/
util_car.py
1203 lines (1002 loc) · 44.9 KB
/
util_car.py
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from subprocess import call
import os, time
import shutil
import io
import base64
from IPython.display import HTML
import numpy as np
from PIL import ImageDraw, Image, ImageFont
from tempfile import NamedTemporaryFile
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation
import matplotlib
import math
import copy
import itertools
import tensorflow as tf
import subprocess
FLAGS = tf.app.flags.FLAGS
import cv2
#from pylab import *
import pylab
from matplotlib.patches import Wedge
from scipy.ndimage.filters import gaussian_filter
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredDrawingArea
from matplotlib.patches import FancyArrowPatch
def images2video_highqual(frame_rate,
name="temp_name", dir_name="temp_dir"):
# make dir if not exists
if not os.path.isdir(dir_name):
os.mkdir(dir_name)
pwd = os.getcwd()
os.chdir(dir_name)
print("converting to video")
video_name = name+'.mp4'
cmd = "ffmpeg -y -f image2 -r " + str(frame_rate) + " -pattern_type glob -i '*.png' -crf 5 -preset veryslow " + \
"-threads 16 -vcodec libx264 -pix_fmt yuv420p " + video_name
call(cmd, shell=True)
call("rm *.png", shell=True)
os.chdir(pwd)
return os.path.join(dir_name, video_name)
def images2video(images, frame_rate,
name="temp_name", dir_name="temp_dir", highquality=True):
images = np.uint8(images)
shape = images.shape
assert (len(shape) == 4)
assert (shape[3] == 3 or shape[3] == 1)
# make dir if not exists
if not os.path.isdir(dir_name):
os.mkdir(dir_name)
pwd = os.getcwd()
os.chdir(dir_name)
# write out images
print("writing images")
for i in range(shape[0]):
j = Image.fromarray(images[i, :, :, :])
j.save("%05d.jpeg" % i, "jpeg", quality=93)
print("converting to video")
video_name = name+'.mp4'
quality_str = '16' if highquality else '28'
cmd = "ffmpeg -y -f image2 -r " + str(frame_rate) + " -pattern_type glob -i '*.jpeg' -crf "+quality_str+" -preset veryfast " + \
"-threads 16 -vcodec libx264 -pix_fmt yuv420p " + video_name
call(cmd, shell=True)
call("rm *.jpeg", shell=True)
os.chdir(pwd)
return os.path.join(dir_name, video_name)
def play_video(path):
video = io.open(path, 'r+b').read()
encoded = base64.b64encode(video)
return HTML(data='''<video alt="test" controls>
<source src="data:video/mp4;base64,{0}" type="video/mp4" />
</video>'''.format(encoded.decode('ascii')))
def visualize_images(images, frame_rate,
name="temp_name", dir_name="temp_dir",delete_temp=True):
path = images2video(images, frame_rate, name, dir_name)
out = play_video(path)
if delete_temp:
assert not("*" in dir_name)
shutil.rmtree(dir_name)
return out
def write_text_on_image(image, string,
lines=[],
fontsize=30,
lines_color=[]):
shape = image.shape
assert (len(shape) == 3)
assert (shape[-1] == 3 or shape[-1] == 1)
image = np.uint8(image)
j = Image.fromarray(image)
draw = ImageDraw.Draw(j)
# font = ImageFont.load_default().font
#font = ImageFont.truetype("/usr/share/fonts/truetype/inconsolata/Inconsolata.otf", fontsize)
font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", fontsize)
if isinstance(string, list):
for s in string:
draw.text(s[0], s[1], s[2], font=font)
else:
draw.text((0, 0), string, (255, 0, 0), font=font)
for line in lines:
draw.line(line, fill=128, width=1)
for line in lines_color:
draw.line(line[0], fill=line[1], width=1)
return np.array(j)
def egomotion2animation(ego):
# ego is a egomotion matrix, with nframes * previous frames * 3
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
line = ax.plot([], [], '.', zs=[])
line = line[0]
def get_range(ego, axis):
data = ego[:, :, axis]
data = np.reshape(data, [-1])
return [np.min(data), np.max(data)]
ax.axis(get_range(ego, 0) + get_range(ego, 1))
zrange = get_range(ego, 2)
ax.set_zlim(zrange[0], zrange[1])
# initialization function: plot the background of each frame
def init():
line.set_data([], [])
return line,
# animation function. This is called sequentially
def animate(i):
line.set_data(ego[i, :, 0], ego[i, :, 1])
line.set_3d_properties(ego[i, :, 2])
return line,
# call the animator. blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=ego.shape[0], blit=True)
plt.close(anim._fig)
return anim
def animation2HTML(anim, frame_rate):
print("animaiton to video...")
