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export_detection.py
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# python export_detection.py --eval_mono --load_weights_folder log/mdp/models/weights_refine74_pal/ --eval_mono --refine2d_deep true --catxy true --refine_depthnet_with_beam false --refine_2d
from __future__ import absolute_import, division, print_function
import os
import cv2
import tqdm
import numpy as np
import open3d
import torch
from torch.utils.data import DataLoader
import wandb
from layers import disp_to_depth
from utils import readlines
from options import MonodepthOptions
import datasets
import networks
from kitti_util_from_pse import Calibration
from gdc_old import GDC
from layers import *
def get_detec_calib(folder, frame_idx):
img_path = os.path.join(folder, "image_02/data/{:06d}.png".format(int(frame_idx)))
imshape = cv2.imread(img_path).shape[:2]
if imshape == (375, 1242):
return 'kitti_data/2011_09_26'
if imshape == (370, 1224):
return 'kitti_data/2011_09_28'
if imshape == (374, 1238):
return 'kitti_data/2011_09_29'
if imshape == (370, 1226):
return 'kitti_data/2011_09_30'
if imshape == (376, 1241):
return 'kitti_data/2011_10_03'
cv2.setNumThreads(0) # This speeds up evaluation 5x on our unix systems (OpenCV 3.3.1)
splits_dir = os.path.join(os.path.dirname(__file__), "splits")
# Models which were trained with stereo supervision were trained with a nominal
# baseline of 0.1 units. The KITTI rig has a baseline of 54cm. Therefore,
# to convert our stereo predictions to real-world scale we multiply our depths by 5.4.
STEREO_SCALE_FACTOR = 5.4
def compute_errors(gt, pred):
"""Computation of error metrics between predicted and ground truth depths
"""
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.log(gt) - np.log(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
def batch_post_process_disparity(l_disp, r_disp):
"""Apply the disparity post-processing method as introduced in Monodepthv1
"""
_, h, w = l_disp.shape
m_disp = 0.5 * (l_disp + r_disp)
l, _ = np.meshgrid(np.linspace(0, 1, w), np.linspace(0, 1, h))
l_mask = (1.0 - np.clip(20 * (l - 0.05), 0, 1))[None, ...]
r_mask = l_mask[:, :, ::-1]
return r_mask * l_disp + l_mask * r_disp + (1.0 - l_mask - r_mask) * m_disp
def evaluate(opt):
"""Evaluates a pretrained model using a specified test set
"""
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
wandb.init(project="mono-eval")
assert sum((opt.eval_mono, opt.eval_stereo)) == 1, \
"Please choose mono or stereo evaluation by setting either --eval_mono or --eval_stereo"
if opt.ext_disp_to_eval is None:
opt.load_weights_folder = os.path.expanduser(opt.load_weights_folder)
assert os.path.isdir(opt.load_weights_folder), \
"Cannot find a folder at {}".format(opt.load_weights_folder)
print("-> Loading weights from {}".format(opt.load_weights_folder))
filenames = readlines(os.path.join(splits_dir, 'detection', "test.txt"))
encoder_path = os.path.join(opt.load_weights_folder, "encoder.pth")
decoder_path = os.path.join(opt.load_weights_folder, "depth.pth")
encoder_dict = torch.load(encoder_path)
