def __init__(self, camera, video, cad, num_cars_mean):
self.sun = Sun()
self.camera = camera
self.video = video
# get the map of azimuths.
# it has gray values (r==g==b=) and alpha, saved as 4-channels
azimuth_path = atcity(
op.join(camera['camera_dir'], camera['azimuth_name']))
azimuth_map = cv2.imread(azimuth_path, cv2.IMREAD_UNCHANGED)
assert azimuth_map is not None and azimuth_map.shape[2] == 4
# black out the invisible azimuth_map regions
if 'mask' in camera and camera['mask']:
mask_path = atcity(op.join(camera['camera_dir'], camera['mask']))
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
assert mask is not None, mask_path
azimuth_map[mask] = 0
self.num_cars_mean = num_cars_mean
self.num_cars_std = num_cars_mean * 1.0
self.azimuth_map = azimuth_map
self.cad = cad
self.pxls_in_meter = camera['pxls_in_meter']
def __init__(self, camera, video, cad, speed_kph, burn_in=True):
self.sun = Sun() # TODO: only load when it's sunny
self.camera = camera
self.video = video
# load mask
if 'mask' in camera:
mask_path = atcity(op.join(camera['camera_dir'], camera['mask']))
self.mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
assert self.mask is not None, mask_path
logging.info('TrafficModel: loaded a mask')
else:
self.mask = None
# create lanes
lanes_path = atcity(op.join(camera['camera_dir'],
camera['lanes_name']))
lanes_dicts = json.load(open(lanes_path))
self.lanes = [
Lane(('%d' % i), l, cad, speed_kph, camera['pxls_in_meter'])
for i, l in enumerate(lanes_dicts)
]
logging.info('TrafficModel: loaded %d lanes' % len(self.lanes))
if burn_in:
for i in range(100):
self.get_next_frame(time=datetime.now())
def generate_video_traffic(job):
''' Generate traffic file for the whole video.
Args:
in_db_file - should have all the images for which traffic is generated
job - the same as for process_video
'''
assertParamIsThere(job, 'in_db_file')
assertParamIsThere(job, 'out_video_dir')
setParamUnlessThere(job, 'frame_range', '[::]')
assertParamIsThere(job, 'video_dir')
video = Video(video_dir=job['video_dir'])
camera = video.build_camera()
assert op.exists(atcity(job['in_db_file'])), \
'in db %s does not exist' % atcity(job['in_db_file'])
conn_in = sqlite3.connect(atcity(job['in_db_file']))
c_in = conn_in.cursor()
c_in.execute('SELECT time FROM images')
timestamps = c_in.fetchall()
conn_in.close()
cad = Cad()
if 'speed_kph' in job:
model = TrafficModel(camera,
video,
cad=cad,
speed_kph=job['speed_kph'])
elif 'num_cars' in job:
model = TrafficModelRandom(camera,
video,
cad=cad,
num_cars_mean=job['num_cars'])
else:
assert False
diapason = Diapason(len(timestamps), job['frame_range'])
traffic = {'in_db_file': job['in_db_file']}
traffic['frames'] = []
for frame_id in diapason.frame_range:
logging.info('generating traffic for frame %d' % frame_id)
timestamp = timestamps[frame_id][0]
time = parseTimeString(timestamp)
traffic_frame = model.get_next_frame(time)
traffic_frame['frame_id'] = frame_id # for validating
traffic['frames'].append(traffic_frame)
return traffic
def combine_frame(background, video, camera):
''' Overlay image onto background '''
jpg_qual = 40
WORK_DIR = '%s-%d' % (WORK_RENDER_DIR, os.getpid())
# load camera dimensions (compare it to everything for extra safety)
width0 = camera.info['camera_dims']['width']
height0 = camera.info['camera_dims']['height']
# get background file
assert background is not None
assert background.shape == (height0, width0, 3), background.shape
# make a completely gray background frame hahaha
#background.fill(128)
cv2.imwrite(op.join(WORK_DIR, BACKGROUND_FILENAME), background)
# get shadows file
#shadow_path = op.join(WORK_DIR, 'render.png')
#shadow = scipy.misc.imread(shadow_path)
