def run_random(path):
import deepirl.utils.vizualization as v
env = EyetrackingEnv(path, actions_width=16, actions_height=16)
wnd = v.Window(800, 600)
state1_drawer = v.ImgPlotDrawer(v.Rect(10, 10, 200, 400))
state2_drawer = v.ImgPlotDrawer(v.Rect(220, 10, 200, 400))
wnd.add_drawer(state1_drawer)
wnd.add_drawer(state2_drawer)
state = env.reset()
while not env.is_terminal():
state1_drawer.set_value(state[..., 0])
state2_drawer.set_value(state[..., 1])
x = env.replay._memory[env._frame]['x']
y = env.replay._memory[env._frame]['y']
action = env.action_from_x_y(x, y)
# action = np.random.choice(env.num_actions)
# action = env.num_actions // 2
state = env.do_action(action)
wnd.draw()
def _test_explain():
import time
import deepirl.utils.vizualization as v
wnd = v.Window(640, 480)
state_drawer1 = v.ImageDrawer(v.Rect(10, 10, 200, 200))
state_drawer2 = v.ImageDrawer(v.Rect(220, 10, 200, 200),
color_map=v.ColorMap.HOT)
wnd.add_drawer(state_drawer1)
wnd.add_drawer(state_drawer2)
env = SymbolsEnvironment(32,
32,
actions_width=10,
actions_height=10,
fixations_per_symbol=10,
same_image=True,
seed=42)
replay = generate_expert_trajectories(env,
num_trajectories=10,
capacity=100)
while True:
for state, action in replay.iterate():
state_drawer1.img = state[..., 0]
state_drawer2.img = state[..., 1]
wnd.draw()
time.sleep(0.01)
def main():
import deepirl.utils.vizualization as v
wnd = v.Window(512, 512)
image_drawer = v.ImageDrawer(v.Rect(0, 0, 512, 512))
wnd.add_drawer(image_drawer)
batch_size = 64
d = Dist(batch_size)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
w = 128
h = 128
d.stats(sess)
#d.plot(sess, w, h)
for _ in range(10):
for i in range(1000):
print('Training: {}'.format(i))
points = np.random.multivariate_normal(mean=[-0.5, 0.5],
cov=[[1.7, 0.0],
[0.0, 0.2]],
size=batch_size)
d.train(sess, points)
if i % 100 == 0:
image_drawer.set_value(d.plot(sess, w, h))
wnd.draw()
d.stats(sess)
#d.plot(sess, w, h)
d.stats(sess)
d.plot(sess, w, h)
def main():
import deepirl.utils.vizualization as v
w = 1080
h = 1920
z_size = 10
batch_size = 512
image = np.zeros((h, w), dtype=np.float32)
z = np.random.standard_normal((z_size,))
z_batch = np.zeros((batch_size, z_size), np.float32)
z_batch[:] = z
pos_batch = np.zeros((batch_size, 3), np.float32)
x_batch = np.zeros((batch_size,), np.uint16)
y_batch = np.zeros((batch_size,), np.uint16)
wnd = v.Window(w // 2, h // 2)
image_drawer = v.ImageDrawer(v.Rect(0, 0, wnd.width, wnd.height))
wnd.add_drawer(image_drawer)
with tf.Session() as sess:
g = Generator(z_num=z_size)
sess.run(tf.global_variables_initializer())
idx = 0
for i in range(h):
for j in range(w):
pos_batch[idx] = get_pos(j, i, w, h)
x_batch[idx] = i
y_batch[idx] = j
idx += 1
if idx >= batch_size:
values = g.sample(sess, z_batch, pos_batch)
for ii, jj, val in zip(x_batch, y_batch, values):
image[ii, jj] = val
image_drawer.img = image
wnd.draw()
idx = 0
imsave('D:/bgr.png', image)
while True:
wnd.draw()
def explain(env: Environment, sess: tf.Session, model: RQModelBase):
wnd = v.Window(120, 220)
state_drawer = v.ImageDrawer(v.Rect(10, 10, 100, 200))
wnd.add_drawer(state_drawer)
state = env.reset()
while True:
r, u, q, p = model.predict_r_u_q_p(sess, [state])
if np.random.rand() < 0.1:
action = np.random.choice(env.num_actions)
else:
action = np.random.choice(env.num_actions, p=p[0])
