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import os
import cv2
import glob
import numpy as np
import imageio
from MiDaS.MiDaS_utils import write_depth
BOOST_BASE = 'BoostingMonocularDepth'
BOOST_INPUTS = 'inputs'
BOOST_OUTPUTS = 'outputs'
def run_boostmonodepth(img_names, src_folder, depth_folder):
if not isinstance(img_names, list):
img_names = [img_names]
# remove irrelevant files first
clean_folder(os.path.join(BOOST_BASE, BOOST_INPUTS))
clean_folder(os.path.join(BOOST_BASE, BOOST_OUTPUTS))
tgt_names = []
for img_name in img_names:
base_name = os.path.basename(img_name)
tgt_name = os.path.join(BOOST_BASE, BOOST_INPUTS, base_name)
os.system(f'cp {img_name} {tgt_name}')
# keep only the file name here.
# they save all depth as .png file
tgt_names.append(os.path.basename(tgt_name).replace('.jpg', '.png'))
os.system(f'cd {BOOST_BASE} && python run.py --Final --data_dir {BOOST_INPUTS}/ --output_dir {BOOST_OUTPUTS} --depthNet 0')
for i, (img_name, tgt_name) in enumerate(zip(img_names, tgt_names)):
img = imageio.imread(img_name)
H, W = img.shape[:2]
scale = 640. / max(H, W)
# resize and save depth
target_height, target_width = int(round(H * scale)), int(round(W * scale))
depth = imageio.imread(os.path.join(BOOST_BASE, BOOST_OUTPUTS, tgt_name))
depth = np.array(depth).astype(np.float32)
depth = resize_depth(depth, target_width, target_height)
np.save(os.path.join(depth_folder, tgt_name.replace('.png', '.npy')), depth / 32768. - 1.)
write_depth(os.path.join(depth_folder, tgt_name.replace('.png', '')), depth)
def clean_folder(folder, img_exts=['.png', '.jpg', '.npy']):
for img_ext in img_exts:
paths_to_check = os.path.join(folder, f'*{img_ext}')
if len(glob.glob(paths_to_check)) == 0:
continue
print(paths_to_check)
os.system(f'rm {paths_to_check}')
def resize_depth(depth, width, height):
"""Resize numpy (or image read by imageio) depth map
Args:
depth (numpy): depth
width (int): image width
height (int): image height
Returns:
array: processed depth
"""
depth = cv2.blur(depth, (3, 3))
return cv2.resize(depth, (width, height), interpolation=cv2.INTER_AREA)
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