"""
Generate optical flow with one of the available models.
This script can display and save optical flow estimated by any of the available models. It accepts multiple types of inputs,
including: individual images, a folder of images, a video, or a webcam stream.
"""
# =============================================================================
# Copyright 2021 Henrique Morimitsu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import sys
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
import cv2 as cv
import numpy as np
import torch
from tqdm import tqdm
from ptlflow import get_model, get_model_reference
from ptlflow.models.base_model.base_model import BaseModel
from ptlflow.utils.flow_utils import flow_to_rgb, flow_write, flow_read
from ptlflow.utils.io_adapter import IOAdapter
from ptlflow.utils.utils import get_list_of_available_models_list, tensor_dict_to_numpy
def _init_parser() -> ArgumentParser:
parser = ArgumentParser()
parser.add_argument(
"model",
type=str,
choices=get_list_of_available_models_list(),
help="Name of the model to use.",
)
parser.add_argument(
"--input_path",
type=str,
nargs="+",
required=True,
help=(
"Path to the inputs. It can be in any of these formats: 1. list of paths of images; 2. path to a folder "
+ "containing images; 3. path to a video; 4. the index of a webcam."
),
)
parser.add_argument(
"--gt_path",
type=str,
default=None,
help=(
"(Optional) Path to the flow groundtruth. The path must point to one file, and --input_path must be composed of paths to two images only."
),
)
parser.add_argument(
"--write_outputs",
action="store_true",
help="If set, the model outputs are saved to disk.",
)
parser.add_argument(
"--output_path",
type=str,
default=str(Path("outputs/inference")),
help="Path to a folder where the results will be saved.",
)
parser.add_argument(
"--flow_format",
type=str,
default="flo",
choices=["flo", "png"],
help="The format to use when saving the estimated optical flow.",
)
parser.add_argument(
"--show",
action="store_true",
help="If set, the results are shown on the screen.",
)
parser.add_argument(
"--auto_forward",
action="store_true",
help=(
"Only relevant if used with --show. If set, consecutive results will be shown without stopping. "
+ "Otherwise, each result remain on the screen until the user press a button."
),
)
parser.add_argument(
"--input_size",
type=int,
nargs=2,
default=[0, 0],
help="If larger than zero, resize the input image before forwarding.",
)
parser.add_argument(
"--scale_factor",
type=float,
default=None,
help=("Multiply the input image by this scale factor before forwarding."),
)
parser.add_argument(
"--max_show_side",
type=int,
default=1000,
help=(
"If max(height, width) of the output image is larger than this value, then the image is downscaled "
"before showing it on the screen."
),
)
parser.add_argument(
"--fp16", action="store_true", help="If set, use half floating point precision."
)
return parser
[docs]
@torch.no_grad()
def infer(args: Namespace, model: BaseModel) -> None:
"""Perform the inference.
Parameters
----------
model : BaseModel
The model to be used for inference.
args : Namespace
Arguments to configure the model and the inference.
See Also
--------
ptlflow.models.base_model.base_model.BaseModel : The parent class of the available models.
"""
model.eval()
if torch.cuda.is_available():
model = model.cuda()
if args.fp16:
model = model.half()
cap, img_paths, num_imgs, prev_img = init_input(args.input_path)
flow_gt = None
if args.gt_path is not None:
assert num_imgs == 2
flow_gt = flow_read(args.gt_path)
if args.scale_factor is not None:
io_adapter = IOAdapter(
model,
prev_img.shape[:2],
target_scale_factor=args.scale_factor,
cuda=torch.cuda.is_available(),
fp16=args.fp16,
)
else:
io_adapter = IOAdapter(
model,
prev_img.shape[:2],
args.input_size,
cuda=torch.cuda.is_available(),
fp16=args.fp16,
)
prev_dir_name = None
for i in tqdm(range(1, num_imgs)):
img, img_dir_name, img_name, is_img_valid = _read_image(cap, img_paths, i)
if prev_dir_name is None:
prev_dir_name = img_dir_name
if not is_img_valid:
break
if img_dir_name == prev_dir_name:
inputs = io_adapter.prepare_inputs([prev_img, img])
preds = model(inputs)
preds["images"] = inputs["images"]
preds = io_adapter.unscale(preds)
preds_npy = tensor_dict_to_numpy(preds)
if flow_gt is not None:
flow_pred = preds_npy["flows"]
valid = ~np.isnan(flow_gt[..., 0])
sq_dist = np.power(flow_pred - flow_gt, 2).sum(2)
epe = np.sqrt(sq_dist[valid])
gt_sq_dist = np.power(flow_gt, 2).sum(2)
gt_dist_valid = np.sqrt(gt_sq_dist[valid])
outlier = (epe > 3) & (epe > 0.05 * gt_dist_valid)
print(
f"EPE: {epe.mean():.03f}, Outlier: {100*outlier.mean():.03f}",
)
preds_npy["flows_viz"] = flow_to_rgb(preds_npy["flows"])[:, :, ::-1]
if preds_npy.get("flows_b") is not None:
preds_npy["flows_b_viz"] = flow_to_rgb(preds_npy["flows_b"])[:, :, ::-1]
if args.write_outputs:
write_outputs(
preds_npy,
args.output_path,
img_name,
args.flow_format,
img_dir_name,
)
if args.show:
img1 = prev_img
img2 = img
if min(args.input_size) > 0:
img1 = cv.resize(prev_img, args.input_size[::-1])
img2 = cv.resize(img, args.input_size[::-1])
key = show_outputs(
img1, img2, preds_npy, args.auto_forward, args.max_show_side
)
if key == 27:
break
prev_dir_name = img_dir_name
prev_img = img
[docs]
def show_outputs(
img1: np.ndarray,
img2: np.ndarray,
preds_npy: Dict[str, np.ndarray],
auto_forward: bool,
max_show_side: int,
) -> int:
"""Show the images on the screen.