dataset = datasets.KITTIDetecDataset(opt.data_path, filenames,
encoder_dict['height'], encoder_dict['width'],
[0], 4, is_train=False, img_ext='.png', opt=opt)
if opt.eval_gdc:
opt.eval_batch_size = 1
dataloader = DataLoader(dataset, opt.eval_batch_size, shuffle=False, num_workers=opt.num_workers,
pin_memory=True, drop_last=False)
encoder = networks.ResnetEncoder(opt.num_layers, False,
cat4beam_to_color=opt.cat_4beam_to_color,
cat2channel=opt.cat2start)
depth_decoder = networks.DepthDecoder(encoder.num_ch_enc,
opt.scales, cat2end=opt.cat2end)
model_dict = encoder.state_dict()
encoder.load_state_dict({k: v for k, v in encoder_dict.items() if k in model_dict})
depth_decoder.load_state_dict(torch.load(decoder_path))
encoder.cuda()
encoder.eval()
depth_decoder.cuda()
depth_decoder.eval()
if opt.beam_encoder:
beam_encoder = networks.ResnetEncoder(opt.num_layers, False,
beam_encoder=True)
beam_encoder_path = os.path.join(opt.load_weights_folder, "beam_encoder.pth")
beam_encoder.load_state_dict(torch.load(beam_encoder_path))
beam_encoder.cuda()
beam_encoder.eval()
if opt.refine_2d:
refine_net = networks.DepthDecoder(encoder.num_ch_enc, opt.scales, road=True,
catxy=(opt.catxy == 'true'),
deep=(opt.refine2d_deep == 'true'))
refine_net_path = os.path.join(opt.load_weights_folder, "refine2d_decoder.pth")
refine_net.load_state_dict(torch.load(refine_net_path))
refine_net.cuda()
refine_net.eval()
pred_disps = []
if opt.eval_gdc:
dates = []
invKs = []
idx = 0
catxy = {}
for scale in opt.scales:
h = opt.height // (2 ** scale)
w = opt.width // (2 ** scale)
if opt.catxy == 'true':
catxy["True", scale] = Cat_xy(opt.eval_batch_size, h, w)
catxy["True", scale].cuda()
print("-> Computing predictions with size {}x{}".format(
encoder_dict['width'], encoder_dict['height']))
with torch.no_grad():
for data in tqdm.tqdm(dataloader):
if opt.eval_gdc:
dates.append(data['date'][0])
invKs.append(data[("inv_K", 0)])
input_color = data[("color", 0, 0)].cuda()
if opt.post_process:
# Post-processed results require each image to have two forward passes
input_color = torch.cat((input_color, torch.flip(input_color, [3])), 0)
if opt.cat_4beam_to_color:
features = encoder(torch.cat((input_color, data["4beam"].cuda()), 1))
elif opt.cat2start:
features = encoder(torch.cat((input_color, data["2channel"].cuda()), 1))
else:
features = encoder(input_color)
if opt.cat2end:
output = depth_decoder(features, two_channel=data["2channel"].cuda())
elif opt.beam_encoder:
beam_features = beam_encoder(data["2channel"].cuda())
if opt.refine_depthnet_with_beam == 'true' or not opt.refine_2d:
output = depth_decoder(features, beam_features=beam_features)
else:
output = depth_decoder(features)
else:
output = depth_decoder(features)
if opt.refine_2d:
for iter in range(opt.refine_iter):
beam = data['4beam']
two_cha = data['2channel'].cuda()
disp_0 = output[("disp", 0)]
for scale in opt.scales:
if not opt.refine_a0 == 'true':
disp = output[("disp", scale)]
else:
disp = disp_0
disp_0 = F.max_pool2d(disp_0, 2, ceil_mode=True)
disp640 = F.interpolate(disp, [opt.height, opt.width], mode="bilinear", align_corners=False)
