :, 2] < 0.5
joints[invisible_joints] = 0
joints = joints[..., :2]
N_test = int(len(joints) * 0.1)
permTest = np.random.permutation(int(len(joints)))[:N_test].tolist()
imagelist = sorted(glob.glob(FLAGS.orimage_dir + '*.jpg'))
fp_train = open(os.path.join(FLAGS.data_dir, FLAGS.trainLabels_fn), 'w')
fp_test = open(os.path.join(FLAGS.data_dir, FLAGS.testLabels_fn), 'w')
for index, img_fn in enumerate(imagelist):
imgFile = Image.open(img_fn)
(imWidth, imHeight) = imgFile.size
imgFile = imgFile.resize((FLAGS.input_size, FLAGS.input_size),
Image.ANTIALIAS)
newFileName = os.path.join(FLAGS.resized_dir,
b(img_fn).replace("jpg", "bin"))
joints[index, :, 0] *= FLAGS.input_size / float(imWidth)
joints[index, :, 1] *= FLAGS.input_size / float(imHeight)
im_label_pack = np.concatenate(
(joints[index, :, :].reshape(LSPGlobals.TotalLabels),
np.asarray(imgFile).reshape(LSPGlobals.TotalImageBytes)))
im_label_pack.astype(np.uint8).tofile(newFileName)
if index in permTest:
print(newFileName, file=fp_test)
else:
print(newFileName, file=fp_train)
if (index % 100 == 0):
sys.stdout.write("\r%d done" % index) #"\r" deletes previous line
sys.stdout.flush()
#"\r" deletes previous line
sys.stdout.write("\r")
sys.stdout.flush()
print('Done.')
def parse_resize_image_and_labels():
print('Resizing and packing images and labels to bin files.\n')
np.random.seed(1701) # to fix test set
if not os.path.exists(FLAGS.resized_dir):
os.mkdir(FLAGS.resized_dir)
jnt_fn = FLAGS.data_dir + 'joints.mat'
joints = loadmat(jnt_fn)
joints = joints['joints'].swapaxes(0, 2).swapaxes(1, 2)
invisible_joints = joints[:, :, 2] < 0.5
joints[invisible_joints] = 0
joints = joints[...,:2]
N_test = int(len(joints) * 0.1)
permTest = np.random.permutation(int(len(joints)))[:N_test].tolist()
imagelist = sorted(glob.glob(FLAGS.orimage_dir + '*.jpg'))
fp_train = open(os.path.join(FLAGS.data_dir, FLAGS.trainLabels_fn), 'w')
fp_test = open(os.path.join(FLAGS.data_dir, FLAGS.testLabels_fn), 'w')
for index, img_fn in enumerate(imagelist):
imgFile = Image.open(img_fn)
(imWidth, imHeight) = imgFile.size
imgFile = imgFile.resize((FLAGS.input_size,FLAGS.input_size), Image.ANTIALIAS)
newFileName = os.path.join(FLAGS.resized_dir, b(img_fn).replace( "jpg", "bin" ))
joints[index, :, 0] *= FLAGS.input_size/float(imWidth)
joints[index, :, 1] *= FLAGS.input_size/float(imHeight)
im_label_pack = np.concatenate(( joints[index, :, :].reshape(LSPGlobals.TotalLabels),
np.asarray(imgFile).reshape(LSPGlobals.TotalImageBytes) ))
im_label_pack.astype(np.uint8).tofile(newFileName)
if index in permTest:
print(newFileName, file=fp_test)
else:
print(newFileName, file=fp_train)
if (index % 100 == 0):
sys.stdout.write("\r%d done" % index) #"\r" deletes previous line
sys.stdout.flush()
#"\r" deletes previous line
sys.stdout.write("\r")
sys.stdout.flush()
print('Done.')
def transform_and_write(im_path, joints, resized_dir, transforms=[[]], dry_run=False):
"""Performs transforms on image and joints, and writes to binary."""
