def test_flownet():
sum_error = 0
sum_px_error = 0
result = open('result.txt', 'wb')
prediction_file = 'flownets-pred-0000000.flo'
img1_list = open('img1_list_test.txt', 'r').readlines()
img2_list = open('img2_list_test.txt', 'r').readlines()
flow_list = open('flo_list_test.txt', 'r').readlines()
length = len(img1_list)
for i in range(length):
img_files = []
img_files.append(img1_list[i].strip())
img_files.append(img2_list[i].strip())
# sanity check
if os.path.exists(prediction_file):
os.remove(prediction_file)
FlowNet.run(this_dir, img_files, './model_simple')
epe = fl.evaluate_flow_file(flow_list[i].strip(), prediction_file)
flow = fl.read_flow(prediction_file)
[height, width, channels] = flow.shape
sum_error += epe
sum_px_error += epe / (height * width)
result.write(str.format("%4d" % i) + ': ' + str(epe) + '\n')
print 'Average Image EPE error: ', sum_error/length
print 'Average Pixel EPE error: ', sum_px_error/length
result.write('Average Image EPE error: ' + str(sum_error / length))
result.write('\n')
result.write('Average Pixel EPE error: ' + str(sum_px_error / length))
result.close()
def test_rain():
rain_image_path = 'haze_rain'
prediction_file = 'flownets-pred-0000000.flo'
left_name_base = 'haze_rain_light/render_haze_left_beta'
right_name_base = 'haze_rain_light/render_haze_right_beta'
flow_file = 'haze_rain_light/flow_left.flo'
result = open('result.txt', 'wb')
sum_error = 0
for beta in range(0, 200, 5):
for contrast in range(120, 201, 5):
img_files = []
left_name = left_name_base + str(beta) + 'contrast' + str(contrast) + '.png'
right_name = right_name_base + str(beta) + 'contrast' + str(contrast) + '.png'
img_files.append(right_name)
img_files.append(left_name)
# sanity check
if os.path.exists(prediction_file):
os.remove(prediction_file)
FlowNet.run(this_dir, img_files, './model_simple')
epe = fl.evaluate_flow_file(flow_file, prediction_file)
flow = fl.read_flow(prediction_file)
flowpic = fl.flow_to_image(flow)
flow_image = Image.fromarray(flowpic)
flow_image.save('beta' + str(beta)+ 'contrast' + str(contrast) + 'flow.png')
sum_error += epe
result.write('beta: ' + str(beta) + ' contrast: ' + str(contrast) + ' epe: ' + str(epe) + '\n')
print 'sum of average end point error: ', sum_error
result.close()
def test_flownet():
sum_error = 0
sum_px_error = 0
result = open('result.txt', 'wb')
prediction_file = 'flownets-pred-0000000.flo'
img1_list = open('img1_list_test.txt', 'r').readlines()
img2_list = open('img2_list_test.txt', 'r').readlines()
flow_list = open('flo_list_test.txt', 'r').readlines()
length = len(img1_list)
for i in range(length):
img_files = []
img_files.append(img1_list[i].strip())
img_files.append(img2_list[i].strip())
# sanity check
if os.path.exists(prediction_file):
os.remove(prediction_file)
FlowNet.run(this_dir, img_files, './model_simple')
epe = fl.evaluate_flow_file(flow_list[i].strip(), prediction_file)
flow = fl.read_flow(prediction_file)
[height, width, channels] = flow.shape
sum_error += epe
sum_px_error += epe / (height * width)
result.write(str.format("%4d" % i) + ': ' + str(epe) + '\n')
print 'Average Image EPE error: ', sum_error / length
print 'Average Pixel EPE error: ', sum_px_error / length
result.write('\n')
result.write('Average Image EPE error: ' + str(sum_error / length))
result.write('Average Pixel EPE error: ' + str(sum_px_error / length))
result.close()
def test_kitti():
kitti_image = '../../../../../../../media/data/LRT_Flow/KITTI/data_scene_flow/training/image_2'
kitti_flow = 'noc'
image_list = os.listdir(kitti_image)
flow_list = os.listdir(kitti_flow)
prediction_file = 'flownets-pred-0000000.flo'
result = open('result.txt', 'wb')
sum_error = 0
# retrieve image and flow information
image_list.sort()
flow_list.sort()
for i in range(0, 308, 2):
# input images and ground truth flow
img_files = []
img_files.append(os.path.join(kitti_image, image_list[i]))
img_files.append(os.path.join(kitti_image, image_list[i + 1]))
ground_truth_file = flow_list[i / 2]
# sanity check
if os.path.exists(prediction_file):
os.remove(prediction_file)
#invoke FlowNet
FlowNet.run(this_dir, img_files, './model_simple')
print i
print img_files[0]
print img_files[1]
print ground_truth_file
