import numpy as np
import cv2
import threading

import Object
import StopLine

from model import NeuralNetwork

class CollectTrainingData(object):

    def __init__(self, client, steer):        

        self.client = client        
        self.steer = steer

        self.stopline = StopLine.Stop()
        self.dect = Object.Object_Detection(self.steer)

        # model create
        self.model = NeuralNetwork()
        self.model.load_model(path = 'model_data/video_model_1.h5')          

    def collect(self):

        print("Start video stream")        

        stream_bytes = b' '  
 
        while True :            
            stream_bytes += self.client.recv(1024)
            first = stream_bytes.find(b'\xff\xd8')
            last = stream_bytes.find(b'\xff\xd9')

            if first != -1 and last != -1:
                try:
                    jpg = stream_bytes[first:last + 2]
                    stream_bytes = stream_bytes[last + 2:]

                    image = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
                    rgb = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8), cv2.IMREAD_COLOR)
                    rgb2 = rgb.copy()
                    roi = image[120:240, :]                    
                    roi2 = rgb2[120:240, :] #for line roi

                    cv2.imshow('Origin', rgb)
                    cv2.imshow('GRAY', image)
                    cv2.imshow('roi', roi)

                    # reshape the roi image into a vector
                    image_array = np.reshape(roi, (-1, 120, 320, 1))                  

                    # neural network makes prediction
                    self.steer.Set_Line(self.model.predict(image_array))
                    self.steer.Set_Stopline(self.stopline.GetStopLine(roi2))
                    self.dect.Detection(rgb)                    
                    self.steer.Control()
                except:
                    continue

                if cv2.waitKey(1) & 0xFF == ord('q'):
                    break





























c8a0488d22ab0178add20e4877362a74d5ba21d6
<mask token>


class CollectTrainingData(object):
    <mask token>
    <mask token>

<mask token>


class CollectTrainingData(object):

    def __init__(self, client, steer):
        self.client = client
        self.steer = steer
        self.stopline = StopLine.Stop()
        self.dect = Object.Object_Detection(self.steer)
        self.model = NeuralNetwork()
        self.model.load_model(path='model_data/video_model_1.h5')
    <mask token>

<mask token>


class CollectTrainingData(object):

    def __init__(self, client, steer):
        self.client = client
        self.steer = steer
        self.stopline = StopLine.Stop()
        self.dect = Object.Object_Detection(self.steer)
        self.model = NeuralNetwork()
        self.model.load_model(path='model_data/video_model_1.h5')

    def collect(self):
        print('Start video stream')
        stream_bytes = b' '
        while True:
            stream_bytes += self.client.recv(1024)
            first = stream_bytes.find(b'\xff\xd8')
            last = stream_bytes.find(b'\xff\xd9')
            if first != -1 and last != -1:
                try:
                    jpg = stream_bytes[first:last + 2]
                    stream_bytes = stream_bytes[last + 2:]
                    image = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8),
                        cv2.IMREAD_GRAYSCALE)
                    rgb = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8),
                        cv2.IMREAD_COLOR)
                    rgb2 = rgb.copy()
                    roi = image[120:240, :]
                    roi2 = rgb2[120:240, :]
                    cv2.imshow('Origin', rgb)
                    cv2.imshow('GRAY', image)
                    cv2.imshow('roi', roi)
                    image_array = np.reshape(roi, (-1, 120, 320, 1))
                    self.steer.Set_Line(self.model.predict(image_array))
                    self.steer.Set_Stopline(self.stopline.GetStopLine(roi2))
                    self.dect.Detection(rgb)
                    self.steer.Control()
                except:
                    continue
                if cv2.waitKey(1) & 255 == ord('q'):
                    break

import numpy as np
import cv2
import threading
import Object
import StopLine
from model import NeuralNetwork


class CollectTrainingData(object):

    def __init__(self, client, steer):
        self.client = client
        self.steer = steer
        self.stopline = StopLine.Stop()
        self.dect = Object.Object_Detection(self.steer)
        self.model = NeuralNetwork()
        self.model.load_model(path='model_data/video_model_1.h5')

    def collect(self):
        print('Start video stream')
        stream_bytes = b' '
        while True:
            stream_bytes += self.client.recv(1024)
            first = stream_bytes.find(b'\xff\xd8')
            last = stream_bytes.find(b'\xff\xd9')
            if first != -1 and last != -1:
                try:
                    jpg = stream_bytes[first:last + 2]
                    stream_bytes = stream_bytes[last + 2:]
                    image = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8),
                        cv2.IMREAD_GRAYSCALE)
                    rgb = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8),
                        cv2.IMREAD_COLOR)
                    rgb2 = rgb.copy()
                    roi = image[120:240, :]
                    roi2 = rgb2[120:240, :]
                    cv2.imshow('Origin', rgb)
                    cv2.imshow('GRAY', image)
                    cv2.imshow('roi', roi)
                    image_array = np.reshape(roi, (-1, 120, 320, 1))
                    self.steer.Set_Line(self.model.predict(image_array))
                    self.steer.Set_Stopline(self.stopline.GetStopLine(roi2))
                    self.dect.Detection(rgb)
                    self.steer.Control()
                except:
                    continue
                if cv2.waitKey(1) & 255 == ord('q'):
                    break

import numpy as np
import cv2
import threading

import Object
import StopLine

from model import NeuralNetwork

class CollectTrainingData(object):

    def __init__(self, client, steer):        

        self.client = client        
        self.steer = steer

        self.stopline = StopLine.Stop()
        self.dect = Object.Object_Detection(self.steer)

        # model create
        self.model = NeuralNetwork()
        self.model.load_model(path = 'model_data/video_model_1.h5')          

    def collect(self):

        print("Start video stream")        

        stream_bytes = b' '  
 
        while True :            
            stream_bytes += self.client.recv(1024)
            first = stream_bytes.find(b'\xff\xd8')
            last = stream_bytes.find(b'\xff\xd9')

            if first != -1 and last != -1:
                try:
                    jpg = stream_bytes[first:last + 2]
                    stream_bytes = stream_bytes[last + 2:]

                    image = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
                    rgb = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8), cv2.IMREAD_COLOR)
                    rgb2 = rgb.copy()
                    roi = image[120:240, :]                    
                    roi2 = rgb2[120:240, :] #for line roi

                    cv2.imshow('Origin', rgb)
                    cv2.imshow('GRAY', image)
                    cv2.imshow('roi', roi)

                    # reshape the roi image into a vector
                    image_array = np.reshape(roi, (-1, 120, 320, 1))                  

                    # neural network makes prediction
                    self.steer.Set_Line(self.model.predict(image_array))
                    self.steer.Set_Stopline(self.stopline.GetStopLine(roi2))
                    self.dect.Detection(rgb)                    
                    self.steer.Control()
                except:
                    continue

                if cv2.waitKey(1) & 0xFF == ord('q'):
                    break




























