Added CARLA brains from experiments

behavior_metrics/brains/CARLA/tensorflow/brain_carla_bird_eye_deep_learning.py

@@ -9,7 +9,7 @@
 import carla
 from os import path
 from albumentations import (
-    Compose, Normalize, RandomRain, RandomBrightness, RandomShadow, RandomSnow, RandomFog, RandomSunFlare
+    Compose, Normalize, RandomRain, RandomBrightness, RandomShadow, RandomSnow, RandomFog, RandomSunFlare, GridDropout
 )
 from utils.constants import PRETRAINED_MODELS_DIR, ROOT_PATH
 from utils.logger import logger
@@ -21,12 +21,13 @@
 from tensorflow.python.framework.errors_impl import UnimplementedError
 import tensorflow as tf
 
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 
-#gpus = tf.config.experimental.list_physical_devices('GPU')
-#for gpu in gpus:
-#    tf.config.experimental.set_memory_growth(gpu, True)
+#import os
+#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+for gpu in gpus:
+    tf.config.experimental.set_memory_growth(gpu, True)
 
 class Brain:
 
@@ -159,21 +160,15 @@ def execute(self):
 
             speed = self.vehicle.get_velocity()
             vehicle_speed = 3.6 * math.sqrt(speed.x**2 + speed.y**2 + speed.z**2)
-            self.previous_speed = vehicle_speed
 
-            if vehicle_speed > 30:
-                self.motors.sendThrottle(0)
+            if vehicle_speed < 5:
+                self.motors.sendThrottle(1.0)
+                self.motors.sendSteer(0.0)
+                self.motors.sendBrake(0)
+            else:
+                self.motors.sendThrottle(throttle)
                 self.motors.sendSteer(steer)
                 self.motors.sendBrake(break_command)
-            else:
-                if vehicle_speed < 5:
-                    self.motors.sendThrottle(1.0)
-                    self.motors.sendSteer(0.0)
-                    self.motors.sendBrake(0)
-                else:
-                    self.motors.sendThrottle(throttle)
-                    self.motors.sendSteer(steer)
-                    self.motors.sendBrake(break_command)
         except NotFoundError as ex:
             logger.info('Error inside brain: NotFoundError!')
             logger.warning(type(ex).__name__)

behavior_metrics/brains/CARLA/tensorflow/brain_carla_bird_eye_deep_learning_70_km_h.py