#shadow[:,:,3] = 0 # assign full transparency
#scipy.misc.imsave(shadow_path, shadow)
# remove previous result so that there is an error if blender fails
if op.exists(op.join(WORK_DIR, COMBINED_FILENAME)):
os.remove(op.join(WORK_DIR, COMBINED_FILENAME))
# overlay
assert video.combine_blend_file is not None
combine_scene_path = atcity(video.combine_blend_file)
command = [
'%s/blender' % os.getenv('BLENDER_ROOT'), combine_scene_path,
'--background', '--python',
'%s/src/augmentation/combineScene.py' % os.getenv('CITY_PATH')
]
returncode = subprocess.call(command,
shell=False,
stdout=FNULL,
stderr=FNULL)
logging.info('combine: blender returned code %s' % str(returncode))
combined_filepath = op.join(WORK_DIR, COMBINED_FILENAME)
assert op.exists(combined_filepath), combined_filepath
image = cv2.imread(combined_filepath)
assert image.shape == (height0, width0,
3), '%s vs %s' % (image.shape, (height0, width0))
# reencode to match jpeg quality
shutil.move(combined_filepath, op.join(WORK_DIR, 'uncompressed.png'))
_, ajpg = cv2.imencode(".jpg", image, [cv2.IMWRITE_JPEG_QUALITY, jpg_qual])
image = cv2.imdecode(ajpg, cv2.CV_LOAD_IMAGE_COLOR)
cv2.imwrite(combined_filepath, image)
return image
import sys, os, os.path as op
sys.path.insert(0, op.join(os.getenv('CITY_PATH'), 'src'))
import json
import logging
import bpy
import numpy as np
from learning.helperSetup import atcity, setupLogging, setParamUnlessThere
''' Make all frame postprocessing and combination in RENDER_DIR '''
WORK_RENDER_DIR = atcity('data/augmentation/blender/current-frame')
BACKGROUND_FILENAME = 'background.png'
NORMAL_FILENAME = 'render.png'
CARSONLY_FILENAME = 'cars-only.png'
COMBINED_FILENAME = 'out.png'
#CORRECTION_FILENAME = 'color-correction.json'
WORK_DIR = '%s-%d' % (WORK_RENDER_DIR, os.getppid())
WORK_DIR_SUFFIX = '-%d' % os.getppid()
#correction_path = op.join(WORK_DIR, CORRECTION_FILENAME)
image_node = bpy.context.scene.node_tree.nodes['Image-Background'].image
image_node.filepath = op.join(WORK_DIR, BACKGROUND_FILENAME)
image_node = bpy.context.scene.node_tree.nodes['Image-Cars-Only'].image
image_node.filepath = op.join(WORK_DIR, CARSONLY_FILENAME)
image_node = bpy.context.scene.node_tree.nodes['Image-Normal'].image
image_node.filepath = op.join(WORK_DIR, NORMAL_FILENAME)
bpy.context.scene.node_tree.nodes[
def __init__(self, video_dir=None, video_info=None):
if video_dir:
video_name = op.basename(video_dir)
video_path = atcity(op.join(video_dir, '%s.json' % video_name))
assert op.exists(video_path), video_path
logging.info('Video: loading info from: %s' % video_path)
video_info = json.load(open(video_path))
elif video_info:
assert 'video_dir' in video_info
video_dir = video_info['video_dir']
assert op.exists(atcity(video_dir)), video_dir
video_name = op.basename(video_dir)
else:
raise Exception('pass video_info or video_dir')
logging.info('Video: parse info for: %s' % video_dir)
self.info = video_info
self.info['video_name'] = video_name
if 'example_frame_name' in video_info:
logging.info('- found example_frame_name: %s' % example_frame_name)
self.example_frame = cv2.imread(
op.join(video_dir, example_frame_name))
assert self.example_frame is not None
else:
# trying possible paths, and take the first to match
example_frame_paths = glob(atcity(op.join(video_dir,
'frame*.png')))
if len(example_frame_paths) > 0:
logging.info('- deduced example_frame: %s' %
example_frame_paths[0])
self.example_frame = cv2.imread(example_frame_paths[0])
self.info['example_frame_name'] = op.basename(
example_frame_paths[0])
assert self.example_frame is not None
else:
logging.warning('- no example_frame for %s' % video_dir)
self.example_frame = None
self.info['example_frame_name'] = None
