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Running
on
Zero
File size: 9,731 Bytes
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import numpy as np
import cv2
import matplotlib.pyplot as plt
import json
import argparse
from scipy.ndimage import map_coordinates
from tqdm import tqdm
import os
import gc
import torch
from metric import *
import metric
device = 'cuda'
eval_metrics = [
"abs_relative_difference",
"rmse_linear",
"delta1_acc",
]
def get_infer(infer_path,args, target_size = None):
if infer_path.split('.')[-1] == 'npy':
img_gray = np.load(infer_path)
img_gray = img_gray.astype(np.float32)
infer_factor = 1.0
else:
img = cv2.imread(infer_path)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray = img_gray.astype(np.float32)
infer_factor = 1.0 / 255.0
infer = img_gray / infer_factor
if target_size is not None:
if infer.shape[0] != target_size[0] or infer.shape[1] != target_size[1]:
infer = cv2.resize(infer, (target_size[1], target_size[0]))
return infer
def get_gt(depth_gt_path, gt_factor, args):
if depth_gt_path.split('.')[-1] == 'npy':
depth_gt = np.load(depth_gt_path)
else:
depth_gt = cv2.imread(depth_gt_path, -1)
depth_gt = np.array(depth_gt)
depth_gt = depth_gt / gt_factor
depth_gt[depth_gt==0] = -1
return depth_gt
def get_flow(flow_path):
assert os.path.exists(flow_path)
flow = np.load(flow_path, allow_pickle=True)
return flow
def depth2disparity(depth, return_mask=False):
if isinstance(depth, np.ndarray):
disparity = np.zeros_like(depth)
non_negtive_mask = depth > 0
disparity[non_negtive_mask] = 1.0 / depth[non_negtive_mask]
if return_mask:
return disparity, non_negtive_mask
else:
return disparity
def eval_depthcrafter(infer_paths, depth_gt_paths, factors, args):
depth_errors = []
gts = []
infs = []
seq_length = args.max_eval_len
dataset_max_depth = args.max_depth_eval
for i in range(len(infer_paths)):
if not os.path.exists(infer_paths[i]):
continue
depth_gt = get_gt(depth_gt_paths[i], factors[i], args)
depth_gt = depth_gt[args.a:args.b, args.c:args.d]
infer = get_infer(infer_paths[i], args, target_size=depth_gt.shape)
gts.append(depth_gt)
infs.append(infer)
gts = np.stack(gts, axis=0)
infs = np.stack(infs, axis=0)
infs = infs[:seq_length]
gts = gts[:seq_length]
valid_mask = np.logical_and((gts>1e-3), (gts<dataset_max_depth))
gt_disp_masked = 1. / (gts[valid_mask].reshape((-1,1)).astype(np.float64) + 1e-8)
infs = np.clip(infs, a_min=1e-3, a_max=None)
pred_disp_masked = infs[valid_mask].reshape((-1,1)).astype(np.float64)
_ones = np.ones_like(pred_disp_masked)
A = np.concatenate([pred_disp_masked, _ones], axis=-1)
X = np.linalg.lstsq(A, gt_disp_masked, rcond=None)[0]
scale, shift = X
aligned_pred = scale * infs + shift
aligned_pred = np.clip(aligned_pred, a_min=1e-3, a_max=None)
pred_depth = depth2disparity(aligned_pred)
gt_depth = gts
pred_depth = np.clip(
pred_depth, a_min=1e-3, a_max=dataset_max_depth
)
sample_metric = []
metric_funcs = [getattr(metric, _met) for _met in eval_metrics]
pred_depth_ts = torch.from_numpy(pred_depth).to(device)
gt_depth_ts = torch.from_numpy(gt_depth).to(device)
valid_mask_ts = torch.from_numpy(valid_mask).to(device)
n = valid_mask.sum((-1, -2))
valid_frame = (n > 0)
pred_depth_ts = pred_depth_ts[valid_frame]
gt_depth_ts = gt_depth_ts[valid_frame]
valid_mask_ts = valid_mask_ts[valid_frame]
for met_func in metric_funcs:
_metric_name = met_func.__name__
_metric = met_func(pred_depth_ts, gt_depth_ts, valid_mask_ts).item()
sample_metric.append(_metric)
return sample_metric
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--infer_path', type=str, default='')
parser.add_argument('--infer_type', type=str, default='npy')
parser.add_argument('--benchmark_path', type=str, default='')
parser.add_argument('--datasets', type=str, nargs='+', default=['vkitti', 'kitti', 'sintel', 'nyu_v2', 'tartanair', 'bonn', 'ip_lidar'])
args = parser.parse_args()
results_save_path = os.path.join(args.infer_path, 'results.txt')