@@ -0,0 +1,191 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+import csv
+import cv2
+import math
+import numpy as np
+import threading
+import time
+import carla
+from os import path
+from albumentations import (
+    Compose, Normalize, RandomRain, RandomBrightness, RandomShadow, RandomSnow, RandomFog, RandomSunFlare
+)
+from utils.constants import PRETRAINED_MODELS_DIR, ROOT_PATH
+from utils.logger import logger
+from traceback import print_exc
+
+PRETRAINED_MODELS = ROOT_PATH + '/' + PRETRAINED_MODELS_DIR + 'carla_tf_models/'
+
+from tensorflow.python.framework.errors_impl import NotFoundError
+from tensorflow.python.framework.errors_impl import UnimplementedError
+import tensorflow as tf
+
+
+#import os
+#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+for gpu in gpus:
+    tf.config.experimental.set_memory_growth(gpu, True)
+
+class Brain:
+
+    def __init__(self, sensors, actuators, handler, model, config=None):
+        self.camera_0 = sensors.get_camera('camera_0')
+        self.camera_1 = sensors.get_camera('camera_1')
+        self.camera_2 = sensors.get_camera('camera_2')
+        self.camera_3 = sensors.get_camera('camera_3')
+
+        self.cameras_first_images = []
+
+        self.pose = sensors.get_pose3d('pose3d_0')
+
+        self.bird_eye_view = sensors.get_bird_eye_view('bird_eye_view_0')
+
+        self.motors = actuators.get_motor('motors_0')
+        self.handler = handler
+        self.config = config
+        self.inference_times = []
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
+
+        self.threshold_image = np.zeros((640, 360, 3), np.uint8)
+        self.color_image = np.zeros((640, 360, 3), np.uint8)
+
+        client = carla.Client('localhost', 2000)
+        client.set_timeout(10.0) # seconds
+        world = client.get_world()
+
+        time.sleep(5)
+        self.vehicle = world.get_actors().filter('vehicle.*')[0]
+
+        if model:
+            if not path.exists(PRETRAINED_MODELS + model):
+                logger.info("File " + model + " cannot be found in " + PRETRAINED_MODELS)
+            logger.info("** Load TF model **")
+            self.net = tf.keras.models.load_model(PRETRAINED_MODELS + model, compile=False)
+            logger.info("** Loaded TF model **")
+        else:
+            logger.info("** Brain not loaded **")
+            logger.info("- Models path: " + PRETRAINED_MODELS)
+            logger.info("- Model: " + str(model))
+
+        self.previous_bird_eye_view_image = 0
+        self.bird_eye_view_images = 0
+        self.bird_eye_view_unique_images = 0
+
+        self.first_acceleration = True
+
+
+    def update_frame(self, frame_id, data):
+        """Update the information to be shown in one of the GUI's frames.
+
+        Arguments:
+            frame_id {str} -- Id of the frame that will represent the data
+            data {*} -- Data to be shown in the frame. Depending on the type of frame (rgbimage, laser, pose3d, etc)
+        """
+        if data.shape[0] != data.shape[1]:
+            if data.shape[0] > data.shape[1]:
+                difference = data.shape[0] - data.shape[1]
+                extra_left, extra_right = int(difference/2), int(difference/2)
+                extra_top, extra_bottom = 0, 0
+            else:
+                difference = data.shape[1] - data.shape[0]
+                extra_left, extra_right = 0, 0
+                extra_top, extra_bottom = int(difference/2), int(difference/2)
+
+
+            data = np.pad(data, ((extra_top, extra_bottom), (extra_left, extra_right), (0, 0)), mode='constant', constant_values=0)
+
+        self.handler.update_frame(frame_id, data)
+
+    def update_pose(self, pose_data):
+        self.handler.update_pose3d(pose_data)
+
+    def execute(self):
+        image = self.camera_0.getImage().data
+        image_1 = self.camera_1.getImage().data
+        image_2 = self.camera_2.getImage().data
+        image_3 = self.camera_3.getImage().data
+
+        bird_eye_view_1 = self.bird_eye_view.getImage(self.vehicle)
+        bird_eye_view_1 = cv2.cvtColor(bird_eye_view_1, cv2.COLOR_BGR2RGB)
+
+        if self.cameras_first_images == []:
+            self.cameras_first_images.append(image)
+            self.cameras_first_images.append(image_1)
+            self.cameras_first_images.append(image_2)
+            self.cameras_first_images.append(image_3)
+            self.cameras_first_images.append(bird_eye_view_1)
+
+        self.cameras_last_images = [
+            image,
+            image_1,
+            image_2,
+            image_3,
+            bird_eye_view_1
+        ]
+
+        self.update_frame('frame_1', image_1)
+        self.update_frame('frame_2', image_2)
+        self.update_frame('frame_3', image_3)
+
+        self.update_frame('frame_0', bird_eye_view_1)
+
+        self.update_pose(self.pose.getPose3d())
+
+        image_shape=(50, 150)
+        img_base = cv2.resize(bird_eye_view_1, image_shape)
+
+        AUGMENTATIONS_TEST = Compose([
+            Normalize()
+        ])
+        image = AUGMENTATIONS_TEST(image=img_base)
+        img = image["image"]
+
+        self.bird_eye_view_images += 1
+        if (self.previous_bird_eye_view_image==img).all() == False:
+            self.bird_eye_view_unique_images += 1
+        self.previous_bird_eye_view_image = img
+
+        img = np.expand_dims(img, axis=0)
+        start_time = time.time()
+        try:
+            prediction = self.net.predict(img, verbose=0)
+            self.inference_times.append(time.time() - start_time)
+            throttle = prediction[0][0]
+            steer = prediction[0][1] * (1 - (-1)) + (-1)
+            break_command = prediction[0][2]
+
+            speed = self.vehicle.get_velocity()
+            vehicle_speed = 3.6 * math.sqrt(speed.x**2 + speed.y**2 + speed.z**2)
+
+            if vehicle_speed < 70 and self.first_acceleration:
+                self.motors.sendThrottle(1.0)
+                self.motors.sendSteer(0.0)
+                self.motors.sendBrake(0)
+            else:
+                self.first_acceleration = False
+                self.motors.sendThrottle(throttle)
+                self.motors.sendSteer(steer)
+                self.motors.sendBrake(break_command)
+        except NotFoundError as ex:
+            logger.info('Error inside brain: NotFoundError!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+        except UnimplementedError as ex:
+            logger.info('Error inside brain: UnimplementedError!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+        except Exception as ex:
+            logger.info('Error inside brain: Exception!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+
+
+
+
+