if not hasattr(anim, '_encoded_video'):
with NamedTemporaryFile(suffix='.mp4') as f:
anim.save(f.name, fps=frame_rate,
extra_args=['-vcodec', 'libx264',
'-pix_fmt', 'yuv420p',
'-crf', '28',
'-preset', 'veryfast'])
video = io.open(f.name, 'r+b').read()
encoded = base64.b64encode(video)
return HTML(data='''<video alt="test" controls>
<source src="data:video/mp4;base64,{0}" type="video/mp4" />
</video>'''.format(encoded.decode('ascii')))
def visualize_egomotion(ego, frame_rate):
anim = egomotion2animation(ego)
return animation2HTML(anim, frame_rate)
def vis_reader(tout, frame_rate, j=0):
decoded, isvalid, ego, name, isstop = tout
images = decoded[j, :, :, :, :]
images_txt = np.zeros_like(images)
this_stop = isstop[j]
this_valid = isvalid[j]
for i in range(images.shape[0]):
stop_str = {1: "STOP",
0: "GO",
-1: "UNKNOWN"}[this_stop[i]]
valid_str = {0: "Egomotion=Invalid",
1: "Egomotion=Valid"}[this_valid[i]]
showing_str = stop_str + "\n" + valid_str
# showing_str = stop_str
images_txt[i, :, :, :] = write_text_on_image(images[i, :, :, :], showing_str)
print("showing visualization for video %s" % name[0])
return visualize_images(images_txt, frame_rate)
def move_to_line(move, h, w, multiplier = 10):
m = copy.deepcopy(move)
m[1] *= multiplier
m = [m[1] * math.sin(m[0]), m[1]*math.cos(m[0])]
return [w / 2, h, w/2+m[0], h-m[1]]
def draw_bar_on_image(image, bar_left_top, fraction, fill=(0,0,0,128), height=20, length=120):
image = np.uint8(image)
j = Image.fromarray(image)
draw = ImageDraw.Draw(j)
l = bar_left_top
draw.rectangle([l, (l[0]+int(length*fraction), l[1]+height)], fill=fill)
return np.array(j)
def vis_reader_stop_go(tout, prediction,frame_rate, j=0, save_visualize = False, dir_name="temp", provider="nexar_large_speed"):
#out_of_date, won't do stop go any more
decoded = tout[0]
speed = tout[1]
name = tout[2]
highres = tout[3]
isstop = tout[4]
turn = tout[5]
locs = tout[6]
decoded = highres
turn = turn[j, :, :]
locs = locs[j, :, :]
images = decoded[j, :, :, :, :]
images_txt = np.zeros_like(images)
stop = isstop[j, :]
speed = speed[j, :, :]
for i in range(images.shape[0]):
showing_str = "STOP" if prediction[i] == 1 else "GO!"
showing_str += "\n" + str(np.linalg.norm(speed[i, :]))
showing_str += "\n" + "GT: STOP" if stop[i] == 1 else "\nGT: GO!"
images_txt[i, :, :, :] = write_text_on_image(images[i, :, :, :],
showing_str)
print("showing visualization for video %s" % name[0])
#vis_speed(speed, frame_rate)
if save_visualize:
_, short_name = os.path.split(name[j])
short_name = short_name.split(".")[0]
return visualize_images(images_txt, frame_rate,
name=short_name,
dir_name=dir_name,
delete_temp=False)
else:
return visualize_images(images_txt, frame_rate)
def vis_discrete(tout, predict, frame_rate,
j=0, save_visualize=False, dir_name="temp"):
import data_providers.nexar_large_speed as provider
int2str = provider.MyDataset.turn_int2str
# city_data and only_seg are mutually exclusive, actually one flag is enough
if FLAGS.city_data == 1:
decoded = tout[0]
speed = tout[1]
name = tout[2]
isstop = tout[5]
turn = tout[6]
locs = tout[7]
elif FLAGS.only_seg == 1:
decoded = tout[0]
speed = tout[1]
name = tout[2]
isstop = tout[6]
turn = tout[7]
locs = tout[8]
else:
decoded = tout[0]
speed = tout[1]
name = tout[2]
highres = tout[3]
isstop = tout[4]
turn = tout[5]
locs = tout[6]
decoded = highres
images = copy.deepcopy(decoded[j, :, :, :, :])
_, hi, wi, _ = images.shape
locs = locs[j, :, :]
turn = turn[j, :, :]
for i in range(images.shape[0]):
# the ground truth course and speed
showing_str = "speed: %.1f m/s \ncourse: %.2f degree/s" % \
(locs[i, 1], locs[i, 0]/math.pi*180)
for k in range(4):
showing_str += "\n"+int2str[k]
gtline = move_to_line(locs[i,:], hi, wi)
FontHeight=18
FontWidth =8
for k in range(4):
