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occlusion.py
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occlusion.py
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#!/usr/bin/env python
# ==============================================================================
# MIT License
#
# Copyright 2020 Institute for Automotive Engineering of RWTH Aachen University.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# ==============================================================================
import os
import argparse
import yaml
import tqdm
import multiprocessing
import numpy as np
import cv2
import skimage.draw
BLOCKING_LABELS = ["building", "wall", "car", "truck", "bus", "caravan", "trailer", "train"]
TALL_NON_BLOCKING_LABELS = ["vegetation"] # will be visible behind small blocking objects (e.g. cars)
COLORS = {
"occluded" : (150, 150, 150),
"static" : ( 0, 0, 0),
"dynamic" : (111, 74, 0),
"ground" : ( 81, 0, 81),
"road" : (128, 64, 128),
"sidewalk" : (244, 35, 232),
"parking" : (250, 170, 160),
"rail track" : (230, 150, 140),
"building" : ( 70, 70, 70),
"wall" : (102, 102, 156),
"fence" : (190, 153, 153),
"guard rail" : (180, 165, 180),
"bridge" : (150, 100, 100),
"tunnel" : (150, 120, 90),
"pole" : (153, 153, 153),
"polegroup" : (153, 153, 153),
"traffic light": (250, 170, 30),
"traffic sign" : (220, 220, 0),
"vegetation" : (107, 142, 35),
"terrain" : (152, 251, 152),
"sky" : ( 70, 130, 180),
"person" : (255, 0, 0),
"rider" : (220, 20, 60),
"car" : ( 0, 0, 142),
"truck" : ( 0, 0, 70),
"bus" : ( 0, 60, 100),
"caravan" : ( 0, 0, 90),
"trailer" : ( 0, 0, 110),
"train" : ( 0, 80, 100),
"motorcycle" : ( 0, 0, 230),
"bicycle" : (119, 11, 32),
"roadmark" : (255, 255, 255)
}
DUMMY_COLOR = tuple(np.random.randint(0, 256, 3))
while DUMMY_COLOR in COLORS.values():
DUMMY_COLOR = tuple(np.random.randint(0, 256, 3))
class Camera:
def __init__(self, config, frame, pxPerM):
self.origin = (frame[0] + config["XCam"] * pxPerM[0], frame[1] - config["YCam"] * pxPerM[1])
self.yaw = -config["yaw"]
self.fov = 2.0 * np.arctan(config["px"] / config["fx"]) * 180.0 / np.pi
thetaMin = self.yaw - self.fov / 2.0
thetaMax = (self.yaw + self.fov / 2.0)
thetaMin = thetaMin % 180 if thetaMin < -180 else thetaMin
thetaMax = thetaMax % -180 if thetaMax > 180 else thetaMax
self.fovBounds = (thetaMin, thetaMax)
def canSee(self, x, y):
dx, dy = x - self.origin[0], y - self.origin[1]
theta = np.arctan2(dy, dx) * 180.0 / np.pi
if self.fovBounds[0] > self.fovBounds[1]:
return (self.fovBounds[0] <= theta) or (theta <= self.fovBounds[1])
else:
return (self.fovBounds[0] <= theta) and (theta <= self.fovBounds[1])
def floodFill(px, color, inputImg, outputImg):
mask = np.zeros((inputImg.shape[0]+2, inputImg.shape[1]+2), np.uint8)
flags = 4 | (255 << 8) | cv2.FLOODFILL_FIXED_RANGE | cv2.FLOODFILL_MASK_ONLY
cv2.floodFill(image=inputImg, mask=mask, seedPoint=(px[0], px[1]), newVal=(255, 255, 255), loDiff=(1,1,1), upDiff=(1,1,1), flags=flags)
outputImg[np.where(mask[1:-1, 1:-1] == 255)] = color
def castRay(fromPoint, toPoint, inputImg, outputImg):
# loop over all pixels along the ray, moving outwards
ray = list(zip(*skimage.draw.line(*(int(fromPoint[0]), int(fromPoint[1])), *(int(toPoint[0]), int(toPoint[1])))))
stopRay = stopTransfer = False
for px in ray:
# out-of-bounds check
if not (0 <= px[0] and px[0] < inputImg.shape[1] and 0 <= px[1] and px[1] < inputImg.shape[0]):
continue
# check if ray hit a blocking object class
for label in BLOCKING_LABELS:
if (inputImg[px[1], px[0], :] == COLORS[label]).all():
# if car, continue ray to look for more blocking objects, else stop ray
if label == "car":
if stopTransfer: # if car behind another car, skip
continue
else: # if first car in line of ray
stopTransfer = True
else:
stopRay = True
# transfer blocking object to output image
if not (outputImg[px[1], px[0], :] == COLORS[label]).all():
floodFill(px, COLORS[label], inputImg, outputImg)
break
if stopRay: # stop ray if blocked
break
if stopTransfer: # if transfer is stopped, still look for tall non-blocking labels to transfer
for label in TALL_NON_BLOCKING_LABELS:
if (inputImg[px[1], px[0], :] == COLORS[label]).all():
outputImg[px[1], px[0], :] = inputImg[px[1], px[0], :]
break
else: # transfer px to output image
outputImg[px[1], px[0], :] = inputImg[px[1], px[0], :]
# ==============================================================================
# parse command line arguments and read image
parser = argparse.ArgumentParser(description="Determines the areas not visible from vehicle cameras and removes them from drone camera footage.")
