class NeuralControl:
def __init__(self):
rospy.init_node('BMW_controller')
self.rate = rospy.Rate(10)
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.drive = DriveRun(sys.argv[1])
rospy.Subscriber('/image_topic_2', Image, self.controller_cb)
rospy.Subscriber('/usb_cam/image_raw', Image, self.recorder1)
self.image = None
self.image_processed = False
def recorder1(self, image):
cam_img = self.ic.imgmsg_to_opencv(image)
cropImg = cam_img[yMin:yMax, xMin:xMax]
global newimg
newimg = cv2.resize(cropImg, (160, 50))
def controller_cb(self, image):
img = self.ic.imgmsg_to_opencv(image)
img_resize = cv2.resize(img, (160, 50))
stacked_img = np.concatenate((img_resize, newimg), axis=0)
self.image = self.image_process.process(stacked_img)
self.image_processed = True
def __init__(self):
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.driveC = DriveRun(sys.argv[2])
rospy.Subscriber('/usb_cam/image_raw', Image, self.recorder1)
self.imageC = None
self.camera_processed = False
def __init__(self):
rospy.init_node('BMW_controller')
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.rate = rospy.Rate(10)
self.drive= DriveRun(sys.argv[1])
rospy.Subscriber('/image_topic_2', Image, self.controller_cb)
self.image = None
self.image_processed = False
def __init__(self):
rospy.init_node('controller')
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.rate = rospy.Rate(10)
self.drive= DriveRun(sys.argv[1])
rospy.Subscriber('/bulldogbolt/camera_left/image_raw_left', Image, self.controller_cb)
self.image = None
self.image_processed = False
def __init__(self):
rospy.init_node('controller')
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.rate = rospy.Rate(30)
self.drive = DriveRun(sys.argv[1])
rospy.Subscriber('/bolt/front_camera/image_raw', Image,
self.controller_cb)
self.image = None
self.image_processed = False
self.config = Config()
def __init__(self, weight_file_name):
rospy.init_node('run_neural')
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.rate = rospy.Rate(30)
self.drive = DriveRun(weight_file_name)
rospy.Subscriber(Config.data_collection['camera_image_topic'], Image,
self._controller_cb)
self.image = None
self.image_processed = False
#self.config = Config()
self.braking = False
def rank_image_cb(self, msg):
logger.info(
'Received message with {} compressed_imgs, {} imgs, {} strs'.
format(len(msg.compressed_imgs), len(msg.imgs), len(msg.strs)))
if self.trial and not self.trial.mode in (Trial.State.ABORTED,
Trial.State.COMPLETED):
print('Aborting due to ongoing trial')
self.action_server.set_aborted()
return
self.reset()
if len(msg.compressed_imgs) > 0:
self.screen.fill((255, 255, 255))
self.screen.blit(
self.font.render(
'Received {} images'.format(len(msg.compressed_imgs)), 1,
(0, 0, 0)), (40, self.size[1] / 2))
pygame.display.flip()
scaled_imgs = [
ImageConverter.from_ros(img) for img in msg.compressed_imgs
]
self.start_trial(
Trial(zip(msg.option_ids, scaled_imgs),
size=self.size,
preview_time=4000,
image_time=self.PRESENTATION_DELAY))
while self.ranking:
pygame.time.wait(100)
else:
self.action_server.set_aborted()
class NeuralControl:
def __init__(self):
rospy.init_node('controller')
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.rate = rospy.Rate(30)
self.drive = DriveRun(sys.argv[1])
rospy.Subscriber('/bolt/front_camera/image_raw', Image,
self.controller_cb)
self.image = None
self.image_processed = False
self.config = Config()
def controller_cb(self, image):
img = self.ic.imgmsg_to_opencv(image)
cropImg = img[self.config.capture_area[1]:self.config.capture_area[3],
self.config.capture_area[0]:self.config.capture_area[2]]
#cropImg = img[yMin:yMax,xMin:xMax]
img = cv2.resize(
cropImg, (self.config.image_size[0], self.config.image_size[1]))
self.image = self.image_process.process(img)
if self.config.typeofModel == 4 or self.config.typeofModel == 5:
self.image = np.array(self.image).reshape(
1, self.config.image_size[1], self.config.image_size[0],
self.config.image_size[2])
self.image_processed = True
def keyboard_rank_cb(self, msg):
rospy.loginfo("received msg")
self.screen.fill((0,0,0))
scaled_imgs = [ImageConverter.from_ros(img) for img in msg.compressed_imgs]
self.options = zip(msg.option_ids, scaled_imgs)
elements_per_side = int(math.ceil(math.sqrt(len(self.options))))
grid_w = self.size[0] / elements_per_side
grid_h = self.size[1] / elements_per_side
counter = 0
for idx in range(len(self.options)):
row = counter / elements_per_side
col = counter % elements_per_side
rect = pygame.Rect((row * grid_w, col * grid_h), (grid_w, grid_h))
subsurf = self.screen.subsurface(rect)
subsurf.blit(pygame.transform.smoothscale(self.options[idx][1], (grid_w, grid_h)), (0, 0))
counter += 1
pygame.display.flip()
while self.options and self.running:
pygame.time.wait(100)
def rank_image_cb(self, msg):
logger.info(
'Received message with {} compressed_imgs, {} imgs, {} strs'.format(len(msg.compressed_imgs), len(msg.imgs),
len(msg.strs)))
if self.trial and not self.trial.mode in (Trial.State.ABORTED, Trial.State.COMPLETED):
print('Aborting due to ongoing trial')
self.action_server.set_aborted()
return
self.reset()
if len(msg.compressed_imgs) > 0:
self.screen.fill((255, 255, 255))
self.screen.blit(self.font.render(
'Received {} images'.format(len(msg.compressed_imgs)), 1, (0, 0, 0)), (40, self.size[1] / 2))
pygame.display.flip()
scaled_imgs = [ImageConverter.from_ros(img) for img in msg.compressed_imgs]