#evaluate result
epe = fl.evaluate_flow_file('noc/' + ground_truth_file,
prediction_file)
sum_error += epe
# write to result file
result.write(
str(i) + ':\n' + img_files[0] + '\n' + img_files[1] + '\n' +
ground_truth_file + '\n')
result.write('Average end point error: ' + str(epe) + '\n')
result.write('Total average point error: ' + str(sum_error) + '\n')
print 'sum of average end point error: ', sum_error
result.close()
def test_middlebury():
# setting up
middlebury_image_1 = 'data/test/t0'
middlebury_image_2 = 'data/test/t1'
middlebury_flow = 'data/test/flow'
img1_name = '00000.png'
img2_name = '00000.png'
flow_name = '00000.flo'
prediction_file = 'flownets-pred-0000000.flo'
result_file = 'result.txt'
result = open(result_file, 'wb')
sum_error = 0
# Retrieve image and flow information
folder_names = os.listdir(data / test)
for folder in folder_names:
# input images and ground truth flow
img_files = []
img_files.append(os.path.join(middlebury_image_1, folder, img1_name))
img_files.append(os.path.join(middlebury_image_2, folder, img2_name))
ground_truth_file = os.path.join(middlebury_flow, folder, flow_name)
# sanity check
if os.path.exists(prediction_file):
os.remove(prediction_file)
# invoke FlowNet
FlowNet.run(this_dir, img_files, './model_corr')
# evaluate result
epe = fl.evaluate_flow_file(ground_truth_file, prediction_file)
sum_error += epe
print folder, " average end point error is:", epe
# write to result file
result.write(folder + ':\n' + img_files[0] + '\n' + img_files[1] +
'\n' + ground_truth_file + '\n')
result.write('Average end point error: ' + str(epe) + '\n')
result.write('Total average point error: ' + str(sum_error) + '\n')
print 'sum of average end point error: ', sum_error
result.close()
def test_middlebury():
# setting up
middlebury_image = 'data/other-data'
middlebury_flow = 'data/other-gt-flow'
img1_name = 'frame10_rain.png'
img2_name = 'frame11_rain.png'
flow_name = 'flow10.flo'
prediction_file = 'flownets-pred-0000000.flo'
result_file = 'result.txt'
result = open(result_file , 'wb')
sum_error = 0
# Retrieve image and flow information
folder_names = os.listdir(middlebury_image)
for folder in folder_names:
# input images and ground truth flow
img_files = []
img_files.append(os.path.join(middlebury_image, folder, img1_name))
img_files.append(os.path.join(middlebury_image, folder, img2_name))
ground_truth_file = os.path.join(middlebury_flow, folder, flow_name)
# sanity check
if os.path.exists(prediction_file):
os.remove(prediction_file)
# invoke FlowNet
FlowNet.run(this_dir, img_files, './model_simple')
# evaluate result
epe = fl.evaluate_flow_file(ground_truth_file, prediction_file)
sum_error += epe
print folder, " average end point error is:", epe
# write to result file
result.write(folder + ':\n' + img_files[0] +'\n' + img_files[1] + '\n' + ground_truth_file + '\n')
result.write('Average end point error: ' + str(epe) + '\n')
result.write('Total average point error: ' + str(sum_error) + '\n')
print 'sum of average end point error: ', sum_error
result.close()
def test_rain():
rain_image_path = 'haze_rain'
prediction_file = 'flownets-pred-0000000.flo'
left_name_base = 'haze_rain_light/render_haze_left_beta'
right_name_base = 'haze_rain_light/render_haze_right_beta'
flow_file = 'haze_rain_light/flow_left.flo'
result = open('result.txt', 'wb')
sum_error = 0
for beta in range(0, 200, 5):
for contrast in range(120, 201, 5):
img_files = []
left_name = left_name_base + str(beta) + 'contrast' + str(
contrast) + '.png'
right_name = right_name_base + str(beta) + 'contrast' + str(
contrast) + '.png'
img_files.append(right_name)
img_files.append(left_name)
# sanity check
if os.path.exists(prediction_file):
os.remove(prediction_file)
FlowNet.run(this_dir, img_files, './model_simple')
epe = fl.evaluate_flow_file(flow_file, prediction_file)
flow = fl.read_flow(prediction_file)
flowpic = fl.flow_to_image(flow)
flow_image = Image.fromarray(flowpic)
flow_image.save('beta' + str(beta) + 'contrast' + str(contrast) +
'flow.png')
sum_error += epe
result.write('beta: ' + str(beta) + ' contrast: ' + str(contrast) +
' epe: ' + str(epe) + '\n')
print 'sum of average end point error: ', sum_error
result.close()