parser.add_argument("img", help="segmented drone image")
parser.add_argument("drone", help="drone camera config file")
parser.add_argument("cam", nargs="+", help="camera config file")
parser.add_argument("--batch", help="process folders of images instead of single images", action="store_true")
parser.add_argument("--output", help="output directory to write output images to")
args = parser.parse_args()
# load image paths
imagePaths = []
if not args.batch:
imagePaths.append(os.path.abspath(args.img))
else:
path = os.path.abspath(args.img)
imagePaths = [os.path.join(path, f) for f in sorted(os.listdir(path)) if f[0] != "."]
# parse camera configs
with open(os.path.abspath(args.drone)) as stream:
droneConfig = yaml.safe_load(stream)
cameraConfigs = []
for cameraConfig in args.cam:
with open(os.path.abspath(cameraConfig)) as stream:
cameraConfigs.append(yaml.safe_load(stream))
# create output directories
if args.output:
outputDir = os.path.abspath(args.output)
if not os.path.exists(outputDir):
os.makedirs(outputDir)
# determine image dimensions in (m)
inputImg = cv2.imread(imagePaths[0])
dxm = inputImg.shape[1] / droneConfig["fx"] * droneConfig["ZCam"]
dym = inputImg.shape[0] / droneConfig["fy"] * droneConfig["ZCam"]
pxPerM = (inputImg.shape[1] / dxm, inputImg.shape[0] / dym)
base_link = (int(inputImg.shape[1] / 2.0 - droneConfig["XCam"] * pxPerM[0]), int(inputImg.shape[0] / 2.0 + droneConfig["YCam"] * pxPerM[0]))
# create cameras
cameras = []
for cameraConfig in cameraConfigs:
cam = Camera(cameraConfig, base_link, pxPerM)
cameras.append(cam)
# define processing of a single image
def processImage(imagePath):
filename = os.path.basename(imagePath)
# read input image and create blank output image
inputImg = cv2.imread(imagePath)
inputImg = cv2.cvtColor(inputImg, cv2.COLOR_BGR2RGB)
outputImg = np.zeros(inputImg.shape, dtype=np.uint8) + np.array(COLORS["occluded"], dtype=np.uint8)
# temporarily recolor ego vehicle (if in image), s.t. it does not block
if base_link[0] > 0 and base_link[1] > 0:
floodFill(base_link, DUMMY_COLOR, inputImg, inputImg)
# loop over all border pixels to determine if ray is visible
rays = []
for cam in cameras:
for x in range(inputImg.shape[1]):
if cam.canSee(x, 0):
rays.append((cam.origin, (x, 0)))
for x in range(inputImg.shape[1]):
if cam.canSee(x, inputImg.shape[0]):
rays.append((cam.origin, (x, inputImg.shape[0])))
for y in range(inputImg.shape[0]):
if cam.canSee(0, y):
rays.append((cam.origin, (0, y)))
for y in range(inputImg.shape[0]):
if cam.canSee(inputImg.shape[1], y):
rays.append((cam.origin, (inputImg.shape[1], y)))
# cast rays
for ray in rays:
castRay(ray[0], ray[1], inputImg, outputImg)
# recolor ego vehicle as car and transfer to output
if base_link[0] > 0 and base_link[1] > 0:
floodFill(base_link, COLORS["car"], inputImg, outputImg)
floodFill(base_link, COLORS["car"], inputImg, outputImg)
# display or export output image
outputImg = cv2.cvtColor(outputImg, cv2.COLOR_RGB2BGR)
if args.output:
cv2.imwrite(os.path.join(outputDir, filename), outputImg)
else:
cv2.namedWindow(filename, cv2.WINDOW_NORMAL)
cv2.imshow(filename, outputImg)
cv2.waitKey(0)
cv2.destroyAllWindows()
# process images in parallel
if args.batch:
print("Warning: This might take an extremely long time, are you sure you need to (re)generate the occluded labels?")
pool = multiprocessing.Pool(multiprocessing.cpu_count())
for _ in tqdm.tqdm(pool.imap(processImage, imagePaths), desc="Processing images", total=len(imagePaths), smoothing=0):
pass
pool.close()
pool.join()
else:
processImage(imagePaths[0])