if 'example_background_name' in video_info:
example_background_name = video_info['example_background_name']
logging.info('- found example_background_name: %s' %
example_background_name)
example_background_path = atcity(
op.join(video_dir, example_background_name))
self.example_background = cv2.imread(example_background_path)
assert self.example_background is not None
else:
# trying possible paths
example_back_paths = glob(
atcity(op.join(video_dir, 'background*.png')))
if len(example_back_paths) > 0:
logging.info('- deduced example_background: %s' %
example_back_paths[0])
self.example_background = cv2.imread(example_back_paths[0])
assert self.example_background is not None
else:
logging.warning('- no example_background for %s' % video_dir)
self.example_background = None
if 'start_timestamp' in video_info:
start_timestamp = video_info['start_timestamp']
logging.info('- found start_timestamp: %s' % start_timestamp)
self.start_time = datetime.strptime(start_timestamp, TIME_FORMAT)
else:
# deduce from the name of the file
self.start_time = datetime.strptime(video_name, VIDEO_DIR_STRPTIME)
logging.info('- deduced start_time: %s' %
self.start_time.strftime(TIME_FORMAT))
if 'frame_range' in video_info:
self.info['frame_range'] = video_info['frame_range']
logging.info('- found frame_range: %s' % video_info['frame_range'])
else:
self.info['frame_range'] = ':'
if 'render_blend_file' in video_info:
self.render_blend_file = video_info['render_blend_file']
logging.info('- found render_blend_file: %s' %
self.render_blend_file)
assert op.exists(atcity(self.render_blend_file))
elif op.exists(atcity(op.join(video_dir, 'render.blend'))):
# if found the default name in the video folder
self.render_blend_file = op.join(video_dir, 'render.blend')
logging.info('- found render_blend_file in video dir: %s' %
self.render_blend_file)
elif op.exists(atcity(op.join(video_dir, 'render-generated.blend'))):
# if found the default name in the video folder
self.render_blend_file = op.join(video_dir,
'render-generated.blend')
logging.warning('- using generated render_blend_file: %s' %
self.render_blend_file)
else:
logging.warning('- could not figure out render_blend_file')
if 'combine_blend_file' in video_info:
self.combine_blend_file = video_info['combine_blend_file']
logging.info('- found combine_blend_file: %s' %
self.combine_blend_file)
op.exists(atcity(self.combine_blend_file))
elif op.exists(atcity(op.join(video_dir, 'combine.blend'))):
# if found the default name in the video folder
self.combine_blend_file = op.join(video_dir, 'combine.blend')
logging.info('- found combine_blend_file in video dir: %s' %
self.combine_blend_file)
elif op.exists(atcity(op.join(video_dir, 'combine-generated.blend'))):
# if found the default name in the video folder
self.combine_blend_file = op.join(video_dir,
'combine-generated.blend')
logging.warning('- using generated combine_blend_file: %s' %
self.combine_blend_file)
else:
logging.warning('- could not figure out combine_blend_file')
if 'camera_dir' in video_info:
self.camera_dir = video_info['camera_dir']
logging.info('- found camera_dir: %s' % self.camera_dir)
else:
# deduce from the name of the file
self.camera_dir = op.dirname(video_dir)
logging.info('- deduced camera_dir: %s' % self.camera_dir)
assert op.exists(atcity(self.camera_dir)), atcity(self.camera_dir)
if 'pose_id' in video_info:
self.pose_id = int(video_info['pose_id'])
logging.info('- found pose_id: %d' % self.pose_id)
else:
self.pose_id = 0
logging.info('- take default pose_id = 0')
def _rename(render_dir, from_name, to_name):
os.rename(atcity(op.join(render_dir, from_name)),
atcity(op.join(render_dir, to_name)))
def make_snapshot(render_dir, car_names, params):