images[i, :, :, :] = draw_bar_on_image(images[i,:,:,:],
(FontWidth*14, FontHeight*(2+k)),
fraction = turn[i, k],
fill=(255, 0, 0, 128),
height=FontHeight * 2 // 3,
length=FontWidth * 4)
images[i, :, :, :] = draw_bar_on_image(images[i, :, :, :],
(FontWidth * 20, FontHeight * (2 + k)),
fraction=predict[i, k],
fill=(0, 255, 0, 128),
height=FontHeight * 2 // 3,
length=FontWidth * 4)
images[i, :, :, :] = write_text_on_image(images[i, :, :, :],
showing_str,
[gtline],
fontsize=15)
print("showing visualization for video %s" % name[j])
if save_visualize:
_, short_name = os.path.split(name[j])
short_name = short_name.split(".")[0]
for i in range(10):
this_name = short_name + "_" + str(i)
if not os.path.isfile(os.path.join(dir_name,this_name+'.mp4')):
break
return visualize_images(images, frame_rate,
name=this_name,
dir_name=dir_name,
delete_temp=False)
else:
return visualize_images(images, frame_rate)
def vis_discrete_simplified(tout, predict, frame_rate,
j=0, save_visualize=False, dir_name="temp"):
import data_providers.nexar_large_speed as provider
int2str = provider.MyDataset.turn_int2str
decoded = tout[0]
speed = tout[1]
name = tout[2]
highres = tout[3]
isstop = tout[4]
turn = tout[5]
locs = tout[6]
decoded = highres
images = copy.deepcopy(decoded[j, :, :, :, :])
_, hi, wi, _ = images.shape
locs = locs[j, :, :]
turn = turn[j, :, :]
for i in range(images.shape[0]):
# the ground truth course and speed
showing_str = ""
for k in range(4):
showing_str += int2str[k] + "\n"
FontHeight = 18
FontWidth = 8
for k in range(4):
images[i, :, :, :] = draw_bar_on_image(images[i, :, :, :],
(FontWidth * 14, FontHeight * k),
fraction=turn[i, k],
fill=(255, 0, 0, 128),
height=FontHeight * 2 // 3,
length=FontWidth * 4)
images[i, :, :, :] = draw_bar_on_image(images[i, :, :, :],
(FontWidth * 20, FontHeight * k),
fraction=predict[i, k],
fill=(0, 255, 0, 128),
height=FontHeight * 2 // 3,
length=FontWidth * 4)
images[i, :, :, :] = write_text_on_image(images[i, :, :, :],
showing_str,
fontsize=15)
print("showing visualization for video %s" % name[j])
if save_visualize:
_, short_name = os.path.split(name[j])
short_name = short_name.split(".")[0]
for i in range(10):
this_name = short_name + "_" + str(i)
if not os.path.isfile(os.path.join(dir_name, this_name + '.mp4')):
break
return visualize_images(images, frame_rate,
name=this_name,
dir_name=dir_name,
delete_temp=False)
else:
return visualize_images(images, frame_rate)
def generate_meshlist(arange1, arange2):
return np.dstack(np.meshgrid(arange1, arange2, indexing='ij')).reshape((-1,2))
def draw_sector(image,
predict,
car_stop_model,
course_delta = 0.5 / 180 * math.pi,
speed_delta=0.3,
pdf_multiplier=255,
speed_multiplier = 5,
h=360, w=640,
max_speed=30,
uniform_speed=False,
consistent_vis=(False, 1e-3, 1e2),
has_alpha_channel=False):
course_samples = np.arange(-math.pi / 2-course_delta,
math.pi / 2+course_delta,
course_delta)
speed_samples = np.arange(0, max_speed+speed_delta, speed_delta)
total_pdf = car_stop_model.continous_pdf([predict],
generate_meshlist(course_samples, speed_samples),
"multi_querys")
total_pdf = np.reshape(total_pdf, (len(course_samples), len(speed_samples)))
if uniform_speed:
total_pdf = total_pdf / np.sum(total_pdf, axis=1, keepdims=True)
speed_scaled = max_speed * speed_multiplier
# potential xy positions to be filled
xy = generate_meshlist(np.arange(w / 2 - speed_scaled, w / 2 + speed_scaled),
np.arange(h - speed_scaled, h))
# filter out invalid speed
v=np.stack((xy[:,0]-w/2, h-xy[:,1]), axis=1)
speed_norm = np.sqrt(v[:,0]**2 + v[:,1]**2) *(1.0/speed_multiplier)
valid_speed = np.less(speed_norm, max_speed)
xy = xy[valid_speed, :]
speed_norm=speed_norm[valid_speed]
v=v[valid_speed]
course_norm = np.arctan(1.0*v[:, 0] / v[:, 1])