#action = np.argmax(q[0])
state = env.do_action(action)
state_drawer.img = state
if env.is_terminal():
state = env.reset()
wnd.draw()
def main(arguments):
import deepirl.utils.vizualization as v
import time
from deepirl.utils.replay import StateActionReplay
wnd = v.Window(420, 420)
screen = v.ImageDrawer(v.Rect(10, 10, 400, 400))
wnd.add_drawer(screen)
env = Snake(width=10, height=10)
state = env.reset()
while True:
screen.img = state
action = np.random.choice(env.num_actions)
state = env.do_action(action)
wnd.draw()
#time.sleep(0.1)
if env.is_terminal():
state = env.reset()
def main2():
import deepirl.utils.vizualization as v
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images # Returns np.array
batch_size = 64
wnd = v.Window(400, 400)
image_drawer = v.ImageDrawer(v.Rect(0, 0, wnd.width, wnd.height))
wnd.add_drawer(image_drawer)
with tf.Session() as sess:
gan = GAN(28, 28, z_size=32, batch_size=batch_size)
sess.run(tf.global_variables_initializer())
i = 0
while True:
indices = np.random.choice(len(train_data), batch_size, replace=False)
samples = np.reshape(train_data[indices], (-1, 28, 28, 1))
gan.train(sess, samples)
i += 1
if i % 10 == 0:
image_drawer.img = gan.generate_sample(sess, 64, 64)
wnd.draw()
def frames_from_video(path: str,
gaze_points: dict,
width: int = 128,
height: int = 128,
output_path='',
draw: bool = True):
import cv2
import deepirl.utils.vizualization as v
# LODS:
# target src at x,y location
# 0: 32x32 32 x 32
# 2: 32x32 64 x 64
# 4: 32x32 128 x 128
# 8: 32x32 256 x 256
# -1: 32x32 original.width x original.height
# 512x512 is not used since it is not very different from original
# So we will have 5 channels
lod_scales = [1, 2, 4, 8, -1]
frame = np.zeros((height, width, len(lod_scales)), dtype=np.uint8)
memory = EyeTrackingReplay(height=height,
width=width,
channels=len(lod_scales))
cap = cv2.VideoCapture(path)
# Check if camera opened successfully
if not cap.isOpened():
raise RuntimeError(
"Error opening video stream or file: {0}".format(path))
drawer_width = 256
wnd = v.Window(len(lod_scales) * drawer_width, 490)
lod_drawers = []
for i in range(len(lod_scales)):
drawer = v.ImgPlotDrawer(v.Rect(i * drawer_width, 0, drawer_width,
480))
wnd.add_drawer(drawer)
lod_drawers.append(drawer)
stats_drawer = v.StringDrawer(10, 440)
wnd.add_drawer(stats_drawer)
frame_i = 0
left, top, right, bottom = 717, 226, 1423, 923
# roi width is 706
# roi height is 697
roi = v.Rect(left, top, right - left, bottom - top)
while cap.isOpened():
# Capture frame-by-frame
ret, frame_raw = cap.read()
if not ret:
break
if frame_i in gaze_points:
x, y = gaze_points[frame_i]
x = float(x - roi.left) / roi.width
y = float(y - roi.top) / roi.height
skip = False
# Skip if x is out of area
if x < 0.0 or x > 1.0:
skip = True
# Skip if y is out of area
if y < 0.0 or y > 1.0:
skip = True
if not skip:
cropped = frame_raw[roi.top:roi.bottom, roi.left:roi.right]
cropped_bw = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY)
for i, scale in enumerate(lod_scales):
if scale < 0:
part = cropped_bw
else:
part = get_part(cropped_bw, x, y, width * scale,
height * scale)
if scale == 1:
frame[..., i] = part
else:
frame[...,
i] = cv2.resize(part, (width, height),
interpolation=cv2.INTER_AREA)
memory.append(frame / 255.0, 2.0 * (x - 0.5), 2.0 * (y - 0.5))