Parameters
----------
img1 : np.ndarray
First image for estimating the optical flow.
img2 : np.ndarray
Second image for estimating the optical flow.
preds_npy : dict[str, np.ndarray]
The model predictions converted to numpy format.
auto_forward : bool
If false, the user needs to press a key to move to the next image.
max_show_side : int
If max(height, width) of the image is larger than this value, then it is downscaled before showing.
Returns
-------
int
A value representing which key the user pressed.
See Also
--------
ptlflow.utils.utils.tensor_dict_to_numpy : This function can generate preds_npy.
"""
preds_npy["img1"] = img1
preds_npy["img2"] = img2
for k, v in preds_npy.items():
if len(v.shape) == 2 or v.shape[2] == 1 or v.shape[2] == 3:
if max(v.shape[:2]) > max_show_side:
scale_factor = float(max_show_side) / max(v.shape[:2])
v = cv.resize(
v, (int(scale_factor * v.shape[1]), int(scale_factor * v.shape[0]))
)
cv.imshow(k, v)
if auto_forward:
w = 1
else:
w = 0
key = cv.waitKey(w)
return key
[docs]
def write_outputs(
preds_npy: Dict[str, np.ndarray],
output_dir: str,
img_name: str,
flow_format: str,
img_dir_name: Optional[str] = None,
) -> None:
"""Show the images on the screen.
Parameters
----------
preds_npy : dict[str, np.ndarray]
The model predictions converted to numpy format.
output_dir : str
The path to the root dir where the outputs will be saved.
img_name : str
The name to be used to save each image (without extension).
flow_format : str
The format (extension) of the flow file to be saved. It can one of {flo, png}.
See Also
--------
ptlflow.utils.utils.tensor_dict_to_numpy : This function can generate preds_npy.
"""
for k, v in preds_npy.items():
out_dir = Path(output_dir) / k
if img_dir_name is not None:
out_dir /= img_dir_name
out_dir.mkdir(parents=True, exist_ok=True)
out_path = out_dir / img_name
if k == "flows" or k == "flows_b":
if flow_format[0] != ".":
flow_format = "." + flow_format
flow_write(out_path.with_suffix(flow_format), v)
print(f"Saved flow at: {out_path}")
elif len(v.shape) == 2 or (
len(v.shape) == 3 and (v.shape[2] == 1 or v.shape[2] == 3)
):
if v.max() <= 1:
v = v * 255
cv.imwrite(str(out_path.with_suffix(".png")), v.astype(np.uint8))
print(f"Saved image at: {out_path}")
def _read_image(
cap: cv.VideoCapture, img_paths: List[Union[str, Path]], i: int
) -> Tuple[np.ndarray, str, bool]:
if cap is not None:
is_img_valid, img = cap.read()
img_dir_name = None
img_name = "{:08d}".format(i)
else:
img = cv.imread(str(img_paths[i]))
img_dir_name = None
if len(img_paths[i].parent.name) > 0:
img_dir_name = img_paths[i].parent.name
img_name = img_paths[i - 1].stem
is_img_valid = True
return img, img_dir_name, img_name, is_img_valid
if __name__ == "__main__":
parser = _init_parser()
# TODO: It is ugly that the model has to be gotten from the argv rather than the argparser.
# However, I do not see another way, since the argparser requires the model to load some of the args.
FlowModel = None
if len(sys.argv) > 1 and sys.argv[1] != "-h" and sys.argv[1] != "--help":
FlowModel = get_model_reference(sys.argv[1])
parser = FlowModel.add_model_specific_args(parser)
args = parser.parse_args()
model_id = args.model
if args.pretrained_ckpt is not None:
model_id += f"_{Path(args.pretrained_ckpt).stem}"
args.output_path = Path(args.output_path) / model_id
model = get_model(sys.argv[1], args.pretrained_ckpt, args)
infer(args, model)