self.start_trial(Trial(zip(msg.option_ids, scaled_imgs), size=self.size, preview_time=4000,
image_time=self.PRESENTATION_DELAY))
while self.ranking:
pygame.time.wait(100)
else:
self.action_server.set_aborted()
class camera:
def __init__(self):
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.driveC = DriveRun(sys.argv[2])
rospy.Subscriber('/usb_cam/image_raw', Image, self.recorder1)
self.imageC = None
self.camera_processed = False
def recorder1(self, image):
cam_img = self.ic.imgmsg_to_opencv(image)
cropImg = cam_img[yMin:yMax, xMin:xMax]
self.imageC = cv2.resize(cropImg, (160, 70))
self.camera_processed = True
class NeuralControl:
def __init__(self):
rospy.init_node('BMW_controller')
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.rate = rospy.Rate(10)
self.drive= DriveRun(sys.argv[1])
rospy.Subscriber('/image_topic_2', Image, self.controller_cb)
self.image = None
self.image_processed = False
def controller_cb(self, image):
img = self.ic.imgmsg_to_opencv(image)
img = cv2.resize(img,(160,70))
self.image = self.image_process.process(img)
self.image_processed = True
class NeuralControl:
def __init__(self):
rospy.init_node('controller')
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.rate = rospy.Rate(10)
self.drive= DriveRun(sys.argv[1])
rospy.Subscriber('/bulldogbolt/camera_left/image_raw_left', Image, self.controller_cb)
self.image = None
self.image_processed = False
def controller_cb(self, image):
img = self.ic.imgmsg_to_opencv(image)
cropImg = img[yMin:yMax,xMin:xMax]
img = cv2.resize(cropImg,(160,70))
self.image = self.image_process.process(img)
self.image_processed = True
class NeuralControl:
def __init__(self, weight_file_name):
rospy.init_node('run_neural')
self.ic = ImageConverter()
self.image_process = ImageProcess()
self.rate = rospy.Rate(30)
self.drive = DriveRun(weight_file_name)
rospy.Subscriber(Config.data_collection['camera_image_topic'], Image,
self._controller_cb)
self.image = None
self.image_processed = False
#self.config = Config()
self.braking = False
def _controller_cb(self, image):
img = self.ic.imgmsg_to_opencv(image)
cropped = img[Config.data_collection['image_crop_y1']:Config.
data_collection['image_crop_y2'],
Config.data_collection['image_crop_x1']:Config.
data_collection['image_crop_x2']]
img = cv2.resize(
cropped,
(config['input_image_width'], config['input_image_height']))
self.image = self.image_process.process(img)
## this is for CNN-LSTM net models
if config['lstm'] is True:
self.image = np.array(self.image).reshape(
1, config['input_image_height'], config['input_image_width'],
config['input_image_depth'])
self.image_processed = True
def _timer_cb(self):
self.braking = False
def apply_brake(self):
self.braking = True
timer = threading.Timer(Config.run_neural['brake_apply_sec'],
self._timer_cb)
timer.start()
def twitch_smile_resizer():
if request.method == 'GET':
return send_from_directory(directory='./', filename='index.html')
if 'file' not in request.files:
flash('No file part')
return jsonify({"error": "please add square image file"})
if request.method == 'POST':
# check if the post request has the file part
file = request.files['file']
filename = file.filename
if file.filename == '':
flash('No selected file')
return jsonify({"error": "please add square image file"})
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
resample_type = request.form['resample']
converted_images_name = ImageConverter().convert_image_to_twitch_format(secure_filename(file.filename), resample=resample_type)
if not converted_images_name:
os.remove(file.filename)
return jsonify({"error": "please add square image file"})
os.remove(file.filename)
return send_file(os.path.join('zip_files', converted_images_name), as_attachment=True)
def handle_dot_file(self, current_cmd):
the_image_converter = ImageConverter()
the_image_converter.produce_image_b()
import sys
from file_manager import FileManager
from image_converter import ImageConverter
print("Start image converter")
source_path = sys.argv[1]
target_path = sys.argv[2]
size = int(sys.argv[3])
print("Source path:" + source_path + " Target path: "+target_path+" Size: "+ str(size))
manager = FileManager(source_path, target_path)
converter = ImageConverter(size)
if(manager.is_source_exists()):
if(not manager.is_target_exists()):
manager.create_target()
print("Target created")
for image in manager.get_all_images():
img = converter.open_image(image.full_path)
converter.resize(img)
manager.save_image(img, image)
else:
print("Source not exists")
print("End image converter")
import os
import rospy
import datetime
import time
import numpy as np
from geometry_msgs.msg import Vector3
from sensor_msgs.msg import Image
from image_converter import ImageConverter
from std_msgs.msg import String
xMin = 0
yMin = 0
xMax = 640
yMax = 310
vehicle_steer = 0
ic = ImageConverter()
path = '/home/yadav/New_Data_collection/' + str(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + '/')
if os.path.exists(path + 'stacked_only'):
print('path exists. continuing...')
else:
os.makedirs(path + 'stacked_only')
stacked_only_text = open(str(path) + 'stacked_only/' + str(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")) + ".txt", "w+")
def callback(value):
global vehicle_steer
#vehicle_vel = value.linear.x
vehicle_steer = value.y
def recorder1(data):
def converter():
return ImageConverter(SOURCE, TARGET)
def src_converter():
return ImageConverter(SOURCE)