written_files = []
pose_image = PoseImage.from_filename(im_path, joints)
for transform in transforms:
cur_image = pose_image.copy()
should_write = True
desc = ''
for obj in transform:
desc += obj['func'] + ''.join(map(str, obj['args']))
if obj['func'] == 'random_crop_square':
bound_sq = scale_square(bounding_rect(cur_image.joints), DEFAULT_SCALE)
side_length = max(
MIN_SIDE_LENGTH_RATIO * FLAGS.input_size, # Bigger than ratio of input_size
(bound_sq[2] - bound_sq[0]) * 1.00001, # Bigger than bound_sq
min(cur_image.im_width, cur_image.im_height) * CROP_RATIO,
)
if side_length <= min(cur_image.im_width, cur_image.im_height):
cur_image.random_crop_square(side_length, bound_sq)
else:
# No transform would have been done
should_write = False
else:
getattr(cur_image, obj['func'])(*obj['args'])
if obj['func'] == 'rotate_and_crop':
for x, y in [p for p in cur_image.joints if not np.array_equal(p, [0., 0.])]:
if x < 0. or x > cur_image.im_width or y < 0. or y > cur_image.im_height:
# Joint out of bounds
should_write = False
cur_image.resize((FLAGS.input_size, FLAGS.input_size))
if should_write:
new_path = os.path.join(resized_dir, b(im_path).replace( ".jpg", desc + '.bin'))
written_files.append(new_path)
if dry_run:
cur_image.show()
else:
cur_image.save_binary(new_path)
return written_files
parser = argparse.ArgumentParser()
parser.add_argument('--datadir', type=str, default='data/lspet_dataset')
args = parser.parse_args()
print(args)
jnt_fn = '%s/joints.mat' % args.datadir
joints = loadmat(jnt_fn)
joints = joints['joints'].swapaxes(0, 2).swapaxes(1, 2)
joints = joints[:, :, :2]
N_test = int(len(joints) * 0.1)
perm = np.random.permutation(int(len(joints)))[:N_test].tolist()
print(perm)
fp_train = open('%s/train_joints.csv' % args.datadir, 'w')
fp_test = open('%s/test_joints.csv' % args.datadir, 'w')
for img_fn in sorted(glob.glob('%s/images/*.jpg' % args.datadir)):
index = int(re.search(ur'im([0-9]+)', b(img_fn)).groups()[0]) - 1
str_j = [str(j) for j in joints[index].flatten().tolist()]
out_list = [b(img_fn)]
out_list.extend(str_j)
out_str = ','.join(out_list)
print(out_str, file=fp)
if index in perm:
print(out_str, file=fp_test)
else:
print(out_str, file=fp_train)
fp_train.close()
fp_test.close()
def evalDeepPose(imagelist):
with tf.Graph().as_default():
# Placeholder to switch between train and test sets.
dataShape = [FLAGS.batch_size, FLAGS.input_size, FLAGS.input_size, FLAGS.input_depth]
input_images = tf.placeholder(tf.float32, shape=dataShape)
# Build a Graph that computes the logits predictions from the inference model.
logits = LSPModels.inference(input_images, FLAGS.batch_size, keep_prob=1)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
sess.run(init)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("No checkpoint found!")
return
numSteps = int(math.floor(len(imagelist)/FLAGS.batch_size)+0.1)
if (len(imagelist)%FLAGS.batch_size > 0):
numSteps = numSteps +1
resizeValues = np.empty([FLAGS.batch_size, 2], dtype=np.float32)
input_pack = np.empty(dataShape, dtype=np.float32)
for step in xrange(numSteps):
for i in xrange(FLAGS.batch_size):
index = i + step*FLAGS.batch_size
if not index >= len(imagelist):
imgFile = Image.open(imagelist[index])
(imWidth, imHeight) = imgFile.size
imgFile = imgFile.resize((FLAGS.input_size,FLAGS.input_size), Image.ANTIALIAS)
resizeValues[i, 0] = float(imWidth)/FLAGS.input_size
resizeValues[i, 1] = float(imHeight)/FLAGS.input_size
input_pack[i,...] = np.asarray(imgFile)
start_time = time.time()
labels = sess.run([logits], feed_dict={input_images: input_pack})
duration = time.time() - start_time
labels_reshaped = np.asarray(labels).reshape([FLAGS.batch_size, FLAGS.label_count, FLAGS.label_size])
resizedLabels = resizeLabels(labels_reshaped, resizeValues)
for i in xrange(FLAGS.batch_size):
index = i + step*FLAGS.batch_size
if not index >= len(imagelist):
imgFile = Image.open(imagelist[index])
img = np.asarray(imgFile).astype(np.uint8)
draw(img, resizedLabels[i,...], FLAGS.output_dir, 0, fname=b(imagelist[index]), reshaped=True)
examples_per_sec = FLAGS.batch_size / duration
format_str = ('%s: Done: %d, (FeedForward Time: %.1f examples/sec)')
print (format_str % (datetime.now(), (step+1)*FLAGS.batch_size, examples_per_sec))
coord.request_stop()
coord.join(threads)
print('Process Finished...')
