#!/usr/bin/env python3
# manual

"""
This script allows you to manually control the simulator or Duckiebot
using the keyboard arrows.
"""

import sys
import argparse
import pyglet
from pyglet.window import key
import numpy as np
import gym
import gym_duckietown
from gym_duckietown.envs import DuckietownEnv
from gym_duckietown.wrappers import UndistortWrapper

####
from PIL import Image
import cv2
import math
from apriltag import Detector
import transformations as tf
####

# from experiments.utils import save_img

parser = argparse.ArgumentParser()
parser.add_argument('--env-name', default='Duckietown')
parser.add_argument('--map-name', default='udem1')
parser.add_argument('--distortion', default=False, action='store_true')
parser.add_argument('--draw-curve', action='store_true', help='draw the lane following curve')
parser.add_argument('--draw-bbox', action='store_true', help='draw collision detection bounding boxes')
parser.add_argument('--domain-rand', action='store_true', help='enable domain randomization')
parser.add_argument('--frame-skip', default=1, type=int, help='number of frames to skip')
parser.add_argument('--seed', default=1, type=int, help='seed')
args = parser.parse_args()

if args.env_name and args.env_name.find('Duckietown') != -1:
    env = DuckietownEnv(
        seed = args.seed,
        map_name = args.map_name,
        draw_curve = args.draw_curve,
        draw_bbox = args.draw_bbox,
        domain_rand = args.domain_rand,
        frame_skip = args.frame_skip,
        distortion = args.distortion,
    )
else:
    env = gym.make(args.env_name)

#env.reset()
env.render()

@env.unwrapped.window.event
def on_key_press(symbol, modifiers):
    """
    This handler processes keyboard commands that
    control the simulation
    """

    if symbol == key.BACKSPACE or symbol == key.SLASH:
        print('RESET')
        env.reset()
        env.render()
    elif symbol == key.PAGEUP:
        env.unwrapped.cam_angle[0] = 0
    elif symbol == key.ESCAPE:
        env.close()
        sys.exit(0)

    # Take a screenshot
    # UNCOMMENT IF NEEDED - Skimage dependency
    # elif symbol == key.RETURN:
    #     print('saving screenshot')
    #     img = env.render('rgb_array')
    #     save_img('screenshot.png', img)

# Register a keyboard handler
key_handler = key.KeyStateHandler()
env.unwrapped.window.push_handlers(key_handler)
def _draw_pose(overlay, camera_params, tag_size, pose, z_sign=1):

    opoints = np.array([
        -1, -1, 0,
         1, -1, 0,
         1,  1, 0,
        -1,  1, 0,
        -1, -1, -2*z_sign,
         1, -1, -2*z_sign,
         1,  1, -2*z_sign,
        -1,  1, -2*z_sign,
    ]).reshape(-1, 1, 3) * 0.5*tag_size

    edges = np.array([
        0, 1,
        1, 2,
        2, 3,
        3, 0,
        0, 4,
        1, 5,
        2, 6,
        3, 7,
        4, 5,
        5, 6,
        6, 7,
        7, 4
    ]).reshape(-1, 2)

    fx, fy, cx, cy = camera_params

    # matriz homogenea
    K = np.array([fx, 0, cx, 0, fy, cy, 0, 0, 1]).reshape(3, 3)
    print("matriz homogenea ", K)

    rvec, _ = cv2.Rodrigues(pose[:3,:3])
    tvec = pose[:3, 3]

    dcoeffs = np.zeros(5)

    ipoints, _ = cv2.projectPoints(opoints, rvec, tvec, K, dcoeffs)
    ipoints = np.round(ipoints).astype(int)
    ipoints = [tuple(pt) for pt in ipoints.reshape(-1, 2)]
    for i, j in edges:
        cv2.line(overlay, ipoints[i], ipoints[j], (0, 255, 0), 1, 16)

def global_pose(matrix,x_ob,y_ob,angle): # matrix es la pose del apriltag x_ob e y_ob es el x e y del apriltag
    tag_size = 0.18
    tile_size = 0.585