'''Set up the weather, and render vehicles into files
Args:
render_dir: path to directory where to put all rendered images
car_names: names of car objects in the scene
params: dictionary with frame information
Returns:
nothing
'''
logging.info('make_snapshot: started')
setParamUnlessThere(params, 'scale', 1)
setParamUnlessThere(params, 'render_individual_cars', True)
# debug options
setParamUnlessThere(params, 'save_blender_file', False)
setParamUnlessThere(params, 'render_satellite', False)
setParamUnlessThere(params, 'render_cars_as_cubes', False)
bpy.data.worlds['World'].light_settings.environment_energy = 0.0
bpy.data.worlds['World'].light_settings.ao_factor = 0.5
bpy.data.objects['-Sky-sunset'].data.energy = np.random.normal(1, 0.5) #2
params['weather'] = np.random.choice(['Sunny', 'Cloudy', 'Rainy', 'Wet'])
set_weather(params)
# render the image from satellite, when debuging
if '-Satellite' in bpy.data.objects:
bpy.data.objects[
'-Satellite'].hide_render = not params['render_satellite']
# make all cars receive shadows
logging.info('materials: %s' % len(bpy.data.materials))
for m in bpy.data.materials:
m.use_transparent_shadows = True
# create render dir
if not op.exists(render_dir):
os.makedirs(render_dir)
# make all cars receive shadows
logging.info('materials: %s' % len(bpy.data.materials))
for m in bpy.data.materials:
m.use_transparent_shadows = True
# # render all cars and shadows
# bpy.context.scene.render.layers['RenderLayer'].use_pass_combined = True
# bpy.context.scene.render.layers['RenderLayer'].use_pass_z = False
# #bpy.data.objects['-Ground'].hide_render = False
# render_scene(op.join(render_dir, 'render'))
# # render cars depth map
# #bpy.context.scene.render.alpha_mode = 'TRANSPARENT'
# bpy.context.scene.render.layers['RenderLayer'].use_pass_combined = False
# bpy.context.scene.render.layers['RenderLayer'].use_pass_z = True
# #bpy.data.objects['-Ground'].hide_render = True
# render_scene(op.join(render_dir, 'depth-all'))
# # render just the car for each car (to extract bbox)
# if params['render_individual_cars'] and not params['render_cars_as_cubes']:
# # hide all cars
# for car_name in car_names:
# hide_car (car_name)
# # show, render, and hide each car one by one
# for i,car_name in enumerate(car_names):
# show_car (car_name)
# render_scene( op.join(render_dir, 'depth-car-%03d.png' % i) )
# hide_car (car_name)
# # clean up
# bpy.data.objects['-Ground'].hide_render = False
# if not params['render_cars_as_cubes']:
# for car_name in car_names:
# show_car (car_name)
def _rename(render_dir, from_name, to_name):
os.rename(atcity(op.join(render_dir, from_name)),
atcity(op.join(render_dir, to_name)))
# set directional blur amount from if given
#bpy.data.node_groups['Compositing Nodetree'].nodes['Camera-Blur'].zoom
# there are two nodes -- "render" and "depth"
# they save images in BW16 or RBG8
# they render layers "Render" and "Depth" with "Combined" and "Z" passes.
bpy.context.scene.node_tree.nodes['depth'].base_path = atcity(render_dir)
bpy.context.scene.node_tree.nodes['render'].base_path = atcity(render_dir)
# leave only shadows
for m in bpy.data.materials:
if m != bpy.data.materials['Material-dry-asphalt'] and \
m != bpy.data.materials['Material-wet-asphalt']:
m.use_only_shadow = True
# render shadows only
bpy.data.objects['-Ground'].hide_render = False
bpy.ops.render.render(write_still=True, layer='Render')
_rename(render_dir, 'render0001', 'render.png')
# materials back to normal
for m in bpy.data.materials:
if m != bpy.data.materials['Material-dry-asphalt'] and \
m != bpy.data.materials['Material-wet-asphalt']:
m.use_only_shadow = False
# render without ground
bpy.data.objects['-Ground'].hide_render = True
