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test_simple.py
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test_simple.py
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from __future__ import absolute_import, division, print_function
import os
import sys
import glob
import argparse
import numpy as np
import PIL.Image as pil
import matplotlib as mpl
import matplotlib.cm as cm
import torch
from torchvision import transforms, datasets
import networks
from layers import disp_to_depth
import cv2
import heapq
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def parse_args():
parser = argparse.ArgumentParser(
description='Simple testing function for Lite-Mono models.')
parser.add_argument('--image_path', type=str,
help='path to a test image or folder of images', required=True)
parser.add_argument('--load_weights_folder', type=str,
help='path of a pretrained model to use',
)
parser.add_argument('--test',
action='store_true',
help='if set, read images from a .txt file',
)
parser.add_argument('--model', type=str,
help='name of a pretrained model to use',
default="lite-mono",
choices=[
"lite-mono",
"lite-mono-small",
"lite-mono-tiny",
"lite-mono-8m"])
parser.add_argument('--ext', type=str,
help='image extension to search for in folder', default="jpg")
parser.add_argument("--no_cuda",
help='if set, disables CUDA',
action='store_true')
return parser.parse_args()
def test_simple(args):
"""Function to predict for a single image or folder of images
"""
assert args.load_weights_folder is not None, \
"You must specify the --load_weights_folder parameter"
if torch.cuda.is_available() and not args.no_cuda:
device = torch.device("cuda")
else:
device = torch.device("cpu")
print("-> Loading model from ", args.load_weights_folder)
encoder_path = os.path.join(args.load_weights_folder, "encoder.pth")
decoder_path = os.path.join(args.load_weights_folder, "depth.pth")
encoder_dict = torch.load(encoder_path)
decoder_dict = torch.load(decoder_path)
# extract the height and width of image that this model was trained with
feed_height = encoder_dict['height']
feed_width = encoder_dict['width']
# LOADING PRETRAINED MODEL
print(" Loading pretrained encoder")
encoder = networks.LiteMono(model=args.model,
height=feed_height,
width=feed_width)
model_dict = encoder.state_dict()
encoder.load_state_dict({k: v for k, v in encoder_dict.items() if k in model_dict})
encoder.to(device)
encoder.eval()
print(" Loading pretrained decoder")
depth_decoder = networks.DepthDecoder(encoder.num_ch_enc, scales=range(3))
depth_model_dict = depth_decoder.state_dict()
depth_decoder.load_state_dict({k: v for k, v in decoder_dict.items() if k in depth_model_dict})
depth_decoder.to(device)
depth_decoder.eval()
# FINDING INPUT IMAGES
if os.path.isfile(args.image_path) and not args.test:
# Only testing on a single image
paths = [args.image_path]
output_directory = os.path.dirname(args.image_path)
elif os.path.isfile(args.image_path) and args.test:
gt_path = os.path.join('splits', 'eigen', "gt_depths.npz")
gt_depths = np.load(gt_path, fix_imports=True, encoding='latin1', allow_pickle=True)["data"]
side_map = {"2": 2, "3": 3, "l": 2, "r": 3}
# reading images from .txt file
paths = []
with open(args.image_path) as f:
filenames = f.readlines()
for i in range(len(filenames)):
filename = filenames[i]
line = filename.split()
folder = line[0]
if len(line) == 3:
frame_index = int(line[1])
side = line[2]
f_str = "{:010d}{}".format(frame_index, '.jpg')
image_path = os.path.join(
'kitti_data',
folder,
"image_0{}/data".format(side_map[side]),
f_str)
paths.append(image_path)
elif os.path.isdir(args.image_path):
# Searching folder for images
paths = glob.glob(os.path.join(args.image_path, '*.{}'.format(args.ext)))
output_directory = args.image_path
else:
raise Exception("Can not find args.image_path: {}".format(args.image_path))
print("-> Predicting on {:d} test images".format(len(paths)))
# PREDICTING ON EACH IMAGE IN TURN
with torch.no_grad():
for idx, image_path in enumerate(paths):
if image_path.endswith("_disp.jpg"):
# don't try to predict disparity for a disparity image!
continue
# Load image and preprocess
input_image = pil.open(image_path).convert('RGB')
original_width, original_height = input_image.size
input_image = input_image.resize((feed_width, feed_height), pil.LANCZOS)
input_image = transforms.ToTensor()(input_image).unsqueeze(0)
# PREDICTION
input_image = input_image.to(device)
features = encoder(input_image)
outputs = depth_decoder(features)
disp = outputs[("disp", 0)]
disp_resized = torch.nn.functional.interpolate(
disp, (original_height, original_width), mode="bilinear", align_corners=False)
# Saving numpy file
output_name = os.path.splitext(os.path.basename(image_path))[0]
# output_name = os.path.splitext(image_path)[0].split('/')[-1]
scaled_disp, depth = disp_to_depth(disp, 0.1, 100)
name_dest_npy = os.path.join(output_directory, "{}_disp.npy".format(output_name))
np.save(name_dest_npy, scaled_disp.cpu().numpy())
# Saving colormapped depth image
disp_resized_np = disp_resized.squeeze().cpu().numpy()
vmax = np.percentile(disp_resized_np, 95)
normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax)
mapper = cm.ScalarMappable(norm=normalizer, cmap='magma')
colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8)
im = pil.fromarray(colormapped_im)
name_dest_im = os.path.join(output_directory, "{}_disp.jpeg".format(output_name))
im.save(name_dest_im)
print(" Processed {:d} of {:d} images - saved predictions to:".format(
idx + 1, len(paths)))
print(" - {}".format(name_dest_im))
print(" - {}".format(name_dest_npy))
print('-> Done!')
if __name__ == '__main__':
args = parse_args()
test_simple(args)