    T_a = tf.translation_matrix([
        -x_ob, -tag_size*3/4, y_ob]) # esto ya viene multiplicado por el tile_size
    R_a = tf.euler_matrix(0,angle,0)

    T_m_a = tf.concatenate_matrices(T_a, R_a)

    # pose tag con respecto al robot
    T_r_a = np.dot(matrix, tf.euler_matrix(0, np.pi, 0))
    # pose tag con respecto al mapa
    T_a_r = np.linalg.inv(T_r_a) # T_r_a-1
    T_m_r = np.dot(T_m_a, T_a_r)

    return T_m_r


def angle2(q,angle,euler):
    return q-(angle-yaw(euler))

def l1(x,y):
    return math.sqrt(x**2,y**2)

def yaw(euler_angles):
    return euler_angles[2]

def dist(matrix):
    return np.linalg.norm([matrix[0][3],matrix[1][3],matrix[2][3]])

def update(dt):
    """
    This function is called at every frame to handle
    movement/stepping and redrawing
    """
    action = np.array([0.0, 0.0])

    if key_handler[key.UP]:
        action = np.array([0.44, 0.0])
    if key_handler[key.DOWN]:
        action = np.array([-0.44, 0])
    if key_handler[key.LEFT]:
        action = np.array([0.35, +1])
    if key_handler[key.RIGHT]:
        action = np.array([0.35, -1])
    if key_handler[key.SPACE]:
        action = np.array([0, 0])

    # Speed boost
    if key_handler[key.LSHIFT]:
        action *= 1.5

    obs, reward, done, info = env.step(action)

    ### apriltags detector
    label = ""
    original = Image.fromarray(obs)
    cv_img = cv2.cvtColor(np.array(original), cv2.COLOR_RGB2BGR)

    detector = Detector()
    gray = cv2.cvtColor(np.array(original), cv2.COLOR_RGB2GRAY)
    detections, dimg = detector.detect(gray, return_image=True)
    camera = [305.57, 308.83, 303.07, 231.88]


    pose = [[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]
    robot_pose = [[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]
    for detection in detections:

        pose, e0, e1 = detector.detection_pose(detection, camera, 0.18 / 2)
        if not np.isnan(pose[0][0]):
            _draw_pose(cv_img,
                       camera,
                       0.18/ 2,
                       pose)
        # aqui si tiene que ir el 0.585
        robot_pose = global_pose(pose, 2.08*0.585, 4.05*0.585, math.pi)
        print(" ROBOT POSE ", robot_pose)
    label = 'detections = %d, dist = %.2f, pos = (%.2f, %.2f)' % (len(detections), pose[2][3], robot_pose[0][3], robot_pose[2][3])

    # cv2.imshow('win', cv_img)
    # cv2.waitKey(5)

    #if done:
    #   print('done!')
    #    env.reset()
    #    env.render()
    extra_label = pyglet.text.Label(
        font_name="Arial",
        font_size=14,
        x=5,
        y=600 - 19*2
    )
    env.render(mode="top_down")
    extra_label.text = label
    extra_label.draw()

pyglet.clock.schedule_interval(update, 1.0 / env.unwrapped.frame_rate)

# Enter main event loop
pyglet.app.run()

env.close()

#!/usr/bin/env python3
# manual

"""
This script allows you to manually control the simulator or Duckiebot
using the keyboard arrows.
"""

import sys
import argparse
import pyglet
from pyglet.window import key
import numpy as np
import gym
import gym_duckietown
from gym_duckietown.envs import DuckietownEnv
from gym_duckietown.wrappers import UndistortWrapper