if draw:
for i, drawer in enumerate(lod_drawers):
drawer.set_value(frame[..., i])
stats_drawer.text = 'Frame {0}\nGaze X {1}\nGaze Y {2}'.format(
frame_i, x, y)
wnd.draw()
else:
print('Frame {0}\tGaze X {1:.3f}\tGaze Y {2:.3f}'.format(
frame_i, x, y))
frame_i += 1
memory.save(output_path)
def play(sess: tf.Session,
replay: EyeTrackingReplay,
model: Model,
channels=5):
import deepirl.utils.vizualization as v
import cv2
drawer_size = 128
wnd = v.Window(drawer_size * channels, drawer_size * 3)
prob_drawer = v.ImageDrawer(
v.Rect(0, drawer_size, drawer_size * 2, drawer_size * 2))
ex_prob_drawer = v.ImageDrawer(
v.Rect(drawer_size * 2, drawer_size, drawer_size * 2, drawer_size * 2))
stats_drawer = v.StringDrawer(drawer_size * 4 + 10, drawer_size + 10)
wnd.add_drawer(prob_drawer)
wnd.add_drawer(ex_prob_drawer)
wnd.add_drawer(stats_drawer)
output_path = 'D:\\deepirl\\xray_bc.avi'
frame_rate = 20
frames = 0
video_frames_to_write = 30 * frame_rate # 30 secs
writer = None
if output_path:
print('Recording video: {0}'.format(output_path))
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
writer = cv2.VideoWriter(output_path, fourcc, frame_rate,
(wnd.width, wnd.height))
w, h = 64, 64
ex_policy = np.zeros((h, w), dtype=np.float32)
indices = np.zeros((h * w, 2), dtype=np.float32)
for j, x in enumerate(np.linspace(-1, 1, w)):
for i, y in enumerate(np.linspace(-1, 1, h)):
indices[i * w + j, 0] = x
indices[i * w + j, 1] = y
drawers = []
for i in range(channels):
drawer = v.ImageDrawer(
v.Rect(i * drawer_size, 0, drawer_size, drawer_size))
drawers.append(drawer)
wnd.add_drawer(drawer)
while True:
#frames, xs, ys = replay.sample(BATCH_SIZE)
#for frame, x, y in zip(frames, xs, ys):
for res in replay.iterate():
frame = res['frame']
x = res['x']
y = res['y']
for i, drawer in enumerate(drawers):
drawer.set_value(frame[..., i])
yy = np.clip((h - 1) * (y + 1.0) * 0.5, 0.0, h - 1)
xx = np.clip((w - 1) * (x + 1.0) * 0.5, 0.0, w - 1)
ex_policy[int(yy), int(xx)] += 1.0
ex_policy *= 0.95
probs, mode = model.get_prob(sess, frame, indices)
prob_drawer.set_value(np.reshape(probs, newshape=(h, w)))
ex_prob_drawer.set_value(ex_policy)
pred_x = mode[0][0]
pred_y = mode[0][1]
mse = np.sqrt(np.square(pred_x - x) + np.square(pred_y - y))
stats_drawer.text = 'Expert:\n X: {0:.3f}\n Y: {1:.3f}\n\nPredicted:\n X: {2:.3f}\n Y: {3:.3f}\n\nMSE: {4:.3f}'.format(
x, y, pred_x, pred_y, mse)
wnd.draw()
frames += 1
if writer is not None:
if frames < video_frames_to_write:
writer.write(wnd.screen)
if frames == video_frames_to_write:
print('Saving video to: {0}'.format(output_path))
writer.release()
for i in range(layers):
x = tf.layers.dense(x, num_hidden, activation=activation, kernel_initializer=tf.initializers.random_normal(stddev=stddev))
x = tf.exp(x)
if use_color:
x = tf.layers.dense(x, 3, activation=tf.nn.sigmoid)
else:
x = tf.reshape(tf.layers.dense(x, 1, activation=tf.nn.sigmoid), [-1])
out = x
if use_color:
image = np.zeros((h, w, 3), dtype=np.float32)
else:
image = np.zeros((h, w), dtype=np.float32)
wnd = v.Window(w, h)
image_drawer = v.ImageDrawer(v.Rect(0, 0, wnd.width, wnd.height))
wnd.add_drawer(image_drawer)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
while True:
z = np.random.standard_normal((noise_dim,)) * 10.0
z_batch = np.zeros((batch_size, noise_dim), np.float32)
z_batch[:] = z
