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main.py
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main.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 cv2
import torch
from torchvision import transforms, datasets
import networks
def parse_args():
parser = argparse.ArgumentParser(
description='Simple testing funtion for Monodepthv2 models.')
parser.add_argument('--image_path', type=str,
help='path to a test image or folder of images', required=True)
parser.add_argument('--model_name', type=str,
help='name of a pretrained model to use',
choices=[
"mono_640x192",
"stereo_640x192",
"mono+stereo_640x192",
"mono_no_pt_640x192",
"stereo_no_pt_640x192",
"mono+stereo_no_pt_640x192",
"mono_1024x320",
"stereo_1024x320",
"mono+stereo_1024x320"])
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')
parser.add_argument("--pred_metric_depth",
help='if set, predicts metric depth instead of disparity. (This only '
'makes sense for stereo-trained KITTI models).',
action='store_true')
parser.add_argument('--output_image_path', type=str,
help='path to folder of output images', required=True)
parser.add_argument('--beta', type=float,
help='degree of haze', default=1.)
parser.add_argument('--airlight', type=float,
help='atmospheric light', default=255.)
return parser.parse_args()
def gen_haze(clean_img, depth_img, beta=1.0, A = 150):
depth_img_3c = np.zeros_like(clean_img)
depth_img_3c[:,:,0] = depth_img
depth_img_3c[:,:,1] = depth_img
depth_img_3c[:,:,2] = depth_img
norm_depth_img = depth_img_3c/255
trans = np.exp(-norm_depth_img*beta)
hazy = clean_img*trans + A*(1-trans)
hazy = np.array(hazy, dtype=np.uint8)
return hazy
def test_simple(args):
assert args.model_name is not None, \
"You must specify the --model_name parameter; see README.md for an example"
if torch.cuda.is_available() and not args.no_cuda:
device = torch.device("cuda")
else:
device = torch.device("cpu")
if args.pred_metric_depth and "stereo" not in args.model_name:
print("Warning: The --pred_metric_depth flag only makes sense for stereo-trained KITTI "
"models. For mono-trained models, output depths will not in metric space.")
# download_model_if_doesnt_exist(args.model_name)
model_path = os.path.join("models", args.model_name)
print("-> Loading model from ", model_path)
encoder_path = os.path.join(model_path, "encoder.pth")
depth_decoder_path = os.path.join(model_path, "depth.pth")
# LOADING PRETRAINED MODEL
print(" Loading pretrained encoder")
encoder = networks.ResnetEncoder(18, False)
loaded_dict_enc = torch.load(encoder_path, map_location=device)
# EXTRACT THE HEIGHT AND WIDTH OF IMAGE THAT THIS MODEL WAS TRAINED WITH
feed_height = loaded_dict_enc['height']
feed_width = loaded_dict_enc['width']
filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in encoder.state_dict()}
encoder.load_state_dict(filtered_dict_enc)
encoder.to(device)
encoder.eval()
print(" Loading pretrained decoder")
depth_decoder = networks.DepthDecoder(
num_ch_enc=encoder.num_ch_enc, scales=range(4))
loaded_dict = torch.load(depth_decoder_path, map_location=device)
depth_decoder.load_state_dict(loaded_dict)
depth_decoder.to(device)
depth_decoder.eval()
# FINDING INPUT IMAGES
if os.path.isfile(args.image_path):
# Only testing on a single image
paths = [args.image_path]
output_directory = os.path.dirname(args.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)))
# CHECK IF OUTPUT FOLDER EXISTS
if not os.path.isdir(args.output_image_path):
os.makedirs(args.output_image_path)
output_dir = args.output_image_path
# 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')
clean_img = input_image.copy()
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)
# EXTRACT 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)
gray_colormapped_im = cv2.cvtColor(colormapped_im, cv2.COLOR_RGB2GRAY)
inv_gray_colormapped_im = 255 - gray_colormapped_im
# MAKE HAZY IMAGE:
# Change degree of haze by changing 'beta' (recommended value of beta: 0.5 - 3.0)
# High beta -> Thick haze
# Low beta -> Sparse haze
hazy = gen_haze(clean_img, inv_gray_colormapped_im, beta=args.beta, A=args.airlight)
# SAVE FILES
output_name = os.path.splitext(os.path.basename(image_path))[0]
cv2.imwrite(f"{output_dir}/{output_name}_synt.jpg", cv2.cvtColor(hazy, cv2.COLOR_RGB2BGR))
print(" Processed {:d} of {:d} images".format(idx + 1, len(paths)))
print(f'-> Done! Find outputs in {output_dir}')
if __name__ == '__main__':
args = parse_args()
test_simple(args)