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test_make3d.py
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test_make3d.py
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# Author: Anurag Ranjan
# Copyright (c) 2019, Anurag Ranjan
# All rights reserved.
# based on github.com/ClementPinard/SfMLearner-Pytorch
import glob
import torch
import cv2
from torch.autograd import Variable
from PIL import Image
from scipy import interpolate, io
from scipy.misc import imresize, imread
from scipy.ndimage.interpolation import zoom
import numpy as np
from path import Path
import argparse
from tqdm import tqdm
from utils import tensor2array
import models
from loss_functions import spatial_normalize
parser = argparse.ArgumentParser(description='Script for DispNet testing with corresponding groundTruth',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--dispnet", dest='dispnet', type=str, default='DispResNet6', help='dispnet architecture')
parser.add_argument("--pretrained-dispnet", required=True, type=str, help="pretrained DispNet path")
parser.add_argument("--img-height", default=256, type=int, help="Image height")
parser.add_argument("--img-width", default=256, type=int, help="Image width")
parser.add_argument("--no-resize", action='store_true', help="no resizing is done")
parser.add_argument("--min-depth", default=1e-3)
parser.add_argument("--max-depth", default=70, type=float)
parser.add_argument("--dataset-dir", default='.', type=str, help="Dataset directory")
parser.add_argument("--output-dir", default=None, type=str, help="Output directory for saving predictions in a big 3D numpy file")
parser.add_argument("--img-exts", default=['png', 'jpg', 'bmp'], nargs='*', type=str, help="images extensions to glob")
class test_framework(object):
def __init__(self, root, min_depth=1e-3, max_depth=70):
self.root = root
self.min_depth, self.max_depth = min_depth, max_depth
self.img_files = sorted(glob.glob(root/'Test134/*.jpg'))
self.depth_files = sorted(glob.glob(root/'Gridlaserdata/*.mat'))
# This test file is corrupted in the original dataset
self.img_files.pop(61)
self.depth_files.pop(61)
self.ratio = 2
self.h_ratio = 1 / (1.33333 * self.ratio)
self.color_new_height = 1704 // 2
self.depth_new_height = 21
def __getitem__(self, i):
img = Image.open(self.img_files[i])
try:
imgarr = np.array(img)
tgt_img = imgarr.astype(np.float32)
except:
imgarr = np.array(img)
tgt_img = imgarr.astype(np.float32)
tgt_img = tgt_img[ (2272 - self.color_new_height)//2:(2272 + self.color_new_height)//2,:]
depth_map = io.loadmat(self.depth_files[i])
depth_gt = depth_map["Position3DGrid"][:,:,3]
depth_gt_cropped = depth_gt[(55 - 21)//2:(55 + 21)//2]
return {'tgt': tgt_img,
'path':self.img_files[i],
'gt_depth': depth_gt_cropped,
'mask': np.logical_and(depth_gt_cropped > self.min_depth, depth_gt_cropped < self.max_depth)
}
def __len__(self):
return len(self.img_files)
def main():
args = parser.parse_args()
disp_net = getattr(models, args.dispnet)().cuda()
weights = torch.load(args.pretrained_dispnet)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
print('no PoseNet specified, scale_factor will be determined by median ratio, which is kiiinda cheating\
(but consistent with original paper)')
seq_length = 0
dataset_dir = Path(args.dataset_dir)
framework = test_framework(dataset_dir, args.min_depth, args.max_depth)
errors = np.zeros((2, 7, len(framework)), np.float32)
if args.output_dir is not None:
output_dir = Path(args.output_dir)
viz_dir = output_dir/'viz'
output_dir.makedirs_p()
viz_dir.makedirs_p()
for j, sample in enumerate(tqdm(framework)):
