def main(_):
new_files = pick_frame(FLAGS.dataset_dir)
basename = os.path.basename(FLAGS.ckpt_file)
im1_pl = tf.placeholder(dtype=tf.float32, shape=(1, FLAGS.img_height, FLAGS.img_width, 3))
im2_pl = tf.placeholder(dtype=tf.float32, shape=(1, FLAGS.img_height, FLAGS.img_width, 3))
pred_flows = flownet(im1_pl, im2_pl, flownet_spec='C')
saver = tf.train.Saver([var for var in tf.all_variables() if 'flow' in var.name])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
errs = np.zeros(NUM)
if not FLAGS.output_dir is None and not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
with tf.Session(config=config) as sess:
saver.restore(sess, FLAGS.ckpt_file)
# For val set
for t in range(0, len(new_files)):
if t % 100 == 0:
print('processing %s: %d/%d' % (basename, t, len(new_files)))
raw_im0 = pil.open(new_files[t][0])
raw_im1 = pil.open(new_files[t][1])
scaled_im0 = raw_im0.resize((FLAGS.img_width, FLAGS.img_height), pil.ANTIALIAS)
scaled_im1 = raw_im1.resize((FLAGS.img_width, FLAGS.img_height), pil.ANTIALIAS)
# Minus ImageNet channel mean
channel_mean = np.array([104.920005, 110.1753, 114.785955])
scaled_im0 = (np.expand_dims(np.array(scaled_im0), axis=0).astype(np.float32)-channel_mean)/255.
scaled_im1 = (np.expand_dims(np.array(scaled_im1), axis=0).astype(np.float32)-channel_mean)/255.
feed_dict = {im1_pl: scaled_im0, im2_pl: scaled_im1}
pred_flows_val = sess.run(pred_flows, feed_dict=feed_dict)
pred_flow_val = pred_flows_val[-1][0]
# Only for training set
if 'train' in FLAGS.dataset_dir:
# no occlusion
#gt_flow, mask = get_flow(new_files[t][0].replace('colored_0', 'flow_noc'))
# all
gt_flow, mask = get_flow(new_files[t][0].replace('colored_0', 'flow_occ'))
errs[t], scaled_pred = compute_flow_error(gt_flow, pred_flow_val[0,:,:,:], mask)
# Save for eval
if 'test' in FLAGS.dataset_dir:
errs[t], scaled_pred = compute_flow_error(np.array(raw_im0)[:,:,:2], pred_flow_val[0,:,:,:], np.array(raw_im0)[:,:,0])
#png_name = os.path.join(FLAGS.output_dir, new_files[t][0].split('/')[-1])
#write_flow_png(png_name, scaled_pred)
# Save for visual colormap
if not 'test' in FLAGS.dataset_dir and not FLAGS.output_dir is None:
flow_im = flow_to_image(scaled_pred)
#png_name = os.path.join(FLAGS.output_dir, new_files[t][0].split('/')[-1]).replace('png', 'jpg')
#cv2.imwrite(png_name, flow_im[:,:,::-1])
print('{:>10}'.format('(valid) endpoint error'))
print('{:10.4f}'.format(errs.mean()))
with open("test.log", 'w') as f:
f.write(str(errs.mean()))
def main(_):
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
new_files = pick_frame(FLAGS.dataset_dir)
basename = os.path.basename(FLAGS.ckpt_file)
im1_pl = tf.placeholder(dtype=tf.float32, shape=(1, FLAGS.img_height, FLAGS.img_width, 3))
im2_pl = tf.placeholder(dtype=tf.float32, shape=(1, FLAGS.img_height, FLAGS.img_width, 3))
# pred_flows = flownet(im1_pl, im2_pl, flownet_spec='C')
hourglassFile = '/media/disk/Backup/02congzhang/git/flow/relative-depth-using-pytorch/hourglass.pickle'
# hourglassdata = np.load(hourglassFile)
with open(hourglassFile, 'rb') as file:
hourglassdata = pickle.load(file)
pred_flows = net(im1_pl, im2_pl, hourglassdata)[2]
saver = tf.train.Saver([var for var in tf.all_variables()])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
errs = np.zeros(NUM)
f1 = np.zeros(NUM)
if not FLAGS.output_dir is None and not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
with tf.Session(config=config) as sess:
saver.restore(sess, '/media/disk/Backup/02congzhang/git/flow/DF-Net/ckpt/hourr1/model-40000')
# For val set
for t in range(0, len(new_files)):
if t % 100 == 0:
print('processing %s: %d/%d' % (basename, t, len(new_files)))
raw_im0 = pil.open(new_files[t][0])
raw_im1 = pil.open(new_files[t][1])
scaled_im0 = raw_im0.resize((FLAGS.img_width, FLAGS.img_height), pil.ANTIALIAS)
scaled_im1 = raw_im1.resize((FLAGS.img_width, FLAGS.img_height), pil.ANTIALIAS)
# Minus ImageNet channel mean
channel_mean = np.array([104.920005, 110.1753, 114.785955])
scaled_im0 = (np.expand_dims(np.array(scaled_im0), axis=0).astype(np.float32)-channel_mean)/255.
scaled_im1 = (np.expand_dims(np.array(scaled_im1), axis=0).astype(np.float32)-channel_mean)/255.
feed_dict = {im1_pl: scaled_im0, im2_pl: scaled_im1}
pred_flows_val = sess.run(pred_flows, feed_dict=feed_dict)
pred_flow_val = pred_flows_val[0]
# Only for training set
if 'train' in FLAGS.dataset_dir:
# no occlusion
#gt_flow, mask = get_flow(new_files[t][0].replace('image_2', 'flow_noc'))
# all
gt_flow, mask = get_flow(new_files[t][0].replace('image_2', 'flow_occ'))
errs[t], scaled_pred, f1[t] = compute_flow_error(gt_flow, pred_flow_val[0,:,:,:], mask)
# Save for eval
if 'test' in FLAGS.dataset_dir:
_, scaled_pred, _ = compute_flow_error(np.array(raw_im0)[:,:,:2], pred_flow_val[0,:,:,:], np.array(raw_im0)[:,:,0])
png_name = os.path.join(FLAGS.output_dir, new_files[t][0].split('/')[-1])
write_flow_png(png_name, scaled_pred)
# Save for visual colormap
if not 'test' in FLAGS.dataset_dir and not FLAGS.output_dir is None:
flow_im = flow_to_image(scaled_pred)
png_name = os.path.join(FLAGS.output_dir, new_files[t][0].split('/')[-1]).replace('png', 'jpg')
cv2.imwrite(png_name, flow_im[:,:,::-1])
print('{:>10}, {:>10}'.format('(valid) endpoint error', 'f1 score'))
print('{:10.4f}, {:10.4f}'.format(errs.mean(), f1.mean()))