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test_flownet_2015.py
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test_flownet_2015.py
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from __future__ import division
import cv2
import tensorflow as tf
import numpy as np
import os
import PIL.Image as pil
import png
import scipy
from core import flow_to_image
from core import flownet
flags = tf.app.flags
flags.DEFINE_integer("batch_size", 1, "The size of of a sample batch")
flags.DEFINE_integer("img_height", 384, "Image height")
flags.DEFINE_integer("img_width", 1280, "Image width")
flags.DEFINE_string("dataset_dir", './dataset/KITTI/flow2015/training/', "Dataset directory")
flags.DEFINE_string("output_dir", None, "Output directory")
flags.DEFINE_string("ckpt_file", 'pretrained/unflowc_pre', "checkpoint file")
FLAGS = flags.FLAGS
FLOW_SCALE = 5.0
# kitti 2012 has 200 training pairs, 200 test pairs
NUM = 200
def get_flow(path):
bgr = cv2.imread(path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
invalid = bgr[:, :, 0] == 0
out_flow = (bgr[:, :, 2:0:-1].astype('f4') - 2**15) / 64.
out_flow[invalid] = 0
return out_flow, bgr[:, :, 0]
def compute_flow_error(gt_flow, pred_flow, mask):
H, W, _ = gt_flow.shape
old_H, old_W, _ = pred_flow.shape
# Reshape predicted flow to have same size as ground truth
pred0 = cv2.resize(pred_flow[:,:,0], (W, H), interpolation=cv2.INTER_LINEAR) * (1.0*W/old_W)
pred1 = cv2.resize(pred_flow[:,:,1], (W, H), interpolation=cv2.INTER_LINEAR) * (1.0*H/old_H)
pred = np.stack((pred0, pred1), axis=-1) * FLOW_SCALE
err = np.sqrt(np.sum(np.square(gt_flow - pred), axis=-1))
err_valid = np.sum(err * mask) / np.sum(mask)
gt_mag = np.sqrt(np.sum(np.square(gt_flow), axis=-1))
mask1 = err > 3.
mask2 = err / (gt_mag+1e-12) > 0.05
final_mask = np.logical_and(np.logical_and(mask1, mask2), mask)
f1 = final_mask.sum()/mask.sum()
return err_valid, pred, f1
def write_flow_png(name, flow):
H, W, _ = flow.shape
out = np.ones((H, W, 3), dtype=np.uint64)
out[:,:,1] = np.minimum(np.maximum(flow[:,:,1]*64.+2**15, 0), 2**16).astype(np.uint64)
out[:,:,0] = np.minimum(np.maximum(flow[:,:,0]*64.+2**15, 0), 2**16).astype(np.uint64)
with open(name, 'wb') as f:
writer = png.Writer(width=W, height=H, bitdepth=16)
im2list = out.reshape(-1, out.shape[1]*out.shape[2]).tolist()
writer.write(f, im2list)
def pick_frame(path):
new_files = []
for i in range(NUM):
frame1 = os.path.join(path, 'image_2', '{:06d}'.format(i) + '_10.png')
frame2 = os.path.join(path, 'image_2', '{:06d}'.format(i) + '_11.png')
new_files.append([frame1, frame2])
return new_files
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()])
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, 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('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()))
if __name__ == '__main__':
tf.app.run()