_, depth = disp_to_depth(disp640, opt.min_depth, opt.max_depth)
mask = beam > 0
crop_mask = torch.zeros_like(mask)
crop_mask[:, :, 78:190, 23:617] = 1 # 375 1242
mask = mask * crop_mask
ratio = torch.median(beam[mask] * 100.0) / torch.median(depth[mask]).detach()
depth *= ratio
scaled_disp = (F.interpolate(1 / depth, disp.shape[2:],
mode="bilinear", align_corners=False) - 0.01) / 9.9
if scale != 0:
two_cha = F.max_pool2d(two_cha, 2, ceil_mode=True)
if opt.catxy == 'true':
for i in range(scale):
depth = F.max_pool2d(depth, 2, ceil_mode=True)
xyz = catxy['True', scale](depth, data[("inv_K", scale)].cuda())
output[("disp", scale)] = torch.cat([scaled_disp, xyz, two_cha], 1)
else:
output[("disp", scale)] = torch.cat([scaled_disp, two_cha], 1)
refine_output = refine_net(features, beam_features=beam_features,
depth_maps=output, tanh=opt.refine_offset)
for i in opt.scales:
output[("disp", i)] = refine_output[("disp", i)]
output[("disp", 0)] = F.interpolate(
output[("disp", 0)], [192, 640], mode="bilinear", align_corners=False)
pred_disp, _ = disp_to_depth(output[("disp", 0)], opt.min_depth, opt.max_depth)
pred_disp = pred_disp.cpu()[:, 0].numpy()
if opt.post_process:
N = pred_disp.shape[0] // 2
pred_disp = batch_post_process_disparity(pred_disp[:N], pred_disp[N:, :, ::-1])
pred_disps.append(pred_disp)
if opt.save_sample == idx:
from matplotlib import pyplot as plt
plt.pcolor(pred_disp[0], cmap='viridis')
plt.axis('equal')
plt.savefig('/home/zfeng/Desktop/depth{}.jpg'.format(idx), bbox_inches='tight', pad_inches=0)
plt.show()
if opt.visualize:
rgb = data[("color", 0, 0)][0].permute(1, 2, 0).numpy() * 255
rgb = cv2.resize(rgb, (1242, 375))
rgb = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
cv2.imwrite('visualization/prediction/{}rgb.jpg'.format(idx),rgb)
idx += 1
pred_disps = np.concatenate(pred_disps)
else:
# Load predictions from file
print("-> Loading predictions from {}".format(opt.ext_disp_to_eval))
pred_disps = np.load(opt.ext_disp_to_eval)
if opt.eval_eigen_to_benchmark:
eigen_to_benchmark_ids = np.load(
os.path.join(splits_dir, "benchmark", "eigen_to_benchmark_ids.npy"))
pred_disps = pred_disps[eigen_to_benchmark_ids]
if opt.save_pred_disps:
output_path = os.path.join(
opt.load_weights_folder, "disps_{}_split.npy".format(opt.eval_split))
print("-> Saving predicted disparities to ", output_path)
np.save(output_path, pred_disps)
if opt.no_eval:
print("-> Evaluation disabled. Done.")
quit()
elif opt.eval_split == 'benchmark':
save_dir = os.path.join(opt.load_weights_folder, "benchmark_predictions")
print("-> Saving out benchmark predictions to {}".format(save_dir))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for idx in range(len(pred_disps)):
disp_resized = cv2.resize(pred_disps[idx], (1216, 352))
depth = STEREO_SCALE_FACTOR / disp_resized
depth = np.clip(depth, 0, 80)
depth = np.uint16(depth * 256)
save_path = os.path.join(save_dir, "{:010d}.png".format(idx))
cv2.imwrite(save_path, depth)
print("-> No ground truth is available for the KITTI benchmark, so not evaluating. Done.")