# search the course and speed
icourse = np.searchsorted(course_samples, course_norm)
ispeed = np.searchsorted(speed_samples, speed_norm)
green_portion = 0.5
total = total_pdf[icourse, ispeed]
if consistent_vis[0] == False:
total_max = np.amax(total)
total = total / total_max * 255*green_portion
else:
# consistent visualization between methods
MIN = consistent_vis[1]
MAX = consistent_vis[2]
total = np.maximum(MIN, total)
total = np.minimum(MAX, total)
#total = np.log(total) # map to log(MIN) to log(MAX)
#total = (total -np.log(MIN)) / (np.log(MAX) - np.log(MIN)) * 255
total = (total - MIN) / (MAX - MIN)
total = np.sqrt(total)
total = total * 255 * green_portion
# assign to image
image[xy[:, 1], xy[:, 0], :] *= (1-green_portion)
image[xy[:, 1], xy[:, 0], 1] += total
if has_alpha_channel:
image[xy[:, 1], xy[:, 0], 3] = 255
return image
def vis_continuous(tout, predict, frame_rate, car_stop_model,
j=0, save_visualize=False, dir_name="temp", return_first=False, **kwargs):
decoded = tout[0]
speed = tout[1]
name = tout[2]
if FLAGS.city_data:
seg_image = tout[3]
highres = tout[4]
isstop = tout[5]
turn = tout[6]
locs = tout[7]
else:
highres = tout[3]
isstop = tout[4]
turn = tout[5]
locs = tout[6]
decoded = highres
images = copy.deepcopy(decoded[j, :, :, :, :])
print(images.shape)
images = images.astype('float64')
_, hi, wi, _ = images.shape
locs = locs[j, :, :]
for i in range(images.shape[0]):
# the ground truth course and speed
showing_str = "speed: %.1f m/s \ncourse: %.2f degree/s" % \
(locs[i, 1], locs[i, 0] / math.pi * 180)
gtline = move_to_line(locs[i, :], hi, wi, 10)
images[i, :, :, :] = draw_sector(images[i, :, :, :],
predict[i:(i+1), :],
car_stop_model,
course_delta=0.3 / 180 * math.pi,
speed_delta=0.3,
pdf_multiplier=255*10,
speed_multiplier=wi/30/3,
h=hi, w=wi,
consistent_vis=(True, 1e-5, 0.3))
# get the MAP prediction
map = car_stop_model.continous_MAP([predict[i:(i+1), :]])
mapline = move_to_line(map.ravel(), hi, wi, 10)
# swap the shorter line to the latter, avoid overwriting
lines_v = [(gtline, (255,0,0)), (mapline, (0, 0, 255))]
if locs[i, 1] < map.ravel()[1]:
lines_v = [lines_v[1], lines_v[0]]
images[i, :, :, :] = write_text_on_image(images[i, :, :, :],
showing_str,
lines_color=lines_v,
fontsize=15)
print("showing visualization for video %s" % name[j])
if return_first:
return images[0, :, :, :].astype(np.uint8)
if save_visualize:
_, short_name = os.path.split(name[j])
short_name = short_name.split(".")[0]
return visualize_images(images, frame_rate,
name=short_name,
dir_name=dir_name,
delete_temp=False)
else:
return visualize_images(images, frame_rate)
def vis_continuous_simplified(tout, predict, frame_rate, car_stop_model,
j=0, save_visualize=False, dir_name="temp", vis_radius=10):
decoded = tout[0]
speed = tout[1]
name = tout[2]
highres = tout[3]
isstop = tout[4]
turn = tout[5]
locs = tout[6]
decoded = highres
images = copy.deepcopy(decoded[j, :, :, :, :])
images = images.astype('float64')
_, hi, wi, _ = images.shape
locs = locs[j, :, :]
locs = copy.deepcopy(locs)
for i in range(images.shape[0]):
# the ground truth course and speed
locs[i, 1] = 10.0
# get the MAP prediction
map = car_stop_model.continous_MAP([predict[i:(i+1), :]])
map = map.ravel()
map[1] = 10.0
mapline = move_to_line(map, hi, wi, 10)
# get map2
map2 = car_stop_model.continous_MAP([predict[i:(i + 1), :]], return_second_best=True)
map2 = map2.ravel()
map2[1] = 10.0
mapline2 = move_to_line(map2, hi, wi, 10)
showing_str = [
[(0, 0), "driver's angular speed: %.2f degree/s" % (locs[i, 0] / math.pi * 180), (255, 0, 0)],
[(0, 20), "predicted angular speed: %.2f degree/s" % (map[0] / math.pi * 180), (0, 0, 255)]]
# disable the small str on top first
showing_str = ""
showing_str = "speed: %.1f m/s \ncourse: %.2f degree/s" % \
(locs[i, 1], locs[i, 0] / math.pi * 180)