####
from PIL import Image
import cv2
import math
from apriltag import Detector
import transformations as tf
####

# from experiments.utils import save_img

parser = argparse.ArgumentParser()
parser.add_argument('--env-name', default='Duckietown')
parser.add_argument('--map-name', default='udem1')
parser.add_argument('--distortion', default=False, action='store_true')
parser.add_argument('--draw-curve', action='store_true', help='draw the lane following curve')
parser.add_argument('--draw-bbox', action='store_true', help='draw collision detection bounding boxes')
parser.add_argument('--domain-rand', action='store_true', help='enable domain randomization')
parser.add_argument('--frame-skip', default=1, type=int, help='number of frames to skip')
parser.add_argument('--seed', default=1, type=int, help='seed')
args = parser.parse_args()

if args.env_name and args.env_name.find('Duckietown') != -1:
    env = DuckietownEnv(
        seed = args.seed,
        map_name = args.map_name,
        draw_curve = args.draw_curve,
        draw_bbox = args.draw_bbox,
        domain_rand = args.domain_rand,
        frame_skip = args.frame_skip,
        distortion = args.distortion,
    )
else:
    env = gym.make(args.env_name)

#env.reset()
env.render()

@env.unwrapped.window.event
def on_key_press(symbol, modifiers):
    """
    This handler processes keyboard commands that
    control the simulation
    """

    if symbol == key.BACKSPACE or symbol == key.SLASH:
        print('RESET')
        env.reset()
        env.render()
    elif symbol == key.PAGEUP:
        env.unwrapped.cam_angle[0] = 0
    elif symbol == key.ESCAPE:
        env.close()
        sys.exit(0)

    # Take a screenshot
    # UNCOMMENT IF NEEDED - Skimage dependency
    # elif symbol == key.RETURN:
    #     print('saving screenshot')
    #     img = env.render('rgb_array')
    #     save_img('screenshot.png', img)

# Register a keyboard handler
key_handler = key.KeyStateHandler()
env.unwrapped.window.push_handlers(key_handler)
def _draw_pose(overlay, camera_params, tag_size, pose, z_sign=1):

    opoints = np.array([
        -1, -1, 0,
         1, -1, 0,
         1,  1, 0,
        -1,  1, 0,
        -1, -1, -2*z_sign,
         1, -1, -2*z_sign,
         1,  1, -2*z_sign,
        -1,  1, -2*z_sign,
    ]).reshape(-1, 1, 3) * 0.5*tag_size

    edges = np.array([
        0, 1,
        1, 2,
        2, 3,
        3, 0,
        0, 4,
        1, 5,
        2, 6,
        3, 7,
        4, 5,
        5, 6,
        6, 7,
        7, 4
    ]).reshape(-1, 2)

    fx, fy, cx, cy = camera_params

    # matriz homogenea
    K = np.array([fx, 0, cx, 0, fy, cy, 0, 0, 1]).reshape(3, 3)
    print("matriz homogenea ", K)

    rvec, _ = cv2.Rodrigues(pose[:3,:3])
    tvec = pose[:3, 3]

    dcoeffs = np.zeros(5)

    ipoints, _ = cv2.projectPoints(opoints, rvec, tvec, K, dcoeffs)
    ipoints = np.round(ipoints).astype(int)
    ipoints = [tuple(pt) for pt in ipoints.reshape(-1, 2)]
    for i, j in edges:
        cv2.line(overlay, ipoints[i], ipoints[j], (0, 255, 0), 1, 16)

def global_pose(matrix,x_ob,y_ob,angle): # matrix es la pose del apriltag x_ob e y_ob es el x e y del apriltag
    tag_size = 0.18
    tile_size = 0.585

    T_a = tf.translation_matrix([
        -x_ob, -tag_size*3/4, y_ob]) # esto ya viene multiplicado por el tile_size
    R_a = tf.euler_matrix(0,angle,0)

    T_m_a = tf.concatenate_matrices(T_a, R_a)