def evalDeepPose(imagelist):
with tf.Graph().as_default():
# Placeholder to switch between train and test sets.
dataShape = [FLAGS.batch_size, FLAGS.input_size, FLAGS.input_size, FLAGS.input_depth]
input_images = tf.placeholder(tf.float32, shape=dataShape)
# Build a Graph that computes the logits predictions from the inference model.
logits = LSPModels.inference(input_images, FLAGS.batch_size, keepProb=1)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
sess.run(init)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("No checkpoint found!")
return
numSteps = int(math.floor(len(imagelist)/FLAGS.batch_size)+0.1)
if (len(imagelist)%FLAGS.batch_size > 0):
numSteps = numSteps +1
resizeValues = np.empty([FLAGS.batch_size, 2], dtype=np.float32)
input_pack = np.empty(dataShape, dtype=np.float32)
for step in xrange(numSteps):
for i in xrange(FLAGS.batch_size):
index = i + step*FLAGS.batch_size
if not index >= len(imagelist):
imgFile = Image.open(imagelist[index])
(imWidth, imHeight) = imgFile.size
imgFile = imgFile.resize((FLAGS.input_size,FLAGS.input_size), Image.ANTIALIAS)
resizeValues[i, 0] = float(imWidth)/FLAGS.input_size
resizeValues[i, 1] = float(imHeight)/FLAGS.input_size
input_pack[i,...] = np.asarray(imgFile)
start_time = time.time()
labels = sess.run([logits], feed_dict={input_images: input_pack})
duration = time.time() - start_time
labels_reshaped = np.asarray(labels).reshape([FLAGS.batch_size, FLAGS.label_count, FLAGS.label_size])
resizedLabels = resizeLabels(labels_reshaped, resizeValues)
for i in xrange(FLAGS.batch_size):
index = i + step*FLAGS.batch_size
if not index >= len(imagelist):
imgFile = Image.open(imagelist[index])
img = np.asarray(imgFile).astype(np.uint8)
draw(img, resizedLabels[i,...], FLAGS.output_dir, 0, fname=b(imagelist[index]), reshaped=True)
examples_per_sec = FLAGS.batch_size / duration
format_str = ('%s: Done: %d, (FeedForward Time: %.1f examples/sec)')
print (format_str % (datetime.now(), (step+1)*FLAGS.batch_size, examples_per_sec))
coord.request_stop()
coord.join(threads)
print('Process Finished...')
print(colored(datum, COL_INFO))
for country in ['kenya']:
print(colored(country, COL_INFO))
### Import data
IMPORT_PATH = j(ROOT, 'uncompressed', datum)
print(IMPORT_PATH)
#EXPORT_PATH = '%s/%s/%s' % (ROOT, datum, country)
EXPORT_PATH = '%s/%s-%s' % (
"/Volumes/DATA/Datasets/Geography_Data/viz2", country, datum)
left, right, top, bottom = convert_bbox(BBOX[country], FORMAT)
months_data = {}
for file_ in os.listdir(IMPORT_PATH):
if not file_.endswith(".txt"): continue
print(" processing", file_)
month = b(file_)[:7]
data = np.loadtxt(j(IMPORT_PATH, file_)).reshape(FORMAT)
months_data[month] = data[bottom:top, left:right]
print(months_data.keys())
output = EXPORT_PATH
np.save("%s.npy" % output, months_data)
save_mat(months_data, "%s.mat" % output)