gtline = move_to_line(locs[i, :], hi, wi, 10)
if FLAGS.is_MKZ_dataset:
# might be problematic since we enable the normalization
higher_bound = 0.3
else:
higher_bound = 3.0
images[i, :, :, :] = draw_sector(images[i, :, :, :],
predict[i:(i+1), :],
car_stop_model,
course_delta=0.1 / 180 * math.pi,
speed_delta=0.1,
pdf_multiplier=255*10,
speed_multiplier=int(wi/30/3),
h=hi, w=wi,
uniform_speed=True,
consistent_vis=(True, 1e-5, higher_bound))
# disable the MAP line first, since many times not the MAP line is considered
'''
# swap the shorter line to the latter, avoid overwriting
lines_v = [(gtline, (255,0,0)), (mapline, (0, 0, 255))]
if locs[i, 1] < map.ravel()[1]:
lines_v = [lines_v[1], lines_v[0]]
'''
lines_v = [(gtline, (255,0,0)), (mapline, (0,0,255)), (mapline2, (0, 255, 0))]
images[i, :, :, :] = write_text_on_image(images[i, :, :, :],
showing_str,
lines_color=lines_v,
fontsize=24)
print("showing visualization for video %s" % name[j])
if save_visualize:
_, short_name = os.path.split(name[j])
short_name = short_name.split(".")[0]
return visualize_images(images, frame_rate,
name=short_name,
dir_name=dir_name,
delete_temp=False)
else:
return visualize_images(images, frame_rate)
# some visualization functions for the speed
def visLoc(locs, label="NotSet"):
axis = lambda i: [loc[i] for loc in locs]
import matplotlib.ticker as ticker
fig, ax = plt.subplots()
#plt.grid(True)
ax.plot(axis(0), axis(1), 'g^', ms=2)
ylim = ax.get_ylim()
xlim = ax.get_xlim()
ax.set_xlim(min(xlim[0],ylim[0]) ,max(xlim[1],ylim[1]))
ax.set_ylim(min(xlim[0],ylim[0]) ,max(xlim[1],ylim[1]))
plt.title("Moving paths from " + label)
plt.xlabel("West -- East")
plt.ylabel("South -- North")
plt.show()
def integral(speed, time0):
out = np.zeros_like(speed)
l = speed.shape[0]
for i in range(l):
s = speed[i, :]
if i > 0:
out[i, :] = out[i - 1, :] + s * time0
return out
def vis_speed(speed, hz):
visLoc(integral(speed, 1.0 / hz), "speed and course")
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
# x has shape: #instances * #classes
maxes = np.max(x, axis=1)
e_x = np.exp(x - maxes[:, None])
sums = np.sum(e_x, axis=1)
return e_x / sums[:, None]
def read_video_file(video_path, HEIGHT, WIDTH):
# take a video's path and return its decoded contents
cmnd = ['ffmpeg',
'-i', video_path,
'-f', 'image2pipe',
'-loglevel', 'panic',
'-pix_fmt', 'rgb24',
'-vcodec', 'rawvideo', '-']
pipe = subprocess.Popen(cmnd, stdout=subprocess.PIPE, bufsize=10 ** 7)
pout, perr = pipe.communicate()
image_buff = np.fromstring(pout, dtype='uint8')
if image_buff.size % (HEIGHT*WIDTH):
print("Height and Width are potentially not correct")
return None
image_buff = image_buff.reshape((-1, HEIGHT, WIDTH, 3))
return image_buff
def vis_discrete_colormap_antialias(tout, predict, frame_rate, j=0, save_visualize=False, dir_name="temp", string_type='image'):
if FLAGS.only_seg:
decoded = tout[0]
speed = tout[1]
name = tout[2]
isstop = tout[6]
turn = tout[7]
locs = tout[8]
else:
decoded = tout[0]
speed = tout[1]
name = tout[2]
highres = tout[3]
isstop = tout[4]
turn = tout[5]
locs = tout[6]
decoded = highres
images = copy.deepcopy(decoded[j, :, :, :, :])
_, hi, wi, _ = images.shape
turn = turn[j, :, :]
def get_color(prob):
cm = pylab.get_cmap('viridis') # inferno
color = cm(prob) # color will now be an RGBA tuple
r = color[0] * 255
g = color[1] * 255
b = color[2] * 255
return r, g, b
def clamp(x):
x = float(x)
return max(0, min(x, 1))
def add_to_ada(ada, pos_x, pos_y, radius, angle_s, angle_e, ring_width, color_code, edge_color, alpha_value):
ada.drawing_area.add_artist(
Wedge((pos_x, pos_y), radius, angle_s, angle_e, width=ring_width # , color=color_code#'#DAF7A6'
, alpha=alpha_value, antialiased=True, ec=edge_color, fc=color_code))