    # pose tag con respecto al robot
    T_r_a = np.dot(matrix, tf.euler_matrix(0, np.pi, 0))
    # pose tag con respecto al mapa
    T_a_r = np.linalg.inv(T_r_a) # T_r_a-1
    T_m_r = np.dot(T_m_a, T_a_r)

    return T_m_r


def angle2(q,angle,euler):
    return q-(angle-yaw(euler))

def l1(x,y):
    return math.sqrt(x**2,y**2)

def yaw(euler_angles):
    return euler_angles[2]

def dist(matrix):
    return np.linalg.norm([matrix[0][3],matrix[1][3],matrix[2][3]])

def update(dt):
    """
    This function is called at every frame to handle
    movement/stepping and redrawing
    """
    action = np.array([0.0, 0.0])

    if key_handler[key.UP]:
        action = np.array([0.44, 0.0])
    if key_handler[key.DOWN]:
        action = np.array([-0.44, 0])
    if key_handler[key.LEFT]:
        action = np.array([0.35, +1])
    if key_handler[key.RIGHT]:
        action = np.array([0.35, -1])
    if key_handler[key.SPACE]:
        action = np.array([0, 0])

    # Speed boost
    if key_handler[key.LSHIFT]:
        action *= 1.5

    obs, reward, done, info = env.step(action)

    ### apriltags detector
    label = ""
    original = Image.fromarray(obs)
    cv_img = cv2.cvtColor(np.array(original), cv2.COLOR_RGB2BGR)

    detector = Detector()
    gray = cv2.cvtColor(np.array(original), cv2.COLOR_RGB2GRAY)
    detections, dimg = detector.detect(gray, return_image=True)
    camera = [305.57, 308.83, 303.07, 231.88]


    pose = [[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]
    robot_pose = [[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]
    for detection in detections:

        pose, e0, e1 = detector.detection_pose(detection, camera, 0.18 / 2)
        if not np.isnan(pose[0][0]):
            _draw_pose(cv_img,
                       camera,
                       0.18/ 2,
                       pose)
        # aqui si tiene que ir el 0.585
        robot_pose = global_pose(pose, 2.08*0.585, 4.05*0.585, math.pi)
        print(" ROBOT POSE ", robot_pose)
    label = 'detections = %d, dist = %.2f, pos = (%.2f, %.2f)' % (len(detections), pose[2][3], robot_pose[0][3], robot_pose[2][3])

    # cv2.imshow('win', cv_img)
    # cv2.waitKey(5)

    #if done:
    #   print('done!')
    #    env.reset()
    #    env.render()
    extra_label = pyglet.text.Label(
        font_name="Arial",
        font_size=14,
        x=5,
        y=600 - 19*2
    )
    env.render(mode="top_down")
    extra_label.text = label
    extra_label.draw()

pyglet.clock.schedule_interval(update, 1.0 / env.unwrapped.frame_rate)

# Enter main event loop
pyglet.app.run()

env.close()

#!/usr/bin/env python3 # manual This script allows you to manually control the simulator or Duckiebot using the keyboard arrows. #### #### # from experiments.utils import save_img #env.reset() This handler processes keyboard commands that     control the simulation # Take a screenshot # UNCOMMENT IF NEEDED - Skimage dependency # elif symbol == key.RETURN: #     print('saving screenshot') #     img = env.render('rgb_array') #     save_img('screenshot.png', img) # Register a keyboard handler # matriz homogenea # matrix es la pose del apriltag x_ob e y_ob es el x e y del apriltag # esto ya viene multiplicado por el tile_size # pose tag con respecto al robot # pose tag con respecto al mapa # T_r_a-1 This function is called at every frame to handle     movement/stepping and redrawing # Speed boost ### apriltags detector # aqui si tiene que ir el 0.585 # cv2.imshow('win', cv_img) # cv2.waitKey(5) #if done: #   print('done!') #    env.reset() #    env.render() # Enter main event loop