for dataset in args.datasets:
file = open(results_save_path, 'a')
if dataset == 'kitti':
args.json_file = os.path.join(args.benchmark_path,'kitti/kitti_video.json')
args.root_path = os.path.join(args.benchmark_path,'kitti')
args.max_depth_eval = 80.0
args.min_depth_eval = 0.1
args.max_eval_len = 110
args.a = 0
args.b = 374
args.c = 0
args.d = 1242
if dataset == 'kitti_500':
dataset = 'kitti'
args.json_file = os.path.join(args.benchmark_path,'kitti/kitti_video_500.json')
args.root_path = os.path.join(args.benchmark_path,'kitti')
args.max_depth_eval = 80.0
args.min_depth_eval = 0.1
args.max_eval_len = 500
args.a = 0
args.b = 374
args.c = 0
args.d = 1242
elif dataset == 'sintel':
args.json_file = os.path.join(args.benchmark_path,'sintel/sintel_video.json')
args.root_path = os.path.join(args.benchmark_path,'sintel')
args.max_depth_eval = 70
args.min_depth_eval = 0.1
args.max_eval_len = 100
args.a = 0
args.b = 436
args.c = 0
args.d = 1024
elif dataset == 'nyuv2_500':
dataset = 'nyuv2'
args.json_file = os.path.join(args.benchmark_path,'nyuv2/nyuv2_video_500.json')
args.root_path = os.path.join(args.benchmark_path,'nyuv2')
args.max_depth_eval = 10.0
args.min_depth_eval = 0.1
args.max_eval_len = 500
args.a = 45
args.b = 471
args.c = 41
args.d = 601
elif dataset == 'bonn':
args.json_file = os.path.join(args.benchmark_path,'bonn/bonn_video.json')
args.root_path = os.path.join(args.benchmark_path,'bonn')
args.max_depth_eval = 10.0
args.min_depth_eval = 0.1
args.max_eval_len = 110
args.a = 0
args.b = 480
args.c = 0
args.d = 640
elif dataset == 'bonn_500':
dataset = 'bonn'
args.json_file = os.path.join(args.benchmark_path,'bonn/bonn_video_500.json')
args.root_path = os.path.join(args.benchmark_path,'bonn')
args.max_depth_eval = 10.0
args.min_depth_eval = 0.1
args.max_eval_len = 500
args.a = 0
args.b = 480
args.c = 0
args.d = 640
elif dataset == 'scannet':
args.json_file = os.path.join(args.benchmark_path,'scannet/scannet_video.json')
args.root_path = os.path.join(args.benchmark_path,'scannet')
args.max_depth_eval = 10.0
args.min_depth_eval = 0.1
args.max_eval_len = 90
args.a = 8
args.b = -8
args.c = 11
args.d = -11
elif dataset == 'scannet_500':
dataset = 'scannet'
args.json_file = os.path.join(args.benchmark_path,'scannet/scannet_video_500.json')
args.root_path = os.path.join(args.benchmark_path,'scannet')
args.max_depth_eval = 10.0
args.min_depth_eval = 0.1
args.max_eval_len = 500
args.a = 8
args.b = -8
args.c = 11
args.d = -11
with open(args.json_file, 'r') as fs:
path_json = json.load(fs)
json_data = path_json[dataset]
scale_stds = shift_stds = stable_result_fulls = stable_result_wins = 0
depth_result_fulls = np.zeros(5)
depth_result_wins = np.zeros(5)
depth_result_onlys = np.zeros(5)
count = 0
line = '-' * 50
print(f'<{line} {dataset} start {line}>')
file.write(f'<{line} {dataset} start {line}>\n')
results_all = []
for data in tqdm(json_data):
for key in data.keys():
value = data[key]
infer_paths = []
depth_gt_paths = []
flow_paths = []
factors = []
for images in value:
infer_path = (args.infer_path + '/'+ dataset + '/' + images['image']).replace('.jpg', '.npy').replace('.png', '.npy')
infer_paths.append(infer_path)
depth_gt_paths.append(args.root_path + '/' + images['gt_depth'])
factors.append(images['factor'])
infer_paths = infer_paths[:args.max_eval_len]
depth_gt_paths = depth_gt_paths[:args.max_eval_len]
factors = factors[:args.max_eval_len]
results_single = eval_depthcrafter(infer_paths, depth_gt_paths, factors, args)
results_all.append(results_single)
final_results = np.array(results_all)
final_results_mean = np.mean(final_results, axis=0)
result_dict = { 'name': dataset }
for i, metric in enumerate(eval_metrics):
result_dict[metric] = final_results_mean[i]
print(f"{metric}: {final_results_mean[i]:04f}")
file.write(f"{metric}: {final_results_mean[i]:04f}\n")
file.write(f'<{line} {dataset} finish {line}>\n')
if __name__ == '__main__':
main() |