bpy.ops.render.render(write_still=True, layer='Render')
_rename(render_dir, 'render0001', 'cars-only.png')
# render depth of all cars
bpy.ops.render.render(write_still=True, layer='Depth')
_rename(render_dir, 'depth0001', 'depth-all.png')
if params['render_individual_cars'] and not params['render_cars_as_cubes']:
for car_i0, car_name0 in enumerate(car_names):
# remove all cars from the only layer, and add car_name0 back to it
for car_name in car_names:
bpy.data.objects[car_name].hide_render = True
bpy.data.objects[car_name0].hide_render = False
# render scene
bpy.ops.render.render(write_still=True, layer='Depth')
_rename(render_dir, 'depth0001', 'depth-%03d.png' % car_i0)
if params['save_blender_files']:
bpy.ops.wm.save_as_mainfile(
filepath=atcity(op.join(render_dir, 'render.blend')))
# logging.info ('objects in the end of frame: %d' % len(bpy.data.objects))
logging.info('make_snapshot: successfully finished a frame')
import bpy
import sys, os, os.path as op
sys.path.insert(0, op.join(os.getenv('CITY_PATH'), 'src'))
import json
import logging
import numpy as np
from augmentation.common import *
from learning.helperSetup import atcity, setupLogging, setParamUnlessThere
WORK_RENDER_DIR = atcity('data/augmentation/blender/current-frame')
TRAFFIC_FILENAME = 'traffic.json'
WORK_DIR = '%s-%d' % (WORK_RENDER_DIR, os.getppid())
def make_snapshot(render_dir, car_names, params):
'''Set up the weather, and render vehicles into files
Args:
render_dir: path to directory where to put all rendered images
car_names: names of car objects in the scene
params: dictionary with frame information
Returns:
nothing
'''
logging.info('make_snapshot: started')
setParamUnlessThere(params, 'scale', 1)
setParamUnlessThere(params, 'render_individual_cars', True)
# debug options
setParamUnlessThere(params, 'save_blender_file', False)
help='python style ranges, e.g. "[5::2]"')
parser.add_argument('--in_db_file', required=True)
parser.add_argument('--video_dir', required=True)
parser.add_argument('--traffic_file',
required=True,
help='output .json file where to write traffic info. '
'Can be "traffic.json" in video output dir.')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--speed_kph', type=int)
group.add_argument('--num_cars', type=int)
args = parser.parse_args()
setupLogging('log/augmentation/GenerateTraffic.log', args.logging_level,
'w')
if not op.exists(atcity(op.dirname(args.traffic_file))):
os.makedirs(atcity(op.dirname(args.traffic_file)))
job = {
'frame_range': args.frame_range,
'in_db_file': args.in_db_file,
'video_dir': args.video_dir,
'out_video_dir': op.dirname(args.in_db_file)
}
if args.speed_kph is not None:
setParamUnlessThere(job, 'speed_kph', args.speed_kph)
elif args.num_cars is not None:
setParamUnlessThere(job, 'num_cars', args.num_cars)
else:
assert False
import sys, os, os.path as op
sys.path.insert(0, os.path.join(os.getenv('CITY_PATH'), 'src'))
import os, os.path as op
import datetime
import pprint
import cPickle
from learning.helperSetup import atcity
txt_file = 'data/augmentation/resources/sun_position.txt'
bin_file = 'data/augmentation/resources/sun_position.pkl'
with open(atcity(txt_file)) as f:
lines = f.readlines()
lines = lines[9:]
positions = {}
for line in lines:
(daystr, clockstr, altitude, azimuth) = tuple(line.split())
(month, day) = tuple(daystr.split('/'))
(hour, minute) = tuple(clockstr.split(':'))
date = datetime.datetime (year=2015, month=int(month), day=int(day),
hour=int(hour), minute=int(minute))
positions[date] = (float(altitude), float(azimuth))
with open(atcity(bin_file), 'wb') as f:
cPickle.dump(positions, f, cPickle.HIGHEST_PROTOCOL)
'''Render a frame using default video's background.
