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('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_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():
sum_error = 0
sum_px_error = 0
result = open('result.txt', 'wb')
prediction_file = 'flownets-pred-0000000.flo'
img1_list = open('img1_kitti_test.txt', 'r').readlines()
img2_list = open('img2_kitti_test.txt', 'r').readlines()
flow_list = open('flo_kitti_test.txt', 'r').readlines()
length = len(img1_list)
for i in range(length):
# input images and ground truth flow
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)
#invoke FlowNet
FlowNet.run(this_dir, img_files, './model_simple')
#evaluate result
flow = fl.read_flow(prediction_file)
gt = kittitool.flow_read(flow_list[i].strip())
epe = fl.evaluate_flow(gt, flow)
sum_error += epe
# write to result file
result.write(
str(i) + ':\n' + img_files[0] + '\n' + img_files[1] + '\n' +
flow_list[i].strip() + '\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_kitti():
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):
# input images and ground truth flow
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)
#invoke FlowNet
FlowNet.run(this_dir, img_files, './model_simple')
#evaluate result
flow = fl.read_flow(prediction_file)
gt = kittitool.flow_read(flow_list[i].strip())
epe = fl.evaluate_flow(gt, flow)
sum_error += epe
# write to result file
result.write(str(i) + ':\n' + img_files[0] +'\n' + img_files[1] + '\n' + flow_list[i].strip() + '\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()