def draw_cake(ada, pos_x, pos_y, radius, angle_s, angle_diff, ring_width, color_code, edge_color, alpha_value,
share, shift=45):
angle_s = angle_s + shift
for i in range(share):
if (angle_s + (i + 1) * (angle_diff) / share) == 360:
angle_end = 360
else:
angle_end = angle_s + (i + 1) * (angle_diff) / share
#print(i,'_______________________________________')
add_to_ada(ada, pos_x, pos_y, radius,
angle_s + i * (angle_diff) / share, angle_end,
ring_width, color_code=color_code, edge_color=edge_color, alpha_value=alpha_value[i])
def draw_pile_cake(ada, pos_x, pos_y, radius, angle_s, angle_diff, ring_width, color_code, edge_color, alpha_value,
share, x_frac, y_frac, split, fontsize=24, shift=45):
# draw the black one
draw_cake(ada, pos_x=pos_x, pos_y=pos_y, radius=radius, angle_s=angle_s, angle_diff=360, ring_width=None,
color_code='k', edge_color=None, alpha_value=[0.6], share=1)
# draw the green one
draw_cake(ada, pos_x=pos_x, pos_y=pos_y, radius=radius, angle_s=angle_s, angle_diff=360, ring_width=ring_width,
color_code=color_code, edge_color='#FFFFFF', alpha_value=alpha_value, share=4)
# draw the white edge
draw_cake(ada, pos_x=pos_x, pos_y=pos_y, radius=radius, angle_s=angle_s, angle_diff=360, ring_width=ring_width,
color_code='none', edge_color='#FFFFFF', alpha_value=[1, 1, 1, 1], share=4)
ada.da.add_artist(
ax.annotate(split, xy=(x_frac, y_frac), xycoords="axes fraction", fontsize=fontsize, color='w'))
def draw_cake_type(ada, string_type, action_mean, predict_mean):
if string_type == 'video':
draw_pile_cake(ada, pos_x=210, pos_y=70, radius=60, angle_s=0, angle_diff=360, ring_width=30,
color_code='#00FF00', edge_color=None, alpha_value=predict_mean, share=1,
x_frac=0.513, y_frac=0.895, split='P')
draw_pile_cake(ada, pos_x=80, pos_y=70, radius=60, angle_s=0, angle_diff=360, ring_width=30,
color_code='#00FF00', edge_color=None, alpha_value=action_mean, share=1,
x_frac=0.185, y_frac=0.895, split='G')
elif string_type == 'image':
draw_pile_cake(ada, pos_x=240, pos_y=70, radius=70, angle_s=0, angle_diff=360, ring_width=40,
color_code='#00FF00', edge_color=None, alpha_value=predict_mean, share=1,
x_frac=0.580, y_frac=0.89, split='P', fontsize=32)
draw_pile_cake(ada, pos_x=80, pos_y=70, radius=70, angle_s=0, angle_diff=360, ring_width=40,
color_code='#00FF00', edge_color=None, alpha_value=action_mean, share=1,
x_frac=0.18, y_frac=0.89, split='G', fontsize=32)
_, short_name = os.path.split(name[j])
short_name = short_name.split(".")[0]
for i in range(images.shape[0]):
action_mean = [clamp(turn[i, 0]+0.05), clamp(turn[i, 2]+0.05),
clamp(turn[i, 1]+0.1), clamp(turn[i, 3]+0.05)]
predict_mean = [clamp(predict[i, 0]+0.05), clamp(predict[i, 2]+0.05),
clamp(predict[i, 1]+0.05), clamp(predict[i, 3]+0.05)]
fig = plt.figure(figsize=(16, 12))
ax_original = plt.gca()
ax_original.set_axis_off()
ax_original.get_xaxis().set_visible(False)
ax_original.get_yaxis().set_visible(False)
plt.imshow(images[i, :, :, :])
plt.axis('off')
ax = fig.add_subplot(121, projection='polar')
ax_2 = fig.add_subplot(122, projection='polar')
ada = AnchoredDrawingArea(200, 100, 0, 0, loc=2, pad=0., frameon=False)
draw_cake_type(ada, string_type, action_mean, predict_mean)
ax.add_artist(ada)
ax.set_axis_off()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax_2.set_axis_off()
ax_2.get_xaxis().set_visible(False)
ax_2.get_yaxis().set_visible(False)
if not os.path.exists(os.path.join(dir_name,'viz')):
os.mkdir(os.path.join(dir_name,'viz'))
if not os.path.exists(os.path.join(dir_name,'viz', short_name+string_type)):
os.mkdir(os.path.join(dir_name, 'viz', short_name+string_type))
fig.savefig(os.path.join(dir_name, 'viz', short_name+string_type,'{0:04}.png'.format(i)),
bbox_inches='tight', pad_inches = -0.04, Transparent=True, dpi=100)
print(short_name,' ', i, 'Done!')