tgt_img = sample['tgt']
h,w,_ = tgt_img.shape
if (not args.no_resize) and (h != args.img_height or w != args.img_width):
tgt_img = imresize(tgt_img, (args.img_height, args.img_width)).astype(np.float32)
tgt_img = np.transpose(tgt_img, (2, 0, 1))
tgt_img = torch.from_numpy(tgt_img).unsqueeze(0)
tgt_img = ((tgt_img/255 - 0.5)/0.5).cuda()
tgt_img_var = Variable(tgt_img, volatile=True)
pred_disp = disp_net(tgt_img_var)
pred_disp = pred_disp.data.cpu().numpy()[0,0]
gt_depth = sample['gt_depth']
if args.output_dir is not None:
if j == 0:
predictions = np.zeros((len(framework), *pred_disp.shape))
predictions[j] = 1/pred_disp
gt_viz = interp_gt_disp(gt_depth)
gt_viz = torch.FloatTensor(gt_viz)
gt_viz[gt_viz == 0] = 1000
gt_viz = (1/gt_viz).clamp(0,10)
tgt_img_viz = tensor2array(tgt_img[0].cpu())
depth_viz = tensor2array(torch.FloatTensor(pred_disp), max_value=None, colormap='hot')
gt_viz = tensor2array(gt_viz, max_value=None, colormap='hot')
tgt_img_viz_im = Image.fromarray((255*tgt_img_viz).astype('uint8'))
tgt_img_viz_im = tgt_img_viz_im.resize(size=(args.img_width, args.img_height), resample=3)
tgt_img_viz_im.save(viz_dir/str(j).zfill(4)+'img.png')
depth_viz_im = Image.fromarray((255*depth_viz).astype('uint8'))
depth_viz_im = depth_viz_im.resize(size=(args.img_width, args.img_height), resample=3)
depth_viz_im.save(viz_dir/str(j).zfill(4)+'depth.png')
gt_viz_im = Image.fromarray((255*gt_viz).astype('uint8'))
gt_viz_im = gt_viz_im.resize(size=(args.img_width, args.img_height), resample=3)
gt_viz_im.save(viz_dir/str(j).zfill(4)+'gt.png')
all_viz_im = Image.fromarray( np.hstack([np.array(tgt_img_viz_im), np.array(gt_viz_im), np.array(depth_viz_im)]) )
all_viz_im.save(viz_dir/str(j).zfill(4)+'all.png')
pred_depth = 1/pred_disp
pred_depth_zoomed = zoom(pred_depth, (gt_depth.shape[0]/pred_depth.shape[0],gt_depth.shape[1]/pred_depth.shape[1])).clip(args.min_depth, args.max_depth)
if sample['mask'] is not None:
pred_depth_zoomed = pred_depth_zoomed[sample['mask']]
gt_depth = gt_depth[sample['mask']]
scale_factor = np.median(gt_depth)/np.median(pred_depth_zoomed)
pred_depth_zoomed = scale_factor*pred_depth_zoomed
pred_depth_zoomed[pred_depth_zoomed>args.max_depth] = args.max_depth
errors[1,:,j] = compute_errors(gt_depth, pred_depth_zoomed)
mean_errors = errors.mean(2)
error_names = ['abs_rel','sq_rel','rms','log_rms','a1','a2','a3']
print("Results with scale factor determined by GT/prediction ratio (like the original paper) : ")
print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format(*error_names))
print("{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}".format(*mean_errors[1]))
if args.output_dir is not None:
np.save(output_dir/'predictions.npy', predictions)
def interp_gt_disp(mat, mask_val=0):
mat[mat==mask_val] = np.nan
x = np.arange(0, mat.shape[1])
y = np.arange(0, mat.shape[0])
mat = np.ma.masked_invalid(mat)
xx, yy = np.meshgrid(x, y)
#get only the valid values
x1 = xx[~mat.mask]
y1 = yy[~mat.mask]
newarr = mat[~mat.mask]
GD1 = interpolate.griddata((x1, y1), newarr.ravel(), (xx, yy), method='linear', fill_value=mask_val)
return GD1
def compute_errors(gt, pred):
thresh = np.maximum((gt / pred), (pred / gt))
a1 = (thresh < 1.25 ).mean()
a2 = (thresh < 1.25 ** 2).mean()
a3 = (thresh < 1.25 ** 3).mean()
rmse = (gt - pred) ** 2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log10(gt) - np.log10(pred)) ** 2
rmse_log = np.sqrt(rmse_log.mean())
abs_rel = np.mean(np.abs(gt - pred) / gt)
sq_rel = np.mean(((gt - pred)**2) / gt)
return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3
if __name__ == '__main__':
main()