Source code for scripts.infer

# Copyright 2020 Toyota Research Institute.  All rights reserved.

import os
import torch
import argparse
from glob import glob
from cv2 import imwrite
import numpy as np

from packnet_sfm import ModelWrapper
from packnet_sfm.datasets.augmentations import resize_image, to_tensor
from packnet_sfm.utils.horovod import hvd_init, rank, world_size, print0
from packnet_sfm.utils.image import load_image
from packnet_sfm.utils.config import parse_test_file
from packnet_sfm.utils.load import set_debug
from packnet_sfm.utils.depth import inv2depth, viz_inv_depth
from packnet_sfm.utils.logging import pcolor


[docs]def is_image(file, ext=('.png', '.jpg',)): """Check if a file is an image with certain extensions""" return file.endswith(ext)
[docs]def parse_args(): parser = argparse.ArgumentParser(description='PackNet-SfM inference of depth maps from images') parser.add_argument('--checkpoint', type=str, help='Checkpoint (.ckpt)') parser.add_argument('--input', type=str, help='Input file or folder') parser.add_argument('--output', type=str, help='Output file or folder') parser.add_argument('--image_shape', type=tuple, default=None, help='Input and output image shape ' '(default: checkpoint\'s config.datasets.augmentation.image_shape)') parser.add_argument('--half', action="store_true", help='Use half precision (fp16)') parser.add_argument('--save_npz', action='store_true', help='save in .npz format') args = parser.parse_args() assert args.checkpoint.endswith('.ckpt'), \ 'You need to provide a .ckpt file as checkpoint' assert args.image_shape is None or len(args.image_shape) == 2, \ 'You need to provide a 2-dimensional tuple as shape (H,W)' assert (is_image(args.input) and is_image(args.output)) or \ (not is_image(args.input) and not is_image(args.input)), \ 'Input and output must both be images or folders' return args
[docs]@torch.no_grad() def infer_and_save_depth(input_file, output_file, model_wrapper, image_shape, half, save_npz): """ Process a single input file to produce and save visualization Parameters ---------- input_file : str Image file output_file : str Output file, or folder where the output will be saved model_wrapper : nn.Module Model wrapper used for inference image_shape : Image shape Input image shape half: bool use half precision (fp16) save_npz: bool save .npz output depth maps if True, else save as png """ if not is_image(output_file): # If not an image, assume it's a folder and append the input name os.makedirs(output_file, exist_ok=True) output_file = os.path.join(output_file, os.path.basename(input_file)) # change to half precision for evaluation if requested dtype = torch.float16 if half else None # Load image image = load_image(input_file) # Resize and to tensor image = resize_image(image, image_shape) image = to_tensor(image).unsqueeze(0) # Send image to GPU if available if torch.cuda.is_available(): image = image.to('cuda:{}'.format(rank()), dtype=dtype) # Depth inference (returns predicted inverse depth) pred_inv_depth = model_wrapper.depth(image)[0] if save_npz: # Get depth from predicted depth map and save to .npz depth = inv2depth(pred_inv_depth).squeeze().detach().cpu().numpy() output_file = os.path.splitext(output_file)[0] + ".npz" print('Saving {} to {}'.format( pcolor(input_file, 'cyan', attrs=['bold']), pcolor(output_file, 'magenta', attrs=['bold']))) np.savez_compressed(output_file, depth=depth) else: # Prepare RGB image rgb = image[0].permute(1, 2, 0).detach().cpu().numpy() * 255 # Prepare inverse depth viz_pred_inv_depth = viz_inv_depth(pred_inv_depth[0]) * 255 # Concatenate both vertically image = np.concatenate([rgb, viz_pred_inv_depth], 0) # Save visualization print('Saving {} to {}'.format( pcolor(input_file, 'cyan', attrs=['bold']), pcolor(output_file, 'magenta', attrs=['bold']))) imwrite(output_file, image[:, :, ::-1])
[docs]def main(args): # Initialize horovod hvd_init() # Parse arguments config, state_dict = parse_test_file(args.checkpoint) # If no image shape is provided, use the checkpoint one image_shape = args.image_shape if image_shape is None: image_shape = config.datasets.augmentation.image_shape # Set debug if requested set_debug(config.debug) # Initialize model wrapper from checkpoint arguments model_wrapper = ModelWrapper(config, load_datasets=False) # Restore monodepth_model state model_wrapper.load_state_dict(state_dict) # change to half precision for evaluation if requested dtype = torch.float16 if args.half else None # Send model to GPU if available if torch.cuda.is_available(): model_wrapper = model_wrapper.to('cuda:{}'.format(rank()), dtype=dtype) # Set to eval mode model_wrapper.eval() if os.path.isdir(args.input): # If input file is a folder, search for image files files = [] for ext in ['png', 'jpg']: files.extend(glob((os.path.join(args.input, '*.{}'.format(ext))))) files.sort() print0('Found {} files'.format(len(files))) else: # Otherwise, use it as is files = [args.input] # Process each file for fn in files[rank()::world_size()]: infer_and_save_depth( fn, args.output, model_wrapper, image_shape, args.half, args.save_npz)
if __name__ == '__main__': args = parse_args() main(args)