pos_batch = np.zeros((batch_size, 3), np.float32)
x_batch = np.zeros((batch_size,), np.uint16)
y_batch = np.zeros((batch_size,), np.uint16)
idx = 0
def explain(env: Environment, sess: tf.Session, model: RQModelBase, replay: StateActionReplay,
frame_rate: float = 20.0, output_path: str=''):
wnd = v.Window(900, 700)
writer = None
frames = 0
video_frames_to_write = 60 * frame_rate # one minute
if output_path:
print('Recording video: {0}'.format(output_path))
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
writer = cv2.VideoWriter(output_path, fourcc, frame_rate, (wnd.width, wnd.height))
w = 200
h = 320
padding = 20
state1_drawer = v.ImgPlotDrawer(v.Rect(padding, padding, w, h),
caption="State channel:0")
state2_drawer = v.ImgPlotDrawer(v.Rect(state1_drawer.bounds.right + padding, padding, w, h),
caption="State channel:1", color_map=v.ColorMap.HOT)
expert_drawer = v.ImgPlotDrawer(v.Rect(state2_drawer.bounds.right + w + padding * 2, padding, w, h),
caption="Expert behaviour", color_map=v.ColorMap.HOT)
r_drawer = v.ImgPlotDrawer(v.Rect(padding, state1_drawer.bounds.bottom + padding, w, h),
caption="Reward R(s,a)", color_map=v.ColorMap.HOT)
u_drawer = v.ImgPlotDrawer(v.Rect(r_drawer.bounds.right + padding, state1_drawer.bounds.bottom + padding, w, h),
caption="Expected return U(s,a)", color_map=v.ColorMap.HOT)
q_drawer = v.ImgPlotDrawer(v.Rect(u_drawer.bounds.right + padding, state1_drawer.bounds.bottom + padding, w, h),
caption="Quality Q(s,a)=R(s,a)+U(s,a)", color_map=v.ColorMap.HOT)
p_drawer = v.ImgPlotDrawer(v.Rect(q_drawer.bounds.right + padding, state1_drawer.bounds.bottom + padding, w, h),
caption="Policy p(s,a)=softmax(Q(s,a))", color_map=v.ColorMap.HOT)
wnd.add_drawer(state1_drawer)
wnd.add_drawer(state2_drawer)
wnd.add_drawer(expert_drawer)
wnd.add_drawer(r_drawer)
wnd.add_drawer(u_drawer)
wnd.add_drawer(q_drawer)
wnd.add_drawer(p_drawer)
for state, action in replay.iterate():
state1_drawer.set_value(state[..., 0])
state2_drawer.set_value(state[..., 1])
r, u, q, p = model.predict_r_u_q_p(sess, [state])
expert = np.zeros_like(p[0])
expert[action] = 1.0
expert_drawer.set_value(square(expert))
r_drawer.set_value(square(r[0]))
u_drawer.set_value(square(u[0]))
q_drawer.set_value(square(q[0]))
p_drawer.set_value(square(p[0]))
wnd.draw()
frames += 1
if writer is not None:
if frames < video_frames_to_write:
writer.write(wnd.screen)
if frames == video_frames_to_write:
print('Saving video to: {0}'.format(output_path))
writer.release()
# No need to sleep. Computation take too much time already
#time.sleep(1.0 / frame_rate)
if writer is not None:
writer.release()
if __name__ == '__main__':
import keyboard
import time
import sys
import deepirl.utils.vizualization as v
from deepirl.utils.replay import StateActionReplay
env = Tetris()
state = env.reset()
replay = StateActionReplay(100000, env.state_shape)
episode = 0
record_episodes = 1
wnd = v.Window(220, 420)
state_drawer = v.ImageDrawer(v.Rect(10, 10, 200, 400), color_map=v.ColorMap.HOT)
wnd.add_drawer(state_drawer)
class Hack(object):
action = None
hack = Hack()
def pressed(event: keyboard.KeyboardEvent, hack_ref):
hack_ref.action = None
if event.name == 'down':
hack_ref.action = ACTION_NO_OP
if event.name == 'left':
hack_ref.action = ACTION_LEFT
if event.name == 'right':
hack_ref.action = ACTION_RIGHT