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"""Compute depth maps for images in the input folder.
"""
import os
import glob
import torch
# from monodepth_net import MonoDepthNet
# import utils
import matplotlib.pyplot as plt
import numpy as np
import cv2
import imageio
def run_depth(img_names, input_path, output_path, model_path, Net, utils, target_w=None):
"""Run MonoDepthNN to compute depth maps.
Args:
input_path (str): path to input folder
output_path (str): path to output folder
model_path (str): path to saved model
"""
print("initialize")
# select device
device = torch.device("cpu")
print("device: %s" % device)
# load network
model = Net(model_path)
model.to(device)
model.eval()
# get input
# img_names = glob.glob(os.path.join(input_path, "*"))
num_images = len(img_names)
# create output folder
os.makedirs(output_path, exist_ok=True)
print("start processing")
for ind, img_name in enumerate(img_names):
print(" processing {} ({}/{})".format(img_name, ind + 1, num_images))
# input
img = utils.read_image(img_name)
w = img.shape[1]
scale = 640. / max(img.shape[0], img.shape[1])
target_height, target_width = int(round(img.shape[0] * scale)), int(round(img.shape[1] * scale))
img_input = utils.resize_image(img)
print(img_input.shape)
img_input = img_input.to(device)
# compute
with torch.no_grad():
out = model.forward(img_input)
depth = utils.resize_depth(out, target_width, target_height)
img = cv2.resize((img * 255).astype(np.uint8), (target_width, target_height), interpolation=cv2.INTER_AREA)
filename = os.path.join(
output_path, os.path.splitext(os.path.basename(img_name))[0]
)
np.save(filename + '.npy', depth)
utils.write_depth(filename, depth, bits=2)
print("finished")
# if __name__ == "__main__":
# # set paths
# INPUT_PATH = "image"
# OUTPUT_PATH = "output"
# MODEL_PATH = "model.pt"
# # set torch options
# torch.backends.cudnn.enabled = True
# torch.backends.cudnn.benchmark = True
# # compute depth maps
# run_depth(INPUT_PATH, OUTPUT_PATH, MODEL_PATH, Net, target_w=640)
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