plt.show()
plt.close()
images2video_highqual(frame_rate = 3,
name=short_name, dir_name=os.path.join(dir_name, 'viz', short_name+string_type))
def vis_continuous_colormap_antialias(tout, predict, frame_rate, car_stop_model,
j=0, save_visualize=False, dir_name="temp", vis_radius=10):
decoded = tout[0]
speed = tout[1]
name = tout[2]
highres = tout[3]
isstop = tout[4]
turn = tout[5]
locs = tout[6]
decoded = highres
images = copy.deepcopy(decoded[j, :, :, :, :])
#images = images.astype('float64')
_, hi, wi, _ = images.shape
locs = locs[j, :, :]
def plot_greens(bin_ends, values, image_width, image_height, radius, driver_action):
# bins are: [0, bin_ends[0]], [bin_ends[0], bin_ends[1]] ...
# and the corresponding values to display are: values[0], values[1]
# the final results are added to ada
ada = AnchoredDrawingArea(radius * 2, radius, 0, 0, loc=10, pad=0., frameon=False)
def add_ada_custom(angle_s, angle_e, value, color):
add_to_ada(ada, radius, -(image_height / 2 - radius / 2), radius, angle_s, angle_e, None, color, value)
def add_to_ada(ada, pos_x, pos_y, radius, angle_s, angle_e, ring_width, color_code, alpha_value):
ada.drawing_area.add_artist(
Wedge((pos_x, pos_y), radius, angle_s, angle_e, width=ring_width, fc=color_code # '#DAF7A6'
,ec = 'none', alpha=alpha_value, antialiased=True))
bin_ends = 180 - np.array(bin_ends)
bin_ends = bin_ends[::-1]
values = np.array(values)
values = np.squeeze(values)
values = values[::-1]
# add a black background
add_ada_custom(0, 180, 0.8, "#000000")
color_shading = "#00FF00"
for i in range(len(values)):
#print(bin_ends.shape, '____all____bin_____')
#print(values.shape, '___all_____values____')
if i < 5:
print(bin_ends[i], bin_ends[i + 1], values[i], '________________________')
add_ada_custom(bin_ends[i], bin_ends[i + 1], values[i], color_shading)
white_border = 1
border_color = '#FFFFFF'
add_to_ada(ada, radius, -(image_height / 2 - radius / 2), radius + white_border, 0, 180, white_border,
border_color, 1)
tick_len = 20
tick_color = '#FFFFFF'
tick_width = 1.0 / 2
for i in range(len(bin_ends)):
add_to_ada(ada, radius, -(image_height / 2 - radius / 2), radius + white_border,
bin_ends[i] - tick_width / 2, bin_ends[i] + tick_width / 2, tick_len, tick_color, 10)
driver_action = driver_action / 180.0 * math.pi
start = np.array([radius, -(image_height / 2 - radius / 2) - 2])
delta = np.array([radius * math.cos(driver_action), radius * math.sin(driver_action)]) * 0.8
color_driver = "#0000FF"
ada.drawing_area.add_artist(FancyArrowPatch(start, start + delta, linewidth=2, color=color_driver))
return ada
_, short_name = os.path.split(name[j])
short_name = short_name.split(".")[0]
for i in range(images.shape[0]):
# the ground truth course and speed
locs[i, 1] = 10.0
# get the MAP prediction
fig = plt.figure(figsize=(16, 12))
course_bin, speed_bin = car_stop_model.get_bins()
course_bin = [-math.pi/2] + course_bin + [math.pi/2]
course_bin = np.array(course_bin)*180/math.pi + 90
ax_original = plt.gca()
ax_original.set_axis_off()
ax_original.get_xaxis().set_visible(False)
ax_original.get_yaxis().set_visible(False)
plt.imshow(images[i, :, :, :])
plt.axis('off')
course = softmax(predict[i:(i + 1), 0:FLAGS.discretize_n_bins])
course = course/np.max(course)
print(course_bin, course, '!'*10)