Leave the frame at the default blender location'''
parser = argparse.ArgumentParser()
parser.add_argument('--work_dir', required=True)
parser.add_argument('--video_dir', required=True)
parser.add_argument('--save_blender_files', action='store_true')
parser.add_argument('--no_combine', action='store_true')
parser.add_argument('--no_render', action='store_true')
parser.add_argument('--no_annotations', action='store_true')
parser.add_argument('--logging_level', default=20, type=int)
parser.add_argument('--background_file',
help='if not given, take the default from video_dir')
args = parser.parse_args()
setupLogging('log/augmentation/ProcessFrame.log', args.logging_level, 'w')
traffic = json.load(open(atcity(op.join(args.work_dir, 'traffic.json'))))
traffic['save_blender_files'] = args.save_blender_files
traffic['render_individual_cars'] = not args.no_annotations
video = Video(video_dir=args.video_dir)
camera = video.build_camera()
if not args.no_render:
render_frame (video, camera, traffic, work_dir=args.work_dir)
if not args.no_combine:
back_file = op.join(args.work_dir, 'background.png')
background = cv2.imread(atcity(back_file))
combine_frame (background, video, camera, work_dir=args.work_dir)
def __init__(self):
sun_pose_file = 'data/augmentation/resources/sun_position.pkl'
with open(atcity(sun_pose_file), 'rb') as f:
self.data = cPickle.load(f)
def process_video(job):
assertParamIsThere(job, 'video_dir')
video = Video(video_dir=job['video_dir'])
camera = video.build_camera()
# some parameters
assertParamIsThere(job, 'traffic_file')
setParamUnlessThere(job, 'save_blender_files', False)
setParamUnlessThere(
job, 'out_video_dir',
op.join('augmentation/video', 'cam%s' % camera.info['cam_id'],
video.info['video_name']))
setParamUnlessThere(job, 'no_annotations', False)
setParamUnlessThere(job, 'timeout', 1000000000)
setParamUnlessThere(job, 'frame_range', '[::]')
setParamUnlessThere(job, 'save_blender_files', False)
job['render_individual_cars'] = not job['no_annotations']
# load camera dimensions (compare it to everything for extra safety)
width0 = camera.info['camera_dims']['width']
height0 = camera.info['camera_dims']['height']
# for checking timeout
start_time = datetime.now()
cad = Cad()
# upload info on parsed vehicles to the monitor server
monitor = None # MonitorDatasetClient (cam_id=camera.info['cam_id'])
# load traffic info
traffic_video = json.load(open(atcity(job['traffic_file'])))
# reader and writer
video_reader = ReaderVideo()
image_vfile = op.join(job['out_video_dir'], 'image.avi')
mask_vfile = op.join(job['out_video_dir'], 'mask.avi')
video_writer = SimpleWriter(image_vfile, mask_vfile, {'unsafe': True})
(conn, c) = dbInit(traffic_video['in_db_file'],
op.join(job['out_video_dir'], 'traffic.db'))
c.execute('SELECT imagefile,maskfile,width,height,time FROM images')
image_entries = c.fetchall()
c.execute('DELETE FROM images')
#assert len(traffic_video['frames']) >= len(image_entries), \
# 'traffic json is too small %d < %d' % (len(traffic_video['frames']), len(image_entries))
diapason = Diapason(len(image_entries), job['frame_range'])
num_processes = int(multiprocessing.cpu_count() / 2 + 1)
pool = multiprocessing.Pool(processes=num_processes)
# each frame_range chunk is processed in parallel
for frame_range in diapason.frame_range_as_chunks(pool._processes):
logging.info('chunk of frames %d to %d' %
(frame_range[0], frame_range[-1]))
# quit, if reached the timeout
time_passed = datetime.now() - start_time
logging.info('passed: %s' % time_passed)
if (time_passed.total_seconds() > job['timeout'] * 60):
logging.warning('reached timeout %d. Passed %s' %
(job['timeout'], time_passed))
break
# collect frame jobs
frame_jobs = []
for frame_id in frame_range:
(in_backfile, in_maskfile, width, height,
_) = image_entries[frame_id]
assert (width0 == width
and height0 == height), (width0, width, height0, height)
logging.info('collect job for frame number %d' % frame_id)
back = video_reader.imread(in_backfile)
traffic = traffic_video['frames'][frame_id]
assert traffic['frame_id'] == frame_id, '%d vs %d' % (
traffic['frame_id'], frame_id)
traffic['save_blender_files'] = job['save_blender_files']
frame_jobs.append((video, camera, traffic, back, job))
#for i, (out_image, out_mask, work_dir) in enumerate(sequentialworker(frame_jobs)):
for i, (out_image, out_mask,
work_dir) in enumerate(pool.imap(worker, frame_jobs)):
frame_id = frame_range[i]
logging.info('processed frame number %d' % frame_id)
assert out_image is not None and out_mask is not None
out_imagefile = video_writer.imwrite(out_image)
out_maskfile = video_writer.maskwrite(out_mask)
logging.info('out_imagefile: %s, out_maskfile: %s' %
(out_imagefile, out_maskfile))
# update out database
(_, _, width, height, time) = image_entries[frame_id]
c.execute(
'INSERT INTO images(imagefile,maskfile,width,height,time) VALUES (?,?,?,?,?)',
(out_imagefile, out_maskfile, width, height, time))
logging.info('wrote frame %d' % c.lastrowid)
if not job['no_annotations']:
extract_annotations(work_dir, c, cad, camera, out_imagefile,
monitor)
if not job['save_blender_files']:
shutil.rmtree(work_dir)
conn.commit()
conn.close()
pool.close()
pool.join()
def render_frame(video, camera, traffic):
''' Write down traffci file for blender and run blender with renderScene.py
All work is in current-frame dir.
