Machine Learning-with-Python

Setting up Environment for AI:
https://github.com/DrewNF/Build-Deep-Learning-Env-with-Tensorflow-Python-OpenCV





http://aishack.in/tutorials/tracking-colored-objects-opencv/

Initialise the capturing device
loop-->
capture a frame from camera.
create scribble Image from first Frame
. Figure out where the yellow ball is in current Frame
  create Threshold Image for  frame:
     threshold the Image:
      HSV colur space 
      keep all the pixels with hue=yellow
      and make remaining pixels to black -
calculate zero order moment(area) and two First order moments(m01,m10) for threshold Image
calc position of yellow ball <x,y> co-ordinetes :x=m01/area  Y= m10/area
keep track of current position and previous position
draw a line from previous position to the current position in scribble Image.
combine Frame and scribble Image
Release the memory ocuupied by threshold Image
Keep doing this till a switch is pressed --->in loop

main()
{

// Initialize capturing live feed from the camera
 CvCapture* capture = 0;
 capture = cvCaptureFromCAM(0); 

// Couldn't get a device? Throw an error and quit
 if(!capture) 
{
 printf("Could not initialize capturing...");
 return -1; 
}

cvNamedWindow("video");
cvNamedWindow("thresh"); 

IplImage* imgScribble = NULL;
// An infinite loop
 while(true)
 { 
// Will hold a frame captured from the camera 
IplImage* frame = 0; 
frame = cvQueryFrame(capture); 
// If we couldn't grab a frame... quit
 if(!frame) 
  break; 
// If this is the first frame, we need to initialize it
 if(imgScribble == NULL) 
  {
   imgScribble = cvCreateImage(cvGetSize(frame), 8, 3);
  } 
// Holds the yellow thresholded image (yellow = white, rest = black) 
 IplImage* imgYellowThresh = GetThresholdedImage(frame);

// Calculate the moments to estimate the position of the ball

 CvMoments *moments = (CvMoments*)malloc(sizeof(CvMoments)); 
 cvMoments(imgYellowThresh, moments, 1); 
// The actual moment values 
double moment10 = cvGetSpatialMoment(moments, 1, 0); 
double moment01 = cvGetSpatialMoment(moments, 0, 1); 
double area = cvGetCentralMoment(moments, 0, 0); 

// Holding the last and current ball positions 
static int posX = 0;
static int posY = 0;
 int lastX = posX; 
int lastY = posY; 
posX = moment10/area; 
posY = moment01/area; 

// Print it out for debugging purposes 
printf("position (%d,%d)", posX, posY); 

// We want to draw a line only if its a valid position
 if(lastX>0 && lastY>0 && posX>0 && posY>0) 
  {
 // Draw a yellow line from the previous point to the current point 
 cvLine(imgScribble, cvPoint(posX, posY), cvPoint(lastX, lastY), cvScalar(0,255,255), 5); 
  } 

// Add the scribbling image and the frame... 
cvAdd(frame, imgScribble, frame);
cvShowImage("thresh", imgYellowThresh);
cvShowImage("video", frame);

// Wait for a keypress
 int c = cvWaitKey(10);
 if(c!=-1)
 {
 // If pressed, break out of the loop 
  break; 
 } 

// Release the thresholded image+moments... we need no memory leaks..
cvReleaseImage(&imgYellowThresh);
 delete moments; 
} 

// We're done using the camera. Other applications can now use it 
cvReleaseCapture(&capture);
 return 0; 
} 


IplImage* GetThresholdedImage(IplImage* img)
{
 // Convert the image into an HSV image
    IplImage* imgHSV = cvCreateImage(cvGetSize(img), 8, 3);
    cvCvtColor(img, imgHSV, CV_BGR2HSV);
IplImage* imgThreshed = cvCreateImage(cvGetSize(img), 8, 1); 
cvInRangeS(imgHSV, cvScalar(20, 100, 100), cvScalar(30, 255, 255), imgThreshed);
cvReleaseImage(&imgHSV);
 return imgThreshed;

} 



   

python:
------------

import numpy as np
import cv2




cap = cv2.VideoCapture(0)

while(True):
    # Capture frame-by-frame
    ret, frame = cap.read()

    # Our operations on the frame come here
    hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # define range of blue color in HSV
    lower_blue = np.array([110,50,50])
    upper_blue = np.array([130,255,255])

  # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv_frame, lower_blue, upper_blue)

   # Bitwise-AND mask and original image
    thresh_img = cv2.bitwise_and(frame,frame, mask= mask)

    cv2.imshow('image',frame)
    cv2.imshow('hsv-im',hsv_frame)
    cv2.imshow('mask',mask)
    cv2.imshow('res',thresh_img)
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break

cap.release()
cv2.destroyAllWindows()