ada2 = plot_greens(course_bin, course, 1280, 501, 200, -locs[i, 0]*180/math.pi+90)
ax_original.add_artist(ada2)
plt.show()
if not os.path.exists(os.path.join(dir_name,'viz')):
os.mkdir(os.path.join(dir_name,'viz'))
if not os.path.exists(os.path.join(dir_name,'viz', short_name)):
os.mkdir(os.path.join(dir_name, 'viz', short_name))
fig.savefig(os.path.join(dir_name, 'viz', short_name, '{0:04}.png'.format(i)),
bbox_inches='tight', pad_inches=-0.04, Transparent=True, dpi=100)
plt.close()
print(short_name)
print("showing visualization for video %s" % name[j])
def vis_continuous_interpolated(tout, predict, frame_rate, car_stop_model,
j=0, save_visualize=False, dir_name="temp", vis_radius=10, need_softmax=True, return_first=False):
decoded = tout[0]
speed = tout[1]
name = tout[2]
highres = tout[3]
isstop = tout[4]
turn = tout[5]
locs = tout[6]
decoded = highres
images = copy.deepcopy(decoded[j, :, :, :, :])
_, hi, wi, _ = images.shape
locs = locs[j, :, :]
def gen_mask(bin_ends, values, radius, height, width):
# convert bin_ends to bin centers
new_ends = []
for i in range(len(bin_ends) - 1):
new_ends.append((bin_ends[i] + bin_ends[i + 1]) / 2)
# RGBA
out = np.zeros((height, width, 4), dtype=np.uint8)
xy = np.dstack(np.meshgrid(np.arange(width / 2 - radius, width / 2 + radius),
np.arange(height - radius, height),
indexing='ij')).reshape((-1, 2))
# filter out invalid speed
v = np.stack((xy[:, 0] - width / 2, height - xy[:, 1]), axis=1)
speed_norm = np.sqrt(v[:, 0] ** 2 + v[:, 1] ** 2)
valid_speed = np.less(speed_norm, radius)
xy = xy[valid_speed, :]
speed_norm = speed_norm[valid_speed]
v = v[valid_speed]
course_norm = np.arccos(1.0 * v[:, 0] / speed_norm)
course_norm = np.degrees(course_norm)
value = np.interp(course_norm, new_ends, values)
out[xy[:, 1], xy[:, 0], 1] = 255 * value
out[xy[:, 1], xy[:, 0], 3] = 255
return out
def plot_greens(bin_ends, values, image_width, image_height, radius, driver_action):
ada = AnchoredDrawingArea(radius * 2, radius, 0, 0, loc=10, pad=0., borderpad=0., frameon=False)
def add_to_ada(ada, pos_x, pos_y, radius, angle_s, angle_e, ring_width, color_code, alpha_value):
ada.drawing_area.add_artist(
Wedge((pos_x, pos_y), radius, angle_s, angle_e, width=ring_width, fc=color_code # '#DAF7A6'
, ec='none', alpha=alpha_value, antialiased=True))
bin_ends = 180 - np.array(bin_ends)
bin_ends = bin_ends[::-1]
values = np.array(values)
values = np.squeeze(values)
values = values[::-1]
mask = gen_mask(bin_ends, values, radius, image_height, image_width)
plt.imshow(mask, alpha=0.8)
white_border = 2
border_color = '#FFFFFF'
add_to_ada(ada, radius, -(image_height / 2 - radius / 2), radius + white_border, 0, 180, white_border + 1,
border_color, 1)
tick_len = 20
tick_color = '#FFFFFF'
tick_width = 1.0 / 2
for i in range(len(bin_ends)):
if abs(bin_ends[i] - 90) > 10:
add_to_ada(ada, radius, -(image_height / 2 - radius / 2), radius + white_border,
bin_ends[i] - tick_width / 2, bin_ends[i] + tick_width / 2, tick_len, tick_color, 10)
driver_action = driver_action / 180.0 * math.pi
start = np.array([radius, -(image_height / 2 - radius / 2)])
delta = np.array([radius * math.cos(driver_action), radius * math.sin(driver_action)]) * 0.8
color_driver = "#0000FF"
ada.drawing_area.add_artist(FancyArrowPatch(start, start + delta, linewidth=2, color=color_driver))