quit()
gt_path = os.path.join(splits_dir, 'detection', "gt_depths.npz")
gt_depths = np.load(gt_path, fix_imports=True, encoding='latin1', allow_pickle=True)["data"]
if opt.random_sample == -1:
beam_path = os.path.join(splits_dir, 'detection', "4beam.npz")
beam_depths = np.load(beam_path, fix_imports=True, encoding='latin1', allow_pickle=True)["data"]
print("-> Evaluating")
if opt.eval_stereo:
print(" Stereo evaluation - "
"disabling median scaling, scaling by {}".format(STEREO_SCALE_FACTOR))
opt.disable_median_scaling = True
opt.pred_depth_scale_factor = STEREO_SCALE_FACTOR
else:
print(" Mono evaluation - using median scaling")
errors = []
ratios = []
#pred_depths = 1 / torch.tensor(pred_disps).unsqueeze(1)
#pred_depths = torch.clamp(F.interpolate(
# pred_depths, [375, 1242], mode="bilinear", align_corners=False), 1e-3, 80).squeeze().numpy()
for i in tqdm.tqdm(range(pred_disps.shape[0])):
gt_depth = gt_depths[i]
beam_depth = beam_depths[i]
gt_height, gt_width = gt_depth.shape[:2]
pred_disp = pred_disps[i]
pred_disp = cv2.resize(pred_disp, (gt_width, gt_height))
pred_depth = 1 / pred_disp
#gt_height, gt_width = 375, 1242
#pred_depth = pred_depths[i]
#import skimage
#gt_depth = skimage.transform.resize(
# gt_depths[i], (375, 1242), order=0, preserve_range=True, mode='constant')
if opt.eval_split == "eigen":
mask = np.logical_and(gt_depth > MIN_DEPTH, gt_depth < MAX_DEPTH)
crop = np.array([0.40810811 * gt_height, 0.99189189 * gt_height,
0.03594771 * gt_width, 0.96405229 * gt_width]).astype(np.int32)
crop_mask = np.zeros(mask.shape)
crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1
mask = np.logical_and(mask, crop_mask)
else:
mask = gt_depth > 0
pred_depth *= opt.pred_depth_scale_factor
if not opt.disable_median_scaling:
ratio = np.median(gt_depth[mask]) / np.median(pred_depth[mask])
ratios.append(ratio)
pred_depth *= ratio
# cv2.imwrite('gt.jpg', gt_depth)
# cv2.imwrite('pred_gdc.jpg', pred_depth)
# cv2.imwrite('4beam.jpg', beam_depth)
# gtd = beam_depth.copy()
# gtd[gtd==0] = -1
# bpd = BackprojectDepth(1, gt_height, gt_width)
# pred_points = bpd(torch.tensor(pred_depth), invKs[i]).squeeze()[:3,].transpose(0,1).numpy()
# beam_points = bpd(torch.tensor(gtd), invKs[i]).squeeze()[:3,].transpose(0,1).numpy()
if opt.eval_gdc:
try:
calib_path = get_detec_calib('kitti_data/kitti_detect/training', i)
calib_path += '/calib_cam_to_cam.txt'
calib = Calibration(calib_path)
gtd = beam_depth
gtd[gtd==0] = -1
if opt.random_sample == -1:
consider_range = (-0.1, 4.0)
else:
consider_range = (-1.5, 9)
corrected = GDC(pred_depth, gtd, calib, W_tol=3e-5, recon_tol=5e-4,
k=10, method='cg', verbose=False, consider_range=consider_range)
pred_depth = corrected
#import pdb
#pdb.set_trace()
except:
print("GDC failed")
if opt.visualize:
diff = abs(pred_depth - gt_depth)
diff = np.ones_like(diff) * 80 - np.clip(diff, 0, 2) * 40
diff_color = cv2.applyColorMap(diff.astype(np.uint8), cv2.COLORMAP_HSV)
ones = np.ones_like(diff_color) * 255
ones[mask] = diff_color[mask]
cv2.imwrite('visualization/prediction/{}{}.jpg'.format(i, opt.vis_name), ones)
pred_save = (pred_depth * 256.0).astype(np.uint16)
save_folder = 'kitti_data/kitti_detect/training/{}'.format(opt.det_name)
if not os.path.exists(save_folder):
os.mkdir(save_folder)
cv2.imwrite(save_folder + '/{:06d}.png'.format(i), pred_save)
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
pred_depth[pred_depth < MIN_DEPTH] = MIN_DEPTH
pred_depth[pred_depth > MAX_DEPTH] = MAX_DEPTH
#print(pred_depth)
#print(gt_depth.asdf)
errors.append(compute_errors(gt_depth, pred_depth))
if not opt.disable_median_scaling:
ratios = np.array(ratios)
med = np.median(ratios)
print(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, np.std(ratios / med)))
mean_errors = np.array(errors).mean(0)
print(mean_errors[0])
print("\n " + ("{:>8} | " * 7).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
print(("&{: 8.3f} " * 7).format(*mean_errors.tolist()) + "\\\\")
print("\n-> Done!")
if __name__ == "__main__":
options = MonodepthOptions()
evaluate(options.parse())