'''
WORK_DIR = '%s-%d' % (WORK_RENDER_DIR, os.getpid())
setParamUnlessThere(traffic, 'save_blender_files', False)
setParamUnlessThere(traffic, 'render_individual_cars', True)
unsharp_mask_params = {'radius': 4.7, 'threshold': 23, 'amount': 1}
# load camera dimensions (compare it to everything for extra safety)
width0 = camera.info['camera_dims']['width']
height0 = camera.info['camera_dims']['height']
logging.debug('camera width,height: %d,%d' % (width0, height0))
image = None
mask = None
# pass traffic info to blender
traffic['scale'] = camera.info['scale']
traffic_path = op.join(WORK_DIR, TRAFFIC_FILENAME)
if not op.exists(op.dirname(traffic_path)):
os.makedirs(op.dirname(traffic_path))
with open(traffic_path, 'w') as f:
f.write(json.dumps(traffic, indent=4))
# remove so that they do not exist if blender fails
if op.exists(op.join(WORK_DIR, RENDERED_FILENAME)):
os.remove(op.join(WORK_DIR, RENDERED_FILENAME))
if op.exists(op.join(WORK_DIR, 'depth-all.png')):
os.remove(op.join(WORK_DIR, 'depth-all.png'))
# render
assert video.render_blend_file is not None
render_blend_path = atcity(video.render_blend_file)
command = [
'%s/blender' % os.getenv('BLENDER_ROOT'), render_blend_path,
'--background', '--python',
'%s/src/augmentation/renderScene.py' % os.getenv('CITY_PATH')
]
logging.debug('WORK_DIR: %s' % WORK_DIR)
logging.debug(' '.join(command))
returncode = subprocess.call(command,
shell=False,
stdout=FNULL,
stderr=FNULL)
logging.info('rendering: blender returned code %s' % str(returncode))
# check and sharpen rendered
rendered_filepath = op.join(WORK_DIR, RENDERED_FILENAME)
image = cv2.imread(rendered_filepath, -1)
assert image is not None
assert image.shape == (height0, width0, 4), image.shape
image = unsharp_mask(image, unsharp_mask_params)
cv2.imwrite(rendered_filepath, image)
# check and sharpen cars-only
carsonly_filepath = op.join(WORK_DIR, CARSONLY_FILENAME)
image = cv2.imread(carsonly_filepath, -1)
assert image is not None
assert image.shape == (height0, width0, 4), image.shape
image = unsharp_mask(image, unsharp_mask_params)
shutil.move(carsonly_filepath, op.join(WORK_DIR, 'unsharpened.png'))
cv2.imwrite(carsonly_filepath, image)
# create mask
if traffic['render_individual_cars'] == True:
mask = _get_masks(WORK_DIR, traffic)
# TODO: visibility is returned via traffic file, NOT straightforward
with open(traffic_path, 'w') as f:
f.write(json.dumps(traffic, indent=4))
# correction_path = op.join(WORK_DIR, CORRECTION_FILENAME)
# if op.exists(correction_path): os.remove(correction_path)
# if not params['no_correction']:
# correction_info = color_correction (video.example_background, background)
# with open(correction_path, 'w') as f:
# f.write(json.dumps(correction_info, indent=4))
return image, mask
